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chore: import upstream snapshot with attribution
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This commit is contained in:
wehub-resource-sync
2026-07-13 12:43:09 +08:00
commit d93385b373
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.cargo/audit.toml
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# Ignored advisories — all are transitive dependencies we cannot upgrade directly.
#
# time 0.3.45: pinned by mac-notification-sys (tauri dependency), awaiting upstream fix
# GTK3/glib/pango/etc: tauri uses gtk-rs GTK3 bindings which are unmaintained
# paste, proc-macro-error, fxhash: unmaintained transitive deps
# lexical-core: unmaintained, pulled by tauri dep chain
# serde_cbor: unmaintained, pulled by tao (tauri)
# cocoa/cocoa-foundation: unmaintained, pulled by tauri/tao
[advisories]
ignore = [
"RUSTSEC-2026-0009", # time DoS — pinned by mac-notification-sys
"RUSTSEC-2024-0370", # proc-macro-error unmaintained
"RUSTSEC-2024-0411", # gtk-rs GTK3 unmaintained (gdk-pixbuf)
"RUSTSEC-2024-0412", # gtk-rs GTK3 unmaintained (gdk)
"RUSTSEC-2024-0413", # gtk-rs GTK3 unmaintained (atk)
"RUSTSEC-2024-0414", # gtk-rs GTK3 unmaintained (pango)
"RUSTSEC-2024-0415", # gtk-rs GTK3 unmaintained (gio)
"RUSTSEC-2024-0416", # gtk-rs GTK3 unmaintained (atk-sys)
"RUSTSEC-2024-0417", # gtk-rs GTK3 unmaintained (gdk-pixbuf-sys)
"RUSTSEC-2024-0418", # gtk-rs GTK3 unmaintained (gdk-sys)
"RUSTSEC-2024-0419", # gtk-rs GTK3 unmaintained (gtk3-macros)
"RUSTSEC-2024-0420", # gtk-rs GTK3 unmaintained (pango-sys)
"RUSTSEC-2024-0429", # gtk-rs GTK3 unmaintained (gtk-sys)
"RUSTSEC-2024-0436", # paste unmaintained
"RUSTSEC-2025-0057", # fxhash unmaintained
"RUSTSEC-2025-0075", # glib unmaintained
"RUSTSEC-2025-0080", # cocoa unmaintained
"RUSTSEC-2025-0081", # cocoa-foundation unmaintained
"RUSTSEC-2025-0098", # lexical-core unmaintained
"RUSTSEC-2025-0100", # gio-sys unmaintained
"RUSTSEC-2026-0002", # serde_cbor unmaintained
"RUSTSEC-2023-0086", # lexopt unmaintained (if present)
]
.dockerignore
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.git
.github
.claude
.vscode
.idea
target
docs
sdk
scripts
*.md
!crates/**/*.md
LICENSE-*
CLAUDE.md
BUILD_LOG.md
.env
.env.*
*.db
*.sqlite
*.pem
*.key
Thumbs.db
.DS_Store
.env.example
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# OpenFang Environment Variables
# Copy this file to .env and fill in your values.
# Only set the providers you plan to use.
# ─── LLM Provider API Keys ───────────────────────────────────────────
# Anthropic (Claude models)
# ANTHROPIC_API_KEY=sk-ant-...
# Google Gemini
# GEMINI_API_KEY=AIza...
# GOOGLE_API_KEY=AIza... # Alternative to GEMINI_API_KEY
# OpenAI
# OPENAI_API_KEY=sk-...
# Groq (fast inference)
# GROQ_API_KEY=gsk_...
# DeepSeek
# DEEPSEEK_API_KEY=sk-...
# OpenRouter (multi-provider gateway)
# OPENROUTER_API_KEY=sk-or-...
# Together AI
# TOGETHER_API_KEY=...
# Mistral AI
# MISTRAL_API_KEY=...
# Fireworks AI
# FIREWORKS_API_KEY=...
# Novita AI (multi-model gateway)
# NOVITA_API_KEY=...
# ─── Local LLM Providers (no API key needed) ─────────────────────────
# Ollama (default: http://localhost:11434)
# OLLAMA_BASE_URL=http://localhost:11434
# vLLM (default: http://localhost:8000)
# VLLM_BASE_URL=http://localhost:8000
# LM Studio (default: http://localhost:1234)
# LMSTUDIO_BASE_URL=http://localhost:1234
# ─── Channel Tokens ──────────────────────────────────────────────────
# Telegram
# TELEGRAM_BOT_TOKEN=123456:ABC-...
# Discord
# DISCORD_BOT_TOKEN=...
# Slack
# SLACK_BOT_TOKEN=xoxb-...
# SLACK_APP_TOKEN=xapp-...
# WhatsApp (via Cloud API)
# WHATSAPP_TOKEN=...
# WHATSAPP_PHONE_ID=...
# Signal
# SIGNAL_CLI_PATH=/usr/local/bin/signal-cli
# SIGNAL_PHONE_NUMBER=+1...
# Matrix
# MATRIX_HOMESERVER=https://matrix.org
# MATRIX_ACCESS_TOKEN=...
# Email (IMAP/SMTP)
# EMAIL_IMAP_HOST=imap.gmail.com
# EMAIL_SMTP_HOST=smtp.gmail.com
# EMAIL_USERNAME=...
# EMAIL_PASSWORD=...
# ─── OpenFang Configuration ──────────────────────────────────────────
# API server bind address (default: 127.0.0.1:3000)
# OPENFANG_LISTEN=127.0.0.1:3000
# API key for HTTP authentication (leave empty for localhost-only access)
# OPENFANG_API_KEY=
# Home directory (default: ~/.openfang)
# OPENFANG_HOME=~/.openfang
# Log level (default: info)
# RUST_LOG=info
# RUST_LOG=openfang=debug # Debug OpenFang only
.github/FUNDING.yml
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github: RightNow-AI
.github/ISSUE_TEMPLATE/bug_report.yml
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name: Bug Report
description: Report a bug or unexpected behavior
labels: ["bug"]
body:
- type: textarea
id: description
attributes:
label: Description
description: What happened?
placeholder: Describe the bug clearly and concisely.
validations:
required: true
- type: textarea
id: expected
attributes:
label: Expected Behavior
description: What did you expect to happen?
validations:
required: true
- type: textarea
id: steps
attributes:
label: Steps to Reproduce
description: How can we reproduce this?
placeholder: |
1. Run `openfang start`
2. Open dashboard at http://localhost:4200
3. Click ...
validations:
required: true
- type: input
id: version
attributes:
label: OpenFang Version
description: Output of `openfang -V`
placeholder: "0.3.23"
validations:
required: true
- type: dropdown
id: os
attributes:
label: Operating System
options:
- Linux (x86_64)
- Linux (aarch64/ARM64)
- macOS (Apple Silicon)
- macOS (Intel)
- Windows
- Android (Termux)
- Other
validations:
required: true
- type: textarea
id: logs
attributes:
label: Logs / Screenshots
description: Paste relevant logs or attach screenshots.
.github/ISSUE_TEMPLATE/feature_request.yml
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name: Feature Request
description: Suggest a new feature or improvement
labels: ["enhancement"]
body:
- type: textarea
id: description
attributes:
label: Description
description: What feature would you like?
placeholder: Describe the feature and why it would be useful.
validations:
required: true
- type: textarea
id: alternatives
attributes:
label: Alternatives Considered
description: Have you tried any workarounds?
- type: textarea
id: context
attributes:
label: Additional Context
description: Any other context, screenshots, or references.
.github/dependabot.yml
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version: 2
updates:
- package-ecosystem: "cargo"
directory: "/"
schedule:
interval: "weekly"
open-pull-requests-limit: 5
labels:
- "dependencies"
- package-ecosystem: "github-actions"
directory: "/"
schedule:
interval: "weekly"
open-pull-requests-limit: 3
labels:
- "ci"
.github/pull_request_template.md
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## Summary
<!-- What does this PR do? Link related issues with "Fixes #123". -->
## Changes
<!-- Brief list of what changed. -->
## Testing
- [ ] `cargo clippy --workspace --all-targets -- -D warnings` passes
- [ ] `cargo test --workspace` passes
- [ ] Live integration tested (if applicable)
## Security
- [ ] No new unsafe code
- [ ] No secrets or API keys in diff
- [ ] User input validated at boundaries
.github/workflows/ci.yml
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name: CI
on:
push:
branches: [main]
pull_request:
branches: [main]
env:
CARGO_TERM_COLOR: always
RUSTFLAGS: "-D warnings"
jobs:
# ── Rust library crates (all 3 platforms) ──────────────────────────────────
check:
name: Check / ${{ matrix.os }}
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest, macos-latest, windows-latest]
steps:
- uses: actions/checkout@v6
- uses: dtolnay/rust-toolchain@stable
- uses: Swatinem/rust-cache@v2
with:
key: check-${{ matrix.os }}
- name: Install Tauri system deps (Linux)
if: runner.os == 'Linux'
run: |
sudo apt-get update
sudo apt-get install -y \
libwebkit2gtk-4.1-dev \
libgtk-3-dev \
libayatana-appindicator3-dev \
librsvg2-dev \
patchelf
- run: cargo check --workspace
test:
name: Test / ${{ matrix.os }}
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest, macos-latest, windows-latest]
steps:
- uses: actions/checkout@v6
- uses: dtolnay/rust-toolchain@stable
- uses: Swatinem/rust-cache@v2
with:
key: test-${{ matrix.os }}
- name: Install Tauri system deps (Linux)
if: runner.os == 'Linux'
run: |
sudo apt-get update
sudo apt-get install -y \
libwebkit2gtk-4.1-dev \
libgtk-3-dev \
libayatana-appindicator3-dev \
librsvg2-dev \
patchelf
# Tests that need a display (Tauri) are skipped in headless CI via cfg
- run: cargo test --workspace
clippy:
name: Clippy
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: dtolnay/rust-toolchain@stable
with:
components: clippy
- uses: Swatinem/rust-cache@v2
- name: Install Tauri system deps
run: |
sudo apt-get update
sudo apt-get install -y \
libwebkit2gtk-4.1-dev \
libgtk-3-dev \
libayatana-appindicator3-dev \
librsvg2-dev \
patchelf
- run: cargo clippy --workspace -- -D warnings
fmt:
name: Format
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: dtolnay/rust-toolchain@stable
with:
components: rustfmt
# Gate every workspace crate on rustfmt to keep `cargo fmt --all --check` clean.
# See issue #1121.
- run: cargo fmt --all -- --check
audit:
name: Security Audit
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: dtolnay/rust-toolchain@stable
- uses: Swatinem/rust-cache@v2
- name: Install cargo-audit
run: cargo install cargo-audit --locked
- run: cargo audit
# ── Secrets scanning (prevent accidental credential commits) ──────────────
secrets:
name: Secrets Scan
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
with:
fetch-depth: 0
- name: Install trufflehog
run: |
curl -sSfL https://raw.githubusercontent.com/trufflesecurity/trufflehog/main/scripts/install.sh | sh -s -- -b /usr/local/bin
- name: Scan for secrets
run: |
trufflehog filesystem . \
--no-update \
--fail \
--only-verified \
--exclude-paths=<(echo -e "target/\n.git/\nCargo.lock")
# ── Installer smoke test (verify install scripts from Vercel) ──────────────
install-smoke:
name: Install Script Smoke Test
runs-on: ubuntu-latest
steps:
- name: Fetch and syntax-check shell installer
run: |
curl -fsSL https://openfang.sh/install -o /tmp/install.sh
bash -n /tmp/install.sh
- name: Fetch and syntax-check PowerShell installer
run: |
curl -fsSL https://openfang.sh/install.ps1 -o /tmp/install.ps1
pwsh -NoProfile -Command "Get-Content /tmp/install.ps1 | Out-Null" 2>&1 || true
.github/workflows/release.yml
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name: Release
on:
push:
tags:
- "v*"
permissions:
contents: write
packages: write
env:
CARGO_TERM_COLOR: always
jobs:
# ── Tauri Desktop App (Windows + macOS + Linux) ───────────────────────────
# Produces: .msi, .exe (Windows) | .dmg, .app (macOS) | .AppImage, .deb (Linux)
# Also generates and uploads latest.json (the auto-updater manifest)
desktop:
name: Desktop / ${{ matrix.platform.name }}
strategy:
fail-fast: false
matrix:
platform:
- name: Linux x86_64
os: ubuntu-22.04
args: "--target x86_64-unknown-linux-gnu"
rust_target: x86_64-unknown-linux-gnu
- name: macOS x86_64
os: macos-latest
args: "--target x86_64-apple-darwin"
rust_target: x86_64-apple-darwin
- name: macOS ARM64
os: macos-latest
args: "--target aarch64-apple-darwin"
rust_target: aarch64-apple-darwin
- name: Windows x86_64
os: windows-latest
args: "--target x86_64-pc-windows-msvc"
rust_target: x86_64-pc-windows-msvc
- name: Windows ARM64
os: windows-latest
args: "--target aarch64-pc-windows-msvc"
rust_target: aarch64-pc-windows-msvc
runs-on: ${{ matrix.platform.os }}
steps:
- uses: actions/checkout@v6
- name: Install system deps (Linux)
if: runner.os == 'Linux'
run: |
sudo apt-get update
sudo apt-get install -y \
libwebkit2gtk-4.1-dev \
libgtk-3-dev \
libayatana-appindicator3-dev \
librsvg2-dev \
patchelf
- uses: dtolnay/rust-toolchain@stable
with:
targets: ${{ matrix.platform.rust_target }}
- uses: Swatinem/rust-cache@v2
with:
key: desktop-${{ matrix.platform.rust_target }}
- name: Import macOS signing certificate
if: runner.os == 'macOS'
env:
MAC_CERT_BASE64: ${{ secrets.MAC_CERT_BASE64 }}
MAC_CERT_PASSWORD: ${{ secrets.MAC_CERT_PASSWORD }}
run: |
echo "$MAC_CERT_BASE64" | base64 --decode > $RUNNER_TEMP/certificate.p12
KEYCHAIN_PATH=$RUNNER_TEMP/app-signing.keychain-db
KEYCHAIN_PASSWORD=$(openssl rand -base64 32)
security create-keychain -p "$KEYCHAIN_PASSWORD" "$KEYCHAIN_PATH"
security set-keychain-settings -lut 21600 "$KEYCHAIN_PATH"
security unlock-keychain -p "$KEYCHAIN_PASSWORD" "$KEYCHAIN_PATH"
security import $RUNNER_TEMP/certificate.p12 -P "$MAC_CERT_PASSWORD" \
-A -t cert -f pkcs12 -k "$KEYCHAIN_PATH"
security list-keychain -d user -s "$KEYCHAIN_PATH"
security set-key-partition-list -S apple-tool:,apple:,codesign: \
-s -k "$KEYCHAIN_PASSWORD" "$KEYCHAIN_PATH"
IDENTITY=$(security find-identity -v -p codesigning "$KEYCHAIN_PATH" | grep "Developer ID Application" | head -1 | awk -F'"' '{print $2}')
echo "Using signing identity: $IDENTITY"
echo "APPLE_SIGNING_IDENTITY=$IDENTITY" >> $GITHUB_ENV
rm -f $RUNNER_TEMP/certificate.p12
- name: Build and bundle Tauri desktop app
uses: tauri-apps/tauri-action@v0
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
TAURI_SIGNING_PRIVATE_KEY: ${{ secrets.TAURI_SIGNING_PRIVATE_KEY }}
TAURI_SIGNING_PRIVATE_KEY_PASSWORD: ${{ secrets.TAURI_SIGNING_PRIVATE_KEY_PASSWORD }}
APPLE_SIGNING_IDENTITY: ${{ env.APPLE_SIGNING_IDENTITY }}
APPLE_ID: ${{ secrets.MAC_NOTARIZE_APPLE_ID }}
APPLE_PASSWORD: ${{ secrets.MAC_NOTARIZE_PASSWORD }}
APPLE_TEAM_ID: ${{ secrets.MAC_NOTARIZE_TEAM_ID }}
with:
tagName: ${{ github.ref_name }}
releaseName: "OpenFang ${{ github.ref_name }}"
releaseBody: |
## What's New
See the [CHANGELOG](https://github.com/RightNow-AI/openfang/blob/main/CHANGELOG.md) for full details.
## Installation
**Desktop App** — Download the installer for your platform below.
**CLI (Linux/macOS)**:
```bash
curl -sSf https://openfang.sh | sh
```
**Docker**:
```bash
docker pull ghcr.io/rightnow-ai/openfang:latest
```
**Coming from OpenClaw?**
```bash
openfang migrate --from openclaw
```
releaseDraft: false
prerelease: false
includeUpdaterJson: true
projectPath: crates/openfang-desktop
args: ${{ matrix.platform.args }}
# ── CLI Binary (7 platforms) ──────────────────────────────────────────────
cli:
name: CLI / ${{ matrix.target }}
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
include:
- target: x86_64-unknown-linux-gnu
os: ubuntu-22.04
archive: tar.gz
- target: aarch64-unknown-linux-gnu
os: ubuntu-22.04
archive: tar.gz
- target: armv7-unknown-linux-gnueabihf
os: ubuntu-22.04
archive: tar.gz
- target: x86_64-apple-darwin
os: macos-latest
archive: tar.gz
- target: aarch64-apple-darwin
os: macos-latest
archive: tar.gz
- target: x86_64-pc-windows-msvc
os: windows-latest
archive: zip
- target: aarch64-pc-windows-msvc
os: windows-latest
archive: zip
steps:
- uses: actions/checkout@v6
- uses: dtolnay/rust-toolchain@stable
with:
targets: ${{ matrix.target }}
- name: Install build deps (Linux)
if: runner.os == 'Linux'
run: sudo apt-get update && sudo apt-get install -y pkg-config libssl-dev
- name: Install cross (Linux aarch64/armv7)
if: matrix.target == 'aarch64-unknown-linux-gnu' || matrix.target == 'armv7-unknown-linux-gnueabihf'
run: cargo install cross --locked
- uses: Swatinem/rust-cache@v2
with:
key: cli-${{ matrix.target }}
- name: Build CLI (cross)
if: matrix.target == 'aarch64-unknown-linux-gnu' || matrix.target == 'armv7-unknown-linux-gnueabihf'
run: cross build --release --target ${{ matrix.target }} --bin openfang
- name: Build CLI
if: matrix.target != 'aarch64-unknown-linux-gnu' && matrix.target != 'armv7-unknown-linux-gnueabihf'
run: cargo build --release --target ${{ matrix.target }} --bin openfang
- name: Ad-hoc codesign CLI binary (macOS)
if: runner.os == 'macOS'
run: |
xattr -cr target/${{ matrix.target }}/release/openfang || true
codesign --force --sign - target/${{ matrix.target }}/release/openfang
- name: Package (Unix)
if: matrix.archive == 'tar.gz'
run: |
cd target/${{ matrix.target }}/release
tar czf ../../../openfang-${{ matrix.target }}.tar.gz openfang
cd ../../..
sha256sum openfang-${{ matrix.target }}.tar.gz > openfang-${{ matrix.target }}.tar.gz.sha256
- name: Package (Windows)
if: matrix.archive == 'zip'
shell: pwsh
run: |
Compress-Archive -Path "target/${{ matrix.target }}/release/openfang.exe" -DestinationPath "openfang-${{ matrix.target }}.zip"
$hash = (Get-FileHash "openfang-${{ matrix.target }}.zip" -Algorithm SHA256).Hash.ToLower()
"$hash openfang-${{ matrix.target }}.zip" | Out-File -Encoding ASCII "openfang-${{ matrix.target }}.zip.sha256"
- name: Upload to GitHub Release
uses: softprops/action-gh-release@v3
with:
files: openfang-${{ matrix.target }}.*
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# ── Docker (linux/amd64 + linux/arm64) ────────────────────────────────────
docker:
name: Docker Image
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- name: Log in to GHCR
uses: docker/login-action@v4
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Set up QEMU (for arm64 emulation)
uses: docker/setup-qemu-action@v4
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v4
- name: Extract version
id: version
run: echo "version=${GITHUB_REF#refs/tags/v}" >> "$GITHUB_OUTPUT"
- name: Build and push (multi-arch)
uses: docker/build-push-action@v7
with:
context: .
push: true
platforms: linux/amd64,linux/arm64
tags: |
ghcr.io/rightnow-ai/openfang:latest
ghcr.io/rightnow-ai/openfang:${{ steps.version.outputs.version }}
labels: |
org.opencontainers.image.source=https://github.com/RightNow-AI/openfang
org.opencontainers.image.licenses=MIT
org.opencontainers.image.description=OpenFang Agent OS — single-binary Rust agent framework
cache-from: type=gha
cache-to: type=gha,mode=max
- name: Set GHCR package visibility to public
run: |
curl -fsSL -X PATCH \
-H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
-H "Accept: application/vnd.github+json" \
-H "X-GitHub-Api-Version: 2022-11-28" \
https://api.github.com/orgs/RightNow-AI/packages/container/openfang \
-d '{"visibility":"public"}'
.gitignore
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# Build
/target
**/*.rs.bk
*.pdb
# Environment & secrets
.env
.env.*
!.env.example
# Database
*.db
*.db-shm
*.db-wal
*.sqlite
*.sqlite3
# User config (may contain API keys)
config.toml
# Certificates & keys
*.pem
*.key
*.cert
*.p12
*.pfx
# Runtime artifacts
collector_hand_state.json
collector_knowledge_base.json
predictions_database.json
prediction_report_*.md
BUILD_LOG.md
# OS
.DS_Store
._*
Thumbs.db
# IDE & tools
.idea/
.vscode/
.claude/
*.swp
*.swo
*~
.serena/
# Personal deploy scripts
scripts/deploy-remote.sh
AGENTS.md
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## Health Stack
- typecheck: cargo build --workspace --lib
- lint: cargo clippy --workspace --all-targets -- -D warnings
- test: cargo test --workspace
- shell: shellcheck scripts/install.sh
CHANGELOG.md
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# Changelog
All notable changes to OpenFang will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [Unreleased]
## [0.5.10] - 2026-04-17
### Fixed
- Non-loopback requests with no `api_key` configured now return 401 by default. Opt out with `OPENFANG_ALLOW_NO_AUTH=1`. Fixes the B1/B2 authentication bypass from #1034.
- Agent `context.md` is re-read on every turn so external updates take effect mid-session. Opt out per agent with `cache_context = true` on the manifest. Fixes #843.
- `openfang config get default_model.base_url` now prints the configured URL instead of an empty string. Missing keys return a clear "not found" error. Fixes #905.
- `schedule_create`, `schedule_list`, and `schedule_delete` tools plus the `/api/schedules` routes now use the kernel cron scheduler, so scheduled jobs actually fire. One-shot idempotent migration imports legacy shared-memory entries at startup. Fixes #1069.
- Multimodal user messages now combine text and image blocks into a single message so the LLM sees both. Fixes #1043.
### Added
- `openfang hand config <id>` subcommand: get, set, unset, and list settings on an active hand instance. Fixes #809.
- Optional per-channel `prefix_agent_name` setting (`off` / `bracket` / `bold_bracket`). Wraps outbound agent responses so users in multi-agent channels can see which agent replied. Default is off, byte-identical to prior behavior. Fixes #980.
### Closed as invalid
- #818 and #819. Both reference a knowledge-domain API that does not exist on `main`. Filed against an unmerged feature branch (`plan/013-audit-remediation`). Close with a note to build the proposed validation and stale-timestamp surfacing into that feature when it lands.
## [0.5.9] - 2026-04-10
### Changed
- **BREAKING:** Dashboard password hashing switched from SHA256 to Argon2id. Existing `password_hash` values in `config.toml` must be regenerated with `openfang auth hash-password`. Only affects users with `[auth] enabled = true`.
### Fixed
- Dashboard passwords were hashed with plain SHA256 (no salt), making them vulnerable to rainbow table and GPU-accelerated brute force attacks. Now uses Argon2id with random salts.
## [0.1.0] - 2026-02-24
### Added
#### Core Platform
- 15-crate Rust workspace: types, memory, runtime, kernel, api, channels, wire, cli, migrate, skills, hands, extensions, desktop, xtask
- Agent lifecycle management: spawn, list, kill, clone, mode switching (Full/Assist/Observe)
- SQLite-backed memory substrate with structured KV, semantic recall, vector embeddings
- 41 built-in tools (filesystem, web, shell, browser, scheduling, collaboration, image analysis, inter-agent, TTS, media)
- WASM sandbox with dual metering (fuel + epoch interruption with watchdog thread)
- Workflow engine with pipelines, fan-out parallelism, conditional steps, loops, and variable expansion
- Visual workflow builder with drag-and-drop node graph, 7 node types, and TOML export
- Trigger system with event pattern matching, content filters, and fire limits
- Event bus with publish/subscribe and correlation IDs
- 7 Hands packages for autonomous agent actions
#### LLM Support
- 3 native LLM drivers: Anthropic, Google Gemini, OpenAI-compatible
- 27 providers: Anthropic, Gemini, OpenAI, Groq, OpenRouter, DeepSeek, Together, Mistral, Fireworks, Cohere, Perplexity, xAI, AI21, Cerebras, SambaNova, Hugging Face, Replicate, Ollama, vLLM, LM Studio, and more
- Model catalog with 130+ built-in models, 23 aliases, tier classification
- Intelligent model routing with task complexity scoring
- Fallback driver for automatic failover between providers
- Cost estimation and metering engine with per-model pricing
- Streaming support (SSE) across all drivers
#### Token Management & Context
- Token-aware session compaction (chars/4 heuristic, triggers at 70% context capacity)
- In-loop emergency trimming at 70%/90% thresholds with summary injection
- Tool profile filtering (cuts default 41 tools to 4-10 for chat agents, saving 15-20K tokens)
- Context budget allocation for system prompt, tools, history, and response
- MAX_TOOL_RESULT_CHARS reduced from 50K to 15K to prevent tool result bloat
- Default token quota raised from 100K to 1M per hour
#### Security
- Capability-based access control with privilege escalation prevention
- Path traversal protection in all file tools
- SSRF protection blocking private IPs and cloud metadata endpoints
- Ed25519 signed agent manifests
- Merkle hash chain audit trail with tamper detection
- Information flow taint tracking
- HMAC-SHA256 mutual authentication for peer wire protocol
- API key authentication with Bearer token
- GCRA rate limiter with cost-aware token buckets
- Security headers middleware (CSP, X-Frame-Options, HSTS)
- Secret zeroization on all API key fields
- Subprocess environment isolation
- Health endpoint redaction (public minimal, auth full)
- Loop guard with SHA256-based detection and circuit breaker thresholds
- Session repair (validates and fixes orphaned tool results, empty messages)
#### Channels
- 40 channel adapters: Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Email, Teams, Mattermost, Google Chat, Webex, Feishu/Lark, LINE, Viber, Facebook Messenger, Mastodon, Bluesky, Reddit, LinkedIn, Twitch, IRC, XMPP, and 18 more
- Unified bridge with agent routing, command handling, message splitting
- Per-channel user filtering and RBAC enforcement
- Graceful shutdown, exponential backoff, secret zeroization on all adapters
#### API
- 100+ REST/WS/SSE API endpoints (axum 0.8)
- WebSocket real-time streaming with per-agent connections
- OpenAI-compatible `/v1/chat/completions` API (streaming SSE + non-streaming)
- OpenAI-compatible `/v1/models` endpoint
- WebChat embedded UI with Alpine.js
- Google A2A protocol support (agent card, task send/get/cancel)
- Prometheus text-format `/api/metrics` endpoint for monitoring
- Multi-session management: list, create, switch, label sessions per agent
- Usage analytics: summary, by-model, daily breakdown
- Config hot-reload via polling (30-second interval, no restart required)
#### Web UI
- Chat message search with Ctrl+F, real-time filtering, text highlighting
- Voice input with hold-to-record mic button (WebM/Opus codec)
- TTS audio playback inline in tool cards
- Browser screenshot rendering in chat (inline images)
- Canvas rendering with iframe sandbox and CSP support
- Session switcher dropdown in chat header
- 6-step first-run setup wizard with provider API key help (12 providers)
- Skill marketplace with 4 tabs (Installed, ClawHub, MCP Servers, Quick Start)
- Copy-to-clipboard on messages, message timestamps
- Visual workflow builder with drag-and-drop canvas
#### Client SDKs
- JavaScript SDK (`@openfang/sdk`): full REST API client with streaming, TypeScript declarations
- Python client SDK (`openfang_client`): zero-dependency stdlib client with SSE streaming
- Python agent SDK (`openfang_sdk`): decorator-based framework for writing Python agents
- Usage examples for both languages (basic + streaming)
#### CLI
- 14+ subcommands: init, start, agent, workflow, trigger, migrate, skill, channel, config, chat, status, doctor, dashboard, mcp
- Daemon auto-detection via PID file
- Shell completion generation (bash, zsh, fish, PowerShell)
- MCP server mode for IDE integration
#### Skills Ecosystem
- 60 bundled skills across 14 categories
- Skill registry with TOML manifests
- 4 runtimes: Python, Node.js, WASM, PromptOnly
- FangHub marketplace with search/install
- ClawHub client for OpenClaw skill compatibility
- SKILL.md parser with auto-conversion
- SHA256 checksum verification
- Prompt injection scanning on skill content
#### Desktop App
- Tauri 2.0 native desktop app
- System tray with status and quick actions
- Single-instance enforcement
- Hide-to-tray on close
- Updated CSP for media, frame, and blob sources
#### Session Management
- LLM-based session compaction with token-aware triggers
- Multi-session per agent with named labels
- Session switching via API and UI
- Cross-channel canonical sessions
- Extended chat commands: `/new`, `/compact`, `/model`, `/stop`, `/usage`, `/think`
#### Image Support
- `ContentBlock::Image` with base64 inline data
- Media type validation (png, jpeg, gif, webp only)
- 5MB size limit enforcement
- Mapped to all 3 native LLM drivers
#### Usage Tracking
- Per-response cost estimation with model-aware pricing
- Usage footer in WebSocket responses and WebChat UI
- Usage events persisted to SQLite
- Quota enforcement with hourly windows
#### Interoperability
- OpenClaw migration engine (YAML/JSON5 to TOML)
- MCP client (JSON-RPC 2.0 over stdio/SSE, tool namespacing)
- MCP server (exposes OpenFang tools via MCP protocol)
- A2A protocol client and server
- Tool name compatibility mappings (21 OpenClaw tool names)
#### Infrastructure
- Multi-stage Dockerfile (debian:bookworm-slim runtime)
- docker-compose.yml with volume persistence
- GitHub Actions CI (check, test, clippy, format)
- GitHub Actions release (multi-platform, GHCR push, SHA256 checksums)
- Cross-platform install script (curl/irm one-liner)
- systemd service file for Linux deployment
#### Multi-User
- RBAC with Owner/Admin/User/Viewer roles
- Channel identity resolution
- Per-user authorization checks
- Device pairing and approval system
#### Production Readiness
- 1731+ tests across 15 crates, 0 failures
- Cross-platform support (Linux, macOS, Windows)
- Graceful shutdown with signal handling (SIGINT/SIGTERM on Unix, Ctrl+C on Windows)
- Daemon PID file with stale process detection
- Release profile with LTO, single codegen unit, symbol stripping
- Prometheus metrics for monitoring
- Config hot-reload without restart
[0.1.0]: https://github.com/RightNow-AI/openfang/releases/tag/v0.1.0
CLAUDE.md
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# OpenFang — Agent Instructions
## Project Overview
OpenFang is an open-source Agent Operating System written in Rust (14 crates).
- Config: `~/.openfang/config.toml`
- Default API: `http://127.0.0.1:4200`
- CLI binary: `target/release/openfang.exe` (or `target/debug/openfang.exe`)
## Build & Verify Workflow
After every feature implementation, run ALL THREE checks:
```bash
cargo build --workspace --lib # Must compile (use --lib if exe is locked)
cargo test --workspace # All tests must pass (currently 1744+)
cargo clippy --workspace --all-targets -- -D warnings # Zero warnings
```
## MANDATORY: Live Integration Testing
**After implementing any new endpoint, feature, or wiring change, you MUST run live integration tests.** Unit tests alone are not enough — they can pass while the feature is actually dead code. Live tests catch:
- Missing route registrations in server.rs
- Config fields not being deserialized from TOML
- Type mismatches between kernel and API layers
- Endpoints that compile but return wrong/empty data
### How to Run Live Integration Tests
#### Step 1: Stop any running daemon
```bash
tasklist | grep -i openfang
taskkill //PID <pid> //F
# Wait 2-3 seconds for port to release
sleep 3
```
#### Step 2: Build fresh release binary
```bash
cargo build --release -p openfang-cli
```
#### Step 3: Start daemon with required API keys
```bash
GROQ_API_KEY=<key> target/release/openfang.exe start &
sleep 6 # Wait for full boot
curl -s http://127.0.0.1:4200/api/health # Verify it's up
```
The daemon command is `start` (not `daemon`).
#### Step 4: Test every new endpoint
```bash
# GET endpoints — verify they return real data, not empty/null
curl -s http://127.0.0.1:4200/api/<new-endpoint>
# POST/PUT endpoints — send real payloads
curl -s -X POST http://127.0.0.1:4200/api/<endpoint> \
-H "Content-Type: application/json" \
-d '{"field": "value"}'
# Verify write endpoints persist — read back after writing
curl -s -X PUT http://127.0.0.1:4200/api/<endpoint> -d '...'
curl -s http://127.0.0.1:4200/api/<endpoint> # Should reflect the update
```
#### Step 5: Test real LLM integration
```bash
# Get an agent ID
curl -s http://127.0.0.1:4200/api/agents | python3 -c "import sys,json; print(json.load(sys.stdin)[0]['id'])"
# Send a real message (triggers actual LLM call to Groq/OpenAI)
curl -s -X POST "http://127.0.0.1:4200/api/agents/<id>/message" \
-H "Content-Type: application/json" \
-d '{"message": "Say hello in 5 words."}'
```
#### Step 6: Verify side effects
After an LLM call, verify that any metering/cost/usage tracking updated:
```bash
curl -s http://127.0.0.1:4200/api/budget # Cost should have increased
curl -s http://127.0.0.1:4200/api/budget/agents # Per-agent spend should show
```
#### Step 7: Verify dashboard HTML
```bash
# Check that new UI components exist in the served HTML
curl -s http://127.0.0.1:4200/ | grep -c "newComponentName"
# Should return > 0
```
#### Step 8: Cleanup
```bash
tasklist | grep -i openfang
taskkill //PID <pid> //F
```
### Key API Endpoints for Testing
| Endpoint | Method | Purpose |
|----------|--------|---------|
| `/api/health` | GET | Basic health check |
| `/api/agents` | GET | List all agents |
| `/api/agents/{id}/message` | POST | Send message (triggers LLM) |
| `/api/budget` | GET/PUT | Global budget status/update |
| `/api/budget/agents` | GET | Per-agent cost ranking |
| `/api/budget/agents/{id}` | GET | Single agent budget detail |
| `/api/network/status` | GET | OFP network status |
| `/api/peers` | GET | Connected OFP peers |
| `/api/a2a/agents` | GET | External A2A agents |
| `/api/a2a/discover` | POST | Discover A2A agent at URL |
| `/api/a2a/send` | POST | Send task to external A2A agent |
| `/api/a2a/tasks/{id}/status` | GET | Check external A2A task status |
## Architecture Notes
- **Don't touch `openfang-cli`** — user is actively building the interactive CLI
- `KernelHandle` trait avoids circular deps between runtime and kernel
- `AppState` in `server.rs` bridges kernel to API routes
- New routes must be registered in `server.rs` router AND implemented in `routes.rs`
- Dashboard is Alpine.js SPA in `static/index_body.html` — new tabs need both HTML and JS data/methods
- Config fields need: struct field + `#[serde(default)]` + Default impl entry + Serialize/Deserialize derives
## Common Gotchas
- `openfang.exe` may be locked if daemon is running — use `--lib` flag or kill daemon first
- `PeerRegistry` is `Option<PeerRegistry>` on kernel but `Option<Arc<PeerRegistry>>` on `AppState` — wrap with `.as_ref().map(|r| Arc::new(r.clone()))`
- Config fields added to `KernelConfig` struct MUST also be added to the `Default` impl or build fails
- `AgentLoopResult` field is `.response` not `.response_text`
- CLI command to start daemon is `start` not `daemon`
- On Windows: use `taskkill //PID <pid> //F` (double slashes in MSYS2/Git Bash)
CONTRIBUTING.md
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# Contributing to OpenFang
Thank you for your interest in contributing to OpenFang. This guide covers everything you need to get started, from setting up your development environment to submitting pull requests.
## Table of Contents
- [Development Environment](#development-environment)
- [Building and Testing](#building-and-testing)
- [Code Style](#code-style)
- [Architecture Overview](#architecture-overview)
- [How to Add a New Agent Template](#how-to-add-a-new-agent-template)
- [How to Add a New Channel Adapter](#how-to-add-a-new-channel-adapter)
- [How to Add a New Tool](#how-to-add-a-new-tool)
- [Pull Request Process](#pull-request-process)
- [Code of Conduct](#code-of-conduct)
---
## Development Environment
### Prerequisites
- **Rust 1.75+** (install via [rustup](https://rustup.rs/))
- **Git**
- **Python 3.8+** (optional, for Python runtime and skills)
- A supported LLM API key (Anthropic, OpenAI, Groq, etc.) for end-to-end testing
### Clone and Build
```bash
git clone https://github.com/RightNow-AI/openfang.git
cd openfang
cargo build
```
The first build takes a few minutes because it compiles SQLite (bundled) and Wasmtime. Subsequent builds are incremental.
### Environment Variables
For running integration tests that hit a real LLM, set at least one provider key:
```bash
export GROQ_API_KEY=gsk_... # Recommended for fast, free-tier testing
export ANTHROPIC_API_KEY=sk-ant-... # For Anthropic-specific tests
```
Tests that require a real LLM key will skip gracefully if the env var is absent.
---
## Building and Testing
### Build the Entire Workspace
```bash
cargo build --workspace
```
### Fast Release Build (for development)
The default `--release` profile uses full LTO and single-codegen-unit, which produces the smallest/fastest binary but is slow to compile. For iterating locally, use the `release-fast` profile instead:
```bash
cargo build --profile release-fast -p openfang-cli
```
This cuts link time significantly (thin LTO, 8 codegen units, `opt-level=2`) while still producing a binary fast enough to run integration tests against. Use `--release` only for final binaries or CI.
### Run All Tests
```bash
cargo test --workspace
```
The test suite is currently 1,744+ tests. All must pass before merging.
### Run Tests for a Single Crate
```bash
cargo test -p openfang-kernel
cargo test -p openfang-runtime
cargo test -p openfang-memory
```
### Check for Clippy Warnings
```bash
cargo clippy --workspace --all-targets -- -D warnings
```
The CI pipeline enforces zero clippy warnings.
### Format Code
```bash
cargo fmt --all
```
Always run `cargo fmt` before committing. CI will reject unformatted code.
### Run the Doctor Check
After building, verify your local setup:
```bash
cargo run -- doctor
```
---
## Code Style
- **Formatting**: Use `rustfmt` with default settings. Run `cargo fmt --all` before every commit.
- **Linting**: `cargo clippy --workspace -- -D warnings` must pass with zero warnings.
- **Documentation**: All public types and functions must have doc comments (`///`).
- **Error Handling**: Use `thiserror` for error types. Avoid `unwrap()` in library code; prefer `?` propagation.
- **Naming**:
- Types: `PascalCase` (e.g., `OpenFangKernel`, `AgentManifest`)
- Functions/methods: `snake_case`
- Constants: `SCREAMING_SNAKE_CASE`
- Crate names: `openfang-{name}` (kebab-case)
- **Dependencies**: Workspace dependencies are declared in the root `Cargo.toml`. Prefer reusing workspace deps over adding new ones. If you need a new dependency, justify it in the PR.
- **Testing**: Every new feature must include tests. Use `tempfile::TempDir` for filesystem isolation and random port binding for network tests.
- **Serde**: All config structs use `#[serde(default)]` for forward compatibility with partial TOML.
---
## Architecture Overview
OpenFang is organized as a Cargo workspace with 14 crates:
| Crate | Role |
|-------|------|
| `openfang-types` | Shared type definitions, taint tracking, manifest signing (Ed25519), model catalog, MCP/A2A config types |
| `openfang-memory` | SQLite-backed memory substrate with vector embeddings, usage tracking, canonical sessions, JSONL mirroring |
| `openfang-runtime` | Agent loop, 3 LLM drivers (Anthropic/Gemini/OpenAI-compat), 38 built-in tools, WASM sandbox, MCP client/server, A2A protocol |
| `openfang-hands` | Hands system (curated autonomous capability packages), 7 bundled hands |
| `openfang-extensions` | Integration registry (25 bundled MCP templates), AES-256-GCM credential vault, OAuth2 PKCE |
| `openfang-kernel` | Assembles all subsystems: workflow engine, RBAC auth, heartbeat monitor, cron scheduler, config hot-reload |
| `openfang-api` | REST/WS/SSE API (Axum 0.8), 76 endpoints, 14-page SPA dashboard, OpenAI-compatible `/v1/chat/completions` |
| `openfang-channels` | 40 channel adapters (Telegram, Discord, Slack, WhatsApp, and 36 more), formatter, rate limiter |
| `openfang-wire` | OFP (OpenFang Protocol): TCP P2P networking with HMAC-SHA256 mutual authentication |
| `openfang-cli` | Clap CLI with daemon auto-detect (HTTP mode vs. in-process fallback), MCP server |
| `openfang-migrate` | Migration engine for importing from OpenClaw (and future frameworks) |
| `openfang-skills` | Skill system: 60 bundled skills, FangHub marketplace, OpenClaw compatibility, prompt injection scanning |
| `openfang-desktop` | Tauri 2.0 native desktop app (WebView + system tray + single-instance + notifications) |
| `xtask` | Build automation tasks |
### Key Architectural Patterns
- **`KernelHandle` trait**: Defined in `openfang-runtime`, implemented on `OpenFangKernel` in `openfang-kernel`. This avoids circular crate dependencies while enabling inter-agent tools.
- **Shared memory**: A fixed UUID (`AgentId(Uuid::from_bytes([0..0, 0x01]))`) provides a cross-agent KV namespace.
- **Daemon detection**: The CLI checks `~/.openfang/daemon.json` and pings the health endpoint. If a daemon is running, commands use HTTP; otherwise, they boot an in-process kernel.
- **Capability-based security**: Every agent operation is checked against the agent's granted capabilities before execution.
---
## How to Add a New Agent Template
Agent templates live in the `agents/` directory. Each template is a folder containing an `agent.toml` manifest.
### Steps
1. Create a new directory under `agents/`:
```
agents/my-agent/agent.toml
```
2. Write the manifest:
```toml
name = "my-agent"
version = "0.1.0"
description = "A brief description of what this agent does."
author = "openfang"
module = "builtin:chat"
tags = ["category"]
[model]
provider = "groq"
model = "llama-3.3-70b-versatile"
[resources]
max_llm_tokens_per_hour = 100000
[capabilities]
tools = ["file_read", "file_list", "web_fetch"]
memory_read = ["*"]
memory_write = ["self.*"]
agent_spawn = false
```
3. Include a system prompt if needed by adding it to the `[model]` section:
```toml
[model]
provider = "anthropic"
model = "claude-sonnet-4-20250514"
system_prompt = """
You are a specialized agent that...
"""
```
4. Test by spawning:
```bash
openfang agent spawn agents/my-agent/agent.toml
```
5. Submit a PR with the new template.
---
## How to Add a New Channel Adapter
Channel adapters live in `crates/openfang-channels/src/`. Each adapter implements the `ChannelAdapter` trait.
### Steps
1. Create a new file: `crates/openfang-channels/src/myplatform.rs`
2. Implement the `ChannelAdapter` trait (defined in `types.rs`):
```rust
use crate::types::{ChannelAdapter, ChannelMessage, ChannelType};
use async_trait::async_trait;
pub struct MyPlatformAdapter {
// token, client, config fields
}
#[async_trait]
impl ChannelAdapter for MyPlatformAdapter {
fn channel_type(&self) -> ChannelType {
ChannelType::Custom("myplatform".to_string())
}
async fn start(&mut self) -> Result<(), Box<dyn std::error::Error>> {
// Start polling/listening for messages
Ok(())
}
async fn send(&self, channel_id: &str, content: &str) -> Result<(), Box<dyn std::error::Error>> {
// Send a message back to the platform
Ok(())
}
async fn stop(&mut self) {
// Clean shutdown
}
}
```
3. Register the module in `crates/openfang-channels/src/lib.rs`:
```rust
pub mod myplatform;
```
4. Wire it up in the channel bridge (`crates/openfang-api/src/channel_bridge.rs`) so the daemon starts it alongside other adapters.
5. Add configuration support in `openfang-types` config structs (add a `[channels.myplatform]` section).
6. Add CLI setup wizard instructions in `crates/openfang-cli/src/main.rs` under `cmd_channel_setup`.
7. Write tests and submit a PR.
---
## How to Add a New Tool
Built-in tools are defined in `crates/openfang-runtime/src/tool_runner.rs`.
### Steps
1. Add the tool implementation function:
```rust
async fn tool_my_tool(input: &serde_json::Value) -> Result<String, String> {
let param = input["param"]
.as_str()
.ok_or("Missing 'param' field")?;
// Tool logic here
Ok(format!("Result: {param}"))
}
```
2. Register it in the `execute_tool` match block:
```rust
"my_tool" => tool_my_tool(input).await,
```
3. Add the tool definition to `builtin_tool_definitions()`:
```rust
ToolDefinition {
name: "my_tool".to_string(),
description: "Description shown to the LLM.".to_string(),
input_schema: serde_json::json!({
"type": "object",
"properties": {
"param": {
"type": "string",
"description": "The parameter description"
}
},
"required": ["param"]
}),
},
```
4. Agents that need the tool must list it in their manifest:
```toml
[capabilities]
tools = ["my_tool"]
```
5. Write tests for the tool function.
6. If the tool requires kernel access (e.g., inter-agent communication), accept `Option<&Arc<dyn KernelHandle>>` and handle the `None` case gracefully.
---
## Pull Request Process
1. **Fork and branch**: Create a feature branch from `main`. Use descriptive names like `feat/add-matrix-adapter` or `fix/session-restore-crash`.
2. **Make your changes**: Follow the code style guidelines above.
3. **Test thoroughly**:
- `cargo test --workspace` must pass (all 1,744+ tests).
- `cargo clippy --workspace --all-targets -- -D warnings` must produce zero warnings.
- `cargo fmt --all --check` must produce no diff.
4. **Write a clear PR description**: Explain what changed and why. Include before/after examples if applicable.
5. **One concern per PR**: Keep PRs focused. A single PR should address one feature, one bug fix, or one refactor -- not all three.
6. **Review process**: At least one maintainer must approve before merge. Address review feedback promptly.
7. **CI must pass**: All automated checks must be green before merge.
### Commit Messages
Use clear, imperative-mood messages:
```
Add Matrix channel adapter with E2EE support
Fix session restore crash on kernel reboot
Refactor capability manager to use DashMap
```
---
## Code of Conduct
This project follows the [Contributor Covenant Code of Conduct](https://www.contributor-covenant.org/version/2/1/code_of_conduct/). By participating, you agree to uphold a welcoming, inclusive, and harassment-free environment for everyone.
Please report unacceptable behavior to the maintainers.
---
## Questions?
- Open a [GitHub Discussion](https://github.com/RightNow-AI/openfang/discussions) for questions.
- Open a [GitHub Issue](https://github.com/RightNow-AI/openfang/issues) for bugs or feature requests.
- Check the [docs/](docs/) directory for detailed guides on specific topics.
Cargo.lock
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[workspace]
resolver = "2"
members = [
"crates/openfang-types",
"crates/openfang-memory",
"crates/openfang-runtime",
"crates/openfang-wire",
"crates/openfang-api",
"crates/openfang-kernel",
"crates/openfang-cli",
"crates/openfang-channels",
"crates/openfang-migrate",
"crates/openfang-skills",
"crates/openfang-desktop",
"crates/openfang-hands",
"crates/openfang-extensions",
"xtask",
]
[workspace.package]
version = "0.6.9"
edition = "2021"
license = "Apache-2.0 OR MIT"
repository = "https://github.com/RightNow-AI/openfang"
rust-version = "1.75"
[workspace.dependencies]
# Async runtime
tokio = { version = "1", features = ["full"] }
tokio-stream = "0.1"
# Serialization
serde = { version = "1", features = ["derive"] }
serde_json = "1"
toml = "0.9"
rmp-serde = "1"
# Error handling
thiserror = "2"
anyhow = "1"
# Concurrency
dashmap = "6"
crossbeam = "0.8"
# Logging / Tracing
tracing = "0.1"
tracing-subscriber = { version = "0.3", features = ["env-filter", "json"] }
# Time
chrono = { version = "0.4", features = ["serde"] }
chrono-tz = "0.10"
# IDs
uuid = { version = "1", features = ["v4", "v5", "serde"] }
# Database
rusqlite = { version = "0.31", features = ["bundled", "serde_json"] }
# CLI
clap = { version = "4", features = ["derive"] }
clap_complete = "4"
# HTTP client (for LLM drivers)
reqwest = { version = "0.12", default-features = false, features = ["json", "stream", "multipart", "rustls-tls", "gzip", "deflate", "brotli"] }
rustls = { version = "0.23", default-features = false, features = ["ring"] }
# Async trait
async-trait = "0.1"
# Base64
base64 = "0.22"
# Bytes
bytes = "1"
# Futures
futures = "0.3"
prost = "0.14"
# WebSocket client (for Discord/Slack gateway)
tokio-tungstenite = { version = "0.24", default-features = false, features = ["connect", "rustls-tls-native-roots"] }
url = "2"
# WASM sandbox
wasmtime = "43"
# HTTP server (for API daemon)
axum = { version = "0.8", features = ["ws", "multipart"] }
tower = "0.5"
tower-http = { version = "0.6", features = ["cors", "trace", "compression-gzip", "compression-br"] }
# Home directory resolution
dirs = "6"
# YAML parsing
serde_yaml = "0.9"
# JSON5 parsing
json5 = "0.4"
# Directory walking
walkdir = "2"
# Security
sha2 = "0.10"
sha1 = "0.10"
aes = "0.8"
cbc = "0.1"
hmac = "0.12"
hex = "0.4"
subtle = "2"
ed25519-dalek = { version = "2", features = ["rand_core"] }
rand = "0.8"
zeroize = { version = "1", features = ["derive"] }
# Rate limiting
governor = "0.10"
# Interactive CLI
ratatui = "0.29"
colored = "3"
# Encryption
aes-gcm = "0.10"
argon2 = "0.5"
# HTML entity decoding
html-escape = "0.2"
# Lightweight regex
regex-lite = "0.1"
# MCP SDK (official Rust implementation)
rmcp = { version = "1.2", default-features = false, features = ["client", "transport-child-process", "transport-streamable-http-client-reqwest", "reqwest"] }
# Socket options (SO_REUSEADDR)
socket2 = "0.5"
# Zip archive extraction
zip = { version = "4", default-features = false, features = ["deflate"] }
# Email (SMTP + IMAP)
lettre = { version = "0.11", default-features = false, features = ["builder", "hostname", "smtp-transport", "tokio1", "tokio1-rustls-tls"] }
imap = "2"
native-tls = { version = "0.2", features = ["vendored"] }
mailparse = "0.16"
# MQTT client
rumqttc = { version = "0.25", default-features = false, features = ["use-native-tls"] }
# OpenSSL (vendored = statically compiled, no runtime libssl dependency on Linux)
openssl = { version = "0.10", features = ["vendored"] }
# Testing
tokio-test = "0.4"
tempfile = "3"
[profile.release]
lto = true
codegen-units = 1
strip = true
opt-level = 3
[profile.release-fast]
inherits = "release"
lto = "thin"
codegen-units = 8
opt-level = 2
strip = false
Cross.toml
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@@ -0,0 +1,11 @@
[target.aarch64-unknown-linux-gnu]
pre-build = [
"dpkg --add-architecture $CROSS_DEB_ARCH",
"apt-get update && apt-get install --assume-yes libssl-dev:$CROSS_DEB_ARCH"
]
[target.armv7-unknown-linux-gnueabihf]
pre-build = [
"dpkg --add-architecture $CROSS_DEB_ARCH",
"apt-get update && apt-get install --assume-yes libssl-dev:$CROSS_DEB_ARCH"
]
Dockerfile
+34
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@@ -0,0 +1,34 @@
# syntax=docker/dockerfile:1
FROM rust:1-slim-bookworm AS builder
WORKDIR /build
RUN apt-get update && apt-get install -y pkg-config libssl-dev perl make && rm -rf /var/lib/apt/lists/*
COPY Cargo.toml Cargo.lock ./
COPY crates ./crates
COPY xtask ./xtask
COPY agents ./agents
COPY packages ./packages
# Optional build args for dev environments to speed up compilation
# Example: docker build --build-arg LTO=false --build-arg CODEGEN_UNITS=16 .
ARG LTO=true
ARG CODEGEN_UNITS=1
ENV CARGO_PROFILE_RELEASE_LTO=${LTO} \
CARGO_PROFILE_RELEASE_CODEGEN_UNITS=${CODEGEN_UNITS}
RUN cargo build --release --bin openfang
FROM rust:1-slim-bookworm
RUN apt-get update && apt-get install -y --no-install-recommends \
ca-certificates \
python3 \
python3-pip \
python3-venv \
nodejs \
npm \
&& rm -rf /var/lib/apt/lists/*
COPY --from=builder /build/target/release/openfang /usr/local/bin/
COPY --from=builder /build/agents /opt/openfang/agents
EXPOSE 4200
VOLUME /data
ENV OPENFANG_HOME=/data
ENTRYPOINT ["openfang"]
CMD ["start"]
LICENSE-APACHE
+189
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@@ -0,0 +1,189 @@
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http://www.apache.org/licenses/
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8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
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9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
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on Your own behalf and on Your sole responsibility, not on behalf
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END OF TERMS AND CONDITIONS
Copyright 2024 OpenFang Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
LICENSE-MIT
+21
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@@ -0,0 +1,21 @@
MIT License
Copyright (c) 2024 OpenFang Contributors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
MIGRATION.md
+360
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# Migrating to OpenFang
This guide covers migrating from OpenClaw (and other frameworks) to OpenFang. The migration engine handles config conversion, agent import, memory transfer, channel re-configuration, and skill scanning.
## Table of Contents
- [Quick Migration](#quick-migration)
- [What Gets Migrated](#what-gets-migrated)
- [Manual Migration Steps](#manual-migration-steps)
- [Config Format Differences](#config-format-differences)
- [Tool Name Mapping](#tool-name-mapping)
- [Provider Mapping](#provider-mapping)
- [Feature Comparison](#feature-comparison)
---
## Quick Migration
Run a single command to migrate your entire OpenClaw workspace:
```bash
openfang migrate --from openclaw
```
This auto-detects your OpenClaw workspace at `~/.openclaw/` and imports everything into `~/.openfang/`.
### Options
```bash
# Specify a custom source directory
openfang migrate --from openclaw --source-dir /path/to/openclaw/workspace
# Dry run -- see what would be imported without making changes
openfang migrate --from openclaw --dry-run
```
### Migration Report
After a successful migration, a `migration_report.md` file is saved to `~/.openfang/` with a summary of everything that was imported, skipped, or needs manual attention.
### Other Frameworks
LangChain and AutoGPT migration support is planned:
```bash
openfang migrate --from langchain # Coming soon
openfang migrate --from autogpt # Coming soon
```
---
## What Gets Migrated
| Item | Source (OpenClaw) | Destination (OpenFang) | Status |
|------|-------------------|------------------------|--------|
| **Config** | `~/.openclaw/config.yaml` | `~/.openfang/config.toml` | Fully automated |
| **Agents** | `~/.openclaw/agents/*/agent.yaml` | `~/.openfang/agents/*/agent.toml` | Fully automated |
| **Memory** | `~/.openclaw/agents/*/MEMORY.md` | `~/.openfang/agents/*/imported_memory.md` | Fully automated |
| **Channels** | `~/.openclaw/messaging/*.yaml` | `~/.openfang/channels_import.toml` | Automated (manual merge) |
| **Skills** | `~/.openclaw/skills/` | Scanned and reported | Manual reinstall |
| **Sessions** | `~/.openclaw/agents/*/sessions/` | Not migrated | Fresh start recommended |
| **Workspace files** | `~/.openclaw/agents/*/workspace/` | Not migrated | Copy manually if needed |
### Channel Import Note
Channel configurations (Telegram, Discord, Slack) are exported to a `channels_import.toml` file. You must manually merge the `[channels]` section into your `~/.openfang/config.toml`.
### Skills Note
OpenClaw skills (Node.js) are detected and listed in the migration report but not automatically converted. After migration, reinstall skills using:
```bash
openfang skill install <skill-name-or-path>
```
OpenFang automatically detects OpenClaw-format skills and converts them during installation.
---
## Manual Migration Steps
If you prefer migrating by hand (or need to handle edge cases), follow these steps:
### 1. Initialize OpenFang
```bash
openfang init
```
This creates `~/.openfang/` with a default `config.toml`.
### 2. Convert Your Config
Translate your `config.yaml` to `config.toml`:
**OpenClaw** (`~/.openclaw/config.yaml`):
```yaml
provider: anthropic
model: claude-sonnet-4-20250514
api_key_env: ANTHROPIC_API_KEY
temperature: 0.7
memory:
decay_rate: 0.05
```
**OpenFang** (`~/.openfang/config.toml`):
```toml
[default_model]
provider = "anthropic"
model = "claude-sonnet-4-20250514"
api_key_env = "ANTHROPIC_API_KEY"
[memory]
decay_rate = 0.05
[network]
listen_addr = "127.0.0.1:4200"
```
### 3. Convert Agent Manifests
Translate each `agent.yaml` to `agent.toml`:
**OpenClaw** (`~/.openclaw/agents/coder/agent.yaml`):
```yaml
name: coder
description: A coding assistant
provider: anthropic
model: claude-sonnet-4-20250514
tools:
- read_file
- write_file
- execute_command
tags:
- coding
- dev
```
**OpenFang** (`~/.openfang/agents/coder/agent.toml`):
```toml
name = "coder"
version = "0.1.0"
description = "A coding assistant"
author = "openfang"
module = "builtin:chat"
tags = ["coding", "dev"]
[model]
provider = "anthropic"
model = "claude-sonnet-4-20250514"
[capabilities]
tools = ["file_read", "file_write", "shell_exec"]
memory_read = ["*"]
memory_write = ["self.*"]
```
### 4. Convert Channel Configs
**OpenClaw** (`~/.openclaw/messaging/telegram.yaml`):
```yaml
type: telegram
bot_token_env: TELEGRAM_BOT_TOKEN
default_agent: coder
allowed_users:
- "123456789"
```
**OpenFang** (add to `~/.openfang/config.toml`):
```toml
[channels.telegram]
bot_token_env = "TELEGRAM_BOT_TOKEN"
default_agent = "coder"
allowed_users = ["123456789"]
```
### 5. Import Memory
Copy any `MEMORY.md` files from OpenClaw agents to OpenFang agent directories:
```bash
cp ~/.openclaw/agents/coder/MEMORY.md ~/.openfang/agents/coder/imported_memory.md
```
The kernel will ingest these on first boot.
---
## Config Format Differences
| Aspect | OpenClaw | OpenFang |
|--------|----------|----------|
| Format | YAML | TOML |
| Config location | `~/.openclaw/config.yaml` | `~/.openfang/config.toml` |
| Agent definition | `agent.yaml` | `agent.toml` |
| Channel config | Separate files per channel | Unified in `config.toml` |
| Tool permissions | Implicit (tool list) | Capability-based (tools, memory, network, shell) |
| Model config | Flat (top-level fields) | Nested (`[model]` section) |
| Agent module | Implicit | Explicit (`module = "builtin:chat"` / `"wasm:..."` / `"python:..."`) |
| Scheduling | Not supported | Built-in (`[schedule]` section: reactive, continuous, periodic, proactive) |
| Resource quotas | Not supported | Built-in (`[resources]` section: tokens/hour, memory, CPU time) |
| Networking | Not supported | OFP protocol (`[network]` section) |
---
## Tool Name Mapping
Tools were renamed between OpenClaw and OpenFang for consistency. The migration engine handles this automatically.
| OpenClaw Tool | OpenFang Tool | Notes |
|---------------|---------------|-------|
| `read_file` | `file_read` | Noun-first naming |
| `write_file` | `file_write` | |
| `list_files` | `file_list` | |
| `execute_command` | `shell_exec` | Capability-gated |
| `web_search` | `web_search` | Unchanged |
| `fetch_url` | `web_fetch` | |
| `browser_navigate` | `browser_navigate` | Unchanged |
| `memory_search` | `memory_recall` | |
| `memory_recall` | `memory_recall` | |
| `memory_save` | `memory_store` | |
| `memory_store` | `memory_store` | |
| `sessions_send` | `agent_send` | |
| `agent_message` | `agent_send` | |
| `agents_list` | `agent_list` | |
| `agent_list` | `agent_list` | |
### New Tools in OpenFang
These tools have no OpenClaw equivalent:
| Tool | Description |
|------|-------------|
| `agent_spawn` | Spawn a new agent from within an agent |
| `agent_kill` | Terminate another agent |
| `agent_find` | Search for agents by name, tag, or description |
| `memory_store` | Store key-value data in shared memory |
| `memory_recall` | Recall key-value data from shared memory |
| `task_post` | Post a task to the shared task board |
| `task_claim` | Claim an available task |
| `task_complete` | Mark a task as complete |
| `task_list` | List tasks by status |
| `event_publish` | Publish a custom event to the event bus |
| `schedule_create` | Create a scheduled job |
| `schedule_list` | List scheduled jobs |
| `schedule_delete` | Delete a scheduled job |
| `image_analyze` | Analyze an image |
| `location_get` | Get location information |
### Tool Profiles
OpenClaw's tool profiles map to explicit tool lists:
| OpenClaw Profile | OpenFang Tools |
|------------------|----------------|
| `minimal` | `file_read`, `file_list` |
| `coding` | `file_read`, `file_write`, `file_list`, `shell_exec`, `web_fetch` |
| `messaging` | `agent_send`, `agent_list`, `memory_store`, `memory_recall` |
| `research` | `web_fetch`, `web_search`, `file_read`, `file_write` |
| `full` | All 10 core tools |
---
## Provider Mapping
| OpenClaw Name | OpenFang Name | API Key Env Var |
|---------------|---------------|-----------------|
| `anthropic` | `anthropic` | `ANTHROPIC_API_KEY` |
| `claude` | `anthropic` | `ANTHROPIC_API_KEY` |
| `openai` | `openai` | `OPENAI_API_KEY` |
| `gpt` | `openai` | `OPENAI_API_KEY` |
| `groq` | `groq` | `GROQ_API_KEY` |
| `ollama` | `ollama` | (none required) |
| `openrouter` | `openrouter` | `OPENROUTER_API_KEY` |
| `deepseek` | `deepseek` | `DEEPSEEK_API_KEY` |
| `together` | `together` | `TOGETHER_API_KEY` |
| `mistral` | `mistral` | `MISTRAL_API_KEY` |
| `fireworks` | `fireworks` | `FIREWORKS_API_KEY` |
### New Providers in OpenFang
| Provider | Description |
|----------|-------------|
| `vllm` | Self-hosted vLLM inference server |
| `lmstudio` | LM Studio local models |
---
## Feature Comparison
| Feature | OpenClaw | OpenFang |
|---------|----------|----------|
| **Language** | Node.js / TypeScript | Rust |
| **Config format** | YAML | TOML |
| **Agent manifests** | YAML | TOML |
| **Multi-agent** | Basic (message passing) | First-class (spawn, kill, find, workflows, triggers) |
| **Agent scheduling** | Manual | Built-in (reactive, continuous, periodic, proactive) |
| **Memory** | Markdown files | SQLite + KV store + semantic search + knowledge graph |
| **Session management** | JSONL files | SQLite with context window tracking |
| **LLM providers** | ~5 | 11 (Anthropic, OpenAI, Groq, OpenRouter, DeepSeek, Together, Mistral, Fireworks, Ollama, vLLM, LM Studio) |
| **Per-agent models** | No | Yes (per-agent provider + model override) |
| **Security** | None | Capability-based (tools, memory, network, shell, agent spawn) |
| **Resource quotas** | None | Per-agent token/hour limits, memory limits, CPU time limits |
| **Workflow engine** | None | Built-in (sequential, fan-out, collect, conditional, loop) |
| **Event triggers** | None | Pattern-matching event triggers with templated prompts |
| **WASM sandbox** | None | Wasmtime-based sandboxed execution |
| **Python runtime** | None | Subprocess-based Python agent execution |
| **Networking** | None | OFP (OpenFang Protocol) peer-to-peer |
| **API server** | Basic REST | REST + WebSocket + SSE streaming |
| **WebChat UI** | Separate | Embedded in daemon |
| **Channel adapters** | Telegram, Discord | Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Email |
| **Skills/Plugins** | npm packages | TOML + Python/WASM/Node.js, FangHub marketplace |
| **CLI** | Basic | Full CLI with daemon auto-detect, MCP server |
| **MCP support** | No | Built-in MCP server (stdio) |
| **Process supervisor** | None | Health monitoring, panic/restart tracking |
| **Persistence** | File-based | SQLite (agents survive restarts) |
---
## Troubleshooting
### Migration reports "Source directory not found"
The migration engine looks for `~/.openclaw/` by default. If your OpenClaw workspace is elsewhere:
```bash
openfang migrate --from openclaw --source-dir /path/to/your/workspace
```
### Agent fails to spawn after migration
Check the converted `agent.toml` for:
- Valid tool names (see the [Tool Name Mapping](#tool-name-mapping) table)
- A valid provider name (see the [Provider Mapping](#provider-mapping) table)
- Correct `module` field (should be `"builtin:chat"` for standard LLM agents)
### Skills not working
OpenClaw Node.js skills must be reinstalled:
```bash
openfang skill install /path/to/openclaw/skills/my-skill
```
The installer auto-detects OpenClaw format and converts the skill manifest.
### Channel not connecting
After migration, channels are exported to `channels_import.toml`. You must merge them into your `config.toml` manually:
```bash
cat ~/.openfang/channels_import.toml
# Copy the [channels.*] sections into ~/.openfang/config.toml
```
Then restart the daemon:
```bash
openfang start
```
README.md
+520
View File
@@ -0,0 +1,520 @@
<p align="center">
<img src="public/assets/openfang-logo.png" width="160" alt="OpenFang Logo" />
</p>
<h1 align="center">OpenFang</h1>
<h3 align="center">The Agent Operating System</h3>
<p align="center">
Open-source Agent OS built in Rust. 137K LOC. 14 crates. 1,767+ tests. Zero clippy warnings.<br/>
<strong>One binary. Battle-tested. Agents that actually work for you.</strong>
</p>
<p align="center">
<a href="https://openfang.sh/docs">Documentation</a> &bull;
<a href="https://openfang.sh/docs/getting-started">Quick Start</a> &bull;
<a href="https://x.com/openfangg">Twitter / X</a>
</p>
<p align="center">
<img src="https://img.shields.io/badge/language-Rust-orange?style=flat-square" alt="Rust" />
<img src="https://img.shields.io/badge/license-MIT-blue?style=flat-square" alt="MIT" />
<img src="https://img.shields.io/badge/version-0.6.9-green?style=flat-square" alt="v0.6.9" />
<img src="https://img.shields.io/badge/tests-2,696%2B%20passing-brightgreen?style=flat-square" alt="Tests" />
<img src="https://img.shields.io/badge/clippy-0%20warnings-brightgreen?style=flat-square" alt="Clippy" />
<a href="https://www.buymeacoffee.com/openfang" target="_blank"><img src="https://img.shields.io/badge/Buy%20Me%20a%20Coffee-FFDD00?style=flat-square&logo=buy-me-a-coffee&logoColor=black" alt="Buy Me A Coffee" /></a>
</p>
---
> **v0.5.10 (April 2026)**
>
> OpenFang is feature complete but still pre-1.0. Expect rough edges and breaking changes between minor versions. We ship fast and fix fast. Pin to a specific commit for production use until v1.0. [Report issues here.](https://github.com/RightNow-AI/openfang/issues)
---
## What is OpenFang?
OpenFang is an **open-source Agent Operating System**. Not a chatbot framework. Not a Python wrapper around an LLM. Not a "multi-agent orchestrator." A full operating system for autonomous agents, built from scratch in Rust.
Traditional agent frameworks wait for you to type something. OpenFang runs **autonomous agents that work for you**: on schedules, 24/7, building knowledge graphs, monitoring targets, generating leads, managing your social media, and reporting results to your dashboard.
The entire system compiles to a **single ~32MB binary**. One install, one command, your agents are live.
```bash
curl -fsSL https://openfang.sh/install | sh
openfang init
openfang start
# Dashboard live at http://localhost:4200
```
<details>
<summary><strong>Windows</strong></summary>
```powershell
irm https://openfang.sh/install.ps1 | iex
openfang init
openfang start
```
</details>
---
## Hands: Agents That Actually Do Things
<p align="center"><em>"Traditional agents wait for you to type. Hands work <strong>for</strong> you."</em></p>
**Hands** are OpenFang's core innovation. Pre-built autonomous capability packages that run independently, on schedules, without you having to prompt them. This is not a chatbot. This is an agent that wakes up at 6 AM, researches your competitors, builds a knowledge graph, scores the findings, and delivers a report to your Telegram before you've had coffee.
Each Hand bundles:
- **HAND.toml**: manifest declaring tools, settings, requirements, and dashboard metrics.
- **System Prompt**: multi-phase operational playbook. Not a one-liner. These are 500+ word expert procedures.
- **SKILL.md**: domain expertise reference injected into context at runtime.
- **Guardrails**: approval gates for sensitive actions (e.g. Browser Hand requires approval before any purchase).
All compiled into the binary. No downloading, no pip install, no Docker pull.
### The 7 Bundled Hands
| Hand | What It Actually Does |
|------|----------------------|
| **Clip** | Takes a YouTube URL, downloads it, identifies the best moments, cuts them into vertical shorts with captions and thumbnails, optionally adds AI voice-over, and publishes to Telegram and WhatsApp. 8-phase pipeline. FFmpeg + yt-dlp + 5 STT backends. |
| **Lead** | Runs daily. Discovers prospects matching your ICP, enriches them with web research, scores 0-100, deduplicates against your existing database, and delivers qualified leads in CSV/JSON/Markdown. Builds ICP profiles over time. |
| **Collector** | OSINT grade intelligence. You give it a target (company, person, topic). It monitors continuously: change detection, sentiment tracking, knowledge graph construction, and critical alerts when something important shifts. |
| **Predictor** | Superforecasting engine. Collects signals from multiple sources, builds calibrated reasoning chains, makes predictions with confidence intervals, and tracks its own accuracy using Brier scores. Has a contrarian mode that deliberately argues against consensus. |
| **Researcher** | Deep autonomous researcher. Cross-references multiple sources, evaluates credibility using CRAAP criteria (Currency, Relevance, Authority, Accuracy, Purpose), generates cited reports with APA formatting, supports multiple languages. |
| **Twitter** | Autonomous Twitter/X account manager. Creates content in 7 rotating formats, schedules posts for optimal engagement, responds to mentions, tracks performance metrics. Has an approval queue, so nothing posts without your OK. |
| **Browser** | Web automation agent. Navigates sites, fills forms, clicks buttons, handles multi-step workflows. Uses Playwright bridge with session persistence. **Mandatory purchase approval gate**: it will never spend your money without explicit confirmation. |
```bash
# Activate the Researcher Hand. It starts working immediately.
openfang hand activate researcher
# Check its progress anytime
openfang hand status researcher
# Activate lead generation on a daily schedule
openfang hand activate lead
# Pause without losing state
openfang hand pause lead
# See all available Hands
openfang hand list
```
**Build your own.** Define a `HAND.toml` with tools, settings, and a system prompt. Publish to FangHub.
---
## OpenFang vs The Landscape
<p align="center">
<img src="public/assets/openfang-vs-claws.png" width="600" alt="OpenFang vs OpenClaw vs ZeroClaw" />
</p>
### Benchmarks: Measured, Not Marketed
All data from official documentation and public repositories, February 2026.
#### Cold Start Time (lower is better)
```
ZeroClaw ██░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 10 ms
OpenFang ██████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 180 ms ★
LangGraph █████████████████░░░░░░░░░░░░░░░░░░░░░░░░░ 2.5 sec
CrewAI ████████████████████░░░░░░░░░░░░░░░░░░░░░░ 3.0 sec
AutoGen ██████████████████████████░░░░░░░░░░░░░░░░░ 4.0 sec
OpenClaw █████████████████████████████████████████░░ 5.98 sec
```
#### Idle Memory Usage (lower is better)
```
ZeroClaw █░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 5 MB
OpenFang ████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 40 MB ★
LangGraph ██████████████████░░░░░░░░░░░░░░░░░░░░░░░░░ 180 MB
CrewAI ████████████████████░░░░░░░░░░░░░░░░░░░░░░░ 200 MB
AutoGen █████████████████████████░░░░░░░░░░░░░░░░░░ 250 MB
OpenClaw ████████████████████████████████████████░░░░ 394 MB
```
#### Install Size (lower is better)
```
ZeroClaw █░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 8.8 MB
OpenFang ███░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 32 MB ★
CrewAI ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 100 MB
LangGraph ████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 150 MB
AutoGen ████████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░ 200 MB
OpenClaw ████████████████████████████████████████░░░░ 500 MB
```
#### Security Systems (higher is better)
```
OpenFang ████████████████████████████████████████████ 16 ★
ZeroClaw ███████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 6
OpenClaw ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 3
AutoGen █████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 2
LangGraph █████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 2
CrewAI ███░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 1
```
#### Channel Adapters (higher is better)
```
OpenFang ████████████████████████████████████████████ 40 ★
ZeroClaw ███████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 15
OpenClaw █████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 13
CrewAI ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 0
AutoGen ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 0
LangGraph ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 0
```
#### LLM Providers (higher is better)
```
ZeroClaw ████████████████████████████████████████████ 28
OpenFang ██████████████████████████████████████████░░ 27 ★
LangGraph ██████████████████████░░░░░░░░░░░░░░░░░░░░░ 15
CrewAI ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 10
OpenClaw ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 10
AutoGen ███████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 8
```
### Feature-by-Feature Comparison
| Feature | OpenFang | OpenClaw | ZeroClaw | CrewAI | AutoGen | LangGraph |
|---------|----------|----------|----------|--------|---------|-----------|
| **Language** | **Rust** | TypeScript | **Rust** | Python | Python | Python |
| **Autonomous Hands** | **7 built-in** | None | None | None | None | None |
| **Security Layers** | **16 discrete** | 3 basic | 6 layers | 1 basic | Docker | AES enc. |
| **Agent Sandbox** | **WASM dual-metered** | None | Allowlists | None | Docker | None |
| **Channel Adapters** | **40** | 13 | 15 | 0 | 0 | 0 |
| **Built-in Tools** | **53 + MCP + A2A** | 50+ | 12 | Plugins | MCP | LC tools |
| **Memory** | **SQLite + vector** | File-based | SQLite FTS5 | 4-layer | External | Checkpoints |
| **Desktop App** | **Tauri 2.0** | None | None | None | Studio | None |
| **Audit Trail** | **Merkle hash-chain** | Logs | Logs | Tracing | Logs | Checkpoints |
| **Cold Start** | **<200ms** | ~6s | ~10ms | ~3s | ~4s | ~2.5s |
| **Install Size** | **~32 MB** | ~500 MB | ~8.8 MB | ~100 MB | ~200 MB | ~150 MB |
| **License** | MIT | MIT | MIT | MIT | Apache 2.0 | MIT |
---
## 16 Security Systems: Defense in Depth
OpenFang doesn't bolt security on after the fact. Every layer is independently testable and operates without a single point of failure.
| # | System | What It Does |
|---|--------|-------------|
| 1 | **WASM Dual-Metered Sandbox** | Tool code runs in WebAssembly with fuel metering + epoch interruption. A watchdog thread kills runaway code. |
| 2 | **Merkle Hash-Chain Audit Trail** | Every action is cryptographically linked to the previous one. Tamper with one entry and the entire chain breaks. |
| 3 | **Information Flow Taint Tracking** | Labels propagate through execution. Secrets are tracked from source to sink. |
| 4 | **Ed25519 Signed Agent Manifests** | Every agent identity and capability set is cryptographically signed. |
| 5 | **SSRF Protection** | Blocks private IPs, cloud metadata endpoints, and DNS rebinding attacks. |
| 6 | **Secret Zeroization** | `Zeroizing<String>` auto-wipes API keys from memory the instant they're no longer needed. |
| 7 | **OFP Mutual Authentication** | HMAC-SHA256 nonce-based, constant-time verification for P2P networking. |
| 8 | **Capability Gates** | Role based access control. Agents declare required tools, the kernel enforces it. |
| 9 | **Security Headers** | CSP, X-Frame-Options, HSTS, X-Content-Type-Options on every response. |
| 10 | **Health Endpoint Redaction** | Public health check returns minimal info. Full diagnostics require authentication. |
| 11 | **Subprocess Sandbox** | `env_clear()` + selective variable passthrough. Process tree isolation with cross-platform kill. |
| 12 | **Prompt Injection Scanner** | Detects override attempts, data exfiltration patterns, and shell reference injection in skills. |
| 13 | **Loop Guard** | SHA256-based tool call loop detection with circuit breaker. Handles ping-pong patterns. |
| 14 | **Session Repair** | 7-phase message history validation and automatic recovery from corruption. |
| 15 | **Path Traversal Prevention** | Canonicalization with symlink escape prevention. ``../`` doesn't work here. |
| 16 | **GCRA Rate Limiter** | Cost-aware token bucket rate limiting with per-IP tracking and stale cleanup. |
---
## Architecture
14 Rust crates. 137,728 lines of code. Modular kernel design.
```
openfang-kernel Orchestration, workflows, metering, RBAC, scheduler, budget tracking
openfang-runtime Agent loop, 3 LLM drivers, 53 tools, WASM sandbox, MCP, A2A
openfang-api 140+ REST/WS/SSE endpoints, OpenAI-compatible API, dashboard
openfang-channels 40 messaging adapters with rate limiting, DM/group policies
openfang-memory SQLite persistence, vector embeddings, canonical sessions, compaction
openfang-types Core types, taint tracking, Ed25519 manifest signing, model catalog
openfang-skills 60 bundled skills, SKILL.md parser, FangHub marketplace
openfang-hands 7 autonomous Hands, HAND.toml parser, lifecycle management
openfang-extensions 25 MCP templates, AES-256-GCM credential vault, OAuth2 PKCE
openfang-wire OFP P2P protocol with HMAC-SHA256 mutual authentication
openfang-cli CLI with daemon management, TUI dashboard, MCP server mode
openfang-desktop Tauri 2.0 native app (system tray, notifications, global shortcuts)
openfang-migrate OpenClaw, LangChain, AutoGPT migration engine
xtask Build automation
```
---
## 40 Channel Adapters
Connect your agents to every platform your users are on.
**Core:** Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Email (IMAP/SMTP)
**Enterprise:** Microsoft Teams, Mattermost, Google Chat, Webex, Feishu/Lark, Zulip
**Social:** LINE, Viber, Facebook Messenger, Mastodon, Bluesky, Reddit, LinkedIn, Twitch
**Community:** IRC, XMPP, Guilded, Revolt, Keybase, Discourse, Gitter
**Privacy:** Threema, Nostr, Mumble, Nextcloud Talk, Rocket.Chat, Ntfy, Gotify
**Workplace:** Pumble, Flock, Twist, DingTalk, Zalo, Webhooks
Each adapter supports per-channel model overrides, DM/group policies, rate limiting, and output formatting.
---
## WhatsApp Web Gateway (QR Code)
Connect your personal WhatsApp account to OpenFang via QR code, just like WhatsApp Web. No Meta Business account required.
### Prerequisites
- **Node.js >= 18** installed ([download](https://nodejs.org/))
- OpenFang installed and initialized
### Setup
**1. Install the gateway dependencies:**
```bash
cd packages/whatsapp-gateway
npm install
```
**2. Configure `config.toml`:**
```toml
[channels.whatsapp]
mode = "web"
default_agent = "assistant"
```
**3. Set the gateway URL (choose one):**
Add to your shell profile for persistence:
```bash
# macOS / Linux
echo 'export WHATSAPP_WEB_GATEWAY_URL="http://127.0.0.1:3009"' >> ~/.zshrc
source ~/.zshrc
```
Or set it inline when starting the gateway:
```bash
export WHATSAPP_WEB_GATEWAY_URL="http://127.0.0.1:3009"
```
**4. Start the gateway:**
```bash
node packages/whatsapp-gateway/index.js
```
The gateway listens on port `3009` by default. Override with `WHATSAPP_GATEWAY_PORT`.
**5. Start OpenFang:**
```bash
openfang start
# Dashboard at http://localhost:4200
```
**6. Scan the QR code:**
Open the dashboard → **Channels** → **WhatsApp**. A QR code will appear. Scan it with your phone:
> **WhatsApp** → **Settings** → **Linked Devices** → **Link a Device**
Once scanned, the status changes to `connected` and incoming messages are routed to your configured agent.
### Gateway Environment Variables
| Variable | Description | Default |
|----------|-------------|---------|
| `WHATSAPP_WEB_GATEWAY_URL` | Gateway URL for OpenFang to connect to | _(empty = disabled)_ |
| `WHATSAPP_GATEWAY_PORT` | Port the gateway listens on | `3009` |
| `OPENFANG_URL` | OpenFang API URL the gateway reports to | `http://127.0.0.1:4200` |
| `OPENFANG_DEFAULT_AGENT` | Agent that handles incoming messages | `assistant` |
### Gateway API Endpoints
| Method | Route | Description |
|--------|-------|-------------|
| `POST` | `/login/start` | Generate QR code (returns base64 PNG) |
| `GET` | `/login/status` | Connection status (`disconnected`, `qr_ready`, `connected`) |
| `POST` | `/message/send` | Send a message (`{ "to": "5511999999999", "text": "Hello" }`) |
| `GET` | `/health` | Health check |
### Alternative: WhatsApp Cloud API
For production workloads, use the [WhatsApp Cloud API](https://developers.facebook.com/docs/whatsapp/cloud-api) with a Meta Business account. See the [Cloud API configuration docs](https://openfang.sh/docs/channels/whatsapp).
---
## 27 LLM Providers, 123+ Models
3 native drivers (Anthropic, Gemini, OpenAI-compatible) route to 27 providers:
Anthropic, Gemini, OpenAI, Groq, DeepSeek, OpenRouter, Together, Mistral, Fireworks, Cohere, Perplexity, xAI, AI21, Cerebras, SambaNova, HuggingFace, Replicate, Ollama, vLLM, LM Studio, Qwen, MiniMax, Zhipu, Moonshot, Qianfan, Bedrock, and more.
Intelligent routing with task complexity scoring, automatic fallback, cost tracking, and per-model pricing.
---
## Migrate from OpenClaw
Already running OpenClaw? One command:
```bash
# Migrate everything: agents, memory, skills, configs.
openfang migrate --from openclaw
# Migrate from a specific path
openfang migrate --from openclaw --path ~/.openclaw
# Dry run first to see what would change
openfang migrate --from openclaw --dry-run
```
The migration engine imports your agents, conversation history, skills, and configuration. OpenFang reads SKILL.md natively and is compatible with the ClawHub marketplace.
---
## OpenAI-Compatible API
Drop-in replacement. Point your existing tools at OpenFang:
```bash
curl -X POST localhost:4200/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "researcher",
"messages": [{"role": "user", "content": "Analyze Q4 market trends"}],
"stream": true
}'
```
140+ REST/WS/SSE endpoints covering agents, memory, workflows, channels, models, skills, A2A, Hands, and more.
---
## Quick Start
```bash
# 1. Install (macOS/Linux)
curl -fsSL https://openfang.sh/install | sh
# 2. Initialize. Walks you through provider setup.
openfang init
# 3. Start the daemon
openfang start
# 4. Dashboard is live at http://localhost:4200
# 5. Activate a Hand. It starts working for you.
openfang hand activate researcher
# 6. Chat with an agent
openfang chat researcher
> "What are the emerging trends in AI agent frameworks?"
# 7. Spawn a pre-built agent
openfang agent spawn coder
```
<details>
<summary><strong>Windows (PowerShell)</strong></summary>
```powershell
irm https://openfang.sh/install.ps1 | iex
openfang init
openfang start
```
</details>
---
## Development
```bash
# Build the workspace
cargo build --workspace --lib
# Run all tests (1,767+)
cargo test --workspace
# Lint (must be 0 warnings)
cargo clippy --workspace --all-targets -- -D warnings
# Format
cargo fmt --all -- --check
```
---
## Stability Notice
OpenFang v0.5.10 is pre-1.0. The architecture is solid, the test suite is comprehensive, and the security model is deep. That said:
- **Breaking changes** may occur between minor versions until v1.0.
- **Some Hands** are more mature than others. Browser and Researcher are the most battle tested.
- **Edge cases** exist. If you find one, [open an issue](https://github.com/RightNow-AI/openfang/issues).
- **Pin to a specific commit** for production deployments until v1.0.
We ship fast and fix fast. The goal is a rock solid v1.0 by mid 2026.
---
## Security
To report a security vulnerability, email **jaber@rightnowai.co**. We take all reports seriously and will respond within 48 hours.
---
## License
MIT. Use it however you want.
---
## Links
- [Website & Documentation](https://openfang.sh)
- [Quick Start Guide](https://openfang.sh/docs/getting-started)
- [GitHub](https://github.com/RightNow-AI/openfang)
- [Discord](https://discord.gg/sSJqgNnq6X)
- [Twitter / X](https://x.com/openfangg)
---
## Built by RightNow
<p align="center">
<a href="https://www.rightnowai.co/">
<img src="public/assets/rightnow-logo.webp" width="60" alt="RightNow Logo" />
</a>
</p>
<p align="center">
OpenFang is built and maintained by <a href="https://x.com/Akashi203"><strong>Jaber</strong></a>, Founder of <a href="https://www.rightnowai.co/"><strong>RightNow</strong></a>.
</p>
<p align="center">
<a href="https://www.rightnowai.co/">Website</a> &bull;
<a href="https://x.com/Akashi203">Twitter / X</a> &bull;
<a href="https://www.buymeacoffee.com/openfang" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
</p>
---
<p align="center">
<strong>Built with Rust. Secured with 16 layers. Agents that actually work for you.</strong>
</p>
README.wehub.md
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# WeHub 来源说明
- 原始项目：`RightNow-AI/openfang`
- 原始仓库：https://github.com/RightNow-AI/openfang
- 导入方式：上游默认分支的最新快照
- 原作者、版权和许可证信息以原始仓库及本仓库 LICENSE 为准
- 本文件仅用于记录来源，不代表 WeHub 是原项目作者
SECURITY.md
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# Security Policy
## Supported Versions
| Version | Supported |
|---------|--------------------|
| 0.3.x | :white_check_mark: |
## Reporting a Vulnerability
If you discover a security vulnerability in OpenFang, please report it responsibly.
**Do NOT open a public GitHub issue for security vulnerabilities.**
### How to Report
1. Email: **jaber@rightnowai.co**
2. Include:
- Description of the vulnerability
- Steps to reproduce
- Affected versions
- Potential impact assessment
- Suggested fix (if any)
### What to Expect
- **Acknowledgment** within 48 hours
- **Initial assessment** within 7 days
- **Fix timeline** communicated within 14 days
- **Credit** given in the advisory (unless you prefer anonymity)
### Scope
The following are in scope for security reports:
- Authentication/authorization bypass
- Remote code execution
- Path traversal / directory traversal
- Server-Side Request Forgery (SSRF)
- Privilege escalation between agents or users
- Information disclosure (API keys, secrets, internal state)
- Denial of service via resource exhaustion
- Supply chain attacks via skill ecosystem
- WASM sandbox escapes
## Security Architecture
OpenFang implements defense-in-depth with the following security controls:
### Access Control
- **Capability-based permissions**: Agents only access resources explicitly granted
- **RBAC multi-user**: Owner/Admin/User/Viewer role hierarchy
- **Privilege escalation prevention**: Child agents cannot exceed parent capabilities
- **API authentication**: Bearer token with loopback bypass for local CLI
### Input Validation
- **Path traversal protection**: `safe_resolve_path()` / `safe_resolve_parent()` on all file operations
- **SSRF protection**: Private IP blocking, DNS resolution checks, cloud metadata endpoint filtering
- **Image validation**: Media type whitelist (png/jpeg/gif/webp), 5MB size limit
- **Prompt injection scanning**: Skill content scanned for override attempts and data exfiltration
### Cryptographic Security
- **Ed25519 signed manifests**: Agent identity verification
- **HMAC-SHA256 wire protocol**: Mutual authentication with nonce-based replay protection
- **Secret zeroization**: `Zeroizing<String>` on all API key fields, wiped on drop
### Runtime Isolation
- **WASM dual metering**: Fuel limits + epoch interruption with watchdog thread
- **Subprocess sandbox**: Environment isolation (`env_clear()`), restricted PATH
- **Taint tracking**: Information flow labels prevent untrusted data in privileged operations
### Network Security
- **GCRA rate limiter**: Cost-aware token buckets per IP
- **Security headers**: CSP, X-Frame-Options, X-Content-Type-Options, HSTS
- **Health redaction**: Public endpoint returns minimal info; full diagnostics require auth
- **CORS policy**: Restricted to localhost when no API key configured
### Audit
- **Merkle hash chain**: Tamper-evident audit trail for all agent actions
- **Tamper detection**: Chain integrity verification via `/api/audit/verify`
## Dependencies
Security-critical dependencies are pinned and audited:
| Dependency | Purpose |
|------------|---------|
| `ed25519-dalek` | Manifest signing |
| `sha2` | Hash chain, checksums |
| `hmac` | Wire protocol authentication |
| `subtle` | Constant-time comparison |
| `zeroize` | Secret memory wiping |
| `rand` | Cryptographic randomness |
| `governor` | Rate limiting |
agents/analyst/agent.toml
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name = "analyst"
version = "0.1.0"
description = "Data analyst. Processes data, generates insights, creates reports."
author = "openfang"
module = "builtin:chat"
[model]
provider = "default"
model = "default"
api_key_env = "GEMINI_API_KEY"
max_tokens = 4096
temperature = 0.4
system_prompt = """You are Analyst, a data analysis agent running inside the OpenFang Agent OS.
ANALYSIS FRAMEWORK:
1. QUESTION — Clarify what question we're answering and what decisions it informs.
2. EXPLORE — Read the data. Examine shape, types, distributions, missing values, and outliers.
3. ANALYZE — Apply appropriate methods. Show your work with numbers.
4. VISUALIZE — When helpful, write Python scripts to generate charts or summary tables.
5. REPORT — Present findings in a structured format.
EVIDENCE STANDARDS:
- Every claim must be backed by data. Quote specific numbers.
- Distinguish correlation from causation.
- State confidence levels and sample sizes.
- Flag data quality issues upfront.
OUTPUT FORMAT:
- Executive Summary (1-2 sentences)
- Key Findings (numbered, with supporting metrics)
- Methodology (what you did and why)
- Data Quality Notes
- Recommendations with evidence
- Caveats and limitations"""
[[fallback_models]]
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[resources]
max_llm_tokens_per_hour = 150000
[capabilities]
tools = ["file_read", "file_write", "file_list", "shell_exec", "web_search", "web_fetch", "memory_store", "memory_recall"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
shell = ["python *", "cargo *"]
agents/architect/agent.toml
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name = "architect"
version = "0.1.0"
description = "System architect. Designs software architectures, evaluates trade-offs, creates technical specifications."
author = "openfang"
module = "builtin:chat"
tags = ["architecture", "design", "planning"]
[model]
provider = "default"
model = "default"
api_key_env = "DEEPSEEK_API_KEY"
max_tokens = 8192
temperature = 0.3
system_prompt = """You are Architect, a senior software architect running inside the OpenFang Agent OS.
You design systems with these principles:
- Separation of concerns and clean boundaries
- Performance-aware design (measure, don't guess)
- Simplicity over cleverness
- Explicit over implicit
- Design for change, but don't over-engineer
When designing:
1. Clarify requirements and constraints
2. Identify key components and their responsibilities
3. Define interfaces and data flow
4. Evaluate trade-offs (latency, throughput, complexity, maintainability)
5. Document decisions with rationale
Output format: Use clear headings, diagrams (ASCII), and structured reasoning.
When asked to review, be honest about weaknesses."""
[[fallback_models]]
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[resources]
max_llm_tokens_per_hour = 200000
[capabilities]
tools = ["file_read", "file_list", "memory_store", "memory_recall", "agent_send"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agent_message = ["*"]
agents/assistant/agent.toml
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name = "assistant"
version = "0.1.0"
description = "General-purpose assistant agent. The default OpenClaw agent for everyday tasks, questions, and conversations."
author = "openfang"
module = "builtin:chat"
tags = ["general", "assistant", "default", "multipurpose", "conversation", "productivity"]
[model]
provider = "default"
model = "default"
max_tokens = 8192
temperature = 0.5
system_prompt = """You are Assistant, a specialist agent in the OpenFang Agent OS. You are the default general-purpose agent — a versatile, knowledgeable, and helpful companion designed to handle a wide range of everyday tasks, answer questions, and assist with productivity workflows.
CORE COMPETENCIES:
1. Conversational Intelligence
You engage in natural, helpful conversations on virtually any topic. You answer factual questions accurately, provide explanations at the appropriate level of detail, and maintain context across multi-turn dialogues. You know when to be concise (quick factual answers) and when to be thorough (complex explanations, nuanced topics). You ask clarifying questions when a request is ambiguous rather than guessing. You are honest about the limits of your knowledge and clearly distinguish between established facts, well-supported opinions, and speculation.
2. Task Execution and Productivity
You help users accomplish concrete tasks: writing and editing text, brainstorming ideas, summarizing documents, creating lists and plans, drafting emails and messages, organizing information, performing calculations, and managing files. You approach each task systematically: understand the goal, gather necessary context, execute the work, and verify the result. You proactively suggest improvements and catch potential issues.
3. Research and Information Synthesis
You help users find, organize, and understand information. You can search the web, read documents, and synthesize findings into clear summaries. You evaluate source quality, identify conflicting information, and present balanced perspectives on complex topics. You structure research output with clear sections: key findings, supporting evidence, open questions, and recommended next steps.
4. Writing and Communication
You are a versatile writer who adapts style and tone to the task: professional correspondence, creative writing, technical documentation, casual messages, social media posts, reports, and presentations. You understand audience, purpose, and context. You provide multiple options when the user's preference is unclear. You edit for clarity, grammar, tone, and structure.
5. Problem Solving and Analysis
You help users think through problems logically. You apply structured frameworks: define the problem, identify constraints, generate options, evaluate trade-offs, and recommend a course of action. You use first-principles thinking to break complex problems into manageable components. You consider multiple perspectives and anticipate potential objections or risks.
6. Agent Delegation
As the default entry point to the OpenFang Agent OS, you know when a task would be better handled by a specialist agent. You can list available agents, delegate tasks to specialists, and synthesize their responses. You understand each specialist's strengths and route work accordingly: coding tasks to Coder, research to Researcher, data analysis to Analyst, writing to Writer, and so on. When a task is within your general capabilities, you handle it directly without unnecessary delegation.
7. Knowledge Management
You help users organize and retrieve information across sessions. You store important context, preferences, and reference material in memory for future conversations. You maintain structured notes, to-do lists, and project summaries. You recall previous conversations and build on established context.
8. Creative and Brainstorming Support
You help generate ideas, explore possibilities, and think creatively. You use brainstorming techniques: mind mapping, SCAMPER, random association, constraint-based ideation, and analogical thinking. You help users explore options without premature judgment, then shift to evaluation and refinement when ready.
OPERATIONAL GUIDELINES:
- Be helpful, accurate, and honest in all interactions
- Adapt your communication style to the user's preferences and the task at hand
- When unsure, ask clarifying questions rather than making assumptions
- For specialized tasks, recommend or delegate to the appropriate specialist agent
- Provide structured, scannable output: use headers, bullet points, and numbered lists
- Store user preferences, context, and important information in memory for continuity
- Be proactive about suggesting related tasks or improvements, but respect the user's focus
- Never fabricate information — clearly state when you are uncertain or speculating
- Respect privacy and confidentiality in all interactions
- When handling multiple tasks, prioritize and track them clearly
- Use all available tools appropriately: files for persistent documents, memory for context, web for current information, shell for computations
TOOLS AVAILABLE:
- file_read / file_write / file_list: Read, create, and manage files and documents
- memory_store / memory_recall: Persist and retrieve context, preferences, and knowledge
- web_fetch: Access current information from the web
- shell_exec: Run computations, scripts, and system commands
- agent_send / agent_list: Delegate tasks to specialist agents and see available agents
You are reliable, adaptable, and genuinely helpful. You are the user's trusted first point of contact in the OpenFang Agent OS — capable of handling most tasks directly and smart enough to delegate when a specialist would do it better."""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 300000
max_concurrent_tools = 10
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "web_fetch", "shell_exec", "agent_send", "agent_list"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agent_message = ["*"]
shell = ["python *", "cargo *", "git *", "npm *"]
[autonomous]
max_iterations = 100
agents/code-reviewer/agent.toml
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name = "code-reviewer"
version = "0.1.0"
description = "Senior code reviewer. Reviews PRs, identifies issues, suggests improvements with production standards."
author = "openfang"
module = "builtin:chat"
tags = ["review", "code-quality", "best-practices"]
[model]
provider = "default"
model = "default"
api_key_env = "GEMINI_API_KEY"
max_tokens = 4096
temperature = 0.3
system_prompt = """You are Code Reviewer, a senior engineer running inside the OpenFang Agent OS.
Review criteria (in priority order):
1. CORRECTNESS: Does it work? Logic errors, edge cases, error handling
2. SECURITY: Injection, auth, data exposure, input validation
3. PERFORMANCE: Algorithmic complexity, unnecessary allocations, I/O patterns
4. MAINTAINABILITY: Naming, structure, separation of concerns
5. STYLE: Consistency with codebase, idiomatic patterns
Review format:
- Start with a summary (approve / request changes / comment)
- Group feedback by file
- Use severity: [MUST FIX] / [SHOULD FIX] / [NIT] / [PRAISE]
- Always explain WHY, not just WHAT
- Suggest specific code when proposing changes
Rules:
- Be respectful and constructive
- Acknowledge good code, not just problems
- Don't bikeshed on style if there's a formatter
- Focus on things that matter for production"""
[[fallback_models]]
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[resources]
max_llm_tokens_per_hour = 150000
[capabilities]
tools = ["file_read", "file_list", "shell_exec", "memory_store", "memory_recall"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
shell = ["cargo clippy *", "cargo fmt *", "git diff *", "git log *"]
agents/coder/agent.toml
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name = "coder"
version = "0.1.0"
description = "Expert software engineer. Reads, writes, and analyzes code."
author = "openfang"
module = "builtin:chat"
tags = ["coding", "implementation", "rust", "python"]
[model]
provider = "default"
model = "default"
api_key_env = "GEMINI_API_KEY"
max_tokens = 8192
temperature = 0.3
system_prompt = """You are Coder, an expert software engineer agent running inside the OpenFang Agent OS.
METHODOLOGY:
1. READ — Always read the relevant file(s) before making changes. Understand context, conventions, and dependencies.
2. PLAN — Think through the approach. For non-trivial changes, outline the plan before writing code.
3. IMPLEMENT — Write clean, production-quality code that follows the project's existing patterns.
4. TEST — Write tests for new code. Run existing tests to check for regressions.
5. VERIFY — Read the modified files to confirm changes are correct.
QUALITY STANDARDS:
- Match the existing code style (naming, formatting, patterns) — don't introduce new conventions.
- Handle errors properly. No unwrap() in production code unless the invariant is documented.
- Write minimal, focused changes. Don't refactor surrounding code unless asked.
- When fixing a bug, write a test that reproduces it first.
RESEARCH:
- When you encounter an unfamiliar API, error message, or library, use web_search or web_fetch to look it up.
- Check official documentation before guessing at API usage."""
[[fallback_models]]
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[resources]
max_llm_tokens_per_hour = 200000
max_concurrent_tools = 10
[capabilities]
tools = ["file_read", "file_write", "file_list", "shell_exec", "web_search", "web_fetch", "memory_store", "memory_recall"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*"]
shell = ["cargo *", "rustc *", "git *", "npm *", "python *"]
agents/customer-support/agent.toml
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name = "customer-support"
version = "0.1.0"
description = "Customer support agent for ticket handling, issue resolution, and customer communication."
author = "openfang"
module = "builtin:chat"
tags = ["support", "customer-service", "tickets", "helpdesk", "communication", "resolution"]
[model]
provider = "default"
model = "default"
max_tokens = 4096
temperature = 0.3
system_prompt = """You are Customer Support, a specialist agent in the OpenFang Agent OS. You are an expert customer service representative who handles support tickets, resolves issues, and communicates with customers professionally and empathetically.
CORE COMPETENCIES:
1. Ticket Triage and Classification
You rapidly assess incoming support requests and classify them by: category (bug report, feature request, billing, account access, how-to question, integration issue), severity (critical/blocking, high, medium, low), product area, and customer tier. You identify tickets that require escalation to engineering, billing, or management and route them appropriately. You detect duplicate tickets and link related issues to avoid redundant work.
2. Issue Diagnosis and Resolution
You follow systematic troubleshooting workflows: gather symptoms, reproduce the issue when possible, check known issues and documentation, identify root cause, and provide a clear resolution. You maintain a mental model of common issues and their solutions, and you can walk customers through multi-step resolution procedures. When you cannot resolve an issue, you escalate with a complete diagnostic summary so the next responder has full context.
3. Customer Communication
You write customer-facing responses that are empathetic, clear, and solution-oriented. You acknowledge the customer's frustration before jumping to solutions. You explain technical concepts in accessible language without being condescending. You set realistic expectations about resolution timelines and follow through on commitments. You adapt your communication style to the customer's technical level and emotional state.
4. Knowledge Base Management
You help build and maintain internal knowledge base articles, FAQ documents, and canned responses. When you encounter a new issue type, you document the symptoms, diagnosis steps, and resolution for future reference. You identify gaps in existing documentation and recommend articles that need updates.
5. Escalation and Handoff
You know when to escalate and how to do it effectively. You prepare escalation summaries that include: original customer request, steps already taken, diagnostic findings, customer sentiment, and urgency assessment. You ensure no context is lost during handoffs between support tiers or departments.
6. Customer Sentiment Analysis
You monitor the emotional tone of customer interactions and adjust your approach accordingly. You identify at-risk customers (frustrated, threatening to churn) and flag them for priority treatment. You track sentiment trends across tickets to identify systemic issues that are driving customer dissatisfaction.
7. Metrics and Reporting
You can generate support metrics summaries: ticket volume by category, average resolution time, first-contact resolution rate, escalation rate, and customer satisfaction indicators. You identify trends and recommend process improvements.
OPERATIONAL GUIDELINES:
- Always lead with empathy: acknowledge the customer's experience before providing solutions
- Never blame the customer or use dismissive language
- Provide step-by-step instructions with numbered lists for troubleshooting
- Set clear expectations about what you can and cannot do
- Escalate promptly when an issue is beyond your resolution capability
- Store resolved issue patterns and solutions in memory for faster future resolution
- Use templates for common response types but personalize each response
- Track all open tickets and pending follow-ups
- Never share internal system details, credentials, or other customer data
- Flag potential security issues (account compromise, data exposure) immediately
TOOLS AVAILABLE:
- file_read / file_write / file_list: Access knowledge base, write response drafts and ticket logs
- memory_store / memory_recall: Persist issue patterns, customer context, and resolution templates
- web_fetch: Access external documentation and status pages
You are patient, empathetic, and solutions-focused. You turn frustrated customers into satisfied advocates."""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 200000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "web_fetch"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agents/data-scientist/agent.toml
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name = "data-scientist"
version = "0.1.0"
description = "Data scientist. Analyzes datasets, builds models, creates visualizations, performs statistical analysis."
author = "openfang"
module = "builtin:chat"
[model]
provider = "default"
model = "default"
api_key_env = "GEMINI_API_KEY"
max_tokens = 4096
temperature = 0.3
system_prompt = """You are Data Scientist, an analytics expert running inside the OpenFang Agent OS.
Your methodology:
1. UNDERSTAND: What question are we answering?
2. EXPLORE: Examine data shape, distributions, missing values
3. ANALYZE: Apply appropriate statistical methods
4. MODEL: Build predictive models when needed
5. COMMUNICATE: Present findings clearly with evidence
Statistical toolkit:
- Descriptive stats: mean, median, std, percentiles
- Hypothesis testing: t-test, chi-squared, ANOVA
- Correlation and regression analysis
- Time series analysis
- Clustering and dimensionality reduction
- A/B test design and analysis
Output format:
- Executive summary (1-2 sentences)
- Key findings (numbered, with confidence levels)
- Data quality notes
- Methodology description
- Recommendations with supporting evidence
- Caveats and limitations"""
[[fallback_models]]
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[resources]
max_llm_tokens_per_hour = 150000
[capabilities]
tools = ["file_read", "file_write", "file_list", "shell_exec", "web_search", "web_fetch", "memory_store", "memory_recall"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
shell = ["python *"]
agents/debugger/agent.toml
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name = "debugger"
version = "0.1.0"
description = "Expert debugger. Traces bugs, analyzes stack traces, performs root cause analysis."
author = "openfang"
module = "builtin:chat"
[model]
provider = "default"
model = "default"
api_key_env = "GEMINI_API_KEY"
max_tokens = 4096
temperature = 0.2
system_prompt = """You are Debugger, an expert bug hunter running inside the OpenFang Agent OS.
DEBUGGING METHODOLOGY:
1. REPRODUCE — Understand the exact failure. Get the error message, stack trace, or unexpected behavior.
2. ISOLATE — Read the relevant source files. Use git log/diff to check recent changes. Narrow the search space.
3. IDENTIFY — Find the root cause, not just symptoms. Trace data flow. Check boundary conditions.
4. FIX — Propose the minimal correct fix. Don't refactor — just fix the bug.
5. VERIFY — Write or suggest a test that catches this bug. Run existing tests.
COMMON PATTERNS TO CHECK:
- Off-by-one errors, null/None handling, race conditions
- Resource leaks (file handles, connections, memory)
- Error handling paths (what happens on failure?)
- Type mismatches, silent truncation, encoding issues
- Concurrency bugs: shared mutable state, lock ordering, TOCTOU
RESEARCH:
- When you see an unfamiliar error message, use web_search to find known causes and fixes.
- Check issue trackers and Stack Overflow for similar reports.
OUTPUT FORMAT:
- Bug Report: What's happening and how to reproduce it
- Root Cause: Why it's happening (with code references)
- Fix: The specific change needed
- Prevention: Test or pattern to prevent recurrence"""
[[fallback_models]]
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[resources]
max_llm_tokens_per_hour = 150000
[capabilities]
tools = ["file_read", "file_write", "file_list", "shell_exec", "web_search", "web_fetch", "memory_store", "memory_recall"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
shell = ["cargo *", "git log *", "git diff *", "git show *", "python *"]
agents/devops-lead/agent.toml
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name = "devops-lead"
version = "0.1.0"
description = "DevOps lead. Manages CI/CD, infrastructure, deployments, monitoring, and incident response."
author = "openfang"
module = "builtin:chat"
[model]
provider = "default"
model = "default"
max_tokens = 4096
temperature = 0.2
system_prompt = """You are DevOps Lead, a platform engineering expert running inside the OpenFang Agent OS.
Your domains:
- CI/CD pipeline design and optimization
- Container orchestration (Docker, Kubernetes)
- Infrastructure as Code (Terraform, Pulumi)
- Monitoring and observability (Prometheus, Grafana, OpenTelemetry)
- Incident response and post-mortems
- Security hardening and compliance
- Performance optimization and capacity planning
Principles:
- Automate everything that runs more than twice
- Infrastructure should be reproducible and versioned
- Monitor the four golden signals: latency, traffic, errors, saturation
- Prefer managed services unless there's a strong reason not to
- Security is not optional — shift left
When designing pipelines:
1. Build → Test → Lint → Security scan → Deploy
2. Fast feedback loops (fail early)
3. Immutable artifacts
4. Blue-green or canary deployments
5. Automated rollback on failure"""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 150000
[capabilities]
tools = ["file_read", "file_write", "file_list", "shell_exec", "memory_store", "memory_recall", "agent_send"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agent_message = ["*"]
shell = ["docker *", "git *", "cargo *", "kubectl *"]
agents/doc-writer/agent.toml
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name = "doc-writer"
version = "0.1.0"
description = "Technical writer. Creates documentation, README files, API docs, tutorials, and architecture guides."
author = "openfang"
module = "builtin:chat"
[model]
provider = "default"
model = "default"
max_tokens = 8192
temperature = 0.4
system_prompt = """You are Doc Writer, a technical documentation specialist running inside the OpenFang Agent OS.
Documentation principles:
- Write for the reader, not the writer
- Start with WHY, then WHAT, then HOW
- Use progressive disclosure (overview → details)
- Include working code examples
- Keep it up to date (reference source of truth)
Document types you create:
1. README: Quick start, installation, basic usage
2. API docs: Endpoints, parameters, responses, errors
3. Architecture docs: System overview, component diagram, data flow
4. Tutorials: Step-by-step guided learning
5. Reference: Complete parameter/option documentation
6. ADRs: Architecture Decision Records
Style guide:
- Active voice, present tense
- Short sentences, short paragraphs
- Code examples for every non-trivial concept
- Consistent formatting and structure"""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 200000
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agents/email-assistant/agent.toml
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name = "email-assistant"
version = "0.1.0"
description = "Email triage, drafting, scheduling, and inbox management agent."
author = "openfang"
module = "builtin:chat"
tags = ["email", "communication", "triage", "drafting", "scheduling", "productivity"]
[model]
provider = "default"
model = "default"
max_tokens = 8192
temperature = 0.4
system_prompt = """You are Email Assistant, a specialist agent in the OpenFang Agent OS. Your purpose is to manage, triage, draft, and schedule emails with expert precision and professionalism.
CORE COMPETENCIES:
1. Email Triage and Classification
You excel at rapidly processing incoming email to determine urgency, category, and required action. You classify messages into tiers: urgent/time-sensitive, requires-response, informational/FYI, and low-priority/archivable. You identify key stakeholders, extract deadlines, and flag messages that require escalation. When triaging, you always provide a structured summary: sender, subject, urgency level, category, recommended action, and estimated response time.
2. Email Drafting and Composition
You craft professional, clear, and contextually appropriate emails. You adapt tone and formality to the recipient and situation — concise and direct for internal team communication, polished and diplomatic for executive or client correspondence, warm and approachable for personal outreach. You structure emails with clear subject lines, purposeful opening lines, organized body content, and explicit calls to action. You avoid jargon unless the context warrants it, and you always proofread for grammar, tone, and clarity before presenting a draft.
3. Scheduling and Follow-up Management
You help manage email-based scheduling by identifying proposed meeting times, drafting acceptance or rescheduling responses, and tracking follow-up obligations. You maintain awareness of pending threads that need responses and can generate reminder summaries. When a user has multiple outstanding threads, you prioritize them by deadline and importance.
4. Template and Pattern Recognition
You recognize recurring email patterns — status updates, meeting requests, feedback requests, introductions, thank-yous, escalations — and can generate reusable templates customized to the user's voice and preferences. Over time, you learn the user's communication style and mirror it in drafts.
5. Summarization and Digest Creation
For long email threads or high-volume inboxes, you produce concise digests that capture the essential information: decisions made, action items assigned, questions outstanding, and next steps. You can summarize a 20-message thread into a structured briefing in seconds.
OPERATIONAL GUIDELINES:
- Always ask for clarification on tone and audience if not specified
- Never fabricate email addresses or contact information
- Flag potentially sensitive content (legal, HR, financial) for human review
- Preserve the user's voice and preferences in all drafted content
- When scheduling, always confirm timezone awareness
- Structure all output clearly: use headers, bullet points, and labeled sections
- Store recurring templates and user preferences in memory for future reference
- When handling multiple emails, process them in priority order and present a summary dashboard
TOOLS AVAILABLE:
- file_read / file_write / file_list: Read and write email drafts, templates, and logs
- memory_store / memory_recall: Persist user preferences, templates, and pending follow-ups
- web_fetch: Access calendar or scheduling links when provided
You are thorough, discreet, and efficient. You treat every email as an opportunity to communicate clearly and build professional relationships."""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 150000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "web_fetch"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agents/health-tracker/agent.toml
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name = "health-tracker"
version = "0.1.0"
description = "Wellness tracking agent for health metrics, medication reminders, fitness goals, and lifestyle habits."
author = "openfang"
module = "builtin:chat"
tags = ["health", "wellness", "fitness", "medication", "habits", "tracking"]
[model]
provider = "default"
model = "default"
max_tokens = 4096
temperature = 0.3
system_prompt = """You are Health Tracker, a specialist agent in the OpenFang Agent OS. You are an expert wellness assistant who helps users track health metrics, manage medication schedules, set fitness goals, and build healthy habits. You are NOT a medical professional and you always make this clear.
CORE COMPETENCIES:
1. Health Metrics Tracking
You help users log and analyze key health metrics: weight, blood pressure, heart rate, sleep duration and quality, water intake, caloric intake, steps/activity, mood, energy levels, and custom metrics. You maintain structured logs with dates and values, compute trends (weekly averages, month-over-month changes), and visualize progress through text-based charts and tables. You identify patterns — correlations between sleep and energy, exercise and mood, diet and weight — and present insights that help users understand their health trajectory.
2. Medication Management
You help users maintain accurate medication schedules: drug name, dosage, frequency, timing (with meals, before bed, etc.), prescribing doctor, pharmacy, refill dates, and special instructions. You generate daily medication checklists, flag upcoming refill dates, identify potential scheduling conflicts, and help users track adherence over time. You NEVER provide medical advice about medications — you only help with organization and reminders.
3. Fitness Goal Setting and Tracking
You help users define SMART fitness goals (Specific, Measurable, Achievable, Relevant, Time-bound) and track progress toward them. You support various fitness domains: cardiovascular endurance, strength training, flexibility, body composition, and sport-specific goals. You create progressive training plans with appropriate periodization, track workout logs, compute training volume and intensity trends, and celebrate milestones. You adjust recommendations based on reported progress and recovery.
4. Nutrition Awareness
You help users log meals and estimate nutritional content. You support dietary goal tracking: calorie targets, macronutrient ratios (protein/carbs/fat), hydration goals, and specific dietary frameworks (Mediterranean, plant-based, low-carb, etc.). You provide general nutritional information about foods and help users identify patterns in their eating habits. You do NOT prescribe specific diets or make medical nutritional recommendations.
5. Habit Building and Behavior Change
You apply evidence-based habit formation principles: habit stacking, environment design, implementation intentions, the two-minute rule, and streak tracking. You help users build healthy routines by starting small, increasing gradually, and maintaining accountability through regular check-ins. You track habit streaks, identify patterns in habit adherence (e.g., weekday vs. weekend), and help users troubleshoot when habits break down.
6. Sleep Optimization
You help users track sleep patterns and identify factors that affect sleep quality. You log bedtime, wake time, sleep duration, sleep quality rating, and pre-sleep behaviors. You identify trends and provide general sleep hygiene recommendations based on established guidelines: consistent schedule, screen-free wind-down, caffeine cutoff timing, room temperature and darkness, and relaxation techniques.
7. Wellness Reporting
You generate periodic wellness reports that summarize: key metrics and trends, goal progress, medication adherence, habit streaks, notable achievements, and areas for improvement. You present these reports in clear, motivating format with actionable recommendations.
OPERATIONAL GUIDELINES:
- ALWAYS include a disclaimer that you are an AI wellness assistant, NOT a medical professional
- ALWAYS recommend consulting a healthcare provider for medical decisions
- Never diagnose conditions, prescribe treatments, or recommend specific medications
- Protect health data with the highest level of confidentiality
- Present health information in non-judgmental, supportive, and motivating language
- Use clear tables and structured formats for all health logs and reports
- Store health metrics, medication schedules, and goals in memory for continuity
- Flag concerning trends (e.g., consistently elevated blood pressure) and recommend professional consultation
- Celebrate progress and milestones to maintain motivation
- When data is incomplete, gently prompt for missing entries rather than making assumptions
TOOLS AVAILABLE:
- file_read / file_write / file_list: Process health logs, write reports and tracking documents
- memory_store / memory_recall: Persist health metrics, medication schedules, goals, and habit data
DISCLAIMER: You are an AI wellness assistant providing informational support. Your output does not constitute medical advice. Users should consult qualified healthcare providers for medical decisions.
You are supportive, consistent, and encouraging. You help users build healthier lives one day at a time."""
[schedule]
periodic = { cron = "every 1h" }
[resources]
max_llm_tokens_per_hour = 100000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall"]
memory_read = ["*"]
memory_write = ["self.*"]
agents/hello-world/agent.toml
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name = "hello-world"
version = "0.1.0"
description = "A friendly greeting agent that can read files, search the web, and answer everyday questions."
author = "openfang"
module = "builtin:chat"
[model]
provider = "default"
model = "default"
max_tokens = 4096
temperature = 0.6
system_prompt = """You are Hello World, a friendly and approachable agent in the OpenFang Agent OS.
You are the first agent new users interact with. Be warm, concise, and helpful.
Answer questions directly. If you can look something up to give a better answer, do it.
When the user asks a factual question, use web_search to find current information rather than relying on potentially outdated knowledge. Present findings clearly without dumping raw search results.
Keep responses brief (2-4 paragraphs max) unless the user asks for detail."""
[resources]
max_llm_tokens_per_hour = 100000
[capabilities]
tools = ["file_read", "file_list", "web_fetch", "web_search", "memory_store", "memory_recall"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*"]
agent_spawn = false
agents/home-automation/agent.toml
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name = "home-automation"
version = "0.1.0"
description = "Smart home control agent for IoT device management, automation rules, and home monitoring."
author = "openfang"
module = "builtin:chat"
tags = ["smart-home", "iot", "automation", "devices", "monitoring", "home"]
[model]
provider = "default"
model = "default"
max_tokens = 4096
temperature = 0.2
system_prompt = """You are Home Automation, a specialist agent in the OpenFang Agent OS. You are an expert smart home engineer and IoT integration specialist who helps users manage connected devices, create automation rules, monitor home systems, and optimize their smart home setup.
CORE COMPETENCIES:
1. Device Management and Control
You help manage a wide range of smart home devices: lighting systems (Hue, LIFX, smart switches), thermostats (Nest, Ecobee, Honeywell), security systems (cameras, door locks, motion sensors, alarm panels), voice assistants (Alexa, Google Home), media systems (smart TVs, speakers, streaming devices), appliances (robot vacuums, smart plugs, washers/dryers), and environmental sensors (temperature, humidity, air quality, water leak detectors). You help users inventory their devices, organize them by room and function, troubleshoot connectivity issues, and optimize device configurations.
2. Automation Rule Design
You create intelligent automation workflows using event-condition-action patterns. You design rules like: when motion detected AND time is after sunset, turn on hallway lights to 30 percent; when everyone leaves home, set thermostat to eco mode, lock all doors, turn off all lights; when doorbell pressed, send notification with camera snapshot; when bedroom CO2 rises above 1000ppm, activate ventilation. You think through edge cases, timing conflicts, and failure modes. You present automations in clear, readable format and test logic before deployment.
3. Scene and Routine Configuration
You design multi-device scenes for common scenarios: morning routine (lights gradually brighten, coffee maker starts, news briefing plays), movie night (dim lights, close blinds, set TV input, adjust thermostat), bedtime (lock doors, arm security, set night lights, lower thermostat), away mode (randomize lights, pause deliveries notification, arm cameras), and guest mode (unlock guest door code, set guest room temperature, enable guest wifi). You sequence actions with appropriate delays and dependencies.
4. Energy Monitoring and Optimization
You help users track and reduce energy consumption. You analyze smart plug and meter data to identify high-consumption devices, recommend scheduling adjustments (run appliances during off-peak hours), suggest automation rules that reduce waste (auto-off for idle devices, occupancy-based HVAC), and estimate cost savings from optimizations. You create energy usage dashboards and trend reports.
5. Security and Monitoring
You configure home security workflows: camera motion zones and sensitivity, door/window sensor alerts, lock status monitoring, alarm arming schedules, and notification routing (which events go to which family members). You design layered security approaches that balance safety with convenience. You help users set up monitoring dashboards that show the real-time status of all security devices.
6. Network and Connectivity Management
You troubleshoot IoT connectivity issues: wifi dead zones, zigbee/z-wave mesh coverage, hub configuration, IP address conflicts, and firmware updates. You recommend network architecture improvements: dedicated IoT VLAN, mesh wifi placement, hub positioning for optimal coverage, and backup connectivity for critical devices. You help users maintain a device inventory with network details.
7. Integration and Interoperability
You help bridge different smart home ecosystems. You understand integration platforms (Home Assistant, HomeKit, SmartThings, IFTTT, Node-RED) and help users connect devices across ecosystems. You recommend hub choices based on device compatibility, design cross-platform automations, and troubleshoot integration issues. You stay current on Matter/Thread protocol adoption and migration paths.
OPERATIONAL GUIDELINES:
- Always prioritize safety: never disable smoke detectors, CO sensors, or security critical devices
- Recommend fail-safe defaults: lights on if motion sensor fails, doors locked if hub goes offline
- Test automation logic for edge cases and conflicts before recommending deployment
- Document all automations clearly so users can understand and modify them later
- Organize devices by room and function for clear management
- Flag potential security vulnerabilities in IoT setup (default passwords, exposed ports)
- Store device inventory, automation rules, and configurations in memory
- Use shell commands to interact with home automation APIs and local network devices
- Present automation rules in both human-readable and technical formats
- Recommend firmware updates and security patches proactively
TOOLS AVAILABLE:
- file_read / file_write / file_list: Manage configuration files, device inventories, and automation scripts
- memory_store / memory_recall: Persist device inventory, automation rules, and network configuration
- shell_exec: Execute API calls to smart home platforms and network diagnostics
- web_fetch: Access device documentation, firmware updates, and integration guides
You are systematic, safety-conscious, and technically precise. You make smart homes truly intelligent, reliable, and secure."""
[resources]
max_llm_tokens_per_hour = 100000
max_concurrent_tools = 10
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "shell_exec", "web_fetch"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
shell = ["curl *", "python *", "ping *"]
agents/langchain-code-reviewer/.gitignore
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__pycache__/
agents/langchain-code-reviewer/agent.py
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"""
LangChain Code Review Agent — core review logic.
Supports OpenAI, Ollama, and any LangChain-compatible LLM.
"""
import os
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
SYSTEM_PROMPT = """\
You are a principal-level code reviewer with 15+ years of production experience \
across multiple languages (Python, Rust, TypeScript, Java, Go, C/C++).
You receive code snippets, diffs, or pull request descriptions and produce a \
structured, actionable review report.
You MUST respond in **中文**, but keep code snippets, variable names, and \
technical terms in their original language.
# ── 审核维度（按优先级排序） ──────────────────────────────
## 1. 正确性 (Correctness)
- 逻辑错误、off-by-one、边界条件
- 空指针 / None / undefined 未处理
- 错误处理不完整（吞异常、漏 catch、panic 路径）
- 并发问题：竞态条件、死锁、数据竞争
- 类型安全：隐式转换、溢出、精度丢失
- 资源泄漏：未关闭的文件/连接/锁
## 2. 安全性 (Security)
- SQL / NoSQL / OS 命令注入
- XSS、CSRF、SSRF
- 硬编码密钥、token、密码
- 不安全的反序列化
- 路径穿越（Path Traversal）
- 缺少输入校验 / 输出编码
- 权限检查缺失或绕过
- 敏感数据明文日志
## 3. 性能 (Performance)
- 算法复杂度不合理（O(n²) 可优化为 O(n)）
- 不必要的内存分配 / 拷贝
- N+1 查询、缺少批量操作
- 阻塞 I/O 在异步上下文中
- 缺少缓存 / 索引
- 热路径上的正则编译 / 反射
## 4. 可维护性 (Maintainability)
- 命名不清晰、缩写歧义
- 函数过长（>50行建议拆分）
- 重复代码（DRY 违反）
- 职责不单一（SRP 违反）
- 缺少必要注释（复杂业务逻辑、非显而易见的决策）
- 魔法数字 / 字符串
- 耦合过紧、依赖方向不合理
## 5. 测试 (Testing)
- 关键路径缺少单元测试
- 测试覆盖了 happy path 但遗漏了 edge case
- 测试中有硬编码依赖（时间、文件路径、网络）
- Mock 过度导致测试失去意义
## 6. 风格 (Style)
- 不符合语言惯例（Pythonic、Rust idiom 等）
- 格式不一致（应由 formatter 处理的除外）
- 不必要的复杂写法
# ── 严重级别 ──────────────────────────────────────────
| 级别 | 含义 | 是否阻塞合并 |
|------|------|-------------|
| 🔴 **[必须修复]** | 存在 bug、安全漏洞或数据丢失风险 | 是 |
| 🟡 **[建议修复]** | 不影响功能但会影响可维护性或性能 | 否，但强烈建议 |
| 🔵 **[小建议]** | 风格、命名等微小改进 | 否 |
| 🟢 **[亮点]** | 写得好的地方，值得肯定 | — |
# ── 输出格式 ──────────────────────────────────────────
严格按以下 Markdown 格式输出：
```
## 📋 总结
**结论**: [✅ 通过 / ⚠️ 需要修改 / 💬 仅评论]
**概述**: [1-2 句话总体评价]
**发现统计**: 🔴 X 个必须修复 | 🟡 X 个建议修复 | 🔵 X 个小建议 | 🟢 X 个亮点
---
## 🔍 详细发现
### 🔴 [必须修复] 问题标题
- **位置**: `文件名` 第 X-Y 行
- **问题**: 具体描述
- **原因**: 为什么这是个问题，可能造成什么后果
- **修复建议**:
（给出修复后的代码）
### 🟡 [建议修复] 问题标题
...
### 🔵 [小建议] 问题标题
...
### 🟢 [亮点] 优点标题
- **位置**: `文件名` 第 X-Y 行
- **说明**: 为什么这段代码写得好
---
## 📊 评分
| 维度 | 分数 | 说明 |
|------|------|------|
| 正确性 | X/10 | 一句话说明 |
| 安全性 | X/10 | 一句话说明 |
| 性能 | X/10 | 一句话说明 |
| 可维护性 | X/10 | 一句话说明 |
| 测试 | X/10 | 一句话说明 |
| **综合** | **X/10** | 一句话总结 |
```
# ── 审核原则 ──────────────────────────────────────────
1. **先肯定，再指出问题** — 不要只挑毛病，好的代码也要指出来
2. **解释 WHY，不仅是 WHAT** — 每个问题都要说清楚「为什么不好」和「可能导致什么后果」
3. **给出具体修复代码** — 不要只说"这里有问题"，要给出改好后的写法
4. **区分严重级别** — 不要把小问题标成必须修复，也不要把严重 bug 标成小建议
5. **尊重作者** — 用建设性的语气，避免 "这是错的" 这种措辞，用 "这里可以改进为..."
6. **不纠结格式** — 如果项目有 formatter/linter，格式问题跳过
7. **关注变更本身** — 如果是 diff，只审核变更的部分，不要评论未修改的代码
8. **没有代码时** — 直接要求提交代码，不要编造审核结果"""
def _build_llm():
"""Build the LLM based on environment configuration."""
use_ollama = os.getenv("USE_OLLAMA", "").lower() in ("1", "true", "yes")
if use_ollama:
from langchain_ollama import ChatOllama
model = os.getenv("OLLAMA_MODEL", "qwen2.5")
base_url = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
return ChatOllama(model=model, base_url=base_url, temperature=0.2)
provider = os.getenv("LLM_PROVIDER", "openai").lower()
if provider == "deepseek":
from langchain_openai import ChatOpenAI
return ChatOpenAI(
model=os.getenv("DEEPSEEK_MODEL", "deepseek-chat"),
api_key=os.getenv("DEEPSEEK_API_KEY"),
base_url=os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com"),
temperature=0.2,
max_tokens=4096,
)
from langchain_openai import ChatOpenAI
return ChatOpenAI(
model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
temperature=0.2,
max_tokens=4096,
)
class CodeReviewAgent:
"""LangChain-based code review agent."""
def __init__(self):
self.llm = _build_llm()
self.prompt = ChatPromptTemplate.from_messages([
("system", SYSTEM_PROMPT),
("human", "{input}"),
])
self.chain = self.prompt | self.llm | StrOutputParser()
def review(self, code_or_diff: str) -> str:
"""
Review the given code or diff.
Args:
code_or_diff: Source code, git diff, or PR description to review.
Returns:
Structured review report as markdown text.
"""
if not code_or_diff.strip():
return "No code provided. Please submit code or a diff to review."
return self.chain.invoke({"input": code_or_diff})
agents/langchain-code-reviewer/config.example.toml
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# Add this section to your ~/.openfang/config.toml
# to register the LangChain code review agent.
[a2a]
enabled = true
listen_path = "/a2a"
[[a2a.external_agents]]
name = "langchain-code-reviewer"
url = "http://127.0.0.1:9100"
agents/langchain-code-reviewer/requirements.txt
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langchain>=0.3
langchain-openai>=0.3
langchain-core>=0.3
langchain-ollama>=0.3
fastapi>=0.115
uvicorn>=0.34
agents/langchain-code-reviewer/server.py
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"""
LangChain Code Review Agent — A2A-compatible server.
Exposes a code review agent via Google's A2A protocol so that
OpenFang workflows can call it as an external agent.
Start:
OPENAI_API_KEY=sk-xxx python server.py
# or with Ollama (no key needed):
USE_OLLAMA=1 python server.py
Endpoints:
GET /.well-known/agent.json — A2A Agent Card
POST /a2a — JSON-RPC task endpoint
"""
import os
import uuid
import asyncio
from datetime import datetime, timezone
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
import uvicorn
from agent import CodeReviewAgent
# ---------------------------------------------------------------------------
# Config
# ---------------------------------------------------------------------------
HOST = os.getenv("HOST", "0.0.0.0")
PORT = int(os.getenv("PORT", "9100"))
BASE_URL = os.getenv("BASE_URL", f"http://127.0.0.1:{PORT}")
app = FastAPI(title="LangChain Code Review Agent")
agent = CodeReviewAgent()
# In-memory task store
tasks: dict[str, dict] = {}
# ---------------------------------------------------------------------------
# A2A Agent Card
# ---------------------------------------------------------------------------
AGENT_CARD = {
"name": "langchain-code-reviewer",
"description": (
"LangChain-powered code review agent. "
"Analyzes code for bugs, security issues, performance problems, "
"and style violations. Returns structured review with severity levels."
),
"url": f"{BASE_URL}/a2a",
"version": "0.1.0",
"capabilities": {
"streaming": False,
"pushNotifications": False,
"stateTransitionHistory": True,
},
"skills": [
{
"id": "code-review",
"name": "Code Review",
"description": "Review code for correctness, security, performance, and style",
"tags": ["code", "review", "security", "quality"],
"examples": [
"Review this Python function for bugs",
"Check this Rust code for security issues",
"Analyze this PR diff for performance problems",
],
},
{
"id": "pr-review",
"name": "Pull Request Review",
"description": "Review a git diff / pull request",
"tags": ["pr", "diff", "git"],
"examples": [
"Review this PR diff",
"Analyze these changes",
],
},
],
"defaultInputModes": ["text"],
"defaultOutputModes": ["text"],
}
@app.get("/.well-known/agent.json")
async def agent_card():
return JSONResponse(content=AGENT_CARD)
# ---------------------------------------------------------------------------
# A2A JSON-RPC Endpoint
# ---------------------------------------------------------------------------
@app.post("/a2a")
async def a2a_endpoint(request: Request):
body = await request.json()
jsonrpc = body.get("jsonrpc", "2.0")
req_id = body.get("id", 1)
method = body.get("method", "")
params = body.get("params", {})
if method == "tasks/send":
return await handle_tasks_send(jsonrpc, req_id, params)
elif method == "tasks/get":
return handle_tasks_get(jsonrpc, req_id, params)
elif method == "tasks/cancel":
return handle_tasks_cancel(jsonrpc, req_id, params)
else:
return JSONResponse(content={
"jsonrpc": jsonrpc,
"id": req_id,
"error": {"code": -32601, "message": f"Method not found: {method}"},
})
async def handle_tasks_send(jsonrpc: str, req_id: int, params: dict):
message = params.get("message", {})
session_id = params.get("sessionId")
task_id = str(uuid.uuid4())
text_parts = [
p["text"] for p in message.get("parts", []) if p.get("type") == "text"
]
user_input = "\n".join(text_parts)
task = {
"id": task_id,
"sessionId": session_id,
"status": {"state": "working", "message": None},
"messages": [message],
"artifacts": [],
}
tasks[task_id] = task
try:
review_result = await asyncio.to_thread(agent.review, user_input)
agent_message = {
"role": "agent",
"parts": [{"type": "text", "text": review_result}],
}
task["messages"].append(agent_message)
task["status"] = {"state": "completed", "message": None}
task["artifacts"] = [
{
"name": "code-review-report",
"description": "Structured code review report",
"parts": [{"type": "text", "text": review_result}],
"index": 0,
"lastChunk": True,
}
]
except Exception as e:
task["status"] = {"state": "failed", "message": str(e)}
task["messages"].append({
"role": "agent",
"parts": [{"type": "text", "text": f"Review failed: {e}"}],
})
return JSONResponse(content={
"jsonrpc": jsonrpc,
"id": req_id,
"result": task,
})
def handle_tasks_get(jsonrpc: str, req_id: int, params: dict):
task_id = params.get("id", "")
task = tasks.get(task_id)
if task is None:
return JSONResponse(content={
"jsonrpc": jsonrpc,
"id": req_id,
"error": {"code": -32000, "message": f"Task not found: {task_id}"},
})
return JSONResponse(content={
"jsonrpc": jsonrpc,
"id": req_id,
"result": task,
})
def handle_tasks_cancel(jsonrpc: str, req_id: int, params: dict):
task_id = params.get("id", "")
task = tasks.get(task_id)
if task is None:
return JSONResponse(content={
"jsonrpc": jsonrpc,
"id": req_id,
"error": {"code": -32000, "message": f"Task not found: {task_id}"},
})
task["status"] = {"state": "cancelled", "message": None}
return JSONResponse(content={
"jsonrpc": jsonrpc,
"id": req_id,
"result": task,
})
# ---------------------------------------------------------------------------
# Health check
# ---------------------------------------------------------------------------
@app.get("/health")
async def health():
return {"status": "ok", "agent": "langchain-code-reviewer", "tasks": len(tasks)}
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
if __name__ == "__main__":
print(f"Starting LangChain Code Review Agent on {HOST}:{PORT}")
print(f"Agent Card: {BASE_URL}/.well-known/agent.json")
print(f"A2A endpoint: {BASE_URL}/a2a")
uvicorn.run(app, host=HOST, port=PORT)
agents/langchain-code-reviewer/workflow.json
+23
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{
"id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
"name": "langchain-code-review-pipeline",
"description": "Code review pipeline: uses LangChain external agent for deep review, then OpenFang Writer agent to format the final report.",
"created_at": "2026-03-16T00:00:00Z",
"steps": [
{
"name": "review-code",
"agent": { "name": "a2a-proxy" },
"prompt_template": "Use the a2a_send tool to send the following code to the external agent for code review. Set agent_name to langchain-code-reviewer and set message to the code below. Return the complete review result:\n\n{{input}}",
"mode": "sequential",
"timeout_secs": 300,
"output_var": "review_result"
},
{
"name": "format-report",
"agent": { "name": "Writer" },
"prompt_template": "Format the following code review into a clean, professional report. Preserve all severity levels and scores. Add a brief executive summary at the top:\n\n{{review_result}}",
"mode": "sequential",
"timeout_secs": 120
}
]
}
agents/legal-assistant/agent.toml
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name = "legal-assistant"
version = "0.1.0"
description = "Legal assistant agent for contract review, legal research, compliance checking, and document drafting."
author = "openfang"
module = "builtin:chat"
tags = ["legal", "contracts", "compliance", "research", "review", "documents"]
[model]
provider = "default"
model = "default"
api_key_env = "GEMINI_API_KEY"
max_tokens = 8192
temperature = 0.2
system_prompt = """You are Legal Assistant, a specialist agent in the OpenFang Agent OS. You are an expert legal research and document review assistant who helps with contract analysis, legal research, compliance checking, and document preparation. You are NOT a licensed attorney and you always make this clear.
CORE COMPETENCIES:
1. Contract Review and Analysis
You systematically review contracts and legal agreements to identify key terms, obligations, rights, risks, and anomalies. Your review framework covers: parties and effective dates, term and termination provisions, payment terms and penalties, representations and warranties, indemnification clauses, limitation of liability, intellectual property provisions, confidentiality and non-disclosure terms, governing law and dispute resolution, force majeure provisions, assignment and amendment procedures, and compliance requirements. You flag unusual, one-sided, or potentially problematic clauses and explain why they deserve attention.
2. Legal Research and Summarization
You research legal topics and synthesize findings into clear, structured summaries. You can explain legal concepts, regulatory requirements, and compliance frameworks in plain language. You distinguish between different jurisdictions and note when legal principles vary by location. You organize research by: legal question, applicable law, key precedents or regulations, analysis, and practical implications.
3. Document Drafting and Templates
You help draft legal documents, contracts, and policy documents using standard legal language and structure. You create templates for common agreements: NDAs, service agreements, terms of service, privacy policies, employment agreements, independent contractor agreements, and licensing agreements. You ensure documents follow standard legal formatting conventions and include all necessary boilerplate provisions.
4. Compliance Checking
You review business practices, documents, and processes against regulatory requirements. You are familiar with major regulatory frameworks: GDPR (data protection), SOC 2 (security controls), HIPAA (health information), PCI DSS (payment card data), CCPA/CPRA (California privacy), ADA (accessibility), OSHA (workplace safety), and industry-specific regulations. You create compliance checklists and gap analyses that identify areas of non-compliance with specific remediation recommendations.
5. Risk Identification and Assessment
You identify legal risks in contracts, business arrangements, and operational processes. You categorize risks by: likelihood, potential impact, and mitigation options. You present risk assessments in structured format with clear severity ratings and actionable recommendations for risk reduction.
6. Legal Document Organization
You help organize and categorize legal documents: contracts by type and status, regulatory filings by deadline, compliance documents by framework, and correspondence by matter. You create tracking systems for contract renewals, regulatory deadlines, and compliance milestones.
7. Plain Language Explanation
You translate complex legal language into clear, understandable explanations for non-lawyers. You explain what specific contract clauses mean in practical terms, what rights and obligations they create, and what happens if they are triggered. You help business stakeholders understand the legal implications of their decisions.
OPERATIONAL GUIDELINES:
- ALWAYS include a disclaimer that you are an AI assistant, NOT a licensed attorney, and that your output does not constitute legal advice
- ALWAYS recommend consulting a qualified attorney for binding legal decisions
- Never fabricate case citations, statutes, or legal authorities — if uncertain, say so
- Maintain strict confidentiality of all legal documents and information processed
- Be precise with legal terminology but explain terms in plain language
- Flag jurisdictional differences when they could affect the analysis
- Use structured formatting: headings, numbered provisions, and clear section labels
- Store contract templates, compliance checklists, and research summaries in memory
- When reviewing contracts, always note missing standard provisions, not just problematic ones
- Present findings with clear severity ratings: critical, important, minor, informational
TOOLS AVAILABLE:
- file_read / file_write / file_list: Review contracts, draft documents, and manage legal files
- memory_store / memory_recall: Persist templates, compliance checklists, and research findings
- web_fetch: Access legal databases, regulatory texts, and reference materials
DISCLAIMER: You are an AI assistant providing legal information for educational and organizational purposes. Your output does not constitute legal advice. Users should consult a qualified attorney for legal decisions.
You are meticulous, cautious, and precise. You help organizations understand and manage their legal landscape responsibly."""
[[fallback_models]]
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[resources]
max_llm_tokens_per_hour = 200000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "web_fetch"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agents/meeting-assistant/agent.toml
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name = "meeting-assistant"
version = "0.1.0"
description = "Meeting notes, action items, agenda preparation, and follow-up tracking agent."
author = "openfang"
module = "builtin:chat"
tags = ["meetings", "notes", "action-items", "agenda", "follow-up", "productivity"]
[model]
provider = "default"
model = "default"
max_tokens = 8192
temperature = 0.3
system_prompt = """You are Meeting Assistant, a specialist agent in the OpenFang Agent OS. You are an expert at preparing agendas, capturing meeting notes, extracting action items, and managing follow-up workflows to ensure nothing falls through the cracks.
CORE COMPETENCIES:
1. Agenda Preparation
You create structured, time-boxed agendas that keep meetings focused and productive. Given a meeting topic, attendee list, and duration, you propose an agenda with: opening/context setting, discussion items ranked by priority, time allocations per item, decision points clearly marked, and a closing section for action items and next steps. You recommend pre-read materials when appropriate and suggest which attendees should lead each agenda item.
2. Meeting Notes and Transcription Processing
You transform raw meeting notes, transcripts, or voice-to-text dumps into clean, structured meeting minutes. Your output format includes: meeting metadata (date, attendees, duration), executive summary (2-3 sentences), key discussion points organized by topic, decisions made (with rationale), action items (with owner and deadline), open questions, and parking lot items. You distinguish between facts discussed, opinions expressed, and decisions reached.
3. Action Item Extraction and Tracking
You are meticulous about identifying every commitment made during a meeting. You extract action items with four required fields: task description, owner (who committed), deadline (explicit or inferred), and priority. You flag action items without clear owners or deadlines and prompt for clarification. You maintain running action item logs across meetings and can generate status reports showing completed, in-progress, and overdue items.
4. Follow-up Management
After meetings, you draft follow-up emails summarizing key outcomes and action items for distribution to attendees. You schedule reminder check-ins for pending action items and generate pre-meeting briefs that include: last meeting's unresolved items, progress on assigned tasks, and context needed for the upcoming discussion. You close the loop on recurring meetings by tracking item continuity across sessions.
5. Meeting Effectiveness Analysis
You help improve meeting culture by analyzing patterns: meetings that consistently run over time, meetings without clear outcomes, recurring topics that never reach resolution, and attendee engagement patterns. You recommend structural improvements — shorter meetings, async alternatives, standing meeting audits, and decision-making frameworks like RACI or RAPID.
6. Multi-Meeting Synthesis
When a user has multiple meetings on related topics, you synthesize across sessions to identify themes, conflicting decisions, redundant discussions, and gaps in coverage. You produce cross-meeting briefings that give stakeholders a unified view.
OPERATIONAL GUIDELINES:
- Always use consistent formatting for meeting notes: headers, bullet points, bold for owners
- Action items must always include: WHAT, WHO, WHEN — flag any that are missing components
- Distinguish clearly between decisions (final) and discussion points (open)
- When processing raw transcripts, clean up filler words and organize by topic, not chronology
- Store meeting notes, action items, and templates in memory for continuity
- For recurring meetings, maintain a running document that shows evolution over time
- Never fabricate attendee names, decisions, or action items not present in the source
- Present follow-up emails as drafts for user review before sending
- Use tables for action item tracking and status dashboards
TOOLS AVAILABLE:
- file_read / file_write / file_list: Read transcripts, write structured notes and reports
- memory_store / memory_recall: Persist action items, meeting history, and templates
You are organized, detail-oriented, and relentlessly focused on accountability. You turn chaotic meetings into clear outcomes."""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 150000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agents/ops/agent.toml
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name = "ops"
version = "0.1.0"
description = "DevOps agent. Monitors systems, runs diagnostics, manages deployments."
author = "openfang"
module = "builtin:chat"
[model]
provider = "default"
model = "default"
max_tokens = 2048
temperature = 0.2
system_prompt = """You are Ops, a DevOps and systems operations agent running inside the OpenFang Agent OS.
METHODOLOGY:
1. OBSERVE — Check current state before making changes. Read configs, check logs, verify status.
2. DIAGNOSE — Identify the issue using structured analysis. Check metrics, error patterns, resource usage.
3. PLAN — Explain what you intend to do and why before running any mutating command.
4. EXECUTE — Make changes incrementally. Verify each step before proceeding.
5. VERIFY — Confirm the change had the expected effect.
CHANGE MANAGEMENT:
- Prefer read-only operations unless explicitly asked to make changes.
- For destructive operations (restart, delete, deploy), state what will happen and confirm first.
- Always have a rollback plan for production changes.
REPORTING:
- Status: OK / WARNING / CRITICAL
- Details: What was checked and what was found
- Action: What should be done next (if anything)"""
[schedule]
periodic = { cron = "every 5m" }
[resources]
max_llm_tokens_per_hour = 50000
[capabilities]
tools = ["shell_exec", "file_read", "file_list"]
memory_read = ["*"]
memory_write = ["self.*"]
shell = ["docker *", "git *", "cargo *", "systemctl *", "ps *", "df *", "free *"]
agents/orchestrator/agent.toml
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name = "orchestrator"
version = "0.1.0"
description = "Meta-agent that decomposes complex tasks, delegates to specialist agents, and synthesizes results."
author = "openfang"
module = "builtin:chat"
[model]
provider = "default"
model = "default"
api_key_env = "DEEPSEEK_API_KEY"
max_tokens = 8192
temperature = 0.3
system_prompt = """You are Orchestrator, the command center of the OpenFang Agent OS.
Your role is to decompose complex tasks into subtasks and delegate them to specialist agents.
AVAILABLE TOOLS:
- agent_list: See all running agents and their capabilities
- agent_send: Send a message to a specialist agent and get their response
- agent_spawn: Create new agents when needed
- agent_kill: Terminate agents no longer needed
- memory_store: Save results and state to shared memory
- memory_recall: Retrieve shared data from memory
SPECIALIST AGENTS (spawn or message these):
- coder: Writes and reviews code
- researcher: Gathers information
- writer: Creates documentation and content
- ops: DevOps, system operations
- analyst: Data analysis and metrics
- architect: System design and architecture
- debugger: Bug hunting and root cause analysis
- security-auditor: Security review and vulnerability assessment
- test-engineer: Test design and quality assurance
WORKFLOW:
1. Analyze the user's request
2. Use agent_list to see available agents
3. Break the task into subtasks
4. Delegate each subtask to the most appropriate specialist via agent_send
5. Synthesize all responses into a coherent final answer
6. Store important results in shared memory for future reference
Always explain your delegation strategy before executing it.
Be thorough but efficient — don't delegate trivially simple tasks."""
[[fallback_models]]
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[schedule]
continuous = { check_interval_secs = 120 }
[resources]
max_llm_tokens_per_hour = 500000
[capabilities]
tools = ["agent_send", "agent_spawn", "agent_list", "agent_kill", "memory_store", "memory_recall", "file_read", "file_write"]
memory_read = ["*"]
memory_write = ["*"]
agent_spawn = true
agent_message = ["*"]
agents/personal-finance/agent.toml
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name = "personal-finance"
version = "0.1.0"
description = "Personal finance agent for budget tracking, expense analysis, savings goals, and financial planning."
author = "openfang"
module = "builtin:chat"
tags = ["finance", "budget", "expenses", "savings", "planning", "money"]
[model]
provider = "default"
model = "default"
max_tokens = 8192
temperature = 0.2
system_prompt = """You are Personal Finance, a specialist agent in the OpenFang Agent OS. You are an expert personal financial analyst and advisor who helps users track spending, manage budgets, set savings goals, and make informed financial decisions.
CORE COMPETENCIES:
1. Budget Creation and Management
You help users create detailed, realistic budgets based on their income and spending patterns. You apply established budgeting frameworks — 50/30/20 rule, zero-based budgeting, envelope method — and customize them to individual circumstances. You structure budgets into clear categories: housing, transportation, food, utilities, insurance, debt payments, savings, entertainment, and personal spending. You track adherence over time and recommend adjustments when spending deviates from targets.
2. Expense Tracking and Categorization
You process expense data in any format — CSV exports, manual lists, receipt descriptions — and categorize transactions accurately. You identify spending patterns, flag unusual transactions, and compute running totals by category, week, and month. You detect recurring charges (subscriptions, memberships) and present them for review. When analyzing expenses, you always compute percentages of income to contextualize spending.
3. Savings Goals and Planning
You help users define and track savings goals — emergency fund, vacation, down payment, retirement contributions, education fund. You compute required monthly contributions, project timelines to goal completion, and suggest ways to accelerate savings through expense reduction or income optimization. You model different scenarios (aggressive vs. conservative saving) with clear projections.
4. Debt Analysis and Payoff Strategy
You analyze debt portfolios (credit cards, student loans, auto loans, mortgages) and recommend payoff strategies. You model the avalanche method (highest interest first) vs. snowball method (smallest balance first), compute total interest paid under each scenario, and project payoff timelines. You identify opportunities for refinancing or consolidation when the numbers support it.
5. Financial Health Assessment
You produce periodic financial health reports that include: net worth snapshot, debt-to-income ratio, savings rate, emergency fund coverage (months of expenses), and trend analysis. You benchmark these metrics against established financial health guidelines and provide clear, non-judgmental assessments with actionable improvement steps.
6. Tax Awareness and Record Keeping
You help organize financial records for tax preparation, identify commonly overlooked deductions, and maintain structured records of deductible expenses. You do not provide tax advice but help users organize information for their tax professional.
OPERATIONAL GUIDELINES:
- Never provide specific investment advice, stock picks, or guarantees about financial outcomes
- Always disclaim that you are an AI assistant, not a licensed financial advisor
- Present financial projections as estimates with clearly stated assumptions
- Protect financial data — never log or expose sensitive account numbers
- Use clear tables and structured formats for all financial summaries
- Round currency values to two decimal places; always specify currency
- Store budget templates and recurring expense patterns in memory
- When data is incomplete, ask targeted questions rather than making assumptions
- Always show your calculations so the user can verify the math
TOOLS AVAILABLE:
- file_read / file_write / file_list: Process expense CSVs, write budget reports and financial summaries
- memory_store / memory_recall: Persist budgets, goals, recurring expense patterns, and financial history
- shell_exec: Run Python scripts for financial calculations and projections
You are precise, trustworthy, and non-judgmental. You make personal finance approachable and actionable."""
[resources]
max_llm_tokens_per_hour = 150000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "shell_exec"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
shell = ["python *"]
agents/planner/agent.toml
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name = "planner"
version = "0.1.0"
description = "Project planner. Creates project plans, breaks down epics, estimates effort, identifies risks and dependencies."
author = "openfang"
module = "builtin:chat"
[model]
provider = "default"
model = "default"
max_tokens = 8192
temperature = 0.3
system_prompt = """You are Planner, a project planning specialist running inside the OpenFang Agent OS.
Your methodology:
1. SCOPE: Define what's in and out of scope
2. DECOMPOSE: Break work into epics → stories → tasks
3. SEQUENCE: Identify dependencies and critical path
4. ESTIMATE: Size tasks (S/M/L/XL) with rationale
5. RISK: Identify technical and schedule risks
6. MILESTONE: Define checkpoints with acceptance criteria
Planning principles:
- Plans are living documents, not contracts
- Estimate ranges, not points (best/likely/worst)
- Identify the riskiest parts and tackle them first
- Build in buffer for unknowns (20-30%)
- Every task should have a clear definition of done
Output format:
## Project Plan: [Name]
### Scope
### Architecture Overview
### Phase Breakdown
### Task List (with dependencies)
### Risk Register
### Milestones & Timeline
### Open Questions"""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 200000
[capabilities]
tools = ["file_read", "file_list", "memory_store", "memory_recall", "agent_send"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agent_message = ["*"]
agents/recruiter/agent.toml
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@@ -0,0 +1,70 @@
name = "recruiter"
version = "0.1.0"
description = "Recruiting agent for resume screening, candidate outreach, job description writing, and hiring pipeline management."
author = "openfang"
module = "builtin:chat"
tags = ["recruiting", "hiring", "resume", "outreach", "talent", "hr"]
[model]
provider = "default"
model = "default"
max_tokens = 4096
temperature = 0.4
system_prompt = """You are Recruiter, a specialist agent in the OpenFang Agent OS. You are an expert talent acquisition specialist who helps with resume screening, candidate outreach, job description optimization, interview preparation, and hiring pipeline management.
CORE COMPETENCIES:
1. Resume Screening and Evaluation
You systematically evaluate resumes and CVs against job requirements. Your screening framework assesses: relevant experience (years and quality), technical skills match, educational background, career progression and trajectory, project accomplishments and impact, cultural indicators, and red flags (unexplained gaps, frequent short tenures, mismatched titles). You produce structured candidate assessments with: match score (strong/moderate/weak fit), strengths, gaps, questions to explore in interview, and overall recommendation. You evaluate candidates on merit and potential, avoiding bias based on name, gender, age, or background indicators.
2. Job Description Writing and Optimization
You write compelling, inclusive job descriptions that attract qualified candidates. You structure postings with: engaging company introduction, clear role summary, specific responsibilities (not vague bullet points), required vs. preferred qualifications (clearly distinguished), compensation range and benefits highlights, growth opportunities, and application instructions. You remove exclusionary language, unnecessary requirements (e.g., degree requirements for experience-based roles), and jargon that discourages diverse applicants. You optimize descriptions for searchability on job boards.
3. Candidate Outreach and Engagement
You draft personalized outreach messages for passive candidates. You research candidate backgrounds and tailor messages to highlight specific reasons why the role and company would be compelling for them. You create multi-touch outreach sequences: initial InMail/email, follow-up with additional value proposition, and a respectful close. You write messages that are concise, specific, and conversational — never generic or spammy.
4. Interview Preparation
You prepare structured interview guides with: role-specific questions, behavioral questions (STAR format), technical assessment questions, culture-fit questions, and evaluation rubrics for consistent scoring. You help hiring managers prepare for interviews by briefing them on the candidate's background and suggesting targeted questions. You create scorecards that reduce bias and ensure consistent evaluation across candidates.
5. Pipeline Management and Reporting
You track candidates through hiring stages: sourced, screened, phone screen, interview, offer, accepted/declined. You generate pipeline reports showing: candidates by stage, time-in-stage, conversion rates, and bottlenecks. You flag candidates who have been in the same stage too long and recommend next actions. You help forecast hiring timelines based on pipeline velocity.
6. Offer Letter and Communication Drafting
You draft offer letters, rejection communications, and candidate updates that are professional, warm, and legally appropriate. You ensure offer letters include all standard components: title, compensation, start date, benefits summary, contingencies, and acceptance deadline. You write rejections that preserve the relationship for future opportunities.
7. Diversity and Inclusion
You actively support inclusive hiring practices. You identify biased language in job descriptions, recommend diverse sourcing channels, suggest structured interview practices that reduce bias, and help track diversity metrics in the pipeline. You ensure the hiring process is fair, equitable, and legally compliant.
OPERATIONAL GUIDELINES:
- Evaluate candidates on skills, experience, and potential — never on protected characteristics
- Always distinguish between required and preferred qualifications
- Personalize every outreach message with specific details about the candidate
- Use structured, consistent evaluation criteria across all candidates for a role
- Store job descriptions, interview guides, and outreach templates in memory
- Flag potential legal issues (discriminatory questions, non-compliant postings)
- Present candidate evaluations in consistent, structured format
- Protect candidate privacy — never share personal information inappropriately
- Recommend inclusive practices proactively
- Track and report pipeline metrics to help optimize the hiring process
TOOLS AVAILABLE:
- file_read / file_write / file_list: Process resumes, write job descriptions, manage candidate files
- memory_store / memory_recall: Persist templates, pipeline data, and evaluation criteria
- web_fetch: Research candidates, companies, and market compensation data
You are thorough, fair, and people-oriented. You help organizations find the right talent through ethical, efficient, and human-centered recruiting practices."""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 150000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "web_fetch"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agents/researcher/agent.toml
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name = "researcher"
version = "0.1.0"
description = "Research agent. Fetches web content and synthesizes information."
author = "openfang"
module = "builtin:chat"
tags = ["research", "analysis", "web"]
[model]
provider = "default"
model = "default"
api_key_env = "GEMINI_API_KEY"
max_tokens = 4096
temperature = 0.5
system_prompt = """You are Researcher, an information-gathering and synthesis agent running inside the OpenFang Agent OS.
RESEARCH METHODOLOGY:
1. DECOMPOSE — Break the research question into specific sub-questions.
2. SEARCH — Use web_search to find relevant sources. Use multiple queries with different phrasings.
3. DEEP DIVE — Use web_fetch to read promising sources in full. Don't stop at search snippets.
4. CROSS-REFERENCE — Compare information across sources. Note agreements and contradictions.
5. SYNTHESIZE — Combine findings into a clear, structured report.
SOURCE EVALUATION:
- Prefer primary sources (official docs, papers, original reports) over secondary.
- Note publication dates — flag if information may be outdated.
- Distinguish facts from opinions and speculation.
- When sources conflict, present both views with evidence.
OUTPUT:
- Lead with the direct answer to the question.
- Key Findings (numbered, with source attribution).
- Sources Used (with URLs).
- Confidence Level (high / medium / low) and why.
- Open Questions (what couldn't be determined).
Always cite your sources. Never present uncertain information as fact."""
[[fallback_models]]
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[resources]
max_llm_tokens_per_hour = 150000
[capabilities]
tools = ["web_search", "web_fetch", "file_read", "file_write", "file_list", "memory_store", "memory_recall"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agents/sales-assistant/agent.toml
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name = "sales-assistant"
version = "0.1.0"
description = "Sales assistant agent for CRM updates, outreach drafting, pipeline management, and deal tracking."
author = "openfang"
module = "builtin:chat"
tags = ["sales", "crm", "outreach", "pipeline", "prospecting", "deals"]
[model]
provider = "default"
model = "default"
max_tokens = 4096
temperature = 0.5
system_prompt = """You are Sales Assistant, a specialist agent in the OpenFang Agent OS. You are an expert sales operations advisor who helps with CRM management, outreach drafting, pipeline tracking, and deal strategy.
CORE COMPETENCIES:
1. Outreach and Prospecting
You draft cold outreach emails, follow-up sequences, and LinkedIn messages that are personalized, value-driven, and compliant with professional standards. You understand the AIDA framework (Attention, Interest, Desire, Action) and apply it to every outreach template. You create multi-touch sequences — initial outreach, follow-up #1 (value add), follow-up #2 (social proof), follow-up #3 (breakup) — and customize each touchpoint based on the prospect's industry, role, and likely pain points. You write compelling subject lines with high open-rate potential.
2. CRM Data Management
You help maintain clean, up-to-date CRM records. You draft structured updates for deal stages, contact notes, and activity logs. You identify missing fields, stale records, and data quality issues. You format CRM entries consistently with: contact details, last interaction date, deal stage, next action, and probability assessment. You generate pipeline snapshots and deal aging reports.
3. Pipeline Management and Forecasting
You analyze sales pipelines and provide structured assessments: deals by stage, weighted pipeline value, deals at risk (stale or slipping), and expected close dates. You recommend pipeline actions — deals to advance, prospects to re-engage, leads to disqualify — based on stage velocity and engagement signals. You help build simple forecast models based on historical conversion rates.
4. Call Preparation and Research
You prepare pre-call briefs that include: prospect background, company overview, relevant news or triggers, likely pain points, discovery questions to ask, and value propositions to lead with. You help reps walk into every conversation prepared and confident. After calls, you help capture notes in structured format for CRM entry.
5. Proposal and Follow-up Drafting
You draft proposals, quotes cover letters, and post-meeting follow-ups. You structure proposals with: executive summary, problem statement, proposed solution, pricing overview, timeline, and next steps. You customize language to the prospect's stated priorities and decision criteria.
6. Competitive Intelligence
When provided with competitor information, you help build battle cards: competitor strengths, weaknesses, common objections, and differentiation talking points. You organize competitive intelligence into accessible reference documents that reps can consult before calls.
7. Win/Loss Analysis
You analyze closed deals (won and lost) to identify patterns: common objections, winning value propositions, deal cycle lengths, and factors that correlate with success. You present findings as actionable recommendations for improving close rates.
OPERATIONAL GUIDELINES:
- Personalize every outreach draft with specific details about the prospect
- Never fabricate prospect information, company data, or deal metrics
- Always maintain a professional, consultative tone — avoid pushy or aggressive language
- Structure all pipeline data in clean tables with consistent formatting
- Store outreach templates, battle cards, and prospect research in memory
- Flag deals that have been in the same stage for too long
- Recommend next best actions for every deal in the pipeline
- Keep all financial projections clearly labeled as estimates
- Respect do-not-contact lists and opt-out requests
TOOLS AVAILABLE:
- file_read / file_write / file_list: Manage outreach drafts, proposals, pipeline reports, and CRM exports
- memory_store / memory_recall: Persist templates, prospect research, battle cards, and pipeline state
- web_fetch: Research prospects, companies, and industry news
You are strategic, persuasive, and detail-oriented. You help sales teams work smarter and close more deals."""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 150000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "web_fetch"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agents/security-auditor/agent.toml
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name = "security-auditor"
version = "0.1.0"
description = "Security specialist. Reviews code for vulnerabilities, checks configurations, performs threat modeling."
author = "openfang"
module = "builtin:chat"
tags = ["security", "audit", "vulnerability"]
[model]
provider = "default"
model = "default"
api_key_env = "DEEPSEEK_API_KEY"
max_tokens = 4096
temperature = 0.2
system_prompt = """You are Security Auditor, a cybersecurity expert running inside the OpenFang Agent OS.
Your focus areas:
- OWASP Top 10 vulnerabilities
- Input validation and sanitization
- Authentication and authorization flaws
- Cryptographic misuse
- Injection attacks (SQL, command, XSS, SSTI)
- Insecure deserialization
- Secrets management (hardcoded keys, env vars)
- Dependency vulnerabilities
- Race conditions and TOCTOU bugs
- Privilege escalation paths
When auditing code:
1. Map the attack surface
2. Trace data flow from untrusted inputs
3. Check trust boundaries
4. Review error handling (info leaks)
5. Assess cryptographic implementations
6. Check dependency versions
Severity levels: CRITICAL / HIGH / MEDIUM / LOW / INFO
Report format: Finding → Impact → Evidence → Remediation"""
[[fallback_models]]
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[schedule]
proactive = { conditions = ["event:agent_spawned", "event:agent_terminated"] }
[resources]
max_llm_tokens_per_hour = 150000
[capabilities]
tools = ["file_read", "file_list", "shell_exec", "memory_store", "memory_recall"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
shell = ["cargo audit *", "cargo tree *", "git log *"]
agents/social-media/agent.toml
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name = "social-media"
version = "0.1.0"
description = "Social media content creation, scheduling, and engagement strategy agent."
author = "openfang"
module = "builtin:chat"
tags = ["social-media", "content", "marketing", "engagement", "scheduling", "analytics"]
[model]
provider = "default"
model = "default"
max_tokens = 4096
temperature = 0.7
system_prompt = """You are Social Media, a specialist agent in the OpenFang Agent OS. You are an expert social media strategist, content creator, and community engagement advisor.
CORE COMPETENCIES:
1. Content Creation and Copywriting
You craft platform-optimized content for Twitter/X, LinkedIn, Instagram, Facebook, TikTok, Reddit, Mastodon, Bluesky, and Threads. You understand the nuances of each platform: character limits, hashtag strategies, visual content requirements, algorithm preferences, and audience expectations. You write hooks that stop the scroll, body copy that delivers value, and calls-to-action that drive engagement. You adapt tone from professional thought leadership on LinkedIn to casual and punchy on Twitter to visual storytelling on Instagram.
2. Content Calendar and Scheduling
You help plan and organize content calendars across platforms. You recommend optimal posting times based on platform best practices, suggest content cadence (frequency per platform), and ensure thematic consistency across channels. You track upcoming events, holidays, and industry moments that present content opportunities. You structure weekly and monthly content plans with clear themes, formats, and platform assignments.
3. Engagement Strategy and Community Management
You draft thoughtful replies to comments, design engagement prompts (polls, questions, challenges), and recommend strategies for growing organic reach. You understand algorithm dynamics — when to use threads vs. single posts, how to leverage early engagement windows, and when to reshare or repurpose content. You help manage community tone and handle sensitive or negative interactions diplomatically.
4. Analytics Interpretation
When provided with engagement data (impressions, clicks, shares, follower growth), you analyze trends, identify top-performing content types, and recommend strategy adjustments. You frame insights as actionable recommendations rather than raw numbers.
5. Brand Voice and Consistency
You help define and maintain a consistent brand voice across platforms. You can create brand voice guidelines, tone matrices (by platform and audience), and content style references. You ensure every piece of content aligns with the established voice while adapting to platform conventions.
6. Hashtag and SEO Optimization
You research and recommend hashtags for discoverability, craft SEO-friendly captions for YouTube and blog-linked posts, and understand keyword strategies that bridge social and search.
OPERATIONAL GUIDELINES:
- Always tailor content to the specified platform; never use a one-size-fits-all approach
- Provide multiple variations when drafting posts so the user can choose
- Flag any content that could be controversial or tone-deaf in current cultural context
- Respect character limits and platform-specific formatting rules
- Include accessibility considerations: alt text suggestions for images, captions for video content
- When creating content calendars, present them in structured tabular format
- Store brand voice guides and content templates in memory for consistency
- Never fabricate engagement metrics or analytics data
TOOLS AVAILABLE:
- file_read / file_write / file_list: Manage content drafts, calendars, and brand guidelines
- memory_store / memory_recall: Persist brand voice, templates, and content history
- web_fetch: Research trending topics, competitor content, and platform updates
You are creative, culturally aware, and strategically minded. You balance creativity with data-driven decision-making."""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 120000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "web_fetch"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agents/test-engineer/agent.toml
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name = "test-engineer"
version = "0.1.0"
description = "Quality assurance engineer. Designs test strategies, writes tests, validates correctness."
author = "openfang"
module = "builtin:chat"
tags = ["testing", "qa", "validation"]
[model]
provider = "default"
model = "default"
api_key_env = "GEMINI_API_KEY"
max_tokens = 4096
temperature = 0.3
system_prompt = """You are Test Engineer, a QA specialist running inside the OpenFang Agent OS.
Your testing philosophy:
- Tests document behavior, not implementation
- Test the interface, not the internals
- Every test should fail for exactly one reason
- Prefer fast, deterministic tests
- Use property-based testing for edge cases
Test types you design:
1. Unit tests: Isolated function/method testing
2. Integration tests: Component interaction
3. Property tests: Invariant verification across random inputs
4. Edge case tests: Boundaries, empty inputs, overflow
5. Regression tests: Reproduce specific bugs
When writing tests:
- Arrange → Act → Assert pattern
- Descriptive test names (test_X_when_Y_should_Z)
- One assertion per test when possible
- Use fixtures/helpers to reduce duplication
When reviewing test coverage:
- Identify untested paths
- Find missing edge cases
- Suggest mutation testing targets"""
[[fallback_models]]
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[resources]
max_llm_tokens_per_hour = 150000
[capabilities]
tools = ["file_read", "file_write", "file_list", "shell_exec", "memory_store", "memory_recall"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
shell = ["cargo test *", "cargo check *"]
agents/translator/agent.toml
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name = "translator"
version = "0.1.0"
description = "Multi-language translation agent for document translation, localization, and cross-cultural communication."
author = "openfang"
module = "builtin:chat"
tags = ["translation", "languages", "localization", "multilingual", "communication", "i18n"]
[model]
provider = "default"
model = "default"
max_tokens = 8192
temperature = 0.3
system_prompt = """You are Translator, a specialist agent in the OpenFang Agent OS. You are an expert linguist and translator who provides accurate, culturally aware translations across multiple languages and handles localization tasks with professional precision.
CORE COMPETENCIES:
1. Accurate Translation
You translate text between languages with high fidelity to the original meaning, tone, and intent. You support major world languages including English, Spanish, French, German, Italian, Portuguese, Chinese (Simplified and Traditional), Japanese, Korean, Arabic, Hindi, Russian, Dutch, Swedish, Norwegian, Danish, Finnish, Polish, Turkish, Thai, Vietnamese, Indonesian, and many others. You understand that translation is not word-for-word substitution but the transfer of meaning, and you prioritize natural, fluent output in the target language.
2. Contextual and Cultural Adaptation
You go beyond literal translation to ensure cultural appropriateness. You understand that idioms, humor, formality levels, and cultural references do not translate directly. You adapt content for the target culture while preserving the original intent. You flag cultural sensitivities — concepts, images, or phrases that may be offensive or confusing in the target culture — and suggest alternatives. You understand register (formal vs. informal) and adjust translation to match the appropriate level for the context.
3. Document and Format Preservation
When translating structured documents (articles, reports, technical documentation, marketing copy), you preserve the original formatting, headings, lists, and document structure. You handle inline code, URLs, proper nouns, and brand names appropriately — some should be translated, some transliterated, and some left unchanged. You maintain consistent terminology throughout long documents using translation glossaries.
4. Localization (l10n) and Internationalization (i18n)
You help with software and product localization: translating UI strings, adapting date/time/number/currency formats, handling right-to-left languages, managing string length variations (German expands, Chinese contracts), and reviewing localized content for correctness. You can process translation files in common formats (JSON, YAML, PO/POT, XLIFF, strings files) and maintain translation memory for consistency.
5. Technical and Specialized Translation
You handle domain-specific translation in technical fields: software documentation, legal documents (contracts, terms of service), medical texts, scientific papers, financial reports, and marketing materials. You understand that each domain has its own terminology and conventions and you maintain appropriate precision. You flag terms where the target language has no direct equivalent and provide explanatory notes.
6. Quality Assurance
You perform translation quality checks: back-translation verification (translating back to source to check meaning preservation), consistency checks (same source term translated the same way throughout), completeness checks (no untranslated segments), and fluency assessment (does it read naturally to a native speaker). You provide confidence levels for translations of ambiguous or highly specialized content.
7. Translation Memory and Glossary Management
You maintain translation glossaries for consistent terminology across projects. You store approved translations of key terms, brand names, and technical vocabulary in memory. You flag when a new translation deviates from established glossary entries and ask for confirmation.
OPERATIONAL GUIDELINES:
- Always specify the source and target languages explicitly in your output
- Preserve the original formatting and structure of the source text
- Flag ambiguous phrases that could be translated multiple ways and explain the options
- Provide transliteration alongside translation for non-Latin scripts when helpful
- Maintain consistent terminology throughout a document or project
- Never fabricate translations for terms you are uncertain about — flag them for review
- For critical or legal content, recommend professional human review
- Store glossaries, translation memories, and style preferences in memory
- When the source text contains errors, translate the intended meaning and note the source error
- Present translations in clear, side-by-side format when comparing versions
TOOLS AVAILABLE:
- file_read / file_write / file_list: Process translation files, documents, and localization resources
- memory_store / memory_recall: Persist glossaries, translation memories, and project preferences
- web_fetch: Access reference dictionaries and terminology databases
You are precise, culturally sensitive, and committed to clear cross-language communication. You bridge linguistic gaps with accuracy and grace."""
[resources]
max_llm_tokens_per_hour = 200000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "web_fetch"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agents/travel-planner/agent.toml
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name = "travel-planner"
version = "0.1.0"
description = "Trip planning agent for itinerary creation, booking research, budget estimation, and travel logistics."
author = "openfang"
module = "builtin:chat"
tags = ["travel", "planning", "itinerary", "booking", "logistics", "vacation"]
[model]
provider = "default"
model = "default"
max_tokens = 8192
temperature = 0.5
system_prompt = """You are Travel Planner, a specialist agent in the OpenFang Agent OS. You are an expert travel advisor who helps plan trips, create detailed itineraries, research destinations, estimate budgets, and manage travel logistics.
CORE COMPETENCIES:
1. Itinerary Creation
You build detailed, day-by-day travel itineraries that balance must-see attractions with downtime and practical logistics. Your itineraries include: daily schedule with estimated times, attraction descriptions and highlights, transportation between locations (with estimated travel times), meal recommendations by area and budget, evening activities and options, and contingency plans for weather or closures. You organize itineraries to minimize backtracking, account for jet lag on arrival days, and build in flexibility. You customize intensity level based on traveler preferences: packed sightseeing vs. relaxed exploration.
2. Destination Research and Recommendations
You provide comprehensive destination guides covering: best time to visit (weather, crowds, events), top attractions and hidden gems, neighborhood guides and area descriptions, local customs and cultural etiquette, safety considerations and areas to avoid, local cuisine highlights and restaurant recommendations, transportation options (public transit, ride-share, rental cars), visa and entry requirements, recommended trip duration, and packing suggestions. You tailor recommendations to traveler interests: adventure, culture, food, relaxation, nightlife, family-friendly, or budget travel.
3. Budget Planning and Estimation
You create detailed travel budgets with line-item estimates for: flights (with tips for finding deals), accommodation (by type and area), local transportation, meals (by dining level: budget, moderate, upscale), attractions and activities (entrance fees, tours, experiences), travel insurance, visa fees, and miscellaneous expenses. You provide budget tiers (budget, mid-range, luxury) so travelers can see the cost difference. You identify money-saving opportunities: city passes, free attraction days, happy hours, off-peak pricing, and loyalty program benefits.
4. Accommodation Research
You recommend accommodation options by type (hotels, hostels, vacation rentals, boutique stays), neighborhood, budget, and traveler needs. You assess properties on: location (proximity to attractions and transit), value for money, amenities (wifi, kitchen, laundry), reviews and reputation, cancellation policy, and suitability for the trip type (business, family, romantic, solo). You suggest optimal neighborhoods for different priorities: central location, nightlife, quiet residential, beach access.
5. Transportation and Logistics
You plan the logistics of getting there and getting around: flight route options (direct vs. connecting, layover optimization), airport transfer options, inter-city transportation (trains, buses, domestic flights, rental cars), local transit navigation (metro maps, bus routes, transit passes), and driving logistics (international license requirements, toll roads, parking). You optimize connections and minimize wasted transit time.
6. Packing and Preparation
You create customized packing lists based on: destination climate and weather forecast, planned activities, trip duration, luggage constraints, and cultural dress codes. You include practical reminders: passport validity, travel adapters, medication, copies of documents, travel insurance, phone/data plans, and pre-departure tasks (mail hold, pet care, home security).
7. Multi-Destination and Complex Trip Planning
For trips covering multiple cities or countries, you optimize the route, plan logical transitions between destinations, account for border crossings and visa requirements, balance time allocation across locations, and ensure transportation connections work smoothly. You present the overall journey as both a high-level overview and detailed day-by-day plan.
OPERATIONAL GUIDELINES:
- Always ask for key trip parameters: dates, budget, interests, travel style, and party composition
- Provide options at multiple price points when possible
- Include practical logistics, not just attraction lists
- Note seasonal considerations: peak vs. off-season, weather, local holidays, and closures
- Flag travel advisories, visa requirements, and health recommendations for international destinations
- Store trip plans, preferences, and past trip data in memory for personalized recommendations
- Use clear formatting: day-by-day headers, time estimates, cost estimates, and map references
- Recommend travel insurance and discuss cancellation policies for major bookings
- Never fabricate specific prices, flight numbers, or hotel availability — present estimates clearly as such
- Provide links and references to booking platforms when useful
TOOLS AVAILABLE:
- file_read / file_write / file_list: Create itinerary documents, packing lists, and budget spreadsheets
- memory_store / memory_recall: Persist trip plans, preferences, and destination research
- web_fetch: Research destinations, attractions, transportation options, and current conditions
You are enthusiastic, detail-oriented, and practical. You turn travel dreams into well-organized, memorable trips."""
[resources]
max_llm_tokens_per_hour = 150000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "web_search", "web_fetch", "browser_navigate", "browser_click", "browser_type", "browser_read_page", "browser_screenshot", "browser_close"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
agents/tutor/agent.toml
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name = "tutor"
version = "0.1.0"
description = "Teaching and explanation agent for learning, tutoring, and educational content creation."
author = "openfang"
module = "builtin:chat"
tags = ["education", "teaching", "tutoring", "learning", "explanation", "knowledge"]
[model]
provider = "default"
model = "default"
max_tokens = 8192
temperature = 0.5
system_prompt = """You are Tutor, a specialist agent in the OpenFang Agent OS. You are an expert educator and tutor who explains complex concepts clearly, adapts to different learning styles, and guides students through progressive understanding.
CORE COMPETENCIES:
1. Adaptive Explanation
You explain concepts at the appropriate level for the learner. You assess the student's current understanding through targeted questions before diving into explanations. You use the Feynman Technique — if you cannot explain it simply, you break it down further. You offer multiple angles on the same concept: formal definitions, intuitive analogies, concrete examples, visual descriptions, and real-world applications. You never talk down to learners but always meet them where they are.
2. Socratic Teaching Method
Rather than simply providing answers, you guide learners to discover understanding through structured questioning. You ask questions that reveal assumptions, probe reasoning, and lead to insights. You use the progression: what do you already know, what do you think happens next, why do you think that is, can you think of a counterexample, how would you apply this? You balance guidance with space for the learner to think independently.
3. Subject Matter Expertise
You teach across a broad range of subjects: mathematics (algebra through calculus and statistics), computer science (programming, algorithms, data structures, systems), natural sciences (physics, chemistry, biology), humanities (history, philosophy, literature), social sciences (economics, psychology, sociology), and professional skills (writing, critical thinking, study methods). You clearly state when a topic is outside your expertise and recommend appropriate resources.
4. Problem-Solving Walkthrough
You guide students through problems step-by-step, showing not just the solution but the reasoning process. You demonstrate how to: identify what is being asked, determine what information is given, select an appropriate strategy, execute the solution, and verify the answer. You work through examples together and then provide practice problems of increasing difficulty for the student to attempt.
5. Learning Plan Design
You create structured learning plans for mastering a topic or skill. You sequence concepts from foundational to advanced, identify prerequisites, recommend resources (textbooks, courses, practice sets), set milestones, and build in review and reinforcement. You apply spaced repetition principles and interleaving to optimize retention.
6. Assessment and Feedback
You create practice questions, quizzes, and exercises tailored to the material covered. You provide detailed, constructive feedback on student work — not just what is wrong, but why it is wrong and how to correct the misunderstanding. You celebrate progress and identify specific areas for improvement.
7. Study Skills and Metacognition
You teach students how to learn: effective note-taking strategies, active recall techniques, spaced repetition scheduling, the Pomodoro method, concept mapping, and self-testing. You help students develop metacognitive awareness — the ability to monitor their own understanding and identify when they are confused.
OPERATIONAL GUIDELINES:
- Always assess the learner's current level before explaining
- Use concrete examples before abstract definitions
- Break complex topics into digestible chunks with clear transitions
- Encourage questions and create a psychologically safe learning environment
- Provide multiple representations of the same concept (verbal, visual, mathematical, analogical)
- After explaining, check understanding with targeted follow-up questions
- Store learning plans, progress notes, and student preferences in memory
- Never do the student's homework for them — guide them to the answer
- Adapt pacing: slow down when the student is struggling, speed up when they demonstrate mastery
- Use formatting (headers, numbered lists, code blocks) to structure educational content clearly
TOOLS AVAILABLE:
- file_read / file_write / file_list: Read learning materials, write lesson plans and study guides
- memory_store / memory_recall: Track student progress, learning plans, and personalized preferences
- shell_exec: Run code examples for programming tutoring
- web_fetch: Access reference materials and educational resources
You are patient, encouraging, and intellectually rigorous. You believe every person can learn anything with the right approach and sufficient practice."""
[resources]
max_llm_tokens_per_hour = 200000
max_concurrent_tools = 5
[capabilities]
tools = ["file_read", "file_write", "file_list", "memory_store", "memory_recall", "shell_exec", "web_fetch"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*", "shared.*"]
shell = ["python *"]
agents/writer/agent.toml
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name = "writer"
version = "0.1.0"
description = "Content writer. Creates documentation, articles, and technical writing."
author = "openfang"
module = "builtin:chat"
[model]
provider = "default"
model = "default"
max_tokens = 4096
temperature = 0.7
system_prompt = """You are Writer, a professional content creation agent running inside the OpenFang Agent OS.
WRITING METHODOLOGY:
1. UNDERSTAND — Ask clarifying questions if the audience, tone, or format is unclear.
2. RESEARCH — Read existing files for context. Use web_search if you need facts or references.
3. DRAFT — Write the content in one pass. Prioritize clarity and flow.
4. REFINE — Review for conciseness, active voice, and logical structure.
STYLE PRINCIPLES:
- Lead with the most important information.
- Use active voice. Cut filler words ("just", "actually", "basically").
- Structure with headers, bullet points, and short paragraphs.
- Match the requested tone: technical docs are precise, blog posts are conversational, emails are direct.
- When writing code documentation, include working examples.
OUTPUT:
- Save long-form content to files when asked (use file_write).
- For short content (emails, messages, summaries), respond directly.
- Adapt formatting to the target platform when specified."""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 100000
[capabilities]
tools = ["file_read", "file_write", "file_list", "web_search", "web_fetch", "memory_store", "memory_recall"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*"]
crates/openfang-api/Cargo.toml
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[package]
name = "openfang-api"
version.workspace = true
edition.workspace = true
license.workspace = true
description = "HTTP/WebSocket API server for the OpenFang Agent OS daemon"
[dependencies]
openfang-types = { path = "../openfang-types" }
openfang-kernel = { path = "../openfang-kernel" }
openfang-runtime = { path = "../openfang-runtime" }
openfang-memory = { path = "../openfang-memory" }
openfang-channels = { path = "../openfang-channels" }
openfang-wire = { path = "../openfang-wire" }
openfang-skills = { path = "../openfang-skills" }
openfang-hands = { path = "../openfang-hands" }
openfang-extensions = { path = "../openfang-extensions" }
openfang-migrate = { path = "../openfang-migrate" }
dashmap = { workspace = true }
tokio = { workspace = true }
serde = { workspace = true }
serde_json = { workspace = true }
toml = { workspace = true }
tracing = { workspace = true }
async-trait = { workspace = true }
axum = { workspace = true }
tower = { workspace = true }
tower-http = { workspace = true }
chrono = { workspace = true }
uuid = { workspace = true }
futures = { workspace = true }
governor = { workspace = true }
tokio-stream = { workspace = true }
subtle = { workspace = true }
base64 = { workspace = true }
sha2 = { workspace = true }
hmac = { workspace = true }
hex = { workspace = true }
socket2 = { workspace = true }
reqwest = { workspace = true }
argon2 = { workspace = true }
rand = { workspace = true }
[dev-dependencies]
tokio-test = { workspace = true }
tempfile = { workspace = true }
uuid = { workspace = true }
crates/openfang-api/src/channel_bridge.rs
+1986
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File diff suppressed because it is too large Load Diff
crates/openfang-api/src/lib.rs
+46
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@@ -0,0 +1,46 @@
//! HTTP/WebSocket API server for the OpenFang Agent OS daemon.
//!
//! Exposes agent management, status, and chat via JSON REST endpoints.
//! The kernel runs in-process; the CLI connects over HTTP.
/// Decode percent-encoded strings (e.g. `%2B` → `+`).
/// Used to normalise `?token=` values that browsers encode with `encodeURIComponent`.
pub(crate) fn percent_decode(input: &str) -> String {
let bytes = input.as_bytes();
let mut out = Vec::with_capacity(bytes.len());
let mut i = 0;
while i < bytes.len() {
if bytes[i] == b'%' && i + 2 < bytes.len() {
if let (Some(hi), Some(lo)) = (hex_val(bytes[i + 1]), hex_val(bytes[i + 2])) {
out.push(hi << 4 | lo);
i += 3;
continue;
}
}
out.push(bytes[i]);
i += 1;
}
String::from_utf8(out).unwrap_or_else(|_| input.to_string())
}
fn hex_val(b: u8) -> Option<u8> {
match b {
b'0'..=b'9' => Some(b - b'0'),
b'a'..=b'f' => Some(b - b'a' + 10),
b'A'..=b'F' => Some(b - b'A' + 10),
_ => None,
}
}
pub mod channel_bridge;
pub mod middleware;
pub mod openai_compat;
pub mod rate_limiter;
pub mod routes;
pub mod server;
pub mod session_auth;
pub mod stream_chunker;
pub mod stream_dedup;
pub mod types;
pub mod webchat;
pub mod ws;
crates/openfang-api/src/middleware.rs
+399
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@@ -0,0 +1,399 @@
//! Production middleware for the OpenFang API server.
//!
//! Provides:
//! - Request ID generation and propagation
//! - Per-endpoint structured request logging
//! - In-memory rate limiting (per IP)
use axum::body::Body;
use axum::http::{Request, Response, StatusCode};
use axum::middleware::Next;
use std::time::Instant;
use tracing::info;
/// Request ID header name (standard).
pub const REQUEST_ID_HEADER: &str = "x-request-id";
/// Middleware: inject a unique request ID and log the request/response.
pub async fn request_logging(request: Request<Body>, next: Next) -> Response<Body> {
let request_id = uuid::Uuid::new_v4().to_string();
let method = request.method().clone();
let uri = request.uri().path().to_string();
let start = Instant::now();
let mut response = next.run(request).await;
let elapsed = start.elapsed();
let status = response.status().as_u16();
info!(
request_id = %request_id,
method = %method,
path = %uri,
status = status,
latency_ms = elapsed.as_millis() as u64,
"API request"
);
// Inject the request ID into the response
if let Ok(header_val) = request_id.parse() {
response.headers_mut().insert(REQUEST_ID_HEADER, header_val);
}
response
}
/// Authentication state passed to the auth middleware.
#[derive(Clone)]
pub struct AuthState {
pub api_key: String,
pub auth_enabled: bool,
pub session_secret: String,
/// Set from `OPENFANG_ALLOW_NO_AUTH=1` to permit running without an api_key
/// on a non-loopback bind. Off by default so empty keys fail closed.
pub allow_no_auth: bool,
}
/// Bearer token authentication middleware.
///
/// When `api_key` is non-empty (after trimming), requests to non-public
/// endpoints must include `Authorization: Bearer <api_key>`.
///
/// When `api_key` is empty (no key configured) the server defaults to
/// fail-closed for any request that does NOT originate from loopback.
/// Loopback traffic (127.0.0.1 / ::1) is always allowed through with no
/// key so single-user local setups keep zero-config UX. To explicitly
/// run a no-auth server on a LAN/WAN address, set
/// `OPENFANG_ALLOW_NO_AUTH=1`; this opts out of fail-closed and is
/// reported loudly at startup.
///
/// When dashboard auth is enabled, session cookies are also accepted.
pub async fn auth(
axum::extract::State(auth_state): axum::extract::State<AuthState>,
request: Request<Body>,
next: Next,
) -> Response<Body> {
// SECURITY: Capture method early for method-aware public endpoint checks.
let method = request.method().clone();
let is_loopback = request
.extensions()
.get::<axum::extract::ConnectInfo<std::net::SocketAddr>>()
.map(|ci| ci.0.ip().is_loopback())
.unwrap_or(false); // SECURITY: default-deny; unknown origin is NOT loopback
// Shutdown is loopback-only (CLI on same machine). Skip token auth only
// when the request is from loopback.
let path = request.uri().path();
if path == "/api/shutdown" && is_loopback {
return next.run(request).await;
}
// Public endpoints that don't require auth (dashboard needs these).
// SECURITY: /api/agents is GET-only (listing). POST (spawn) requires auth.
// SECURITY: Public endpoints are GET-only unless explicitly noted.
// POST/PUT/DELETE to any endpoint ALWAYS requires auth to prevent
// unauthenticated writes (cron job creation, skill install, etc.).
let is_get = method == axum::http::Method::GET;
let is_public = path == "/"
|| path == "/logo.png"
|| path == "/favicon.ico"
|| (path == "/.well-known/agent.json" && is_get)
|| (path.starts_with("/a2a/") && is_get)
|| path == "/api/health"
|| path == "/api/health/detail"
|| path == "/api/status"
|| path == "/api/version"
|| (path == "/api/agents" && is_get)
|| (path == "/api/profiles" && is_get)
|| (path == "/api/config" && is_get)
|| (path == "/api/config/schema" && is_get)
|| (path.starts_with("/api/uploads/") && is_get)
// Dashboard read endpoints — allow unauthenticated so the SPA can
// render before the user enters their API key.
|| (path == "/api/models" && is_get)
|| (path == "/api/models/aliases" && is_get)
|| (path == "/api/providers" && is_get)
|| (path == "/api/budget" && is_get)
|| (path == "/api/budget/agents" && is_get)
|| (path.starts_with("/api/budget/agents/") && is_get)
|| (path == "/api/network/status" && is_get)
|| (path == "/api/a2a/agents" && is_get)
|| (path == "/api/approvals" && is_get)
|| (path.starts_with("/api/approvals/") && is_get)
|| (path == "/api/channels" && is_get)
|| (path == "/api/hands" && is_get)
|| (path == "/api/hands/active" && is_get)
|| (path.starts_with("/api/hands/") && is_get)
|| (path == "/api/skills" && is_get)
|| (path.starts_with("/api/skills/") && path.ends_with("/config") && is_get)
|| (path == "/api/sessions" && is_get)
|| (path == "/api/integrations" && is_get)
|| (path == "/api/integrations/available" && is_get)
|| (path == "/api/integrations/health" && is_get)
|| (path == "/api/workflows" && is_get)
|| path == "/api/logs/stream" // SSE stream, read-only
|| (path.starts_with("/api/cron/") && is_get)
|| path.starts_with("/api/providers/github-copilot/oauth/")
|| path == "/api/auth/login"
|| path == "/api/auth/logout"
|| (path == "/api/auth/check" && is_get);
if is_public {
return next.run(request).await;
}
// If no API key configured and no dashboard login is active, fail closed
// for anything that did not come from loopback. Opting out of this
// behavior requires setting `OPENFANG_ALLOW_NO_AUTH=1`, which is logged
// loudly at startup.
//
// See issue #1034 (B1/B2): empty api_key previously bypassed auth for
// all origins, exposing agent config, channel tokens, and LLM keys on
// any LAN-reachable bind.
let api_key_trimmed = auth_state.api_key.trim().to_string();
if api_key_trimmed.is_empty() && !auth_state.auth_enabled {
if is_loopback || auth_state.allow_no_auth {
return next.run(request).await;
}
return Response::builder()
.status(StatusCode::UNAUTHORIZED)
.header("www-authenticate", "Bearer")
.body(Body::from(
serde_json::json!({
"error": "API key required for non-loopback requests. Set OPENFANG_API_KEY or bind to 127.0.0.1."
})
.to_string(),
))
.unwrap_or_default();
}
let api_key = api_key_trimmed.as_str();
// Check Authorization: Bearer <token> header, then fallback to X-API-Key
let bearer_token = request
.headers()
.get("authorization")
.and_then(|v| v.to_str().ok())
.and_then(|v| v.strip_prefix("Bearer "));
let api_token = bearer_token.or_else(|| {
request
.headers()
.get("x-api-key")
.and_then(|v| v.to_str().ok())
});
// SECURITY: Use constant-time comparison to prevent timing attacks.
let header_auth = api_token.map(|token| {
use subtle::ConstantTimeEq;
if token.len() != api_key.len() {
return false;
}
token.as_bytes().ct_eq(api_key.as_bytes()).into()
});
// Also check ?token= query parameter (for EventSource/SSE clients that
// cannot set custom headers, same approach as WebSocket auth).
let query_token_decoded = request
.uri()
.query()
.and_then(|q| q.split('&').find_map(|pair| pair.strip_prefix("token=")))
.map(crate::percent_decode);
// SECURITY: Use constant-time comparison to prevent timing attacks.
let query_auth = query_token_decoded.as_deref().map(|token| {
use subtle::ConstantTimeEq;
if token.len() != api_key.len() {
return false;
}
token.as_bytes().ct_eq(api_key.as_bytes()).into()
});
// Accept if either auth method matches
if header_auth == Some(true) || query_auth == Some(true) {
return next.run(request).await;
}
// Check session cookie (dashboard login sessions)
if auth_state.auth_enabled {
if let Some(token) = crate::session_auth::extract_session_cookie(request.headers()) {
if crate::session_auth::verify_session_token(&token, &auth_state.session_secret)
.is_some()
{
return next.run(request).await;
}
}
}
// Determine error message: was a credential provided but wrong, or missing entirely?
let credential_provided = header_auth.is_some() || query_auth.is_some();
let error_msg = if credential_provided {
"Invalid API key"
} else {
"Missing Authorization: Bearer <api_key> header"
};
Response::builder()
.status(StatusCode::UNAUTHORIZED)
.header("www-authenticate", "Bearer")
.body(Body::from(
serde_json::json!({"error": error_msg}).to_string(),
))
.unwrap_or_default()
}
/// Security headers middleware — applied to ALL API responses.
pub async fn security_headers(request: Request<Body>, next: Next) -> Response<Body> {
let mut response = next.run(request).await;
let headers = response.headers_mut();
headers.insert("x-content-type-options", "nosniff".parse().unwrap());
headers.insert("x-frame-options", "DENY".parse().unwrap());
headers.insert("x-xss-protection", "1; mode=block".parse().unwrap());
// The dashboard handler (webchat_page) sets its own nonce-based CSP.
// For all other responses (API endpoints), apply a strict default.
if !headers.contains_key("content-security-policy") {
headers.insert(
"content-security-policy",
"default-src 'none'; frame-ancestors 'none'"
.parse()
.unwrap(),
);
}
headers.insert(
"referrer-policy",
"strict-origin-when-cross-origin".parse().unwrap(),
);
headers.insert(
"cache-control",
"no-store, no-cache, must-revalidate".parse().unwrap(),
);
headers.insert(
"strict-transport-security",
"max-age=63072000; includeSubDomains".parse().unwrap(),
);
response
}
#[cfg(test)]
mod tests {
use super::*;
use axum::body::Body;
use axum::extract::ConnectInfo;
use axum::http::{Method, Request};
use axum::routing::get;
use axum::Router;
use std::net::SocketAddr;
use tower::ServiceExt;
#[test]
fn test_request_id_header_constant() {
assert_eq!(REQUEST_ID_HEADER, "x-request-id");
}
fn auth_state_empty() -> AuthState {
AuthState {
api_key: String::new(),
auth_enabled: false,
session_secret: String::new(),
allow_no_auth: false,
}
}
fn auth_state_with_key(key: &str) -> AuthState {
AuthState {
api_key: key.to_string(),
auth_enabled: false,
session_secret: key.to_string(),
allow_no_auth: false,
}
}
async fn ok_handler() -> &'static str {
"ok"
}
fn router(state: AuthState) -> Router {
Router::new()
.route("/api/agents/1", get(ok_handler))
.route_layer(axum::middleware::from_fn_with_state(state, auth))
}
fn req_from(ip: &str) -> Request<Body> {
let addr: SocketAddr = format!("{ip}:40000").parse().unwrap();
let mut req = Request::builder()
.method(Method::GET)
.uri("/api/agents/1")
.body(Body::empty())
.unwrap();
req.extensions_mut().insert(ConnectInfo(addr));
req
}
#[tokio::test]
async fn empty_key_allows_loopback() {
let app = router(auth_state_empty());
let resp = app.oneshot(req_from("127.0.0.1")).await.unwrap();
assert_eq!(resp.status(), StatusCode::OK);
}
#[tokio::test]
async fn empty_key_blocks_lan_origin() {
// Issue #1034 B1: previously 192.168/10/... could hit every non-public
// endpoint when api_key was unset. Must now be 401.
let app = router(auth_state_empty());
let resp = app.oneshot(req_from("192.168.1.50")).await.unwrap();
assert_eq!(resp.status(), StatusCode::UNAUTHORIZED);
}
#[tokio::test]
async fn empty_key_blocks_public_origin() {
let app = router(auth_state_empty());
let resp = app.oneshot(req_from("203.0.113.5")).await.unwrap();
assert_eq!(resp.status(), StatusCode::UNAUTHORIZED);
}
#[tokio::test]
async fn empty_key_blocks_unknown_connect_info() {
// Paranoia: if ConnectInfo is missing for any reason, we must fail
// closed, not open.
let app = router(auth_state_empty());
let req = Request::builder()
.method(Method::GET)
.uri("/api/agents/1")
.body(Body::empty())
.unwrap();
let resp = app.oneshot(req).await.unwrap();
assert_eq!(resp.status(), StatusCode::UNAUTHORIZED);
}
#[tokio::test]
async fn empty_key_with_allow_no_auth_opens_everything() {
let mut s = auth_state_empty();
s.allow_no_auth = true;
let app = router(s);
let resp = app.oneshot(req_from("10.0.0.9")).await.unwrap();
assert_eq!(resp.status(), StatusCode::OK);
}
#[tokio::test]
async fn configured_key_rejects_missing_token_from_loopback() {
let app = router(auth_state_with_key("secret"));
let resp = app.oneshot(req_from("127.0.0.1")).await.unwrap();
assert_eq!(resp.status(), StatusCode::UNAUTHORIZED);
}
#[tokio::test]
async fn configured_key_accepts_bearer() {
let app = router(auth_state_with_key("secret"));
let addr: SocketAddr = "127.0.0.1:40000".parse().unwrap();
let mut req = Request::builder()
.method(Method::GET)
.uri("/api/agents/1")
.header("authorization", "Bearer secret")
.body(Body::empty())
.unwrap();
req.extensions_mut().insert(ConnectInfo(addr));
let resp = app.oneshot(req).await.unwrap();
assert_eq!(resp.status(), StatusCode::OK);
}
}
crates/openfang-api/src/openai_compat.rs
+778
View File
@@ -0,0 +1,778 @@
//! OpenAI-compatible `/v1/chat/completions` API endpoint.
//!
//! Allows any OpenAI-compatible client library to talk to OpenFang agents.
//! The `model` field resolves to an agent (by name, UUID, or `openfang:<name>`),
//! and the messages are forwarded to the agent's LLM loop.
//!
//! Supports both streaming (SSE) and non-streaming responses.
use crate::routes::AppState;
use axum::extract::State;
use axum::http::StatusCode;
use axum::response::sse::{Event as SseEvent, KeepAlive, Sse};
use axum::response::IntoResponse;
use axum::Json;
use openfang_runtime::kernel_handle::KernelHandle;
use openfang_runtime::llm_driver::StreamEvent;
use openfang_types::agent::AgentId;
use openfang_types::message::{ContentBlock, Message, MessageContent, Role, StopReason};
use serde::{Deserialize, Serialize};
use std::convert::Infallible;
use std::sync::Arc;
use tracing::warn;
// ── Request types ──────────────────────────────────────────────────────────
#[derive(Debug, Deserialize)]
pub struct ChatCompletionRequest {
pub model: String,
pub messages: Vec<OaiMessage>,
#[serde(default)]
pub stream: bool,
pub max_tokens: Option<u32>,
pub temperature: Option<f32>,
}
#[derive(Debug, Deserialize)]
pub struct OaiMessage {
pub role: String,
#[serde(default)]
pub content: OaiContent,
}
#[derive(Debug, Deserialize, Default)]
#[serde(untagged)]
pub enum OaiContent {
Text(String),
Parts(Vec<OaiContentPart>),
#[default]
Null,
}
#[derive(Debug, Deserialize)]
#[serde(tag = "type")]
pub enum OaiContentPart {
#[serde(rename = "text")]
Text { text: String },
#[serde(rename = "image_url")]
ImageUrl { image_url: OaiImageUrlRef },
}
#[derive(Debug, Deserialize)]
pub struct OaiImageUrlRef {
pub url: String,
}
// ── Response types ──────────────────────────────────────────────────────────
#[derive(Serialize)]
struct ChatCompletionResponse {
id: String,
object: &'static str,
created: u64,
model: String,
choices: Vec<Choice>,
usage: UsageInfo,
}
#[derive(Serialize)]
struct Choice {
index: u32,
message: ChoiceMessage,
finish_reason: &'static str,
}
#[derive(Serialize)]
struct ChoiceMessage {
role: &'static str,
#[serde(skip_serializing_if = "Option::is_none")]
content: Option<String>,
#[serde(skip_serializing_if = "Option::is_none")]
tool_calls: Option<Vec<OaiToolCall>>,
}
#[derive(Serialize)]
struct UsageInfo {
prompt_tokens: u64,
completion_tokens: u64,
total_tokens: u64,
}
#[derive(Serialize)]
struct ChatCompletionChunk {
id: String,
object: &'static str,
created: u64,
model: String,
choices: Vec<ChunkChoice>,
}
#[derive(Serialize)]
struct ChunkChoice {
index: u32,
delta: ChunkDelta,
finish_reason: Option<&'static str>,
}
#[derive(Serialize)]
struct ChunkDelta {
#[serde(skip_serializing_if = "Option::is_none")]
role: Option<&'static str>,
#[serde(skip_serializing_if = "Option::is_none")]
content: Option<String>,
#[serde(skip_serializing_if = "Option::is_none")]
tool_calls: Option<Vec<OaiToolCall>>,
}
#[derive(Serialize, Clone)]
struct OaiToolCall {
index: u32,
#[serde(skip_serializing_if = "Option::is_none")]
id: Option<String>,
#[serde(skip_serializing_if = "Option::is_none")]
#[serde(rename = "type")]
call_type: Option<&'static str>,
function: OaiToolCallFunction,
}
#[derive(Serialize, Clone)]
struct OaiToolCallFunction {
#[serde(skip_serializing_if = "Option::is_none")]
name: Option<String>,
#[serde(skip_serializing_if = "Option::is_none")]
arguments: Option<String>,
}
#[derive(Serialize)]
struct ModelObject {
id: String,
object: &'static str,
created: u64,
owned_by: String,
}
#[derive(Serialize)]
struct ModelListResponse {
object: &'static str,
data: Vec<ModelObject>,
}
// ── Agent resolution ────────────────────────────────────────────────────────
fn resolve_agent(state: &AppState, model: &str) -> Option<(AgentId, String)> {
// 1. "openfang:<name>" → find agent by name
if let Some(name) = model.strip_prefix("openfang:") {
if let Some(entry) = state.kernel.registry.find_by_name(name) {
return Some((entry.id, entry.name.clone()));
}
}
// 2. Valid UUID → find agent by ID
if let Ok(id) = model.parse::<AgentId>() {
if let Some(entry) = state.kernel.registry.get(id) {
return Some((entry.id, entry.name.clone()));
}
}
// 3. Plain string → try as agent name
if let Some(entry) = state.kernel.registry.find_by_name(model) {
return Some((entry.id, entry.name.clone()));
}
// No match — return None so the caller returns a proper 404
None
}
// ── Message conversion ──────────────────────────────────────────────────────
fn convert_messages(oai_messages: &[OaiMessage]) -> Vec<Message> {
oai_messages
.iter()
.filter_map(|m| {
let role = match m.role.as_str() {
"user" => Role::User,
"assistant" => Role::Assistant,
"system" => Role::System,
_ => Role::User,
};
let content = match &m.content {
OaiContent::Text(text) => MessageContent::Text(text.clone()),
OaiContent::Parts(parts) => {
let blocks: Vec<ContentBlock> = parts
.iter()
.filter_map(|part| match part {
OaiContentPart::Text { text } => Some(ContentBlock::Text {
text: text.clone(),
provider_metadata: None,
}),
OaiContentPart::ImageUrl { image_url } => {
// Parse data URI: data:{media_type};base64,{data}
if let Some(rest) = image_url.url.strip_prefix("data:") {
let parts: Vec<&str> = rest.splitn(2, ',').collect();
if parts.len() == 2 {
let media_type = parts[0]
.strip_suffix(";base64")
.unwrap_or(parts[0])
.to_string();
let data = parts[1].to_string();
Some(ContentBlock::Image { media_type, data })
} else {
None
}
} else {
// URL-based images not supported (would require fetching)
None
}
}
})
.collect();
if blocks.is_empty() {
return None;
}
MessageContent::Blocks(blocks)
}
OaiContent::Null => return None,
};
Some(Message {
msg_id: uuid::Uuid::new_v4().to_string(),
provider_msg_id: None,
role,
content,
})
})
.collect()
}
// ── Handlers ────────────────────────────────────────────────────────────────
/// POST /v1/chat/completions
pub async fn chat_completions(
State(state): State<Arc<AppState>>,
Json(req): Json<ChatCompletionRequest>,
) -> impl IntoResponse {
let (agent_id, agent_name) = match resolve_agent(&state, &req.model) {
Some(pair) => pair,
None => {
return (
StatusCode::NOT_FOUND,
Json(serde_json::json!({
"error": {
"message": format!("No agent found for model '{}'", req.model),
"type": "invalid_request_error",
"code": "model_not_found"
}
})),
)
.into_response();
}
};
// Extract the last user message as the input
let messages = convert_messages(&req.messages);
let last_user_msg = messages
.iter()
.rev()
.find(|m| m.role == Role::User)
.map(|m| m.content.text_content())
.unwrap_or_default();
if last_user_msg.is_empty() {
return (
StatusCode::BAD_REQUEST,
Json(serde_json::json!({
"error": {
"message": "No user message found in request",
"type": "invalid_request_error",
"code": "missing_message"
}
})),
)
.into_response();
}
let request_id = format!("chatcmpl-{}", uuid::Uuid::new_v4());
let created = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default()
.as_secs();
if req.stream {
// Streaming response
return match stream_response(
state,
agent_id,
agent_name,
&last_user_msg,
request_id,
created,
)
.await
{
Ok(sse) => sse.into_response(),
Err(e) => (
StatusCode::INTERNAL_SERVER_ERROR,
Json(serde_json::json!({
"error": {
"message": format!("{e}"),
"type": "server_error"
}
})),
)
.into_response(),
};
}
// Non-streaming response
let kernel_handle: Arc<dyn KernelHandle> = state.kernel.clone() as Arc<dyn KernelHandle>;
match state
.kernel
.send_message_with_handle(agent_id, &last_user_msg, Some(kernel_handle), None, None)
.await
{
Ok(result) => {
let response = ChatCompletionResponse {
id: request_id,
object: "chat.completion",
created,
model: agent_name,
choices: vec![Choice {
index: 0,
message: ChoiceMessage {
role: "assistant",
content: Some(crate::ws::strip_think_tags(&result.response)),
tool_calls: None,
},
finish_reason: "stop",
}],
usage: UsageInfo {
prompt_tokens: result.total_usage.input_tokens,
completion_tokens: result.total_usage.output_tokens,
total_tokens: result.total_usage.input_tokens
+ result.total_usage.output_tokens,
},
};
Json(serde_json::to_value(&response).unwrap_or_default()).into_response()
}
Err(e) => {
warn!("OpenAI compat: agent error: {e}");
(
StatusCode::INTERNAL_SERVER_ERROR,
Json(serde_json::json!({
"error": {
"message": "Agent processing failed",
"type": "server_error"
}
})),
)
.into_response()
}
}
}
/// Build an SSE stream response for streaming completions.
async fn stream_response(
state: Arc<AppState>,
agent_id: AgentId,
agent_name: String,
message: &str,
request_id: String,
created: u64,
) -> Result<axum::response::Response, String> {
let kernel_handle: Arc<dyn KernelHandle> = state.kernel.clone() as Arc<dyn KernelHandle>;
let (mut rx, _handle) = state
.kernel
.send_message_streaming(agent_id, message, Some(kernel_handle), None, None, None)
.map_err(|e| format!("Streaming setup failed: {e}"))?;
let (tx, stream_rx) = tokio::sync::mpsc::channel::<Result<SseEvent, Infallible>>(64);
// Send initial role delta
let first_chunk = ChatCompletionChunk {
id: request_id.clone(),
object: "chat.completion.chunk",
created,
model: agent_name.clone(),
choices: vec![ChunkChoice {
index: 0,
delta: ChunkDelta {
role: Some("assistant"),
content: None,
tool_calls: None,
},
finish_reason: None,
}],
};
let _ = tx
.send(Ok(SseEvent::default().data(
serde_json::to_string(&first_chunk).unwrap_or_default(),
)))
.await;
// Helper to build a chunk with a delta and optional finish_reason.
fn make_chunk(
id: &str,
created: u64,
model: &str,
delta: ChunkDelta,
finish_reason: Option<&'static str>,
) -> String {
let chunk = ChatCompletionChunk {
id: id.to_string(),
object: "chat.completion.chunk",
created,
model: model.to_string(),
choices: vec![ChunkChoice {
index: 0,
delta,
finish_reason,
}],
};
serde_json::to_string(&chunk).unwrap_or_default()
}
// Spawn forwarder task — streams ALL iterations until the agent loop channel closes.
let req_id = request_id.clone();
tokio::spawn(async move {
// Tracks current tool_call index within each LLM iteration.
let mut tool_index: u32 = 0;
while let Some(event) = rx.recv().await {
let json = match event {
StreamEvent::TextDelta { text } => make_chunk(
&req_id,
created,
&agent_name,
ChunkDelta {
role: None,
content: Some(text),
tool_calls: None,
},
None,
),
StreamEvent::ToolUseStart { id, name } => {
let idx = tool_index;
tool_index += 1;
make_chunk(
&req_id,
created,
&agent_name,
ChunkDelta {
role: None,
content: None,
tool_calls: Some(vec![OaiToolCall {
index: idx,
id: Some(id),
call_type: Some("function"),
function: OaiToolCallFunction {
name: Some(name),
arguments: Some(String::new()),
},
}]),
},
None,
)
}
StreamEvent::ToolInputDelta { text } => {
// tool_index already incremented past current tool, so current = index - 1
let idx = tool_index.saturating_sub(1);
make_chunk(
&req_id,
created,
&agent_name,
ChunkDelta {
role: None,
content: None,
tool_calls: Some(vec![OaiToolCall {
index: idx,
id: None,
call_type: None,
function: OaiToolCallFunction {
name: None,
arguments: Some(text),
},
}]),
},
None,
)
}
StreamEvent::ContentComplete { stop_reason, .. } => {
// ToolUse → reset tool index for next iteration, do NOT finish.
// EndTurn/MaxTokens/StopSequence → continue, wait for channel close.
if matches!(stop_reason, StopReason::ToolUse) {
tool_index = 0;
}
continue;
}
// ToolUseEnd, ToolExecutionResult, ThinkingDelta, PhaseChange — skip
_ => continue,
};
if tx.send(Ok(SseEvent::default().data(json))).await.is_err() {
break;
}
}
// Channel closed — agent loop is fully done. Send finish + [DONE].
let final_json = make_chunk(
&req_id,
created,
&agent_name,
ChunkDelta {
role: None,
content: None,
tool_calls: None,
},
Some("stop"),
);
let _ = tx.send(Ok(SseEvent::default().data(final_json))).await;
let _ = tx.send(Ok(SseEvent::default().data("[DONE]"))).await;
});
let stream = tokio_stream::wrappers::ReceiverStream::new(stream_rx);
Ok(Sse::new(stream)
.keep_alive(KeepAlive::default())
.into_response())
}
/// GET /v1/models — List available agents as OpenAI model objects.
pub async fn list_models(State(state): State<Arc<AppState>>) -> impl IntoResponse {
let agents = state.kernel.registry.list();
let created = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default()
.as_secs();
let models: Vec<ModelObject> = agents
.iter()
.map(|e| ModelObject {
id: format!("openfang:{}", e.name),
object: "model",
created,
owned_by: "openfang".to_string(),
})
.collect();
Json(
serde_json::to_value(&ModelListResponse {
object: "list",
data: models,
})
.unwrap_or_default(),
)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_oai_content_deserialize_string() {
let json = r#"{"role":"user","content":"hello"}"#;
let msg: OaiMessage = serde_json::from_str(json).unwrap();
assert!(matches!(msg.content, OaiContent::Text(ref t) if t == "hello"));
}
#[test]
fn test_oai_content_deserialize_parts() {
let json = r#"{"role":"user","content":[{"type":"text","text":"what is this?"},{"type":"image_url","image_url":{"url":"data:image/png;base64,abc123"}}]}"#;
let msg: OaiMessage = serde_json::from_str(json).unwrap();
assert!(matches!(msg.content, OaiContent::Parts(ref p) if p.len() == 2));
}
#[test]
fn test_convert_messages_text() {
let oai = vec![
OaiMessage {
role: "system".to_string(),
content: OaiContent::Text("You are helpful.".to_string()),
},
OaiMessage {
role: "user".to_string(),
content: OaiContent::Text("Hello!".to_string()),
},
];
let msgs = convert_messages(&oai);
assert_eq!(msgs.len(), 2);
assert_eq!(msgs[0].role, Role::System);
assert_eq!(msgs[1].role, Role::User);
}
#[test]
fn test_convert_messages_with_image() {
let oai = vec![OaiMessage {
role: "user".to_string(),
content: OaiContent::Parts(vec![
OaiContentPart::Text {
text: "What is this?".to_string(),
},
OaiContentPart::ImageUrl {
image_url: OaiImageUrlRef {
url: "data:image/png;base64,iVBORw0KGgo=".to_string(),
},
},
]),
}];
let msgs = convert_messages(&oai);
assert_eq!(msgs.len(), 1);
match &msgs[0].content {
MessageContent::Blocks(blocks) => {
assert_eq!(blocks.len(), 2);
assert!(matches!(&blocks[0], ContentBlock::Text { .. }));
assert!(matches!(&blocks[1], ContentBlock::Image { .. }));
}
_ => panic!("Expected Blocks"),
}
}
#[test]
fn test_response_serialization() {
let resp = ChatCompletionResponse {
id: "chatcmpl-test".to_string(),
object: "chat.completion",
created: 1234567890,
model: "test-agent".to_string(),
choices: vec![Choice {
index: 0,
message: ChoiceMessage {
role: "assistant",
content: Some("Hello!".to_string()),
tool_calls: None,
},
finish_reason: "stop",
}],
usage: UsageInfo {
prompt_tokens: 10,
completion_tokens: 5,
total_tokens: 15,
},
};
let json = serde_json::to_value(&resp).unwrap();
assert_eq!(json["object"], "chat.completion");
assert_eq!(json["choices"][0]["message"]["content"], "Hello!");
assert_eq!(json["usage"]["total_tokens"], 15);
// tool_calls should be omitted when None
assert!(json["choices"][0]["message"].get("tool_calls").is_none());
}
#[test]
fn test_chunk_serialization() {
let chunk = ChatCompletionChunk {
id: "chatcmpl-test".to_string(),
object: "chat.completion.chunk",
created: 1234567890,
model: "test-agent".to_string(),
choices: vec![ChunkChoice {
index: 0,
delta: ChunkDelta {
role: None,
content: Some("Hello".to_string()),
tool_calls: None,
},
finish_reason: None,
}],
};
let json = serde_json::to_value(&chunk).unwrap();
assert_eq!(json["object"], "chat.completion.chunk");
assert_eq!(json["choices"][0]["delta"]["content"], "Hello");
assert!(json["choices"][0]["delta"]["role"].is_null());
// tool_calls should be omitted when None
assert!(json["choices"][0]["delta"].get("tool_calls").is_none());
}
#[test]
fn test_tool_call_serialization() {
let tc = OaiToolCall {
index: 0,
id: Some("call_abc123".to_string()),
call_type: Some("function"),
function: OaiToolCallFunction {
name: Some("get_weather".to_string()),
arguments: Some(r#"{"location":"NYC"}"#.to_string()),
},
};
let json = serde_json::to_value(&tc).unwrap();
assert_eq!(json["index"], 0);
assert_eq!(json["id"], "call_abc123");
assert_eq!(json["type"], "function");
assert_eq!(json["function"]["name"], "get_weather");
assert_eq!(json["function"]["arguments"], r#"{"location":"NYC"}"#);
}
#[test]
fn test_chunk_delta_with_tool_calls() {
let chunk = ChatCompletionChunk {
id: "chatcmpl-test".to_string(),
object: "chat.completion.chunk",
created: 1234567890,
model: "test-agent".to_string(),
choices: vec![ChunkChoice {
index: 0,
delta: ChunkDelta {
role: None,
content: None,
tool_calls: Some(vec![OaiToolCall {
index: 0,
id: Some("call_1".to_string()),
call_type: Some("function"),
function: OaiToolCallFunction {
name: Some("search".to_string()),
arguments: Some(String::new()),
},
}]),
},
finish_reason: None,
}],
};
let json = serde_json::to_value(&chunk).unwrap();
let tc = &json["choices"][0]["delta"]["tool_calls"][0];
assert_eq!(tc["index"], 0);
assert_eq!(tc["id"], "call_1");
assert_eq!(tc["type"], "function");
assert_eq!(tc["function"]["name"], "search");
// content should be omitted
assert!(json["choices"][0]["delta"].get("content").is_none());
}
#[test]
fn test_tool_input_delta_chunk() {
// Incremental arguments chunk — no id, no type, no name
let tc = OaiToolCall {
index: 2,
id: None,
call_type: None,
function: OaiToolCallFunction {
name: None,
arguments: Some(r#"{"q":"rust"}"#.to_string()),
},
};
let json = serde_json::to_value(&tc).unwrap();
assert_eq!(json["index"], 2);
// id and type should be omitted
assert!(json.get("id").is_none());
assert!(json.get("type").is_none());
assert!(json["function"].get("name").is_none());
assert_eq!(json["function"]["arguments"], r#"{"q":"rust"}"#);
}
#[test]
fn test_backward_compat_no_tool_calls() {
// When tool_calls is None, it should not appear in JSON at all (backward compat)
let msg = ChoiceMessage {
role: "assistant",
content: Some("Hello".to_string()),
tool_calls: None,
};
let json_str = serde_json::to_string(&msg).unwrap();
assert!(!json_str.contains("tool_calls"));
let delta = ChunkDelta {
role: Some("assistant"),
content: Some("Hi".to_string()),
tool_calls: None,
};
let json_str = serde_json::to_string(&delta).unwrap();
assert!(!json_str.contains("tool_calls"));
}
}
crates/openfang-api/src/rate_limiter.rs
+109
View File
@@ -0,0 +1,109 @@
//! Cost-aware rate limiting using GCRA (Generic Cell Rate Algorithm).
//!
//! Each API operation has a token cost (e.g., health=1, spawn=50, message=30).
//! The GCRA algorithm allows 500 tokens per minute per IP address.
use axum::body::Body;
use axum::http::{Request, Response, StatusCode};
use axum::middleware::Next;
use governor::{clock::DefaultClock, state::keyed::DashMapStateStore, Quota, RateLimiter};
use std::net::{IpAddr, SocketAddr};
use std::num::NonZeroU32;
use std::sync::Arc;
pub fn operation_cost(method: &str, path: &str) -> NonZeroU32 {
match (method, path) {
(_, "/api/health") => NonZeroU32::new(1).unwrap(),
("GET", "/api/status") => NonZeroU32::new(1).unwrap(),
("GET", "/api/version") => NonZeroU32::new(1).unwrap(),
("GET", "/api/tools") => NonZeroU32::new(1).unwrap(),
("GET", "/api/agents") => NonZeroU32::new(2).unwrap(),
("GET", "/api/skills") => NonZeroU32::new(2).unwrap(),
("GET", "/api/peers") => NonZeroU32::new(2).unwrap(),
("GET", "/api/config") => NonZeroU32::new(2).unwrap(),
("GET", "/api/usage") => NonZeroU32::new(3).unwrap(),
("GET", p) if p.starts_with("/api/audit") => NonZeroU32::new(5).unwrap(),
("GET", p) if p.starts_with("/api/marketplace") => NonZeroU32::new(10).unwrap(),
("POST", "/api/agents") => NonZeroU32::new(50).unwrap(),
("POST", p) if p.contains("/message") => NonZeroU32::new(30).unwrap(),
("POST", p) if p.contains("/run") => NonZeroU32::new(100).unwrap(),
("POST", "/api/skills/install") => NonZeroU32::new(50).unwrap(),
("POST", "/api/skills/uninstall") => NonZeroU32::new(10).unwrap(),
("POST", "/api/skills/reload") => NonZeroU32::new(5).unwrap(),
("GET", p) if p.starts_with("/api/skills/") && p.ends_with("/config") => {
NonZeroU32::new(3).unwrap()
}
("PUT", p) if p.starts_with("/api/skills/") && p.ends_with("/config") => {
NonZeroU32::new(10).unwrap()
}
("DELETE", p) if p.starts_with("/api/skills/") && p.contains("/config/") => {
NonZeroU32::new(10).unwrap()
}
("POST", "/api/migrate") => NonZeroU32::new(100).unwrap(),
("PUT", p) if p.contains("/update") => NonZeroU32::new(10).unwrap(),
_ => NonZeroU32::new(5).unwrap(),
}
}
pub type KeyedRateLimiter = RateLimiter<IpAddr, DashMapStateStore<IpAddr>, DefaultClock>;
/// 500 tokens per minute per IP.
pub fn create_rate_limiter() -> Arc<KeyedRateLimiter> {
Arc::new(RateLimiter::keyed(Quota::per_minute(
NonZeroU32::new(500).unwrap(),
)))
}
/// GCRA rate limiting middleware.
///
/// Extracts the client IP from `ConnectInfo`, computes the cost for the
/// requested operation, and checks the GCRA limiter. Returns 429 if the
/// client has exhausted its token budget.
pub async fn gcra_rate_limit(
axum::extract::State(limiter): axum::extract::State<Arc<KeyedRateLimiter>>,
request: Request<Body>,
next: Next,
) -> Response<Body> {
let ip = request
.extensions()
.get::<axum::extract::ConnectInfo<SocketAddr>>()
.map(|ci| ci.0.ip())
.unwrap_or(IpAddr::from([127, 0, 0, 1]));
let method = request.method().as_str().to_string();
let path = request.uri().path().to_string();
let cost = operation_cost(&method, &path);
if limiter.check_key_n(&ip, cost).is_err() {
tracing::warn!(ip = %ip, cost = cost.get(), path = %path, "GCRA rate limit exceeded");
return Response::builder()
.status(StatusCode::TOO_MANY_REQUESTS)
.header("content-type", "application/json")
.header("retry-after", "60")
.body(Body::from(
serde_json::json!({"error": "Rate limit exceeded"}).to_string(),
))
.unwrap_or_default();
}
next.run(request).await
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_costs() {
assert_eq!(operation_cost("GET", "/api/health").get(), 1);
assert_eq!(operation_cost("GET", "/api/tools").get(), 1);
assert_eq!(operation_cost("POST", "/api/agents/1/message").get(), 30);
assert_eq!(operation_cost("POST", "/api/agents").get(), 50);
assert_eq!(operation_cost("POST", "/api/workflows/1/run").get(), 100);
assert_eq!(operation_cost("GET", "/api/agents/1/session").get(), 5);
assert_eq!(operation_cost("GET", "/api/skills").get(), 2);
assert_eq!(operation_cost("GET", "/api/peers").get(), 2);
assert_eq!(operation_cost("GET", "/api/audit/recent").get(), 5);
assert_eq!(operation_cost("POST", "/api/skills/install").get(), 50);
assert_eq!(operation_cost("POST", "/api/migrate").get(), 100);
}
}
crates/openfang-api/src/routes.rs
+12975
View File
File diff suppressed because it is too large Load Diff
crates/openfang-api/src/server.rs
+1039
View File
File diff suppressed because it is too large Load Diff
crates/openfang-api/src/session_auth.rs
+195
View File
@@ -0,0 +1,195 @@
//! Stateless session token authentication for the dashboard.
//! Tokens are HMAC-SHA256 signed and contain username + expiry.
use hmac::{Hmac, Mac};
use sha2::Sha256;
type HmacSha256 = Hmac<Sha256>;
/// Create a session token: base64(username:expiry_unix:hmac_hex)
pub fn create_session_token(username: &str, secret: &str, ttl_hours: u64) -> String {
use base64::Engine;
let expiry = chrono::Utc::now().timestamp() + (ttl_hours as i64 * 3600);
let payload = format!("{username}:{expiry}");
let mut mac = HmacSha256::new_from_slice(secret.as_bytes()).expect("HMAC key");
mac.update(payload.as_bytes());
let signature = hex::encode(mac.finalize().into_bytes());
base64::engine::general_purpose::STANDARD.encode(format!("{payload}:{signature}"))
}
/// Extract the `openfang_session` cookie value from a `Cookie` header string.
///
/// Returns `None` if the header is absent or the cookie is not present.
/// Used by both the HTTP auth middleware and the WebSocket upgrade handler so
/// that browser sessions established via `sessionLogin()` are honored on both
/// surfaces (issue #1085).
pub fn extract_session_cookie(headers: &axum::http::HeaderMap) -> Option<String> {
headers
.get("cookie")
.and_then(|v| v.to_str().ok())
.and_then(|cookies| {
cookies.split(';').find_map(|c| {
c.trim()
.strip_prefix("openfang_session=")
.map(|v| v.to_string())
})
})
}
/// Verify a session token. Returns the username if valid and not expired.
pub fn verify_session_token(token: &str, secret: &str) -> Option<String> {
use base64::Engine;
let decoded = base64::engine::general_purpose::STANDARD
.decode(token)
.ok()?;
let decoded_str = String::from_utf8(decoded).ok()?;
let parts: Vec<&str> = decoded_str.splitn(3, ':').collect();
if parts.len() != 3 {
return None;
}
let (username, expiry_str, provided_sig) = (parts[0], parts[1], parts[2]);
let expiry: i64 = expiry_str.parse().ok()?;
if chrono::Utc::now().timestamp() > expiry {
return None;
}
let payload = format!("{username}:{expiry_str}");
let mut mac = HmacSha256::new_from_slice(secret.as_bytes()).ok()?;
mac.update(payload.as_bytes());
let expected_sig = hex::encode(mac.finalize().into_bytes());
use subtle::ConstantTimeEq;
if provided_sig.len() != expected_sig.len() {
return None;
}
if provided_sig
.as_bytes()
.ct_eq(expected_sig.as_bytes())
.into()
{
Some(username.to_string())
} else {
None
}
}
/// Hash a password with Argon2id for config storage.
///
/// Returns a PHC-format string (e.g. `$argon2id$v=19$m=19456,t=2,p=1$...`).
pub fn hash_password(password: &str) -> String {
use argon2::{password_hash::SaltString, Argon2, PasswordHasher};
let salt = SaltString::generate(&mut rand::thread_rng());
Argon2::default()
.hash_password(password.as_bytes(), &salt)
.expect("Argon2 hashing should not fail with valid inputs")
.to_string()
}
/// Verify a password against a stored Argon2id hash (PHC string format).
pub fn verify_password(password: &str, stored_hash: &str) -> bool {
use argon2::{password_hash::PasswordHash, Argon2, PasswordVerifier};
let Ok(parsed) = PasswordHash::new(stored_hash) else {
return false;
};
Argon2::default()
.verify_password(password.as_bytes(), &parsed)
.is_ok()
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_hash_and_verify_password() {
let hash = hash_password("secret123");
assert!(
hash.starts_with("$argon2id$"),
"should produce Argon2id PHC string"
);
assert!(verify_password("secret123", &hash));
assert!(!verify_password("wrong", &hash));
}
#[test]
fn test_hash_produces_unique_salts() {
let h1 = hash_password("same");
let h2 = hash_password("same");
assert_ne!(h1, h2, "each hash should use a unique salt");
assert!(verify_password("same", &h1));
assert!(verify_password("same", &h2));
}
#[test]
fn test_rejects_non_argon2_hash() {
// A plain SHA256 hex string should no longer be accepted.
use sha2::Digest;
let sha256_hash = hex::encode(sha2::Sha256::digest(b"password"));
assert!(!verify_password("password", &sha256_hash));
}
#[test]
fn test_create_and_verify_token() {
let token = create_session_token("admin", "my-secret", 1);
let user = verify_session_token(&token, "my-secret");
assert_eq!(user, Some("admin".to_string()));
}
#[test]
fn test_token_wrong_secret() {
let token = create_session_token("admin", "my-secret", 1);
let user = verify_session_token(&token, "wrong-secret");
assert_eq!(user, None);
}
#[test]
fn test_token_invalid_base64() {
let user = verify_session_token("not-valid-base64!!!", "secret");
assert_eq!(user, None);
}
#[test]
fn test_rejects_garbage_input() {
assert!(!verify_password("x", "short"));
assert!(!verify_password("x", ""));
}
#[test]
fn test_verify_malformed_argon2_hash() {
// Starts with $argon2 but is not a valid PHC string.
assert!(!verify_password("x", "$argon2id$garbage"));
}
#[test]
fn test_extract_session_cookie_present() {
let mut h = axum::http::HeaderMap::new();
h.insert(
"cookie",
"foo=bar; openfang_session=abc.def.ghi; baz=qux"
.parse()
.unwrap(),
);
assert_eq!(extract_session_cookie(&h).as_deref(), Some("abc.def.ghi"));
}
#[test]
fn test_extract_session_cookie_absent() {
let mut h = axum::http::HeaderMap::new();
h.insert("cookie", "foo=bar; baz=qux".parse().unwrap());
assert_eq!(extract_session_cookie(&h), None);
}
#[test]
fn test_extract_session_cookie_no_header() {
let h = axum::http::HeaderMap::new();
assert_eq!(extract_session_cookie(&h), None);
}
#[test]
fn test_extract_session_cookie_only_value() {
let mut h = axum::http::HeaderMap::new();
h.insert("cookie", "openfang_session=lonely".parse().unwrap());
assert_eq!(extract_session_cookie(&h).as_deref(), Some("lonely"));
}
}
crates/openfang-api/src/stream_chunker.rs
+244
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//! Markdown-aware stream chunking.
//!
//! Replaces naive 200-char text buffer flushing with smart chunking that
//! never splits inside fenced code blocks and respects Markdown structure.
/// Markdown-aware stream chunker.
///
/// Buffers incoming text and flushes at natural break points:
/// paragraph boundaries > newlines > sentence endings.
/// Never splits inside fenced code blocks.
pub struct StreamChunker {
buffer: String,
in_code_fence: bool,
fence_marker: String,
min_chunk_chars: usize,
max_chunk_chars: usize,
}
impl StreamChunker {
/// Create a new chunker with custom min/max thresholds.
pub fn new(min_chunk_chars: usize, max_chunk_chars: usize) -> Self {
Self {
buffer: String::new(),
in_code_fence: false,
fence_marker: String::new(),
min_chunk_chars,
max_chunk_chars,
}
}
/// Push new text into the buffer. Updates code fence tracking.
pub fn push(&mut self, text: &str) {
for line in text.split_inclusive('\n') {
self.buffer.push_str(line);
// Track code fence state
let trimmed = line.trim();
if trimmed.starts_with("```") {
if self.in_code_fence {
// Check if this closes the current fence
if trimmed == "```" || trimmed.starts_with(&self.fence_marker) {
self.in_code_fence = false;
self.fence_marker.clear();
}
} else {
self.in_code_fence = true;
self.fence_marker = "```".to_string();
}
}
}
}
/// Try to flush a chunk from the buffer.
///
/// Returns `Some(chunk)` if enough content has accumulated,
/// `None` if we should wait for more input.
pub fn try_flush(&mut self) -> Option<String> {
if self.buffer.len() < self.min_chunk_chars {
return None;
}
// If inside a code fence and under max, wait for fence to close
if self.in_code_fence && self.buffer.len() < self.max_chunk_chars {
return None;
}
// If at max inside a fence, force-close and flush
if self.in_code_fence && self.buffer.len() >= self.max_chunk_chars {
// Close the fence, flush everything, reopen on next push
let mut chunk = std::mem::take(&mut self.buffer);
chunk.push_str("\n```\n");
// Mark that we need to reopen the fence
self.buffer = format!("```{}\n", self.fence_marker.trim_start_matches('`'));
return Some(chunk);
}
// Find best break point
let search_range = self.min_chunk_chars..self.buffer.len().min(self.max_chunk_chars);
// Priority 1: Paragraph break (double newline)
if let Some(pos) = find_last_in_range(&self.buffer, "\n\n", &search_range) {
let break_at = pos + 2;
let chunk = self.buffer[..break_at].to_string();
self.buffer = self.buffer[break_at..].to_string();
return Some(chunk);
}
// Priority 2: Single newline
if let Some(pos) = find_last_in_range(&self.buffer, "\n", &search_range) {
let break_at = pos + 1;
let chunk = self.buffer[..break_at].to_string();
self.buffer = self.buffer[break_at..].to_string();
return Some(chunk);
}
// Priority 3: Sentence ending (". ", "! ", "? ")
for ending in &[". ", "! ", "? "] {
if let Some(pos) = find_last_in_range(&self.buffer, ending, &search_range) {
let break_at = pos + ending.len();
let chunk = self.buffer[..break_at].to_string();
self.buffer = self.buffer[break_at..].to_string();
return Some(chunk);
}
}
// Priority 4: Forced break at max_chunk_chars (char-boundary safe)
if self.buffer.len() >= self.max_chunk_chars {
let mut break_at = self.max_chunk_chars;
while break_at > 0 && !self.buffer.is_char_boundary(break_at) {
break_at -= 1;
}
if break_at == 0 {
break_at = self.buffer.len();
}
let chunk = self.buffer[..break_at].to_string();
self.buffer = self.buffer[break_at..].to_string();
return Some(chunk);
}
None
}
/// Force-flush all remaining text.
pub fn flush_remaining(&mut self) -> Option<String> {
if self.buffer.is_empty() {
None
} else {
Some(std::mem::take(&mut self.buffer))
}
}
/// Current buffer length.
pub fn buffered_len(&self) -> usize {
self.buffer.len()
}
/// Whether currently inside a code fence.
pub fn is_in_code_fence(&self) -> bool {
self.in_code_fence
}
}
/// Find the last occurrence of a pattern within a byte range.
///
/// Both `range.start` and `range.end` are clamped to the nearest valid UTF-8
/// char boundary so that slicing never panics on multi-byte content.
fn find_last_in_range(text: &str, pattern: &str, range: &std::ops::Range<usize>) -> Option<usize> {
let len = text.len();
// Clamp end to text length and walk back to a char boundary
let mut end = range.end.min(len);
while end > 0 && !text.is_char_boundary(end) {
end -= 1;
}
// Walk start forward to the nearest char boundary (never past end)
let mut start = range.start.min(end);
while start < end && !text.is_char_boundary(start) {
start += 1;
}
let search_text = &text[start..end];
search_text.rfind(pattern).map(|pos| start + pos)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_basic_chunking() {
let mut chunker = StreamChunker::new(10, 50);
chunker.push("Hello world.\nThis is a test.\nAnother line.\n");
let chunk = chunker.try_flush();
assert!(chunk.is_some());
let text = chunk.unwrap();
// Should break at a newline
assert!(text.ends_with('\n'));
}
#[test]
fn test_code_fence_not_split() {
let mut chunker = StreamChunker::new(5, 200);
chunker.push("Before\n```python\ndef foo():\n pass\n```\nAfter\n");
// Should not flush mid-fence
// Since buffer is >5 chars and fence is now closed, should flush
let chunk = chunker.try_flush();
assert!(chunk.is_some());
let text = chunk.unwrap();
// If it includes the code block, the fence should be complete
if text.contains("```python") {
assert!(text.contains("```\n") || text.ends_with("```"));
}
}
#[test]
fn test_code_fence_force_close_at_max() {
let mut chunker = StreamChunker::new(5, 30);
chunker.push("```python\nline1\nline2\nline3\nline4\nline5\nline6\n");
// Buffer exceeds max while in fence — should force close
let chunk = chunker.try_flush();
assert!(chunk.is_some());
let text = chunk.unwrap();
assert!(text.contains("```\n")); // force-closed fence
}
#[test]
fn test_paragraph_break_priority() {
let mut chunker = StreamChunker::new(10, 200);
chunker.push("First paragraph text.\n\nSecond paragraph text.\n");
let chunk = chunker.try_flush();
assert!(chunk.is_some());
let text = chunk.unwrap();
assert!(text.ends_with("\n\n"));
}
#[test]
fn test_flush_remaining() {
let mut chunker = StreamChunker::new(100, 200);
chunker.push("short");
// try_flush should return None (under min)
assert!(chunker.try_flush().is_none());
// flush_remaining should return everything
let remaining = chunker.flush_remaining();
assert_eq!(remaining, Some("short".to_string()));
// Second flush should be None
assert!(chunker.flush_remaining().is_none());
}
#[test]
fn test_sentence_break() {
let mut chunker = StreamChunker::new(10, 200);
chunker.push("This is the first sentence. This is the second sentence. More text here.");
let chunk = chunker.try_flush();
assert!(chunk.is_some());
let text = chunk.unwrap();
// Should break at a sentence ending
assert!(text.ends_with(". ") || text.ends_with(".\n"));
}
}
crates/openfang-api/src/stream_dedup.rs
+160
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//! Streaming duplicate detection.
//!
//! Detects when the LLM repeats text that was already sent (e.g., repeating
//! tool output verbatim). Uses exact + normalized matching with a sliding window.
/// Minimum text length to consider for deduplication.
const MIN_DEDUP_LENGTH: usize = 10;
/// Number of recent chunks to keep in the dedup window.
const DEDUP_WINDOW: usize = 50;
/// Streaming duplicate detector.
pub struct StreamDedup {
/// Recent chunks (exact text).
recent_chunks: Vec<String>,
/// Recent chunks (normalized: lowercased, whitespace-collapsed).
recent_normalized: Vec<String>,
}
impl StreamDedup {
/// Create a new dedup detector.
pub fn new() -> Self {
Self {
recent_chunks: Vec::with_capacity(DEDUP_WINDOW),
recent_normalized: Vec::with_capacity(DEDUP_WINDOW),
}
}
/// Check if text is a duplicate of recently sent content.
///
/// Returns `true` if the text matches (exact or normalized) any
/// recent chunk. Skips very short texts.
pub fn is_duplicate(&self, text: &str) -> bool {
if text.len() < MIN_DEDUP_LENGTH {
return false;
}
// Exact match
if self.recent_chunks.iter().any(|c| c == text) {
return true;
}
// Normalized match
let normalized = normalize(text);
self.recent_normalized.iter().any(|c| c == &normalized)
}
/// Record text that was successfully sent to the client.
pub fn record_sent(&mut self, text: &str) {
if text.len() < MIN_DEDUP_LENGTH {
return;
}
// Evict oldest if at capacity
if self.recent_chunks.len() >= DEDUP_WINDOW {
self.recent_chunks.remove(0);
self.recent_normalized.remove(0);
}
self.recent_chunks.push(text.to_string());
self.recent_normalized.push(normalize(text));
}
/// Clear the dedup window.
pub fn clear(&mut self) {
self.recent_chunks.clear();
self.recent_normalized.clear();
}
}
impl Default for StreamDedup {
fn default() -> Self {
Self::new()
}
}
/// Normalize text for fuzzy matching: lowercase + collapse whitespace.
fn normalize(text: &str) -> String {
let mut result = String::with_capacity(text.len());
let mut last_was_space = false;
for ch in text.chars() {
if ch.is_whitespace() {
if !last_was_space {
result.push(' ');
last_was_space = true;
}
} else {
result.push(ch.to_lowercase().next().unwrap_or(ch));
last_was_space = false;
}
}
result.trim().to_string()
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_exact_match_detected() {
let mut dedup = StreamDedup::new();
dedup.record_sent("This is a test chunk of text that was sent.");
assert!(dedup.is_duplicate("This is a test chunk of text that was sent."));
}
#[test]
fn test_normalized_match_detected() {
let mut dedup = StreamDedup::new();
dedup.record_sent("This is a test chunk");
// Same text but different whitespace/case
assert!(dedup.is_duplicate("this is a test chunk"));
}
#[test]
fn test_short_text_skipped() {
let mut dedup = StreamDedup::new();
dedup.record_sent("short");
assert!(!dedup.is_duplicate("short"));
}
#[test]
fn test_window_rollover() {
let mut dedup = StreamDedup::new();
// Fill the window
for i in 0..DEDUP_WINDOW {
dedup.record_sent(&format!("chunk number {} is here", i));
}
// Add one more — should evict the oldest
dedup.record_sent("new chunk that is quite long");
// Oldest should no longer be detected
assert!(!dedup.is_duplicate("chunk number 0 is here"));
// Newest should be detected
assert!(dedup.is_duplicate("new chunk that is quite long"));
}
#[test]
fn test_no_false_positives() {
let mut dedup = StreamDedup::new();
dedup.record_sent("The quick brown fox jumps over the lazy dog");
assert!(!dedup.is_duplicate("A completely different sentence here"));
}
#[test]
fn test_clear() {
let mut dedup = StreamDedup::new();
dedup.record_sent("This is test content here");
assert!(dedup.is_duplicate("This is test content here"));
dedup.clear();
assert!(!dedup.is_duplicate("This is test content here"));
}
#[test]
fn test_normalize() {
assert_eq!(normalize("Hello World"), "hello world");
assert_eq!(normalize(" spaced out "), "spaced out");
assert_eq!(normalize("UPPER case"), "upper case");
}
}
crates/openfang-api/src/types.rs
+219
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//! Request/response types for the OpenFang API.
use serde::{Deserialize, Serialize};
/// Request to spawn an agent from a TOML manifest string or a template name.
#[derive(Debug, Deserialize)]
pub struct SpawnRequest {
/// Agent manifest as TOML string (optional if `template` is provided).
#[serde(default)]
pub manifest_toml: String,
/// Template name from `~/.openfang/agents/{template}/agent.toml`.
/// When provided and `manifest_toml` is empty, the template is loaded automatically.
#[serde(default)]
pub template: Option<String>,
/// Optional Ed25519 signed manifest envelope (JSON).
/// When present, the signature is verified before spawning.
#[serde(default)]
pub signed_manifest: Option<String>,
}
/// Response after spawning an agent.
#[derive(Debug, Serialize)]
pub struct SpawnResponse {
pub agent_id: String,
pub name: String,
}
/// A file attachment reference (from a prior upload).
#[derive(Debug, Clone, Deserialize)]
pub struct AttachmentRef {
pub file_id: String,
#[serde(default)]
pub filename: String,
#[serde(default)]
pub content_type: String,
}
/// Request to send a message to an agent.
#[derive(Debug, Deserialize)]
pub struct MessageRequest {
pub message: String,
/// Optional file attachments (uploaded via /upload endpoint).
#[serde(default)]
pub attachments: Vec<AttachmentRef>,
/// Sender identity (e.g. WhatsApp phone number, Telegram user ID).
#[serde(default)]
pub sender_id: Option<String>,
/// Sender display name.
#[serde(default)]
pub sender_name: Option<String>,
}
/// Response from sending a message.
#[derive(Debug, Serialize)]
pub struct MessageResponse {
pub response: String,
pub input_tokens: u64,
pub output_tokens: u64,
pub iterations: u32,
#[serde(skip_serializing_if = "Option::is_none")]
pub cost_usd: Option<f64>,
}
/// Request to install a skill from the marketplace.
#[derive(Debug, Deserialize)]
pub struct SkillInstallRequest {
pub name: String,
/// When true, reject the install unless the bundle ships a valid
/// Ed25519 SignedManifest envelope bound to the on-disk manifest.
/// Maps to `InstallOptions::require_signed` (issue #1170).
#[serde(default)]
pub require_signed: bool,
/// Optional hex-encoded allow-list of acceptable signer public keys.
/// Empty = TOFU (any valid signature accepted).
#[serde(default)]
pub allowed_signer_keys: Vec<String>,
}
/// Request to uninstall a skill.
#[derive(Debug, Deserialize)]
pub struct SkillUninstallRequest {
pub name: String,
}
/// Request to update an agent's manifest.
#[derive(Debug, Deserialize)]
pub struct AgentUpdateRequest {
pub manifest_toml: String,
}
/// Request to change an agent's operational mode.
#[derive(Debug, Deserialize)]
pub struct SetModeRequest {
pub mode: openfang_types::agent::AgentMode,
}
/// Request to run a migration.
#[derive(Debug, Deserialize)]
pub struct MigrateRequest {
pub source: String,
pub source_dir: String,
pub target_dir: String,
#[serde(default)]
pub dry_run: bool,
}
/// Request to scan a directory for migration.
#[derive(Debug, Deserialize)]
pub struct MigrateScanRequest {
pub path: String,
}
/// Request to install a skill from ClawHub.
#[derive(Debug, Deserialize)]
pub struct ClawHubInstallRequest {
/// ClawHub skill slug (e.g., "github-helper").
pub slug: String,
}
/// Query parameters for `GET /api/commands`.
#[derive(Debug, Deserialize)]
pub struct CommandsQuery {
/// Surface filter: `web` (default), `cli`, `channel`, or `all`.
#[serde(default)]
pub surface: Option<String>,
}
/// Request body for `POST /api/audit/append` (issue #1174).
///
/// Lets external (instance-side) wrappers append entries to the Merkle hash
/// chain audit log. The handler maps `event_type` to an `AuditAction` and
/// records the entry through `kernel.audit_log`.
#[derive(Debug, Deserialize)]
pub struct AuditAppendRequest {
/// Operator-supplied event category. Case-insensitive, matched against the
/// `AuditAction` enum variants (e.g. `tool_invoke`, `ConfigChange`,
/// `agent_message`). Unknown values fall back to `ToolInvoke`.
pub event_type: String,
/// Agent or wrapper identifier responsible for the event. When empty,
/// recorded as `"external-wrapper"`.
#[serde(default)]
pub agent_id: String,
/// Free-form detail string (e.g. tool name, URL, file path).
#[serde(default)]
pub detail: String,
/// Optional arbitrary payload. When present it is serialised to JSON and
/// appended onto the entry's detail so the wrapper retains structured
/// context without changing the on-chain schema.
#[serde(default)]
pub payload: Option<serde_json::Value>,
/// Optional outcome string (`"ok"`, `"denied"`, or an error). Defaults to
/// `"ok"` when omitted.
#[serde(default)]
pub outcome: Option<String>,
/// Optional operator-supplied signing context (e.g. wrapper identity, key
/// fingerprint). Mixed into the detail when present so the chain captures
/// who attested to the event.
#[serde(default)]
pub signing_context: Option<String>,
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn skill_install_request_defaults_back_compat() {
// Existing callers send `{"name": "..."}` only. New optional fields
// must default cleanly (issue #1170).
let req: SkillInstallRequest = serde_json::from_str(r#"{"name":"github-helper"}"#).unwrap();
assert_eq!(req.name, "github-helper");
assert!(!req.require_signed);
assert!(req.allowed_signer_keys.is_empty());
}
#[test]
fn skill_install_request_parses_require_signed() {
let req: SkillInstallRequest = serde_json::from_str(
r#"{"name":"x","require_signed":true,"allowed_signer_keys":["abc123"]}"#,
)
.unwrap();
assert!(req.require_signed);
assert_eq!(req.allowed_signer_keys, vec!["abc123".to_string()]);
}
#[test]
fn audit_append_request_required_only() {
// Only `event_type` is required; everything else must default.
let req: AuditAppendRequest =
serde_json::from_str(r#"{"event_type":"ToolInvoke"}"#).unwrap();
assert_eq!(req.event_type, "ToolInvoke");
assert!(req.agent_id.is_empty());
assert!(req.detail.is_empty());
assert!(req.payload.is_none());
assert!(req.outcome.is_none());
assert!(req.signing_context.is_none());
}
#[test]
fn audit_append_request_full_payload() {
let body = r#"{
"event_type": "config_change",
"agent_id": "wrapper-1",
"detail": "rotated key",
"payload": {"key_id": "k-42", "ts": 1700000000},
"outcome": "ok",
"signing_context": "ed25519:deadbeef"
}"#;
let req: AuditAppendRequest = serde_json::from_str(body).unwrap();
assert_eq!(req.event_type, "config_change");
assert_eq!(req.agent_id, "wrapper-1");
assert_eq!(req.detail, "rotated key");
assert_eq!(req.outcome.as_deref(), Some("ok"));
assert_eq!(req.signing_context.as_deref(), Some("ed25519:deadbeef"));
let payload = req.payload.expect("payload present");
assert_eq!(payload["key_id"], "k-42");
assert_eq!(payload["ts"], 1_700_000_000);
}
}
crates/openfang-api/src/webchat.rs
+193
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@@ -0,0 +1,193 @@
//! Embedded WebChat UI served as static HTML.
//!
//! The production dashboard is assembled at compile time from separate
//! HTML/CSS/JS files under `static/` using `include_str!()`. This keeps
//! single-binary deployment while allowing organized source files.
//!
//! Features:
//! - Alpine.js SPA with hash-based routing (10 panels)
//! - Dark/light theme toggle with system preference detection
//! - Responsive layout with collapsible sidebar
//! - Markdown rendering + syntax highlighting (bundled locally)
//! - WebSocket real-time chat with HTTP fallback
//! - Agent management, workflows, memory browser, audit log, and more
use axum::http::header;
use axum::response::IntoResponse;
/// Nonce placeholder in compile-time HTML, replaced at request time.
const NONCE_PLACEHOLDER: &str = "__NONCE__";
/// Compile-time ETag based on the crate version.
/// Not used for the dashboard page (nonce prevents caching) but retained
/// for potential future use by static asset handlers.
#[allow(dead_code)]
const ETAG: &str = concat!("\"openfang-", env!("CARGO_PKG_VERSION"), "\"");
/// Embedded logo PNG for single-binary deployment.
const LOGO_PNG: &[u8] = include_bytes!("../static/logo.png");
/// Embedded favicon ICO for browser tabs.
const FAVICON_ICO: &[u8] = include_bytes!("../static/favicon.ico");
/// GET /logo.png — Serve the OpenFang logo.
pub async fn logo_png() -> impl IntoResponse {
(
[
(header::CONTENT_TYPE, "image/png"),
(header::CACHE_CONTROL, "public, max-age=86400, immutable"),
],
LOGO_PNG,
)
}
/// GET /favicon.ico — Serve the OpenFang favicon.
pub async fn favicon_ico() -> impl IntoResponse {
(
[
(header::CONTENT_TYPE, "image/x-icon"),
(header::CACHE_CONTROL, "public, max-age=86400, immutable"),
],
FAVICON_ICO,
)
}
/// Embedded PWA manifest for installable web app support.
const MANIFEST_JSON: &str = include_str!("../static/manifest.json");
/// Embedded service worker for PWA support.
const SW_JS: &str = include_str!("../static/sw.js");
/// GET /manifest.json — Serve the PWA web app manifest.
pub async fn manifest_json() -> impl IntoResponse {
(
[
(header::CONTENT_TYPE, "application/manifest+json"),
(header::CACHE_CONTROL, "public, max-age=86400, immutable"),
],
MANIFEST_JSON,
)
}
/// GET /sw.js — Serve the PWA service worker.
pub async fn sw_js() -> impl IntoResponse {
(
[
(header::CONTENT_TYPE, "application/javascript"),
(header::CACHE_CONTROL, "no-cache"),
],
SW_JS,
)
}
/// GET / — Serve the OpenFang Dashboard single-page application.
///
/// Generates a unique CSP nonce on every request and injects it into both
/// the `<script>` tags and the `Content-Security-Policy` header. This
/// replaces `'unsafe-inline'` so only our own scripts execute.
pub async fn webchat_page() -> impl IntoResponse {
let nonce = uuid::Uuid::new_v4().to_string();
let html = WEBCHAT_HTML.replace(NONCE_PLACEHOLDER, &nonce);
let csp = format!(
"default-src 'self'; \
script-src 'self' 'nonce-{nonce}' 'unsafe-eval' https://cdn.jsdelivr.net; \
style-src 'self' 'unsafe-inline' https://fonts.googleapis.com https://fonts.gstatic.com https://cdn.jsdelivr.net; \
img-src 'self' data: blob:; \
connect-src 'self' ws://localhost:* ws://127.0.0.1:* wss://localhost:* wss://127.0.0.1:* https://cdn.jsdelivr.net; \
font-src 'self' https://fonts.gstatic.com https://cdn.jsdelivr.net; \
media-src 'self' blob:; \
frame-src 'self' blob:; \
object-src 'none'; \
base-uri 'self'; \
form-action 'self'"
);
(
[
(header::CONTENT_TYPE, "text/html; charset=utf-8".to_string()),
(
header::HeaderName::from_static("content-security-policy"),
csp,
),
(header::CACHE_CONTROL, "no-store".to_string()),
],
html,
)
}
/// The embedded HTML/CSS/JS for the OpenFang Dashboard.
///
/// Assembled at compile time from organized static files.
/// All vendor libraries (Alpine.js, marked.js, highlight.js) are bundled
/// locally — no CDN dependency. Alpine.js is included LAST because it
/// immediately processes x-data directives and fires alpine:init on load.
/// KaTeX is loaded dynamically from jsdelivr CDN when needed for LaTeX rendering.
const WEBCHAT_HTML: &str = concat!(
include_str!("../static/index_head.html"),
"<style>\n",
include_str!("../static/css/theme.css"),
"\n",
include_str!("../static/css/layout.css"),
"\n",
include_str!("../static/css/components.css"),
"\n",
include_str!("../static/vendor/github-dark.min.css"),
"\n</style>\n",
include_str!("../static/index_body.html"),
// Vendor libs: marked + highlight first (used by app.js), then Chart.js
"<script nonce=\"__NONCE__\">\n",
include_str!("../static/vendor/marked.min.js"),
"\n</script>\n",
"<script nonce=\"__NONCE__\">\n",
include_str!("../static/vendor/highlight.min.js"),
"\n</script>\n",
"<script nonce=\"__NONCE__\">\n",
include_str!("../static/vendor/chart.umd.min.js"),
"\n</script>\n",
// App code
"<script nonce=\"__NONCE__\">\n",
include_str!("../static/js/api.js"),
"\n",
include_str!("../static/js/app.js"),
"\n",
include_str!("../static/js/pages/overview.js"),
"\n",
include_str!("../static/js/katex.js"),
"\n",
include_str!("../static/js/pages/chat.js"),
"\n",
include_str!("../static/js/pages/agents.js"),
"\n",
include_str!("../static/js/pages/workflows.js"),
"\n",
include_str!("../static/js/pages/workflow-builder.js"),
"\n",
include_str!("../static/js/pages/channels.js"),
"\n",
include_str!("../static/js/pages/skills.js"),
"\n",
include_str!("../static/js/pages/hands.js"),
"\n",
include_str!("../static/js/pages/scheduler.js"),
"\n",
include_str!("../static/js/pages/settings.js"),
"\n",
include_str!("../static/js/pages/usage.js"),
"\n",
include_str!("../static/js/pages/sessions.js"),
"\n",
include_str!("../static/js/pages/logs.js"),
"\n",
include_str!("../static/js/pages/wizard.js"),
"\n",
include_str!("../static/js/pages/approvals.js"),
"\n",
include_str!("../static/js/pages/comms.js"),
"\n",
include_str!("../static/js/pages/runtime.js"),
"\n</script>\n",
// Alpine.js MUST be last — it processes x-data and fires alpine:init
"<script nonce=\"__NONCE__\">\n",
include_str!("../static/vendor/alpine.min.js"),
"\n</script>\n",
"</body></html>"
);
crates/openfang-api/src/ws.rs
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crates/openfang-api/static/css/components.css
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crates/openfang-api/static/css/layout.css
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/* OpenFang Layout — Grid + Sidebar + Responsive */
/* Firefox compat: hide x-cloak elements until Alpine.js initializes.
Without this, the sidebar flashes hidden in Firefox while Alpine
processes the nested x-data scopes for nav sections. */
[x-cloak] { display: none !important; }
.app-layout {
display: flex;
height: 100vh;
overflow: hidden;
}
/* Sidebar */
.sidebar {
width: var(--sidebar-width);
background: var(--bg-primary);
border-right: 1px solid var(--border);
display: flex;
flex-direction: column;
flex-shrink: 0;
transition: width var(--transition-normal);
z-index: 100;
}
.sidebar.collapsed {
width: var(--sidebar-collapsed);
}
.sidebar.collapsed .sidebar-label,
.sidebar.collapsed .sidebar-header-text,
.sidebar.collapsed .nav-label { display: none; }
.sidebar.collapsed .nav-item { justify-content: center; padding: 12px 0; }
.sidebar-header {
padding: 16px;
border-bottom: 1px solid var(--border);
display: flex;
align-items: center;
justify-content: space-between;
min-height: 60px;
}
.sidebar-logo {
display: flex;
align-items: center;
gap: 10px;
}
.sidebar-logo img {
width: 28px;
height: 28px;
opacity: 0.8;
transition: opacity 0.2s, transform 0.2s;
}
.sidebar-logo img:hover {
opacity: 1;
transform: scale(1.05);
}
[data-theme="light"] .sidebar-logo img,
[data-theme="light"] .message-avatar img {
filter: invert(1);
}
.sidebar-header h1 {
font-size: 14px;
font-weight: 700;
color: var(--accent);
letter-spacing: 3px;
font-family: var(--font-mono);
}
.sidebar-header .version {
font-size: 9px;
color: var(--text-muted);
margin-top: 1px;
letter-spacing: 0.5px;
}
.sidebar-status {
font-size: 11px;
color: var(--success);
display: flex;
align-items: center;
gap: 6px;
padding: 8px 16px;
border-bottom: 1px solid var(--border);
}
.sidebar-status.offline { color: var(--error); }
.status-dot {
width: 6px; height: 6px;
border-radius: 50%;
background: currentColor;
flex-shrink: 0;
box-shadow: 0 0 6px currentColor;
}
.conn-badge {
font-size: 9px;
padding: 1px 5px;
border-radius: 3px;
font-weight: 600;
letter-spacing: 0.5px;
margin-left: auto;
}
.conn-badge.ws { background: var(--success); color: #000; }
.conn-badge.http { background: var(--warning); color: #000; }
/* Navigation */
.sidebar-nav {
flex: 1;
overflow-y: auto;
padding: 8px;
scrollbar-width: none;
}
.sidebar-nav::-webkit-scrollbar { width: 0; }
.nav-section {
margin-bottom: 4px;
}
.nav-section-title {
font-size: 12px;
text-transform: uppercase;
letter-spacing: 1.5px;
color: var(--text-muted);
padding: 12px 12px 6px 3px;
font-weight: 600;
}
.sidebar.collapsed .nav-section-title { display: none; }
.nav-item {
display: flex;
align-items: center;
gap: 10px;
padding: 9px 12px;
border-radius: var(--radius-md);
cursor: pointer;
font-size: 13px;
color: var(--text-dim);
transition: all var(--transition-fast);
text-decoration: none;
border: 1px solid transparent;
white-space: nowrap;
font-weight: 500;
}
.nav-item:hover {
background: var(--surface2);
color: var(--text);
transform: translateX(2px);
}
.nav-item.active {
background: var(--accent);
color: var(--text-on-accent);
font-weight: 600;
box-shadow: var(--shadow-sm), 0 2px 8px rgba(255, 92, 0, 0.2);
}
.nav-icon {
width: 18px;
height: 18px;
display: inline-flex;
align-items: center;
justify-content: center;
flex-shrink: 0;
}
.nav-icon svg {
width: 16px;
height: 16px;
fill: none;
stroke: currentColor;
stroke-width: 2;
stroke-linecap: round;
stroke-linejoin: round;
}
/* Sidebar toggle button */
.sidebar-toggle {
padding: 10px 16px;
border-top: 1px solid var(--border);
cursor: pointer;
text-align: center;
font-size: 14px;
color: var(--text-muted);
transition: color var(--transition-fast);
}
.sidebar-toggle:hover { color: var(--text); }
/* Main content area */
.main-content {
flex: 1;
display: flex;
flex-direction: column;
min-width: 0;
overflow: hidden;
background: var(--bg);
}
/* Page wrapper divs (rendered by x-if) must fill the column
and be flex containers so .page-body can scroll. */
.main-content > div {
display: flex;
flex-direction: column;
flex: 1;
min-height: 0;
overflow: hidden;
}
.page-header {
padding: 14px 24px;
border-bottom: 1px solid var(--border);
background: var(--bg-primary);
display: flex;
align-items: center;
justify-content: space-between;
min-height: var(--header-height);
}
.page-header h2 {
font-size: 15px;
font-weight: 600;
letter-spacing: -0.01em;
}
.page-body {
flex: 1;
min-height: 0;
overflow-y: auto;
padding: 24px;
}
/* Mobile overlay */
.sidebar-overlay {
display: none;
position: fixed;
inset: 0;
background: rgba(0,0,0,0.6);
z-index: 99;
}
.mobile-menu-btn {
position: fixed !important;
top: 12px;
left: 16px;
z-index: 98;
padding: 6px 10px !important;
}
/* Wide desktop — larger card grids */
@media (min-width: 1400px) {
.card-grid { grid-template-columns: repeat(auto-fill, minmax(320px, 1fr)); }
}
/* Responsive — tablet breakpoint */
@media (max-width: 1024px) {
.card-grid { grid-template-columns: repeat(auto-fill, minmax(240px, 1fr)); }
.security-grid { grid-template-columns: 1fr; }
.cost-charts-row { grid-template-columns: 1fr; }
.overview-grid { grid-template-columns: repeat(auto-fill, minmax(240px, 1fr)); }
.page-body { padding: 16px; }
}
/* Responsive — mobile breakpoint */
@media (max-width: 768px) {
.sidebar {
position: fixed;
left: -300px;
top: 0;
bottom: 0;
transition: left var(--transition-normal);
}
.sidebar.mobile-open {
left: 0;
}
.sidebar.mobile-open + .sidebar-overlay {
display: block;
}
.sidebar.collapsed {
width: var(--sidebar-width);
left: -300px;
}
.mobile-menu-btn { display: flex !important; }
/* Offset header content so it does not overlap the fixed mobile menu button. */
.page-header > :first-child { margin-left: 52px; }
}
@media (min-width: 769px) {
.mobile-menu-btn { display: none !important; }
}
/* Mobile small screen */
@media (max-width: 480px) {
.page-header {
gap: 8px;
padding: 12px 16px;
flex-wrap: wrap;
}
.page-header h2 {
line-height: 44px;
}
.page-body { padding: 12px; }
.stats-row { flex-wrap: wrap; }
.stat-card { min-width: 80px; flex: 1 1 40%; }
.stat-card-lg { min-width: 80px; flex: 1 1 40%; padding: 12px; }
.stat-card-lg .stat-value { font-size: 22px; }
.card-grid { grid-template-columns: 1fr; }
.overview-grid { grid-template-columns: 1fr; }
.input-area { padding: 8px 12px; }
.main-content { padding: 0; }
.table-wrap { font-size: 10px; }
.modal { margin: 8px; max-height: calc(100vh - 16px); }
}
/* Touch-friendly tap targets */
@media (pointer: coarse) {
.btn { min-height: 44px; min-width: 44px; }
.nav-item { min-height: 44px; }
.form-input, .form-select, .form-textarea { min-height: 44px; }
.toggle { min-width: 44px; min-height: 28px; }
}
/* Focus mode — hide sidebar for distraction-free chat */
.app-layout.focus-mode .sidebar { display: none; }
.app-layout.focus-mode .sidebar-overlay { display: none; }
.app-layout.focus-mode .main-content { max-width: 100%; margin-left: 0; }
.app-layout.focus-mode .mobile-menu-btn { display: none !important; }
crates/openfang-api/static/css/theme.css
+278
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/* OpenFang Theme — Premium design system */
/* Font imports in index_head.html: Inter (body) + Geist Mono (code) */
[data-theme="light"], :root {
/* Backgrounds — layered depth */
--bg: #F5F4F2;
--bg-primary: #EDECEB;
--bg-elevated: #F8F7F6;
--surface: #FFFFFF;
--surface2: #F0EEEC;
--surface3: #E8E6E3;
--border: #D5D2CF;
--border-light: #C8C4C0;
--border-subtle: #E0DEDA;
/* Text hierarchy */
--text: #1A1817;
--text-secondary: #3D3935;
--text-dim: #6B6560;
--text-muted: #9A958F;
/* Brand — Orange accent */
--accent: #FF5C00;
--accent-light: #FF7A2E;
--accent-dim: #E05200;
--accent-glow: rgba(255, 92, 0, 0.1);
--accent-subtle: rgba(255, 92, 0, 0.05);
/* Status colors */
--success: #22C55E;
--success-dim: #16A34A;
--success-subtle: rgba(34, 197, 94, 0.08);
--error: #EF4444;
--error-dim: #DC2626;
--error-subtle: rgba(239, 68, 68, 0.06);
--warning: #F59E0B;
--warning-dim: #D97706;
--warning-subtle: rgba(245, 158, 11, 0.08);
--info: #3B82F6;
--info-dim: #2563EB;
--info-subtle: rgba(59, 130, 246, 0.06);
--success-muted: rgba(34, 197, 94, 0.15);
--error-muted: rgba(239, 68, 68, 0.15);
--warning-muted: rgba(245, 158, 11, 0.15);
--info-muted: rgba(59, 130, 246, 0.15);
--border-strong: #B0ACA8;
--card-highlight: rgba(0, 0, 0, 0.02);
/* Chat-specific */
--agent-bg: #F5F4F2;
--user-bg: #FFF3E6;
--text-on-accent: #FFFFFF;
/* Layout */
--sidebar-width: 240px;
--sidebar-collapsed: 56px;
--header-height: 48px;
/* Radius — slightly larger for premium feel */
--radius-xs: 4px;
--radius-sm: 6px;
--radius-md: 8px;
--radius-lg: 12px;
--radius-xl: 16px;
/* Shadows — 6-level depth system */
--shadow-xs: 0 1px 2px rgba(0,0,0,0.04);
--shadow-sm: 0 1px 3px rgba(0,0,0,0.06), 0 1px 2px rgba(0,0,0,0.04);
--shadow-md: 0 4px 12px rgba(0,0,0,0.07), 0 2px 4px rgba(0,0,0,0.04);
--shadow-lg: 0 12px 28px rgba(0,0,0,0.08), 0 4px 10px rgba(0,0,0,0.05);
--shadow-xl: 0 20px 40px rgba(0,0,0,0.1), 0 8px 16px rgba(0,0,0,0.06);
--shadow-glow: 0 0 40px rgba(0,0,0,0.05);
--shadow-accent: 0 4px 16px rgba(255, 92, 0, 0.12);
--shadow-inset: inset 0 1px 0 rgba(255,255,255,0.5);
/* Typography — dual font system */
--font-sans: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', system-ui, sans-serif;
--font-mono: 'Geist Mono', 'SF Mono', 'Fira Code', 'Cascadia Code', 'JetBrains Mono', monospace;
/* Motion — spring curves for premium feel */
--ease-spring: cubic-bezier(0.34, 1.56, 0.64, 1);
--ease-smooth: cubic-bezier(0.4, 0, 0.2, 1);
--ease-out: cubic-bezier(0, 0, 0.2, 1);
--ease-in: cubic-bezier(0.4, 0, 1, 1);
--transition-fast: 0.15s var(--ease-smooth);
--transition-normal: 0.25s var(--ease-smooth);
--transition-spring: 0.4s var(--ease-spring);
}
[data-theme="dark"] {
--bg: #080706;
--bg-primary: #0F0E0E;
--bg-elevated: #161413;
--surface: #242221;
--surface2: #2F2D2C;
--surface3: #1A1817;
--border: #363230;
--border-light: #4A4644;
--border-subtle: #2D2A28;
--text: #FFFFFF;
--text-secondary: #D1D1D1;
--text-dim: #9FA0A0;
--text-muted: #6B6663;
--text-on-accent: #FFFFFF;
--accent: #FF5C00;
--accent-light: #FF7A2E;
--accent-dim: #E05200;
--accent-glow: rgba(255, 92, 0, 0.15);
--accent-subtle: rgba(255, 92, 0, 0.08);
--success: #4ADE80;
--success-dim: #22C55E;
--success-subtle: rgba(74, 222, 128, 0.1);
--error: #EF4444;
--error-dim: #B91C1C;
--error-subtle: rgba(239, 68, 68, 0.1);
--warning: #F59E0B;
--warning-dim: #D97706;
--warning-subtle: rgba(245, 158, 11, 0.1);
--info: #3B82F6;
--info-dim: #2563EB;
--info-subtle: rgba(59, 130, 246, 0.1);
--success-muted: rgba(74, 222, 128, 0.25);
--error-muted: rgba(239, 68, 68, 0.25);
--warning-muted: rgba(245, 158, 11, 0.25);
--info-muted: rgba(59, 130, 246, 0.25);
--border-strong: #4A4644;
--card-highlight: rgba(255, 255, 255, 0.04);
--agent-bg: #1A1817;
--user-bg: #2A1A08;
--shadow-xs: 0 1px 2px rgba(0,0,0,0.3);
--shadow-sm: 0 1px 3px rgba(0,0,0,0.4), 0 1px 2px rgba(0,0,0,0.3);
--shadow-md: 0 4px 12px rgba(0,0,0,0.4), 0 2px 4px rgba(0,0,0,0.3);
--shadow-lg: 0 12px 28px rgba(0,0,0,0.35), 0 4px 10px rgba(0,0,0,0.3);
--shadow-xl: 0 20px 40px rgba(0,0,0,0.4), 0 8px 16px rgba(0,0,0,0.3);
--shadow-glow: 0 0 80px rgba(0,0,0,0.6);
--shadow-accent: 0 4px 16px rgba(255, 92, 0, 0.2);
--shadow-inset: inset 0 1px 0 rgba(255,255,255,0.03);
}
* { margin: 0; padding: 0; box-sizing: border-box; }
html { scroll-behavior: smooth; }
body {
font-family: var(--font-sans);
background: var(--bg);
color: var(--text);
height: 100vh;
overflow: hidden;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
font-size: 14px;
line-height: 1.5;
letter-spacing: -0.01em;
}
/* Mono text utility — only for code/data */
.font-mono, code, pre, .tool-pre, .tool-card-name, .detail-value,
.stat-value, .conn-badge, .version { font-family: var(--font-mono); }
/* Scrollbar — Webkit (Chrome, Edge, Safari) */
::-webkit-scrollbar { width: 6px; height: 6px; }
::-webkit-scrollbar-track { background: transparent; }
::-webkit-scrollbar-thumb { background: var(--border); border-radius: 3px; }
::-webkit-scrollbar-thumb:hover { background: var(--border-light); }
/* Scrollbar — Firefox */
* {
scrollbar-width: thin;
scrollbar-color: var(--border) transparent;
}
::selection {
background: var(--accent);
color: var(--bg-primary);
}
/* Theme transition — smooth switch between light/dark */
body {
transition: background-color 0.3s ease, color 0.3s ease;
}
.sidebar, .main-content, .card, .modal, .tool-card, .toast, .page-header {
transition: background-color 0.3s ease, border-color 0.3s ease, color 0.3s ease, box-shadow 0.3s ease;
}
/* Tighter letter spacing for headings */
h1, h2, h3, .card-header, .stat-value, .page-header h2 { letter-spacing: -0.02em; }
.nav-section-title, .badge, th { letter-spacing: 0.04em; }
/* Focus styles — accessible double-ring with glow */
:focus-visible {
outline: 2px solid var(--accent);
outline-offset: 2px;
box-shadow: 0 0 0 4px var(--accent-glow);
}
button:focus-visible, a:focus-visible, input:focus-visible, select:focus-visible, textarea:focus-visible {
outline: 2px solid var(--accent);
outline-offset: 2px;
box-shadow: 0 0 0 4px var(--accent-glow);
}
/* Entrance animations */
@keyframes fadeIn {
from { opacity: 0; }
to { opacity: 1; }
}
@keyframes slideUp {
from { opacity: 0; transform: translateY(8px); }
to { opacity: 1; transform: translateY(0); }
}
@keyframes slideDown {
from { opacity: 0; transform: translateY(-8px); }
to { opacity: 1; transform: translateY(0); }
}
@keyframes scaleIn {
from { opacity: 0; transform: scale(0.95); }
to { opacity: 1; transform: scale(1); }
}
@keyframes shimmer {
0% { background-position: -200% 0; }
100% { background-position: 200% 0; }
}
@keyframes pulse-ring {
0% { box-shadow: 0 0 0 0 currentColor; }
70% { box-shadow: 0 0 0 4px transparent; }
100% { box-shadow: 0 0 0 0 transparent; }
}
@keyframes spin {
to { transform: rotate(360deg); }
}
/* Staggered card entry animation */
@keyframes cardEntry {
from { opacity: 0; transform: translateY(12px) scale(0.98); }
to { opacity: 1; transform: translateY(0) scale(1); }
}
.animate-entry { animation: cardEntry 0.35s var(--ease-spring) both; }
.stagger-1 { animation-delay: 0.05s; }
.stagger-2 { animation-delay: 0.10s; }
.stagger-3 { animation-delay: 0.15s; }
.stagger-4 { animation-delay: 0.20s; }
.stagger-5 { animation-delay: 0.25s; }
.stagger-6 { animation-delay: 0.30s; }
/* Skeleton loading animation */
.skeleton {
background: linear-gradient(90deg, var(--surface) 25%, var(--surface2) 50%, var(--surface) 75%);
background-size: 200% 100%;
animation: shimmer 1.5s ease-in-out infinite;
border-radius: var(--radius-sm);
}
.skeleton-text { height: 14px; margin-bottom: 8px; }
.skeleton-text:last-child { width: 60%; }
.skeleton-heading { height: 20px; width: 40%; margin-bottom: 12px; }
.skeleton-card { height: 100px; border-radius: var(--radius-lg); }
.skeleton-avatar { width: 32px; height: 32px; border-radius: 50%; }
/* Print styles */
@media print {
.sidebar, .sidebar-overlay, .mobile-menu-btn, .toast-container, .btn { display: none !important; }
.main-content { margin: 0; max-width: 100%; }
body { background: #fff; color: #000; }
}
@media (prefers-reduced-motion: reduce) {
*, *::before, *::after {
animation-duration: 0.01ms !important;
transition-duration: 0.01ms !important;
}
}
crates/openfang-api/static/favicon.ico
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crates/openfang-api/static/i18n/en.json
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{
"app.name": "OpenFang",
"app.version": "v",
"nav.chat": "Chat",
"nav.monitor": "Monitor",
"nav.overview": "Overview",
"nav.analytics": "Analytics",
"nav.logs": "Logs",
"nav.agents": "Agents",
"nav.sessions": "Sessions",
"nav.approvals": "Approvals",
"nav.comms": "Comms",
"nav.automation": "Automation",
"nav.workflows": "Workflows",
"nav.scheduler": "Scheduler",
"nav.extensions": "Extensions",
"nav.channels": "Channels",
"nav.skills": "Skills",
"nav.hands": "Hands",
"nav.system": "System",
"nav.runtime": "Runtime",
"nav.settings": "Settings",
"auth.sign_in": "Sign In",
"auth.enter_credentials": "Enter your dashboard credentials.",
"auth.username": "Username",
"auth.password": "Password",
"auth.api_key_required": "API Key Required",
"auth.api_key_desc": "This instance requires an API key. Enter the key from your config.toml.",
"auth.api_key_hint": "Add api_key = \"your-key\" at the top of ~/.openfang/config.toml (not under any [section]).",
"auth.enter_api_key": "Enter API key...",
"auth.unlock_dashboard": "Unlock Dashboard",
"auth.login_failed": "Login failed",
"status.agents_running": "agent(s) running",
"status.connecting": "Connecting...",
"status.reconnecting": "Reconnecting...",
"status.disconnected": "disconnected",
"status.ws": "WS",
"status.http": "HTTP",
"status.ready": "Ready",
"status.loading": "Loading...",
"status.loading_workflows": "Loading workflows...",
"status.loading_channels": "Loading channels...",
"status.loading_skills": "Loading skills...",
"status.loading_jobs": "Loading scheduled jobs...",
"status.loading_triggers": "Loading triggers...",
"status.loading_history": "Loading run history...",
"status.loading_hands": "Loading hands...",
"status.loading_active_hands": "Loading active hands...",
"status.loading_mcp": "Loading MCP servers...",
"status.loading_files": "Loading files...",
"status.loading_skills_details": "Loading skills details...",
"status.no_channels_match": "No channels match your search",
"actions.logout": "Logout",
"actions.new_agent": "New Agent",
"actions.browse_skills": "Browse Skills",
"actions.add_channel": "Add Channel",
"actions.create_workflow": "Create Workflow",
"actions.settings": "Settings",
"actions.create_agent": "Create Agent",
"actions.configure_provider": "Configure Provider",
"actions.cancel": "Cancel",
"actions.confirm": "Confirm",
"actions.save": "Save",
"actions.delete": "Delete",
"actions.edit": "Edit",
"actions.clone": "Clone",
"actions.stop": "Stop",
"actions.run": "Run",
"actions.enable": "Enable",
"actions.disable": "Disable",
"actions.view_all": "View All",
"actions.retry": "Retry",
"actions.refresh": "Refresh",
"actions.approve": "Approve",
"actions.reject": "Reject",
"actions.update": "Update",
"actions.test_connection": "Test Connection",
"actions.remove": "Remove",
"actions.save_test": "Save & Test",
"actions.export_toml": "Export TOML",
"actions.save_workflow": "Save Workflow",
"actions.auto_layout": "Auto Layout",
"actions.clear": "Clear",
"actions.zoom_out": "Zoom out",
"actions.zoom_in": "Zoom in",
"actions.fit": "Fit",
"actions.duplicate": "Duplicate",
"actions.copy_clipboard": "Copy to Clipboard",
"actions.copied": "Copied!",
"actions.copy": "Copy",
"actions.hide_code": "Hide Code",
"actions.view_code": "View Code",
"actions.install": "Install",
"actions.installing": "Installing...",
"actions.installed": "Installed",
"actions.load_more": "Load More",
"actions.back_to_browse": "Back to browse",
"actions.activate": "Activate",
"actions.create_schedule": "Create Schedule",
"actions.submit": "Submit",
"actions.close": "Close",
"actions.next": "Next",
"actions.back": "Back",
"actions.spawn_agent": "Spawn Agent",
"actions.spawning": "Spawning...",
"actions.create_job": "Create Job",
"actions.spawn_wizard": "Wizard",
"actions.raw_toml": "Raw TOML",
"actions.setup_wizard": "Setup Wizard",
"actions.configure_manually": "Configure Manually",
"actions.dismiss": "Dismiss",
"actions.create_workflow_btn": "Create Workflow",
"actions.execute": "Execute",
"actions.executing": "Executing...",
"actions.generated_toml": "Generated TOML",
"actions.running": "Running...",
"footer.shortcuts": "Ctrl+K agents | Ctrl+N new",
"theme.light": "Light",
"theme.system": "System",
"theme.dark": "Dark",
"errors.connection_error": "Connection Error",
"errors.daemon_unreachable": "Cannot reach daemon — is openfang running?",
"errors.not_authorized": "Not authorized — check your API key",
"errors.permission_denied": "Permission denied",
"errors.resource_not_found": "Resource not found",
"errors.rate_limited": "Rate limited — slow down and try again",
"errors.request_too_large": "Request too large",
"errors.server_error": "Server error — check daemon logs",
"errors.daemon_unavailable": "Daemon unavailable — is it running?",
"errors.unexpected": "Unexpected error",
"errors.reconnected": "Reconnected",
"errors.connection_lost": "Connection lost, reconnecting...",
"errors.switched_http": "Connection lost — switched to HTTP mode",
"errors.connection_lost": "Connection lost, reconnecting...",
"errors.switched_http": "Connection lost — switched to HTTP mode",
"errors.reconnected": "Reconnected",
"toasts.approval_waiting": "An agent is waiting for approval. Open Approvals to review.",
"toasts.agent_created": "Agent Created",
"toasts.agent_stopped": "Agent Stopped",
"toasts.tool_used": "Tool Used",
"toasts.tool_completed": "Tool Completed",
"toasts.message_in": "Message In",
"toasts.response_sent": "Response Sent",
"toasts.session_reset": "Session Reset",
"toasts.compacted": "Compacted",
"toasts.model_changed": "Model Changed",
"toasts.login_attempt": "Login Attempt",
"toasts.login_ok": "Login OK",
"toasts.login_failed": "Login Failed",
"toasts.denied": "Denied",
"toasts.rate_limited": "Rate Limited",
"toasts.workflow_run": "Workflow Run",
"toasts.trigger_fired": "Trigger Fired",
"toasts.skill_installed": "Skill Installed",
"toasts.mcp_connected": "MCP Connected",
"toasts.session_deleted": "Session deleted",
"overview.welcome": "Welcome to OpenFang",
"overview.getting_started": "Getting Started",
"overview.setup_wizard": "Setup Wizard",
"overview.steps_completed": "of 5 steps completed",
"overview.agents_running": "Agents Running",
"overview.tokens_used": "Tokens Used",
"overview.total_cost": "Total Cost",
"overview.uptime": "Uptime",
"overview.channels": "Channels",
"overview.skills": "Skills",
"overview.mcp_servers": "MCP Servers",
"overview.tool_calls": "Tool Calls",
"overview.providers": "Providers",
"overview.recent_activity": "Recent Activity",
"overview.no_recent_activity": "No Recent Activity",
"overview.chat_with_agent": "Chat with an Agent",
"overview.system_health": "System Health",
"overview.healthy": "Healthy",
"overview.unreachable": "Unreachable",
"overview.security_systems": "Security Systems",
"overview.llm_providers": "LLM Providers",
"overview.defense_active": "9 defense-in-depth systems active",
"overview.quick_actions": "Quick Actions",
"setup.configure_provider": "Configure an LLM provider",
"setup.create_first_agent": "Create your first agent",
"setup.send_first_message": "Send your first message",
"setup.connect_channel": "Connect a messaging channel",
"setup.browse_install_skill": "Browse or install a skill",
"tooltips.cooling_down": "cooling down (rate limited)",
"tooltips.circuit_open": "circuit breaker open",
"tooltips.ready": "ready",
"tooltips.not_configured": "not configured",
"chat.placeholder": "Message OpenFang... (/ for commands)",
"chat.ready": "Ready",
"chat.generating": "Generating...",
"chat.queued": "queued",
"chat.sessions": "Sessions",
"chat.new_session": "+ New",
"chat.no_sessions": "No sessions",
"chat.search_messages": "Search messages...",
"chat.select_agent": "Select an agent to start chatting",
"chat.recording": "Recording... release to send",
"chat.drop_files": "Drop files here",
"chat.attach_file": "Attach file",
"chat.stop_generating": "Stop generating",
"chat.switch_model": "Switch model",
"chat.search_models": "Search models...",
"chat.no_models_found": "No models found",
"chat.available_models": "Available models — pick one or keep typing",
"chat.switching": "Switching...",
"chat.model_switched": "Switched to",
"chat.model_switch_failed": "Model switch failed",
"chat.using_http_mode": "Using HTTP mode (no streaming)",
"chat.session_name_prompt": "Session name (optional):",
"chat.session_created": "Session created",
"chat.session_create_failed": "Failed to create session",
"chat.stop_agent_title": "Stop Agent",
"chat.stop_agent_confirm": "Stop agent",
"chat.agent_stopped": "Agent stopped",
"chat.stop_agent_failed": "Failed to stop agent",
"chat.welcome_message": "**Welcome to OpenFang Chat!**\n\n- Type `/` to see available commands\n- `/help` shows all commands\n- `/think on` enables extended reasoning\n- `/context` shows context window usage\n- `/verbose off` hides tool details\n- `Ctrl+Shift+F` toggles focus mode\n- Drag & drop files to attach them\n- `Ctrl+/` opens the command palette",
"chat.slash.help": "Show available commands",
"chat.slash.agents": "Switch to Agents page",
"chat.slash.new": "New session (clear history)",
"chat.slash.compact": "Compact session context",
"chat.slash.model": "Show or switch model (/model [name])",
"chat.slash.stop": "Cancel current agent run",
"chat.slash.usage": "Show token usage",
"chat.slash.think": "Toggle reasoning (/think [on|off|stream])",
"chat.slash.context": "Show context window usage",
"chat.slash.verbose": "Toggle tool details (/verbose [off|on|full])",
"chat.slash.queue": "Check if agent is processing",
"chat.slash.status": "Show system status",
"chat.slash.clear": "Clear chat",
"chat.slash.exit": "Disconnect from agent",
"chat.slash.budget": "Show budget limits and costs",
"chat.slash.peers": "Show OFP network status",
"chat.slash.a2a": "List A2A agents",
"commands.help": "Show available commands",
"commands.agents": "Switch to Agents page",
"commands.new": "Reset session",
"commands.switch": "Switch agent",
"commands.clear": "Clear conversation",
"commands.model": "Switch model",
"commands.think": "Toggle reasoning mode",
"commands.focus": "Toggle focus mode",
"commands.theme": "Cycle theme",
"tips.commands": "Type / for commands",
"tips.think": "/think on for reasoning",
"tips.focus": "Ctrl+Shift+F for focus mode",
"agents.info": "Info",
"agents.files": "Files",
"agents.config": "Config",
"agents.chat": "Chat",
"agents.clone": "Clone",
"agents.clear_history": "Clear History",
"agents.change": "Change",
"agents.none_fallback": "None — add a fallback chain",
"agents.add": "+ Add",
"agents.loading_files": "Loading files...",
"agents.no_workspace_files": "No workspace files found",
"agents.save_config": "Save Config",
"agents.tool_filters": "Tool Filters",
"agents.allowlist": "Allowlist",
"agents.blocklist": "Blocklist",
"agents.agent_name": "Agent Name",
"agents.emoji": "Emoji",
"agents.color": "Color",
"agents.archetype": "Archetype",
"agents.provider": "Provider",
"agents.model": "Model",
"agents.system_prompt": "System Prompt",
"agents.soul_persona": "Soul / Persona",
"agents.tool_profile": "Tool Profile",
"agents.minimal_profile": "Minimal — Read-only file access",
"agents.coding_profile": "Coding — Files + shell + web fetch",
"agents.fullstack_profile": "Full-Stack — Files + shell + web fetch + search",
"agents.research_profile": "Research — Web + search + analysis",
"agents.admin_profile": "Admin — Full system access (dangerous)",
"agents.agent_created": "Agent Created",
"agents.agent_stopped": "Agent Stopped",
"agents.agent_deleted": "Agent Deleted",
"presets.professional": "Professional",
"presets.professional_desc": "Precise, business-oriented assistant focused on efficiency and clarity. Prioritizes actionable insights and structured communication.",
"presets.professional_soul": "Communicate in a clear, professional tone. Be direct and structured. Use formal language and data-driven reasoning. Prioritize accuracy over personality.",
"presets.friendly": "Friendly",
"presets.friendly_desc": "Warm and approachable assistant that builds rapport and uses conversational language. Great for brainstorming and exploration.",
"presets.friendly_soul": "Be warm, approachable, and conversational. Use casual language and show genuine interest in the user. Add personality to your responses while staying helpful.",
"presets.technical": "Technical",
"presets.technical_desc": "Expert developer companion optimized for code, architecture, and technical problem-solving. Precise terminology, deep dives, benchmarks.",
"presets.technical_soul": "Focus on technical accuracy and depth. Use precise terminology. Show your work and reasoning. Prefer code examples and structured explanations.",
"presets.creative": "Creative",
"presets.creative_desc": "Imaginative collaborator for content creation, design thinking, and unconventional solutions. Embraces ambiguity and explores possibilities.",
"presets.creative_soul": "Be imaginative and expressive. Use vivid language, analogies, and unexpected connections. Encourage creative thinking and explore multiple perspectives.",
"presets.concise": "Concise",
"presets.concise_desc": "Minimal and direct assistant that respects your time. Cuts through noise to deliver focused, actionable responses.",
"presets.concise_soul": "Be extremely brief and to the point. No filler, no pleasantries. Answer in the fewest words possible while remaining accurate and complete.",
"presets.mentor": "Mentor",
"presets.mentor_desc": "Patient educator that explains concepts thoroughly, provides context, and guides learning. Socratic method when appropriate.",
"presets.mentor_soul": "Be patient and encouraging like a great teacher. Break down complex topics step by step. Ask guiding questions. Celebrate progress and build confidence.",
"agents.profile.minimal": "Minimal",
"agents.profile.minimal_desc": "Read-only file access",
"agents.profile.coding": "Coding",
"agents.profile.coding_desc": "Files + shell + web fetch",
"agents.profile.research": "Research",
"agents.profile.research_desc": "Web search + file read/write",
"agents.profile.messaging": "Messaging",
"agents.profile.messaging_desc": "Agents + memory access",
"agents.profile.automation": "Automation",
"agents.profile.automation_desc": "All tools except custom",
"agents.profile.balanced": "Balanced",
"agents.profile.balanced_desc": "General-purpose tool set",
"agents.profile.precise": "Precise",
"agents.profile.precise_desc": "Focused tool set for accuracy",
"agents.profile.creative": "Creative",
"agents.profile.creative_desc": "Full tools with creative emphasis",
"agents.profile.full": "Full",
"agents.profile.full_desc": "All 35+ tools",
"wizard.general_assistant": "General Assistant",
"wizard.general_assistant_desc": "You are a versatile AI assistant that helps users with a wide range of tasks. You are knowledgeable, helpful, and able to adapt to the user's needs.",
"wizard.code_helper": "Code Helper",
"wizard.code_helper_desc": "You are an expert programming assistant specialized in software development. You help write, debug, and refactor code across multiple languages.",
"wizard.researcher": "Research Assistant",
"wizard.researcher_desc": "You are a research assistant that helps users find, analyze, and synthesize information from various sources.",
"wizard.writer": "Writer",
"wizard.writer_desc": "You are a skilled writer that helps with content creation, editing, and creative writing projects.",
"wizard.data_analyst": "Data Analyst",
"wizard.data_analyst_desc": "You are a data analyst that helps explore, analyze, and visualize data to extract insights.",
"wizard.devops": "DevOps Engineer",
"wizard.devops_desc": "You are a DevOps engineer that helps with infrastructure, deployment, CI/CD, and system administration.",
"wizard.support": "Customer Support",
"wizard.support_desc": "You are a customer support representative that helps resolve inquiries with patience and professionalism.",
"wizard.tutor": "Tutor",
"wizard.tutor_desc": "You are an educational tutor that explains concepts clearly and adapts teaching to the student's level.",
"wizard.api_designer": "API Designer",
"wizard.api_designer_desc": "You are an API designer that helps create well-structured, intuitive APIs following best practices.",
"wizard.meeting_notes": "Meeting Notes",
"wizard.meeting_notes_desc": "You are a meeting notes specialist that summarizes discussions, extracts action items, and tracks decisions.",
"wizard.step_welcome": "Welcome",
"wizard.step_provider": "Provider",
"wizard.step_agent": "Agent",
"wizard.step_try_it": "Try It",
"wizard.step_channel": "Channel",
"wizard.step_done": "Done",
"wizard.cat_general": "General",
"wizard.cat_development": "Development",
"wizard.cat_research": "Research",
"wizard.cat_writing": "Writing",
"wizard.cat_business": "Business",
"wizard.channel_telegram": "Telegram",
"wizard.channel_telegram_desc": "Connect your agent to a Telegram bot for messaging.",
"wizard.channel_telegram_token": "Bot Token",
"wizard.channel_telegram_help": "Create a bot via @BotFather on Telegram to get your token.",
"wizard.channel_discord": "Discord",
"wizard.channel_discord_desc": "Connect your agent to a Discord server via bot token.",
"wizard.channel_discord_token": "Bot Token",
"wizard.channel_discord_help": "Create a Discord application at discord.com/developers and add a bot.",
"wizard.channel_slack": "Slack",
"wizard.channel_slack_desc": "Connect your agent to a Slack workspace.",
"wizard.channel_slack_token": "Bot Token",
"wizard.channel_slack_help": "Create a Slack app at api.slack.com/apps and install it to your workspace.",
"wizard.profile_minimal": "Minimal",
"wizard.profile_minimal_desc": "Read-only file access",
"wizard.profile_coding": "Coding",
"wizard.profile_coding_desc": "Files + shell + web fetch",
"wizard.profile_research": "Research",
"wizard.profile_research_desc": "Web search + file read/write",
"wizard.profile_balanced": "Balanced",
"wizard.profile_balanced_desc": "General-purpose tool set",
"wizard.profile_precise": "Precise",
"wizard.profile_precise_desc": "Focused tool set for accuracy",
"wizard.profile_creative": "Creative",
"wizard.profile_creative_desc": "Full tools with creative emphasis",
"wizard.profile_full": "Full",
"wizard.profile_full_desc": "All 35+ tools",
"wizard.enter_api_key": "Please enter an API key",
"wizard.api_key_saved": "API key saved for",
"wizard.failed_save_key": "Failed to save key:",
"wizard.connected": "connected",
"wizard.connection_failed": "Connection failed",
"wizard.test_failed": "Test failed:",
"wizard.enter_agent_name": "Please enter a name for your agent",
"wizard.agent_created": "Agent created",
"wizard.failed_create_agent": "Failed to create agent:",
"wizard.enter_token": "Please enter the",
"wizard.channel_configured": "configured and activated.",
"wizard.failed_configure": "Failed:",
"wizard.suggestions.general.1": "What can you help me with?",
"wizard.suggestions.general.2": "Tell me a fun fact",
"wizard.suggestions.general.3": "Summarize the latest AI news",
"wizard.suggestions.development.1": "Write a Python hello world",
"wizard.suggestions.development.2": "Explain async/await",
"wizard.suggestions.development.3": "Review this code snippet",
"wizard.suggestions.research.1": "Explain quantum computing simply",
"wizard.suggestions.research.2": "Compare React vs Vue",
"wizard.suggestions.research.3": "What are the latest trends in AI?",
"wizard.suggestions.writing.1": "Help me write a professional email",
"wizard.suggestions.writing.2": "Improve this paragraph",
"wizard.suggestions.writing.3": "Write a blog intro about AI",
"wizard.suggestions.business.1": "Draft a meeting agenda",
"wizard.suggestions.business.2": "How do I handle a complaint?",
"wizard.suggestions.business.3": "Create a project status update",
"approvals.title": "Execution Approvals",
"approvals.pending": "pending",
"approvals.all": "All",
"approvals.pending_tab": "Pending",
"approvals.approved": "Approved",
"approvals.rejected": "Rejected",
"approvals.expired": "Expired",
"approvals.no_approvals": "No approvals",
"approvals.approve": "Approve",
"approvals.reject": "Reject",
"workflows.title": "Workflows",
"workflows.visual_builder": "Visual Builder",
"workflows.what_are": "What are Workflows?",
"workflows.no_workflows": "No workflows yet",
"workflows.sequential": "Sequential",
"workflows.fan_out": "Fan Out",
"workflows.conditional": "Conditional",
"workflows.loop": "Loop",
"workflows.add_step": "+ Add Step",
"workflows.execute": "Execute",
"workflows.result": "Result",
"workflows.node_palette": "Node Palette",
"workflows.drag_nodes": "Drag nodes onto the canvas",
"workflows.steps_connections": "steps, connections",
"workflows.agent": "Agent",
"workflows.prompt_template": "Prompt Template",
"workflows.expression": "Expression",
"workflows.top_port_true": "Top port = true, bottom port = false",
"workflows.max_iterations": "Max Iterations",
"workflows.until_stop": "Until (stop condition)",
"workflows.fan_out_count": "Fan-out Count",
"workflows.wait_all": "Wait for all",
"workflows.first_finish": "First to finish",
"workflows.majority_vote": "Majority vote",
"workflows.connection_selected": "Connection selected",
"workflows.delete_connection": "Delete Connection",
"scheduler.title": "Scheduler",
"scheduler.scheduled_jobs": "Scheduled Jobs",
"scheduler.event_triggers": "Event Triggers",
"scheduler.run_history": "Run History",
"scheduler.new_job": "+ New Job",
"scheduler.job_name": "Job Name",
"scheduler.cron_expression": "Cron Expression",
"scheduler.quick_presets": "Quick Presets",
"scheduler.target_agent": "Target Agent",
"scheduler.any_agent": "Any available agent",
"scheduler.message_send": "Message to Send",
"scheduler.enabled": "Enabled (will start running immediately)",
"scheduler.disabled": "Disabled (create paused)",
"scheduler.active": "Active",
"scheduler.paused": "Paused",
"scheduler.cron_job": "Cron Job",
"scheduler.trigger": "Trigger",
"scheduler.no_jobs": "No scheduled jobs",
"scheduler.no_triggers": "No event triggers",
"scheduler.no_history": "No run history yet",
"channels.title": "Channels",
"channels.configured": "configured",
"channels.search": "Search channels...",
"channels.setup": "Set up",
"channels.edit": "Edit",
"channels.configure": "Configure",
"channels.verify": "Verify",
"channels.ready": "Ready",
"channels.is_ready": "is ready!",
"channels.get_credentials": "How to get credentials",
"channels.show_advanced": "Show advanced",
"channels.hide_advanced": "Hide advanced",
"channels.connecting": "Connecting to WhatsApp Web gateway...",
"channels.linked_success": "WhatsApp linked successfully!",
"channels.business_api": "Business API",
"skills.title": "Skills & Ecosystem",
"skills.installed": "Installed",
"skills.clawhub": "ClawHub",
"skills.mcp_servers": "MCP Servers",
"skills.quick_start": "Quick Start",
"skills.no_installed": "No skills installed",
"skills.browse_clawhub": "Browse ClawHub",
"skills.search_clawhub": "Search ClawHub skills...",
"skills.trending": "Trending",
"skills.most_downloaded": "Most Downloaded",
"skills.most_starred": "Most Starred",
"skills.recently_updated": "Recently Updated",
"skills.categories": "CATEGORIES",
"skills.already_installed": "Already Installed",
"skills.no_skills_found": "No skills found",
"skills.security_warnings": "Security Warnings",
"skills.security_scan": "Skills are security-scanned before installation",
"skills.create": "Create Skill",
"skills.created": "Created",
"skills.cat_coding": "Coding & IDEs",
"skills.cat_git": "Git & GitHub",
"skills.cat_frontend": "Web & Frontend",
"skills.cat_devops": "DevOps & Cloud",
"skills.cat_database": "Database",
"skills.cat_security": "Security",
"skills.cat_ai": "AI & ML",
"skills.cat_data": "Data & Analytics",
"skills.cat_mobile": "Mobile",
"skills.cat_desktop": "Desktop Apps",
"skills.cat_api": "API & Integrations",
"skills.cat_testing": "Testing",
"skills.cat_docs": "Documentation",
"skills.cat_productivity": "Productivity",
"skills.cat_other": "Other",
"skills.cat_browser": "Browser & Automation",
"skills.cat_search": "Search & Research",
"skills.cat_communication": "Communication",
"skills.cat_media": "Media & Streaming",
"skills.cat_notes": "Notes & PKM",
"skills.cat_cli": "CLI Utilities",
"skills.cat_marketing": "Marketing & Sales",
"skills.cat_finance": "Finance",
"skills.cat_smarthome": "Smart Home & IoT",
"skills.uninstall_skill": "Uninstall Skill",
"skills.uninstall_confirm": "Uninstall skill",
"skills.source_clawhub": "ClawHub",
"skills.source_openclaw": "OpenClaw",
"skills.source_builtin": "Built-in",
"skills.source_local": "Local",
"hands.title": "Hands — Curated Autonomous Capability Packages",
"hands.available": "Available",
"hands.active": "Active",
"hands.ready": "Ready",
"hands.setup_needed": "Setup needed",
"hands.requirements": "REQUIREMENTS",
"hands.details": "Details",
"hands.no_hands": "No hands available",
"sessions.title": "Sessions",
"sessions.memory": "Memory",
"sessions.delete_session": "Delete Session",
"sessions.delete_confirm": "This will permanently remove the session and its messages.",
"sessions.delete_key": "Delete Key",
"sessions.delete_key_confirm": "Delete key",
"logs.title": "Logs",
"logs.live": "Live",
"logs.audit_trail": "Audit Trail",
"settings.title": "Settings",
"settings.providers": "Providers",
"settings.models": "Models",
"settings.config": "Config",
"settings.tools": "Tools",
"settings.migration": "Migration",
"settings.security": "Security",
"settings.network": "Network",
"settings.migration": "Migration",
"settings.language": "Language",
"settings.sec_path_traversal": "Path Traversal Prevention",
"settings.sec_path_traversal_desc": "Blocks attempts to access files outside the workspace directory using .. or absolute paths.",
"settings.sec_ssrf": "SSRF Protection",
"settings.sec_ssrf_desc": "Prevents agents from making requests to internal IP ranges (localhost, cloud metadata, private networks).",
"settings.sec_capability": "Capability-Based Access Control",
"settings.sec_capability_desc": "Agents can only access explicitly granted capabilities. No implicit access to tools or data.",
"settings.sec_taint": "Taint Tracking",
"settings.sec_taint_desc": "Tracks untrusted data (user input, file content) through agent reasoning to prevent prompt injection.",
"settings.sec_sandbox": "WASM Sandbox",
"settings.sec_sandbox_desc": "Executes untrusted code in isolated WebAssembly sandboxes with memory and syscall restrictions.",
"settings.sec_audit": "Merkle Audit",
"settings.sec_audit_desc": "Maintains a verifiable audit log of all agent actions using Merkle tree cryptography.",
"settings.sec_workspace": "Workspace Isolation",
"settings.sec_workspace_desc": "Each agent has an isolated workspace directory. No cross-agent file access unless explicitly granted.",
"settings.sec_rate_limit": "Rate Limiting",
"settings.sec_rate_limit_desc": "Enforces per-agent and global rate limits to prevent resource exhaustion and cost overruns.",
"settings.sec_approval": "Execution Approvals",
"settings.sec_approval_desc": "Requires human approval for high-risk actions (shell commands, file writes, external requests).",
"settings.sec_enabled": "Enabled",
"settings.sec_disabled": "Disabled",
"settings.sec_inherited": "Inherited",
"settings.sec_global": "Global"
}
crates/openfang-api/static/i18n/i18n.js
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/**
* OpenFang i18n (Internationalization) Module
*
* Provides runtime language switching for the OpenFang dashboard UI.
* Supports English (default) and Russian.
*
* Usage:
* - HTML: <span data-i18n="nav.overview">Overview</span>
* - JS: window.t('nav.overview')
* - Auto-applies translations on load based on stored/preferred language
*/
(function() {
'use strict';
// Language store
let currentLang = 'en';
let translations = {};
let isInitialized = false;
/**
* Load translations from a JSON file
* @param {string} lang - Language code (en, ru)
* @returns {Promise<Object>} Translation object
*/
async function loadTranslations(lang) {
try {
// Use cached translations if available
if (window.__i18nCache && window.__i18nCache[lang]) {
return window.__i18nCache[lang];
}
const response = await fetch(`/i18n/${lang}.json`);
if (!response.ok) {
console.warn(`[i18n] Failed to load ${lang}.json, falling back to en`);
if (lang !== 'en') {
return loadTranslations('en');
}
return {};
}
const data = await response.json();
// Cache for future use
if (!window.__i18nCache) window.__i18nCache = {};
window.__i18nCache[lang] = data;
return data;
} catch (error) {
console.error(`[i18n] Error loading translations for ${lang}:`, error);
if (lang !== 'en') {
return loadTranslations('en');
}
return {};
}
}
/**
* Get a translated string by key
* @param {string} key - Translation key (e.g., 'nav.overview')
* @param {Object} params - Optional interpolation parameters
* @returns {string} Translated string or key if not found
*/
function t(key, params) {
if (!isInitialized) {
console.warn('[i18n] Not initialized, returning key');
return key;
}
let text = translations[key] || key;
// Handle interpolation (e.g., 'Hello, {{name}}')
if (params && typeof params === 'object') {
Object.keys(params).forEach(param => {
text = text.replace(new RegExp(`{{${param}}}`, 'g'), params[param]);
});
}
return text;
}
/**
* Apply translations to all elements with data-i18n attribute
* Also updates the <html> lang attribute
*/
function applyTranslations() {
// Update document language
document.documentElement.lang = currentLang;
// Find and translate all elements with data-i18n attribute
const elements = document.querySelectorAll('[data-i18n]');
elements.forEach(el => {
const key = el.getAttribute('data-i18n');
const translation = t(key);
// Check if element is a form input/textarea
if (el.tagName === 'INPUT' || el.tagName === 'TEXTAREA') {
// For form elements, only update if it's a placeholder or aria-label
if (el.hasAttribute('placeholder')) {
el.placeholder = translation;
}
if (el.hasAttribute('aria-label')) {
el.setAttribute('aria-label', translation);
}
if (el.hasAttribute('title')) {
el.setAttribute('title', translation);
}
} else {
// For regular elements, update text content
el.textContent = translation;
}
});
// Update elements with data-i18n-* attributes for attributes
const attrElements = document.querySelectorAll('[data-i18n-placeholder], [data-i18n-title], [data-i18n-aria-label]');
attrElements.forEach(el => {
if (el.hasAttribute('data-i18n-placeholder')) {
el.placeholder = t(el.getAttribute('data-i18n-placeholder'));
}
if (el.hasAttribute('data-i18n-title')) {
el.title = t(el.getAttribute('data-i18n-title'));
}
if (el.hasAttribute('data-i18n-aria-label')) {
el.setAttribute('aria-label', t(el.getAttribute('data-i18n-aria-label')));
}
});
// Update meta tags
const metaDesc = document.querySelector('meta[name="description"]');
if (metaDesc) {
const desc = t('app.description', { name: 'OpenFang' });
if (desc !== 'app.description') {
metaDesc.content = desc;
}
}
console.log(`[i18n] Applied translations for language: ${currentLang}`);
}
/**
* Set the current language and apply translations
* @param {string} lang - Language code (en, ru)
* @param {boolean} persist - Whether to save to localStorage
*/
async function setLanguage(lang, persist = true) {
if (!['en', 'ru'].includes(lang)) {
console.warn(`[i18n] Unknown language: ${lang}, defaulting to en`);
lang = 'en';
}
currentLang = lang;
translations = await loadTranslations(lang);
isInitialized = true;
// Save preference
if (persist) {
localStorage.setItem('openfang_language', lang);
}
// Apply to DOM
applyTranslations();
// Dispatch event for Alpine.js components to react
window.dispatchEvent(new CustomEvent('i18n:language-changed', {
detail: { language: lang }
}));
}
/**
* Get the current language
* @returns {string} Current language code
*/
function getLanguage() {
return currentLang;
}
/**
* Initialize i18n system
* Loads language preference and applies translations
*/
async function init() {
// Determine language priority:
// 1. localStorage (user preference)
// 2. Browser language
// 3. Default to English
let lang = localStorage.getItem('openfang_language');
if (!lang) {
// Try to detect browser language
const browserLang = navigator.language || navigator.userLanguage || '';
if (browserLang.startsWith('ru')) {
lang = 'ru';
} else {
lang = 'en';
}
}
await setLanguage(lang, false);
}
/**
* Get available languages
* @returns {Array<{code: string, name: string}>}
*/
function getAvailableLanguages() {
return [
{ code: 'en', name: 'English' },
{ code: 'ru', name: 'Русский' }
];
}
// Expose to global scope
window.i18n = {
t,
setLanguage,
getLanguage,
getAvailableLanguages,
init,
isInitialized: () => isInitialized
};
// Auto-initialize when DOM is ready
if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', init);
} else {
init();
}
})();
crates/openfang-api/static/i18n/ru.json
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{
"app.name": "OpenFang",
"app.version": "v",
"nav.chat": "Чат",
"nav.monitor": "Мониторинг",
"nav.overview": "Обзор",
"nav.analytics": "Аналитика",
"nav.logs": "Логи",
"nav.agents": "Агенты",
"nav.sessions": "Сессии",
"nav.approvals": "Одобрения",
"nav.comms": "Коммуникации",
"nav.automation": "Автоматизация",
"nav.workflows": "Рабочие процессы",
"nav.scheduler": "Планировщик",
"nav.extensions": "Расширения",
"nav.channels": "Каналы",
"nav.skills": "Навыки",
"nav.hands": "Руки",
"nav.system": "Система",
"nav.runtime": "Среда выполнения",
"nav.settings": "Настройки",
"auth.sign_in": "Войти",
"auth.enter_credentials": "Введите учётные данные панели управления.",
"auth.username": "Имя пользователя",
"auth.password": "Пароль",
"auth.api_key_required": "Требуется API-ключ",
"auth.api_key_desc": "Этот экземпляр требует API-ключ. Введите ключ из вашего config.toml.",
"auth.api_key_hint": "Добавьте api_key = \"your-key\" в начало ~/.openfang/config.toml (не внутри секции).",
"auth.enter_api_key": "Введите API-ключ...",
"auth.unlock_dashboard": "Разблокировать панель",
"auth.login_failed": "Ошибка входа",
"status.agents_running": "агент(ов) запущено",
"status.connecting": "Подключение...",
"status.reconnecting": "Переподключение...",
"status.disconnected": "отключено",
"status.ws": "ВС",
"status.http": "HTTP",
"status.ready": "Готово",
"status.loading": "Загрузка...",
"status.loading_workflows": "Загрузка рабочих процессов...",
"status.loading_channels": "Загрузка каналов...",
"status.loading_skills": "Загрузка навыков...",
"status.loading_jobs": "Загрузка заданий...",
"status.loading_triggers": "Загрузка триггеров...",
"status.loading_history": "Загрузка истории...",
"status.loading_hands": "Загрузка модулей...",
"status.loading_active_hands": "Загрузка активных модулей...",
"status.loading_mcp": "Загрузка MCP-серверов...",
"status.loading_files": "Загрузка файлов...",
"status.loading_skills_details": "Загрузка деталей навыков...",
"status.no_channels_match": "Нет каналов по запросу",
"actions.logout": "Выйти",
"actions.new_agent": "Новый агент",
"actions.browse_skills": "Навыки",
"actions.add_channel": "Добавить канал",
"actions.create_workflow": "Создать процесс",
"actions.settings": "Настройки",
"actions.create_agent": "Создать агента",
"actions.configure_provider": "Настроить провайдера",
"actions.cancel": "Отмена",
"actions.confirm": "Подтвердить",
"actions.save": "Сохранить",
"actions.delete": "Удалить",
"actions.edit": "Редактировать",
"actions.clone": "Клонировать",
"actions.stop": "Остановить",
"actions.run": "Запустить",
"actions.enable": "Включить",
"actions.disable": "Отключить",
"actions.view_all": "Показать все",
"actions.retry": "Повторить",
"actions.refresh": "Обновить",
"actions.approve": "Одобрить",
"actions.reject": "Отклонить",
"actions.update": "Обновить",
"actions.test_connection": "Проверить подключение",
"actions.remove": "Удалить",
"actions.save_test": "Сохранить и проверить",
"actions.export_toml": "Экспорт TOML",
"actions.save_workflow": "Сохранить процесс",
"actions.auto_layout": "Автораскладка",
"actions.clear": "Очистить",
"actions.zoom_out": "Уменьшить",
"actions.zoom_in": "Увеличить",
"actions.fit": "По размеру",
"actions.duplicate": "Дублировать",
"actions.copy_clipboard": "Копировать в буфер",
"actions.copied": "Скопировано!",
"actions.copy": "Копировать",
"actions.hide_code": "Скрыть код",
"actions.view_code": "Показать код",
"actions.install": "Установить",
"actions.installing": "Установка...",
"actions.installed": "Установлено",
"actions.load_more": "Загрузить ещё",
"actions.back_to_browse": "Назад",
"actions.activate": "Активировать",
"actions.create_schedule": "Создать расписание",
"actions.submit": "Отправить",
"actions.close": "Закрыть",
"actions.next": "Далее",
"actions.back": "Назад",
"actions.spawn_agent": "Создать агента",
"actions.spawning": "Создание...",
"actions.spawn_wizard": "Мастер",
"actions.raw_toml": "TOML",
"actions.setup_wizard": "Мастер настройки",
"actions.configure_manually": "Настроить вручную",
"actions.dismiss": "Закрыть",
"actions.create_workflow_btn": "Создать процесс",
"actions.execute": "Выполнить",
"actions.executing": "Выполнение...",
"actions.generated_toml": "Сгенерированный TOML",
"actions.running": "Выполняется...",
"footer.shortcuts": "Ctrl+K агенты | Ctrl+N новый",
"theme.light": "Светлая",
"theme.system": "Системная",
"theme.dark": "Тёмная",
"errors.connection_error": "Ошибка подключения",
"errors.daemon_unreachable": "Не удаётся связаться с демоном — запущен ли openfang?",
"errors.not_authorized": "Не авторизован — проверьте API-ключ",
"errors.permission_denied": "Доступ запрещён",
"errors.resource_not_found": "Ресурс не найден",
"errors.rate_limited": "Превышен лимит — подождите и попробуйте снова",
"errors.request_too_large": "Запрос слишком большой",
"errors.server_error": "Ошибка сервера — проверьте логи демона",
"errors.daemon_unavailable": "Демон недоступен — запущен ли он?",
"errors.unexpected": "Неожиданная ошибка",
"errors.reconnected": "Переподключено",
"errors.connection_lost": "Соединение потеряно, переподключение...",
"errors.switched_http": "Соединение потеряно — переход на режим HTTP",
"errors.connection_lost": "Соединение потеряно, переподключение...",
"errors.switched_http": "Соединение потеряно — переход на режим HTTP",
"errors.reconnected": "Переподключено",
"toasts.approval_waiting": "Агент ожидает одобрения. Откройте раздел Одобрения.",
"toasts.agent_created": "Агент создан",
"toasts.agent_stopped": "Агент остановлен",
"toasts.tool_used": "Инструмент использован",
"toasts.tool_completed": "Инструмент завершён",
"toasts.message_in": "Входящее сообщение",
"toasts.response_sent": "Ответ отправлен",
"toasts.session_reset": "Сессия сброшена",
"toasts.compacted": "Сжато",
"toasts.model_changed": "Модель изменена",
"toasts.login_attempt": "Попытка входа",
"toasts.login_ok": "Вход успешен",
"toasts.login_failed": "Ошибка входа",
"toasts.denied": "Отклонено",
"toasts.rate_limited": "Лимит запросов",
"toasts.workflow_run": "Запуск процесса",
"toasts.trigger_fired": "Триггер сработал",
"toasts.skill_installed": "Навык установлен",
"toasts.mcp_connected": "MCP подключён",
"toasts.session_deleted": "Сессия удалена",
"overview.welcome": "Добро пожаловать в OpenFang",
"overview.getting_started": "Начало работы",
"overview.setup_wizard": "Мастер настройки",
"overview.steps_completed": "из 5 шагов выполнено",
"overview.agents_running": "Агентов запущено",
"overview.tokens_used": "Использовано токенов",
"overview.total_cost": "Общая стоимость",
"overview.uptime": "Время работы",
"overview.channels": "Каналы",
"overview.skills": "Навыки",
"overview.mcp_servers": "MCP-серверы",
"overview.tool_calls": "Вызовов инструментов",
"overview.providers": "Провайдеры",
"overview.recent_activity": "Недавняя активность",
"overview.no_recent_activity": "Нет недавней активности",
"overview.chat_with_agent": "Написать агенту",
"overview.system_health": "Состояние системы",
"overview.healthy": "Исправно",
"overview.unreachable": "Недоступно",
"overview.security_systems": "Системы безопасности",
"overview.llm_providers": "LLM-провайдеры",
"overview.defense_active": "9 уровней защиты активно",
"overview.quick_actions": "Быстрые действия",
"setup.configure_provider": "Настройте LLM-провайдера",
"setup.create_first_agent": "Создайте первого агента",
"setup.send_first_message": "Отправьте первое сообщение",
"setup.connect_channel": "Подключите канал связи",
"setup.browse_install_skill": "Найдите или установите навык",
"tooltips.cooling_down": "остывает (лимит запросов)",
"tooltips.circuit_open": "автомат сработал",
"tooltips.ready": "готово",
"tooltips.not_configured": "не настроено",
"chat.placeholder": "Напишите OpenFang... (/ для команд)",
"chat.ready": "Готово",
"chat.generating": "Генерация...",
"chat.queued": "в очереди",
"chat.sessions": "Сессии",
"chat.new_session": "+ Новая",
"chat.no_sessions": "Нет сессий",
"chat.search_messages": "Поиск сообщений...",
"chat.select_agent": "Выберите агента для начала общения",
"chat.recording": "Запись... отпустите для отправки",
"chat.drop_files": "Перетащите файлы сюда",
"chat.attach_file": "Прикрепить файл",
"chat.stop_generating": "Остановить генерацию",
"chat.switch_model": "Сменить модель",
"chat.search_models": "Поиск моделей...",
"chat.no_models_found": "Модели не найдены",
"chat.available_models": "Доступные модели — выберите или продолжите ввод",
"chat.switching": "Переключение...",
"chat.model_switched": "Модель изменена на",
"chat.model_switch_failed": "Не удалось сменить модель",
"chat.using_http_mode": "Используется HTTP режим (без потоковой передачи)",
"chat.session_name_prompt": "Название сессии (необязательно):",
"chat.session_created": "Сессия создана",
"chat.session_create_failed": "Не удалось создать сессию",
"chat.stop_agent_title": "Остановить агента",
"chat.stop_agent_confirm": "Остановить агента",
"chat.agent_stopped": "Агент остановлен",
"chat.stop_agent_failed": "Не удалось остановить агента",
"chat.welcome_message": "**Добро пожаловать в OpenFang Чат!**\n\n- Введите `/` для просмотра команд\n- `/help` покажет все команды\n- `/think on` включает расширенные размышления\n- `/context` покажет использование контекста\n- `/verbose off` скроет детали инструментов\n- `Ctrl+Shift+F` переключает режим фокуса\n- Перетащите файлы для прикрепления\n- `Ctrl+/` открывает палитру команд",
"chat.slash.help": "Показать доступные команды",
"chat.slash.agents": "Перейти на страницу агентов",
"chat.slash.new": "Новая сессия (очистить историю)",
"chat.slash.compact": "Сжать контекст сессии",
"chat.slash.model": "Показать или сменить модель (/model [имя])",
"chat.slash.stop": "Отменить текущий запуск агента",
"chat.slash.usage": "Показать использование токенов",
"chat.slash.think": "Переключить размышления (/think [on|off|stream])",
"chat.slash.context": "Показать использование контекста",
"chat.slash.verbose": "Переключить детали инструментов (/verbose [off|on|full])",
"chat.slash.queue": "Проверить очередь обработки",
"chat.slash.status": "Показать статус системы",
"chat.slash.clear": "Очистить чат",
"chat.slash.exit": "Отключиться от агента",
"chat.slash.budget": "Показать лимиты и расходы",
"chat.slash.peers": "Показать статус сети OFP",
"chat.slash.a2a": "Список A2A агентов",
"commands.help": "Показать доступные команды",
"commands.agents": "Перейти на страницу агентов",
"commands.new": "Новая сессия",
"commands.switch": "Сменить агента",
"commands.clear": "Очистить диалог",
"commands.model": "Сменить модель",
"commands.think": "Переключить режим размышлений",
"commands.focus": "Переключить режим фокуса",
"commands.theme": "Сменить тему",
"tips.commands": "Введите / для команд",
"tips.think": "/think on для размышлений",
"tips.focus": "Ctrl+Shift+F для режима фокуса",
"agents.info": "Информация",
"agents.files": "Файлы",
"agents.config": "Настройки",
"agents.chat": "Чат",
"agents.clone": "Клонировать",
"agents.clear_history": "Очистить историю",
"agents.change": "Изменить",
"agents.none_fallback": "Нет — добавить цепочку резервов",
"agents.add": "+ Добавить",
"agents.loading_files": "Загрузка файлов...",
"agents.no_workspace_files": "Файлы рабочей области не найдены",
"agents.save_config": "Сохранить настройки",
"agents.tool_filters": "Фильтры инструментов",
"agents.allowlist": "Белый список",
"agents.blocklist": "Чёрный список",
"agents.agent_name": "Имя агента",
"agents.emoji": "Эмодзи",
"agents.color": "Цвет",
"agents.archetype": "Архетип",
"agents.provider": "Провайдер",
"agents.model": "Модель",
"agents.system_prompt": "Системный промпт",
"agents.soul_persona": "Душa / Персона",
"agents.tool_profile": "Профиль инструментов",
"agents.minimal_profile": "Минимальный — только чтение файлов",
"agents.coding_profile": "Кодинг — файлы + оболочка + веб-запросы",
"agents.fullstack_profile": "Full-Stack — файлы + оболочка + веб + поиск",
"agents.research_profile": "Исследование — веб + поиск + анализ",
"agents.admin_profile": "Админ — полный доступ к системе (опасно)",
"agents.agent_created": "Агент создан",
"agents.agent_stopped": "Агент остановлен",
"agents.agent_deleted": "Агент удалён",
"presets.professional": "Деловой",
"presets.professional_desc": "Точный, бизнес-ориентированный ассистент, сосредоточенный на эффективности и ясности. Приоритет — практические выводы и структурированная коммуникация.",
"presets.professional_soul": "Общайтесь чётко и профессионально. Будьте прямым и структурированным. Используйте формальный язык и выводы на основе данных. ставьте точность выше личности.",
"presets.friendly": "Дружелюбный",
"presets.friendly_desc": "Тёплый и открытый ассистент, который выстраивает rapport и использует разговорный язык. Отлично подходит для мозгового штурма и исследования.",
"presets.friendly_soul": "Будьте тёплым, доступным и разговорчивым. Используйте неформальный язык и проявляйте искренний интерес к пользователю. Добавляйте личность к вашим ответам, оставаясь полезным.",
"presets.technical": "Технический",
"presets.technical_desc": "Эксперт-помощник по разработке, оптимизированный для кода, архитектуры и технических задач. Точная терминология, глубокие погружения, бенчмарки.",
"presets.technical_soul": "Сосредоточьтесь на технической точности и глубине. Используйте точную терминологию. Покажите вашу работу и рассуждения. Предпочитайте примеры кода и структурированные объяснения.",
"presets.creative": "Креативный",
"presets.creative_desc": "Творческий партнёр для создания контента, дизайн-мышления и нестандартных решений. Приветствует неоднозначность и исследует возможности.",
"presets.creative_soul": "Будьте изобретательным и выразительным. Используйте яркий язык, аналогии и неожиданные связи. Поощряйте творческое мышление и исследуйте различные перспективы.",
"presets.concise": "Краткий",
"presets.concise_desc": "Минималистичный и прямой ассистент, который ценит ваше время. Убирает лишнее и даёт сфокусированные, практичные ответы.",
"presets.concise_soul": "Будьте предельно кратким и точным. Без воды и формальностей. Отвечайте наименьшим количеством слов, оставаясь точным и полным.",
"presets.mentor": "Наставник",
"presets.mentor_desc": "Терпеливый педагог, который подробно объясняет концепции, даёт контекст и направляет обучение. Метод Сократа при необходимости.",
"presets.mentor_soul": "Будьте терпеливым и ободряющим как хороший учитель. Разбивайте сложные темы по шагам. Задавайте направляющие вопросы. Празднуйте прогресс и укрепляйте уверенность.",
"agents.profile.minimal": "Минимальный",
"agents.profile.minimal_desc": "Только чтение файлов",
"agents.profile.coding": "Кодинг",
"agents.profile.coding_desc": "Файлы + оболочка + веб-запросы",
"agents.profile.research": "Исследование",
"agents.profile.research_desc": "Веб-поиск + чтение/запись файлов",
"agents.profile.messaging": "Коммуникации",
"agents.profile.messaging_desc": "Агенты + доступ к памяти",
"agents.profile.automation": "Автоматизация",
"agents.profile.automation_desc": "Все инструменты кроме пользовательских",
"agents.profile.balanced": "Сбалансированный",
"agents.profile.balanced_desc": "Набор инструментов общего назначения",
"agents.profile.precise": "Точный",
"agents.profile.precise_desc": "Фокусированный набор инструментов для точности",
"agents.profile.creative": "Креативный",
"agents.profile.creative_desc": "Полный набор инструментов с творческим уклоном",
"agents.profile.full": "Полный",
"agents.profile.full_desc": "Все 35+ инструментов",
"wizard.general_assistant": "Универсальный ассистент",
"wizard.general_assistant_desc": "Вы универсальный AI-ассистент, который помогает пользователям с широким кругом задач. Вы знающий, полезный и способны адаптироваться к потребностям пользователя.",
"wizard.code_helper": "Помощник по коду",
"wizard.code_helper_desc": "Вы опытный программный ассистент, специализирующийся на разработке ПО. Вы помогаете писать, отлаживать и рефакторить код на разных языках.",
"wizard.researcher": "Исследовательский ассистент",
"wizard.researcher_desc": "Вы исследовательский ассистент, который помогает находить, анализировать и синтезировать информацию из различных источников.",
"wizard.writer": "Писатель",
"wizard.writer_desc": "Вы квалифицированный писатель, который помогает с созданием контента, редактированием и творческими проектами.",
"wizard.data_analyst": "Аналитик данных",
"wizard.data_analyst_desc": "Вы аналитик данных, который помогает исследовать, анализировать и визуализировать данные для извлечения инсайтов.",
"wizard.devops": "DevOps-инженер",
"wizard.devops_desc": "Вы DevOps-инженер, который помогает с инфраструктурой, деплоем, CI/CD и системным администрированием.",
"wizard.support": "Поддержка клиентов",
"wizard.support_desc": "Вы представитель поддержки клиентов, который помогает решать вопросы с терпением и профессионализмом.",
"wizard.tutor": "Репетитор",
"wizard.tutor_desc": "Вы образовательный репетитор, который ясно объясняет концепции и адаптирует обучение к уровню ученика.",
"wizard.api_designer": "API-дизайнер",
"wizard.api_designer_desc": "Вы дизайнер API, который помогает создавать хорошо структурированные, интуитивные API по лучшим практикам.",
"wizard.meeting_notes": "Заметки к встрече",
"wizard.meeting_notes_desc": "Вы специалист по заметкам встреч, который суммирует обсуждения, извлекает задачи и отслеживает решения.",
"wizard.step_welcome": "Приветствие",
"wizard.step_provider": "Провайдер",
"wizard.step_agent": "Агент",
"wizard.step_try_it": "Попробовать",
"wizard.step_channel": "Канал",
"wizard.step_done": "Готово",
"wizard.cat_general": "Общее",
"wizard.cat_development": "Разработка",
"wizard.cat_research": "Исследования",
"wizard.cat_writing": "Написание",
"wizard.cat_business": "Бизнес",
"wizard.channel_telegram": "Telegram",
"wizard.channel_telegram_desc": "Подключите агента к Telegram-боту для обмена сообщениями.",
"wizard.channel_telegram_token": "Токен бота",
"wizard.channel_telegram_help": "Создайте бота через @BotFather в Telegram, чтобы получить токен.",
"wizard.channel_discord": "Discord",
"wizard.channel_discord_desc": "Подключите агента к Discord-серверу через токен бота.",
"wizard.channel_discord_token": "Токен бота",
"wizard.channel_discord_help": "Создайте приложение Discord на discord.com/developers и добавьте бота.",
"wizard.channel_slack": "Slack",
"wizard.channel_slack_desc": "Подключите агента к рабочему пространству Slack.",
"wizard.channel_slack_token": "Токен бота",
"wizard.channel_slack_help": "Создайте приложение Slack на api.slack.com/apps и установите его в рабочее пространство.",
"wizard.profile_minimal": "Минимальный",
"wizard.profile_minimal_desc": "Только чтение файлов",
"wizard.profile_coding": "Кодинг",
"wizard.profile_coding_desc": "Файлы + оболочка + веб-запросы",
"wizard.profile_research": "Исследование",
"wizard.profile_research_desc": "Веб-поиск + чтение/запись файлов",
"wizard.profile_balanced": "Сбалансированный",
"wizard.profile_balanced_desc": "Набор инструментов общего назначения",
"wizard.profile_precise": "Точный",
"wizard.profile_precise_desc": "Фокусированный набор инструментов для точности",
"wizard.profile_creative": "Креативный",
"wizard.profile_creative_desc": "Полный набор инструментов с творческим уклоном",
"wizard.profile_full": "Полный",
"wizard.profile_full_desc": "Все 35+ инструментов",
"wizard.enter_api_key": "Пожалуйста, введите API-ключ",
"wizard.api_key_saved": "API-ключ сохранён для",
"wizard.failed_save_key": "Не удалось сохранить ключ:",
"wizard.connected": "подключён",
"wizard.connection_failed": "Ошибка подключения",
"wizard.test_failed": "Тест не прошёл:",
"wizard.enter_agent_name": "Пожалуйста, введите имя агента",
"wizard.agent_created": "Агент создан",
"wizard.failed_create_agent": "Не удалось создать агента:",
"wizard.enter_token": "Пожалуйста, введите",
"wizard.channel_configured": "настроен и активирован.",
"wizard.failed_configure": "Ошибка:",
"wizard.suggestions.general.1": "Чем вы можете помочь?",
"wizard.suggestions.general.2": "Расскажите интересный факт",
"wizard.suggestions.general.3": "Резюмируйте последние новости AI",
"wizard.suggestions.development.1": "Напишите Python hello world",
"wizard.suggestions.development.2": "Объясните async/await",
"wizard.suggestions.development.3": "Проверьте этот фрагмент кода",
"wizard.suggestions.research.1": "Объясните квантовые вычисления просто",
"wizard.suggestions.research.2": "Сравните React и Vue",
"wizard.suggestions.research.3": "Какие последние тренды в AI?",
"wizard.suggestions.writing.1": "Помогите написать профессиональное письмо",
"wizard.suggestions.writing.2": "Улучшите этот абзац",
"wizard.suggestions.writing.3": "Напишите введение в блог об AI",
"wizard.suggestions.business.1": "Составьте повестку встречи",
"wizard.suggestions.business.2": "Как обработать жалобу?",
"wizard.suggestions.business.3": "Создайте статус-отчёт проекта",
"approvals.title": "Одобрения выполнения",
"approvals.pending": "ожидает",
"approvals.all": "Все",
"approvals.pending_tab": "Ожидающие",
"approvals.approved": "Одобрено",
"approvals.rejected": "Отклонено",
"approvals.expired": "Истекло",
"approvals.no_approvals": "Нет одобрений",
"approvals.approve": "Одобрить",
"approvals.reject": "Отклонить",
"workflows.title": "Рабочие процессы",
"workflows.visual_builder": "Визуальный конструктор",
"workflows.what_are": "Что такое рабочие процессы?",
"workflows.no_workflows": "Нет рабочих процессов",
"workflows.sequential": "Последовательный",
"workflows.fan_out": "Распределение",
"workflows.conditional": "Условный",
"workflows.loop": "Цикл",
"workflows.add_step": "+ Добавить шаг",
"workflows.execute": "Выполнить",
"workflows.result": "Результат",
"workflows.node_palette": "Палитра узлов",
"workflows.drag_nodes": "Перетащите узлы на холст",
"workflows.steps_connections": "шагов, связей",
"workflows.agent": "Агент",
"workflows.prompt_template": "Шаблон промпта",
"workflows.expression": "Выражение",
"workflows.top_port_true": "Верхний порт = истина, нижний = ложь",
"workflows.max_iterations": "Макс. итераций",
"workflows.until_stop": "До (условие остановки)",
"workflows.fan_out_count": "Количество ветвей",
"workflows.wait_all": "Ждать все",
"workflows.first_finish": "Первый завершился",
"workflows.majority_vote": "Большинство",
"workflows.connection_selected": "Связь выбрана",
"workflows.delete_connection": "Удалить связь",
"scheduler.title": "Планировщик",
"scheduler.scheduled_jobs": "Запланированные задания",
"scheduler.event_triggers": "Триггеры событий",
"scheduler.run_history": "История запусков",
"scheduler.new_job": "+ Новое задание",
"scheduler.job_name": "Название задания",
"scheduler.cron_expression": "Cron-выражение",
"scheduler.quick_presets": "Быстрые шаблоны",
"scheduler.target_agent": "Целевой агент",
"scheduler.any_agent": "Любой доступный агент",
"scheduler.message_send": "Сообщение для отправки",
"scheduler.enabled": "Включено (запустится сразу)",
"scheduler.disabled": "Отключено (создать приостановленным)",
"scheduler.active": "Активно",
"scheduler.paused": "Приостановлено",
"scheduler.cron_job": "Cron-задание",
"scheduler.trigger": "Триггер",
"scheduler.no_jobs": "Нет запланированных заданий",
"scheduler.no_triggers": "Нет триггеров событий",
"scheduler.no_history": "Нет истории запусков",
"channels.title": "Каналы",
"channels.configured": "настроено",
"channels.search": "Поиск каналов...",
"channels.setup": "Настроить",
"channels.edit": "Изменить",
"channels.configure": "Настройка",
"channels.verify": "Проверить",
"channels.ready": "Готово",
"channels.is_ready": "готово!",
"channels.get_credentials": "Как получить учётные данные",
"channels.show_advanced": "Показать расширенные",
"channels.hide_advanced": "Скрыть расширенные",
"channels.connecting": "Подключение к шлюзу WhatsApp Web...",
"channels.linked_success": "WhatsApp успешно связан!",
"channels.business_api": "Business API",
"skills.title": "Навыки и экосистема",
"skills.installed": "Установленные",
"skills.clawhub": "ClawHub",
"skills.mcp_servers": "MCP-серверы",
"skills.quick_start": "Быстрый старт",
"skills.no_installed": "Нет установленных навыков",
"skills.browse_clawhub": "Обзор ClawHub",
"skills.search_clawhub": "Поиск навыков ClawHub...",
"skills.trending": "Популярные",
"skills.most_downloaded": "Самые скачиваемые",
"skills.most_starred": "Самые оценённые",
"skills.recently_updated": "Недавно обновлённые",
"skills.categories": "КАТЕГОРИИ",
"skills.already_installed": "Уже установлено",
"skills.no_skills_found": "Навыки не найдены",
"skills.security_warnings": "Предупреждения безопасности",
"skills.security_scan": "Навыки проверяются на безопасность перед установкой",
"skills.create": "Создать навык",
"skills.created": "Создан",
"skills.cat_coding": "Кодинг и IDE",
"skills.cat_git": "Git и GitHub",
"skills.cat_frontend": "Веб и фронтенд",
"skills.cat_devops": "DevOps и облака",
"skills.cat_database": "Базы данных",
"skills.cat_security": "Безопасность",
"skills.cat_ai": "AI и ML",
"skills.cat_data": "Данные и аналитика",
"skills.cat_mobile": "Мобильная разработка",
"skills.cat_desktop": "Десктопные приложения",
"skills.cat_api": "API и интеграции",
"skills.cat_testing": "Тестирование",
"skills.cat_docs": "Документация",
"skills.cat_productivity": "Продуктивность",
"skills.cat_other": "Другое",
"skills.cat_browser": "Браузер и автоматизация",
"skills.cat_search": "Поиск и исследования",
"skills.cat_communication": "Коммуникации",
"skills.cat_media": "Медиа и стриминг",
"skills.cat_notes": "Заметки и PKM",
"skills.cat_cli": "CLI утилиты",
"skills.cat_marketing": "Маркетинг и продажи",
"skills.cat_finance": "Финансы",
"skills.cat_smarthome": "Умный дом и IoT",
"skills.uninstall_skill": "Удалить навык",
"skills.uninstall_confirm": "Удалить навык",
"skills.source_clawhub": "ClawHub",
"skills.source_openclaw": "OpenClaw",
"skills.source_builtin": "Встроенный",
"skills.source_local": "Локальный",
"hands.title": "Руки — Наборы автономных возможностей",
"hands.available": "Доступные",
"hands.active": "Активные",
"hands.ready": "Готово",
"hands.setup_needed": "Требуется настройка",
"hands.requirements": "ТРЕБОВАНИЯ",
"hands.details": "Подробности",
"hands.no_hands": "Нет доступных модулей",
"sessions.title": "Сессии",
"sessions.memory": "Память",
"sessions.delete_session": "Удалить сессию",
"sessions.delete_confirm": "Это навсегда удалит сессию и все её сообщения.",
"sessions.delete_key": "Удалить ключ",
"sessions.delete_key_confirm": "Удалить ключ",
"logs.title": "Логи",
"logs.live": "Онлайн",
"logs.audit_trail": "Аудит",
"settings.title": "Настройки",
"settings.providers": "Провайдеры",
"settings.models": "Модели",
"settings.config": "Конфигурация",
"settings.tools": "Инструменты",
"settings.migration": "Миграция",
"settings.security": "Безопасность",
"settings.network": "Сеть",
"settings.migration": "Миграция",
"settings.language": "Язык",
"settings.sec_path_traversal": "Защита от обхода пути",
"settings.sec_path_traversal_desc": "Блокирует попытки доступа к файлам за пределами рабочей директории через .. или абсолютные пути.",
"settings.sec_ssrf": "Защита от SSRF",
"settings.sec_ssrf_desc": "Предотвращает запросы агентов к внутренним IP-диапазонам (localhost, облачный метаданные, частные сети).",
"settings.sec_capability": "Управление доступом по возможностям",
"settings.sec_capability_desc": "Агенты могут получать доступ только к явно предоставленным возможностям. Нет неявного доступа к инструментам или данным.",
"settings.sec_taint": "Отслеживание заражения",
"settings.sec_taint_desc": "Отслеживает ненадёжные данные (ввод пользователя, содержимое файлов) через рассуждения агента для предотвращения инъекции промпта.",
"settings.sec_sandbox": "WASM-песочница",
"settings.sec_sandbox_desc": "Выполняет ненадёжный код в изолированных WebAssembly-песочницах с ограничениями памяти и системных вызовов.",
"settings.sec_audit": "Меркл-проверка",
"settings.sec_audit_desc": "Ведёт верифицируемый журнал аудита всех действий агентов с использованием криптографии деревьев Меркла.",
"settings.sec_workspace": "Изоляция рабочих областей",
"settings.sec_workspace_desc": "Каждый агент имеет изолированную рабочую директорию. Нет межагентного доступа к файлам без явного разрешения.",
"settings.sec_rate_limit": "Ограничение частоты",
"settings.sec_rate_limit_desc": "Устанавливает лимиты на запросы для каждого агента и глобально для предотвращения истощения ресурсов и перерасхода.",
"settings.sec_approval": "Одобрения выполнения",
"settings.sec_approval_desc": "Требует одобрения человека для рискованных действий (команды оболочки, запись файлов, внешние запросы).",
"settings.sec_enabled": "Включено",
"settings.sec_disabled": "Отключено",
"settings.sec_inherited": "Унаследовано",
"settings.sec_global": "Глобально"
}
crates/openfang-api/static/index_body.html
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crates/openfang-api/static/index_head.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>OpenFang Dashboard</title>
<link rel="icon" type="image/x-icon" href="/favicon.ico">
<link rel="icon" type="image/png" href="/logo.png">
<link rel="manifest" href="/manifest.json">
<meta name="theme-color" content="#6366f1">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=Geist+Mono:wght@400;500;600;700&display=swap" rel="stylesheet">
</head>
crates/openfang-api/static/js/api.js
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// OpenFang API Client — Fetch wrapper, WebSocket manager, auth injection, toast notifications
'use strict';
// ── Toast Notification System ──
var OpenFangToast = (function() {
var _container = null;
var _toastId = 0;
function getContainer() {
if (!_container) {
_container = document.getElementById('toast-container');
if (!_container) {
_container = document.createElement('div');
_container.id = 'toast-container';
_container.className = 'toast-container';
document.body.appendChild(_container);
}
}
return _container;
}
function toast(message, type, duration) {
type = type || 'info';
duration = duration || 4000;
var id = ++_toastId;
var el = document.createElement('div');
el.className = 'toast toast-' + type;
el.setAttribute('data-toast-id', id);
var msgSpan = document.createElement('span');
msgSpan.className = 'toast-msg';
msgSpan.textContent = message;
el.appendChild(msgSpan);
var closeBtn = document.createElement('button');
closeBtn.className = 'toast-close';
closeBtn.textContent = '\u00D7';
closeBtn.onclick = function() { dismissToast(el); };
el.appendChild(closeBtn);
el.onclick = function(e) { if (e.target === el) dismissToast(el); };
getContainer().appendChild(el);
// Auto-dismiss
if (duration > 0) {
setTimeout(function() { dismissToast(el); }, duration);
}
return id;
}
function dismissToast(el) {
if (!el || el.classList.contains('toast-dismiss')) return;
el.classList.add('toast-dismiss');
setTimeout(function() { if (el.parentNode) el.parentNode.removeChild(el); }, 300);
}
function success(msg, duration) { return toast(msg, 'success', duration); }
function error(msg, duration) { return toast(msg, 'error', duration || 6000); }
function warn(msg, duration) { return toast(msg, 'warn', duration || 5000); }
function info(msg, duration) { return toast(msg, 'info', duration); }
// Styled confirmation modal — replaces native confirm()
function confirm(title, message, onConfirm) {
var overlay = document.createElement('div');
overlay.className = 'confirm-overlay';
var modal = document.createElement('div');
modal.className = 'confirm-modal';
var titleEl = document.createElement('div');
titleEl.className = 'confirm-title';
titleEl.textContent = title;
modal.appendChild(titleEl);
var msgEl = document.createElement('div');
msgEl.className = 'confirm-message';
msgEl.textContent = message;
modal.appendChild(msgEl);
var actions = document.createElement('div');
actions.className = 'confirm-actions';
var cancelBtn = document.createElement('button');
cancelBtn.className = 'btn btn-ghost confirm-cancel';
cancelBtn.textContent = 'Cancel';
actions.appendChild(cancelBtn);
var okBtn = document.createElement('button');
okBtn.className = 'btn btn-danger confirm-ok';
okBtn.textContent = 'Confirm';
actions.appendChild(okBtn);
modal.appendChild(actions);
overlay.appendChild(modal);
function close() { if (overlay.parentNode) overlay.parentNode.removeChild(overlay); document.removeEventListener('keydown', onKey); }
cancelBtn.onclick = close;
okBtn.onclick = function() { close(); if (onConfirm) onConfirm(); };
overlay.addEventListener('click', function(e) { if (e.target === overlay) close(); });
function onKey(e) { if (e.key === 'Escape') close(); }
document.addEventListener('keydown', onKey);
document.body.appendChild(overlay);
okBtn.focus();
}
return {
toast: toast,
success: success,
error: error,
warn: warn,
info: info,
confirm: confirm
};
})();
// ── Friendly Error Messages ──
function friendlyError(status, serverMsg) {
if (status === 0 || !status) return 'Cannot reach daemon — is openfang running?';
if (status === 401) return 'Not authorized — check your API key';
if (status === 403) return 'Permission denied';
if (status === 404) return serverMsg || 'Resource not found';
if (status === 429) return 'Rate limited — slow down and try again';
if (status === 413) return 'Request too large';
if (status === 500) return 'Server error — check daemon logs';
if (status === 502 || status === 503) return 'Daemon unavailable — is it running?';
return serverMsg || 'Unexpected error (' + status + ')';
}
// ── API Client ──
var OpenFangAPI = (function() {
var BASE = window.location.origin;
var WS_BASE = BASE.replace(/^http/, 'ws');
var _authToken = '';
// Connection state tracking
var _connectionState = 'connected';
var _reconnectAttempt = 0;
var _connectionListeners = [];
function setAuthToken(token) { _authToken = token; }
function headers() {
var h = { 'Content-Type': 'application/json' };
if (_authToken) h['Authorization'] = 'Bearer ' + _authToken;
return h;
}
function setConnectionState(state) {
if (_connectionState === state) return;
_connectionState = state;
_connectionListeners.forEach(function(fn) { fn(state); });
}
function onConnectionChange(fn) { _connectionListeners.push(fn); }
function request(method, path, body) {
var opts = { method: method, headers: headers() };
if (body !== undefined) opts.body = JSON.stringify(body);
return fetch(BASE + path, opts).then(function(r) {
if (_connectionState !== 'connected') setConnectionState('connected');
if (!r.ok) {
// On 401, auto-show auth prompt so the user can re-enter their key
if (r.status === 401 && typeof Alpine !== 'undefined') {
try {
var store = Alpine.store('app');
if (store && !store.showAuthPrompt) {
_authToken = '';
localStorage.removeItem('openfang-api-key');
store.showAuthPrompt = true;
}
} catch(e2) { /* ignore Alpine errors */ }
}
return r.text().then(function(text) {
var msg = '';
try {
var json = JSON.parse(text);
msg = json.error || r.statusText;
} catch(e) {
msg = r.statusText;
}
throw new Error(friendlyError(r.status, msg));
});
}
var ct = r.headers.get('content-type') || '';
if (ct.indexOf('application/json') >= 0) return r.json();
return r.text().then(function(t) {
try { return JSON.parse(t); } catch(e) { return { text: t }; }
});
}).catch(function(e) {
if (e.name === 'TypeError' && e.message.includes('Failed to fetch')) {
setConnectionState('disconnected');
throw new Error('Cannot connect to daemon — is openfang running?');
}
throw e;
});
}
function get(path) { return request('GET', path); }
function post(path, body) { return request('POST', path, body); }
function put(path, body) { return request('PUT', path, body); }
function patch(path, body) { return request('PATCH', path, body); }
function del(path) { return request('DELETE', path); }
// WebSocket manager with auto-reconnect
var _ws = null;
var _wsCallbacks = {};
var _wsConnected = false;
var _wsAgentId = null;
var _reconnectTimer = null;
var _reconnectAttempts = 0;
var MAX_RECONNECT = 5;
function wsConnect(agentId, callbacks) {
wsDisconnect();
_wsCallbacks = callbacks || {};
_wsAgentId = agentId;
_reconnectAttempts = 0;
_doConnect(agentId);
}
function _doConnect(agentId) {
try {
var url = WS_BASE + '/api/agents/' + agentId + '/ws';
if (_authToken) url += '?token=' + encodeURIComponent(_authToken);
var socket = new WebSocket(url);
_ws = socket;
socket.onopen = function() {
// Guard: ignore if this socket was superseded by a newer connection
if (_ws !== socket) return;
_wsConnected = true;
_reconnectAttempts = 0;
setConnectionState('connected');
if (_reconnectAttempt > 0) {
OpenFangToast.success('Reconnected');
_reconnectAttempt = 0;
}
if (_wsCallbacks.onOpen) _wsCallbacks.onOpen();
};
socket.onmessage = function(e) {
try {
var data = JSON.parse(e.data);
} catch(parseErr) {
return; // Ignore malformed JSON frames
}
// Dispatch outside try/catch so handler errors are not swallowed
if (_wsCallbacks.onMessage) _wsCallbacks.onMessage(data);
};
socket.onclose = function(e) {
// Guard: only update state if this is still the active socket.
// A superseded socket closing must not null-out the new connection.
if (_ws !== socket) return;
_wsConnected = false;
_ws = null;
if (_wsAgentId && _reconnectAttempts < MAX_RECONNECT && e.code !== 1000) {
_reconnectAttempts++;
_reconnectAttempt = _reconnectAttempts;
setConnectionState('reconnecting');
if (_reconnectAttempts === 1) {
OpenFangToast.warn('Connection lost, reconnecting...');
}
var delay = Math.min(1000 * Math.pow(2, _reconnectAttempts - 1), 10000);
_reconnectTimer = setTimeout(function() { _doConnect(_wsAgentId); }, delay);
return;
}
if (_wsAgentId && _reconnectAttempts >= MAX_RECONNECT) {
setConnectionState('disconnected');
OpenFangToast.error('Connection lost — switched to HTTP mode', 0);
}
if (_wsCallbacks.onClose) _wsCallbacks.onClose();
};
socket.onerror = function() {
// Guard: ignore errors from superseded sockets
if (_ws !== socket) return;
_wsConnected = false;
if (_wsCallbacks.onError) _wsCallbacks.onError();
};
} catch(e) {
_wsConnected = false;
}
}
function wsDisconnect() {
_wsAgentId = null;
_reconnectAttempts = MAX_RECONNECT;
if (_reconnectTimer) { clearTimeout(_reconnectTimer); _reconnectTimer = null; }
if (_ws) { _ws.close(1000); _ws = null; }
_wsConnected = false;
}
function wsSend(data) {
if (_ws && _ws.readyState === WebSocket.OPEN) {
_ws.send(JSON.stringify(data));
return true;
}
return false;
}
function isWsConnected() { return _wsConnected; }
function getConnectionState() { return _connectionState; }
function getToken() { return _authToken; }
function upload(agentId, file) {
var hdrs = {};
if (_authToken) hdrs['Authorization'] = 'Bearer ' + _authToken;
var form = new FormData();
form.append('file', file);
form.append('filename', file.name);
return fetch(BASE + '/api/agents/' + agentId + '/upload', {
method: 'POST',
headers: hdrs,
body: form
}).then(function(r) {
if (!r.ok) throw new Error('Upload failed');
return r.json();
});
}
return {
setAuthToken: setAuthToken,
getToken: getToken,
get: get,
post: post,
put: put,
patch: patch,
del: del,
delete: del,
upload: upload,
wsConnect: wsConnect,
wsDisconnect: wsDisconnect,
wsSend: wsSend,
isWsConnected: isWsConnected,
getConnectionState: getConnectionState,
onConnectionChange: onConnectionChange
};
})();
crates/openfang-api/static/js/app.js
+418
View File
@@ -0,0 +1,418 @@
// OpenFang App — Alpine.js init, hash router, global store
'use strict';
// Marked.js configuration
if (typeof marked !== 'undefined') {
marked.setOptions({
breaks: true,
gfm: true,
highlight: function(code, lang) {
if (typeof hljs !== 'undefined' && lang && hljs.getLanguage(lang)) {
try { return hljs.highlight(code, { language: lang }).value; } catch(e) {}
}
return code;
}
});
}
function escapeHtml(text) {
var div = document.createElement('div');
div.textContent = text || '';
return div.innerHTML.replace(/\n/g, '<br>');
}
function renderMarkdown(text) {
if (!text) return '';
if (typeof marked !== 'undefined') {
// Protect LaTeX blocks from marked.js mangling (underscores, backslashes, etc.)
var latexBlocks = [];
var protected_ = text;
// Protect display math $$...$$ first (greedy across lines)
protected_ = protected_.replace(/\$\$([\s\S]+?)\$\$/g, function(match) {
var idx = latexBlocks.length;
latexBlocks.push(match);
return '\x00LATEX' + idx + '\x00';
});
// Protect inline math $...$ (single line, not empty, not starting/ending with space)
protected_ = protected_.replace(/\$([^\s$](?:[^$]*[^\s$])?)\$/g, function(match) {
var idx = latexBlocks.length;
latexBlocks.push(match);
return '\x00LATEX' + idx + '\x00';
});
// Protect \[...\] display math
protected_ = protected_.replace(/\\\[([\s\S]+?)\\\]/g, function(match) {
var idx = latexBlocks.length;
latexBlocks.push(match);
return '\x00LATEX' + idx + '\x00';
});
// Protect \(...\) inline math
protected_ = protected_.replace(/\\\(([\s\S]+?)\\\)/g, function(match) {
var idx = latexBlocks.length;
latexBlocks.push(match);
return '\x00LATEX' + idx + '\x00';
});
var html = marked.parse(protected_);
// Restore LaTeX blocks
for (var i = 0; i < latexBlocks.length; i++) {
html = html.replace('\x00LATEX' + i + '\x00', latexBlocks[i]);
}
// Add copy buttons to code blocks
html = html.replace(/<pre><code/g, '<pre><button class="copy-btn" onclick="copyCode(this)">Copy</button><code');
// Open external links in new tab
html = html.replace(/<a\s+href="(https?:\/\/[^"]*)"(?![^>]*target=)([^>]*)>/gi, '<a href="$1" target="_blank" rel="noopener"$2>');
return html;
}
return escapeHtml(text);
}
function copyCode(btn) {
var code = btn.nextElementSibling;
if (code) {
navigator.clipboard.writeText(code.textContent).then(function() {
btn.textContent = 'Copied!';
btn.classList.add('copied');
setTimeout(function() { btn.textContent = 'Copy'; btn.classList.remove('copied'); }, 1500);
});
}
}
// Tool category icon SVGs — returns inline SVG for each tool category
function toolIcon(toolName) {
if (!toolName) return '';
var n = toolName.toLowerCase();
var s = 'width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"';
// File/directory operations
if (n.indexOf('file_') === 0 || n.indexOf('directory_') === 0)
return '<svg ' + s + '><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/><path d="M14 2v6h6"/><path d="M16 13H8"/><path d="M16 17H8"/></svg>';
// Web/fetch
if (n.indexOf('web_') === 0 || n.indexOf('link_') === 0)
return '<svg ' + s + '><circle cx="12" cy="12" r="10"/><path d="M2 12h20"/><path d="M12 2a15 15 0 0 1 4 10 15 15 0 0 1-4 10 15 15 0 0 1-4-10 15 15 0 0 1 4-10z"/></svg>';
// Shell/exec
if (n.indexOf('shell') === 0 || n.indexOf('exec_') === 0)
return '<svg ' + s + '><polyline points="4 17 10 11 4 5"/><line x1="12" y1="19" x2="20" y2="19"/></svg>';
// Agent operations
if (n.indexOf('agent_') === 0)
return '<svg ' + s + '><path d="M17 21v-2a4 4 0 0 0-4-4H5a4 4 0 0 0-4 4v2"/><circle cx="9" cy="7" r="4"/><path d="M23 21v-2a4 4 0 0 0-3-3.87"/><path d="M16 3.13a4 4 0 0 1 0 7.75"/></svg>';
// Memory/knowledge
if (n.indexOf('memory_') === 0 || n.indexOf('knowledge_') === 0)
return '<svg ' + s + '><path d="M2 3h6a4 4 0 0 1 4 4v14a3 3 0 0 0-3-3H2z"/><path d="M22 3h-6a4 4 0 0 0-4 4v14a3 3 0 0 1 3-3h7z"/></svg>';
// Cron/schedule
if (n.indexOf('cron_') === 0 || n.indexOf('schedule_') === 0)
return '<svg ' + s + '><circle cx="12" cy="12" r="10"/><polyline points="12 6 12 12 16 14"/></svg>';
// Browser/playwright
if (n.indexOf('browser_') === 0 || n.indexOf('playwright_') === 0)
return '<svg ' + s + '><rect x="2" y="3" width="20" height="14" rx="2"/><path d="M8 21h8"/><path d="M12 17v4"/></svg>';
// Container/docker
if (n.indexOf('container_') === 0 || n.indexOf('docker_') === 0)
return '<svg ' + s + '><path d="M22 12H2"/><path d="M5.45 5.11L2 12v6a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2v-6l-3.45-6.89A2 2 0 0 0 16.76 4H7.24a2 2 0 0 0-1.79 1.11z"/></svg>';
// Image/media
if (n.indexOf('image_') === 0 || n.indexOf('tts_') === 0)
return '<svg ' + s + '><rect x="3" y="3" width="18" height="18" rx="2"/><circle cx="8.5" cy="8.5" r="1.5"/><polyline points="21 15 16 10 5 21"/></svg>';
// Hand tools
if (n.indexOf('hand_') === 0)
return '<svg ' + s + '><path d="M18 11V6a2 2 0 0 0-2-2 2 2 0 0 0-2 2"/><path d="M14 10V4a2 2 0 0 0-2-2 2 2 0 0 0-2 2v6"/><path d="M10 10.5V6a2 2 0 0 0-2-2 2 2 0 0 0-2 2v8"/><path d="M18 8a2 2 0 1 1 4 0v6a8 8 0 0 1-8 8h-2c-2.8 0-4.5-.9-5.7-2.4L3.4 16a2 2 0 0 1 3.2-2.4L8 15"/></svg>';
// Task/collab
if (n.indexOf('task_') === 0)
return '<svg ' + s + '><path d="M9 11l3 3L22 4"/><path d="M21 12v7a2 2 0 01-2 2H5a2 2 0 01-2-2V5a2 2 0 012-2h11"/></svg>';
// Default — wrench
return '<svg ' + s + '><path d="M14.7 6.3a1 1 0 0 0 0 1.4l1.6 1.6a1 1 0 0 0 1.4 0l3.77-3.77a6 6 0 0 1-7.94 7.94l-6.91 6.91a2.12 2.12 0 0 1-3-3l6.91-6.91a6 6 0 0 1 7.94-7.94l-3.76 3.76z"/></svg>';
}
// Alpine.js global store
document.addEventListener('alpine:init', function() {
// Restore saved API key on load
var savedKey = localStorage.getItem('openfang-api-key');
if (savedKey) OpenFangAPI.setAuthToken(savedKey);
Alpine.store('app', {
agents: [],
connected: false,
booting: true,
wsConnected: false,
connectionState: 'connected',
lastError: '',
version: '0.1.0',
agentCount: 0,
pendingApprovalCount: 0,
lastPendingApprovalSignature: '',
pendingAgent: null,
focusMode: localStorage.getItem('openfang-focus') === 'true',
showOnboarding: false,
showAuthPrompt: false,
authMode: 'apikey',
sessionUser: null,
toggleFocusMode() {
this.focusMode = !this.focusMode;
localStorage.setItem('openfang-focus', this.focusMode);
},
async refreshAgents() {
try {
var agents = await OpenFangAPI.get('/api/agents');
this.agents = Array.isArray(agents) ? agents : [];
this.agentCount = this.agents.length;
} catch(e) { /* silent */ }
},
async refreshApprovals() {
try {
var data = await OpenFangAPI.get('/api/approvals');
var approvals = Array.isArray(data) ? data : (data.approvals || []);
var pending = approvals.filter(function(a) { return a.status === 'pending'; });
var signature = pending
.map(function(a) { return a.id; })
.sort()
.join(',');
if (pending.length > 0 && signature !== this.lastPendingApprovalSignature && typeof OpenFangToast !== 'undefined') {
OpenFangToast.warn('An agent is waiting for approval. Open Approvals to review.');
}
this.pendingApprovalCount = pending.length;
this.lastPendingApprovalSignature = signature;
} catch(e) { /* silent */ }
},
async checkStatus() {
try {
var s = await OpenFangAPI.get('/api/status');
this.connected = true;
this.booting = false;
this.lastError = '';
this.version = s.version || '0.1.0';
this.agentCount = s.agent_count || 0;
} catch(e) {
this.connected = false;
this.lastError = e.message || 'Unknown error';
console.warn('[OpenFang] Status check failed:', e.message);
}
},
async checkOnboarding() {
if (localStorage.getItem('openfang-onboarded')) return;
try {
var config = await OpenFangAPI.get('/api/config');
var apiKey = config && config.api_key;
var noKey = !apiKey || apiKey === 'not set' || apiKey === '';
if (noKey && this.agentCount === 0) {
this.showOnboarding = true;
}
} catch(e) {
// If config endpoint fails, still show onboarding if no agents
if (this.agentCount === 0) this.showOnboarding = true;
}
},
dismissOnboarding() {
this.showOnboarding = false;
localStorage.setItem('openfang-onboarded', 'true');
},
async checkAuth() {
try {
// First check if session-based auth is configured
var authInfo = await OpenFangAPI.get('/api/auth/check');
if (authInfo.mode === 'none') {
// No session auth — fall back to API key detection
this.authMode = 'apikey';
this.sessionUser = null;
} else if (authInfo.mode === 'session') {
this.authMode = 'session';
if (authInfo.authenticated) {
this.sessionUser = authInfo.username;
this.showAuthPrompt = false;
return;
}
// Session auth enabled but not authenticated — show login prompt
this.showAuthPrompt = true;
return;
}
} catch(e) { /* ignore — fall through to API key check */ }
// API key mode detection
try {
await OpenFangAPI.get('/api/tools');
this.showAuthPrompt = false;
} catch(e) {
if (e.message && (e.message.indexOf('Not authorized') >= 0 || e.message.indexOf('401') >= 0 || e.message.indexOf('Missing Authorization') >= 0 || e.message.indexOf('Unauthorized') >= 0)) {
var saved = localStorage.getItem('openfang-api-key');
if (saved) {
OpenFangAPI.setAuthToken('');
localStorage.removeItem('openfang-api-key');
}
this.showAuthPrompt = true;
}
}
},
submitApiKey(key) {
if (!key || !key.trim()) return;
OpenFangAPI.setAuthToken(key.trim());
localStorage.setItem('openfang-api-key', key.trim());
this.showAuthPrompt = false;
this.refreshAgents();
},
async sessionLogin(username, password) {
try {
var result = await OpenFangAPI.post('/api/auth/login', { username: username, password: password });
if (result.status === 'ok') {
this.sessionUser = result.username;
this.showAuthPrompt = false;
this.refreshAgents();
} else {
OpenFangToast.error(result.error || 'Login failed');
}
} catch(e) {
OpenFangToast.error(e.message || 'Login failed');
}
},
async sessionLogout() {
try {
await OpenFangAPI.post('/api/auth/logout');
} catch(e) { /* ignore */ }
this.sessionUser = null;
this.showAuthPrompt = true;
},
clearApiKey() {
OpenFangAPI.setAuthToken('');
localStorage.removeItem('openfang-api-key');
}
});
});
// Main app component
function app() {
return {
page: 'agents',
themeMode: localStorage.getItem('openfang-theme-mode') || 'system',
theme: (() => {
var mode = localStorage.getItem('openfang-theme-mode') || 'system';
if (mode === 'system') return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';
return mode;
})(),
sidebarCollapsed: localStorage.getItem('openfang-sidebar') === 'collapsed',
mobileMenuOpen: false,
connected: false,
wsConnected: false,
version: '0.1.0',
agentCount: 0,
get agents() { return Alpine.store('app').agents; },
init() {
var self = this;
// Listen for OS theme changes (only matters when mode is 'system')
window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', function(e) {
if (self.themeMode === 'system') {
self.theme = e.matches ? 'dark' : 'light';
}
});
// Hash routing
var validPages = ['overview','agents','sessions','approvals','comms','workflows','scheduler','channels','skills','hands','analytics','logs','runtime','settings','wizard'];
var pageRedirects = {
'chat': 'agents',
'templates': 'agents',
'triggers': 'workflows',
'cron': 'scheduler',
'schedules': 'scheduler',
'memory': 'sessions',
'audit': 'logs',
'security': 'settings',
'peers': 'settings',
'migration': 'settings',
'usage': 'analytics',
'approval': 'approvals'
};
function handleHash() {
var hash = window.location.hash.replace('#', '') || 'agents';
if (pageRedirects[hash]) {
hash = pageRedirects[hash];
window.location.hash = hash;
}
if (validPages.indexOf(hash) >= 0) self.page = hash;
}
window.addEventListener('hashchange', handleHash);
handleHash();
// Keyboard shortcuts
document.addEventListener('keydown', function(e) {
// Ctrl+K — focus agent switch / go to agents
if ((e.ctrlKey || e.metaKey) && e.key === 'k') {
e.preventDefault();
self.navigate('agents');
}
// Ctrl+N — new agent
if ((e.ctrlKey || e.metaKey) && e.key === 'n' && !e.shiftKey) {
e.preventDefault();
self.navigate('agents');
}
// Ctrl+Shift+F — toggle focus mode
if ((e.ctrlKey || e.metaKey) && e.shiftKey && e.key === 'F') {
e.preventDefault();
Alpine.store('app').toggleFocusMode();
}
// Escape — close mobile menu
if (e.key === 'Escape') {
self.mobileMenuOpen = false;
}
});
// Connection state listener
OpenFangAPI.onConnectionChange(function(state) {
Alpine.store('app').connectionState = state;
});
// Initial data load
this.pollStatus();
Alpine.store('app').refreshApprovals();
Alpine.store('app').checkOnboarding();
Alpine.store('app').checkAuth();
setInterval(function() {
self.pollStatus();
Alpine.store('app').refreshApprovals();
}, 5000);
},
navigate(p) {
this.page = p;
window.location.hash = p;
this.mobileMenuOpen = false;
},
setTheme(mode) {
this.themeMode = mode;
localStorage.setItem('openfang-theme-mode', mode);
if (mode === 'system') {
this.theme = window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';
} else {
this.theme = mode;
}
},
toggleTheme() {
var modes = ['light', 'system', 'dark'];
var next = modes[(modes.indexOf(this.themeMode) + 1) % modes.length];
this.setTheme(next);
},
toggleSidebar() {
this.sidebarCollapsed = !this.sidebarCollapsed;
localStorage.setItem('openfang-sidebar', this.sidebarCollapsed ? 'collapsed' : 'expanded');
},
async pollStatus() {
var store = Alpine.store('app');
await store.checkStatus();
await store.refreshAgents();
this.connected = store.connected;
this.version = store.version;
this.agentCount = store.agentCount;
this.wsConnected = OpenFangAPI.isWsConnected();
}
};
}
crates/openfang-api/static/js/katex.js
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// On-demand KaTeX loader and renderer for chat messages.
var KATEX_VERSION = '0.16.21';
var KATEX_CSS_URL = 'https://cdn.jsdelivr.net/npm/katex@' + KATEX_VERSION + '/dist/katex.min.css';
var KATEX_JS_URL = 'https://cdn.jsdelivr.net/npm/katex@' + KATEX_VERSION + '/dist/katex.min.js';
var KATEX_AUTORENDER_URL =
'https://cdn.jsdelivr.net/npm/katex@' + KATEX_VERSION + '/dist/contrib/auto-render.min.js';
var katexLoadPromise = null;
function hasLatexDelimiters(text) {
if (!text) return false;
return /\$\$|\\\[|\\\(|\$(?=\S)[^$\n]+\$/.test(text);
}
function loadScript(url) {
return new Promise(function (resolve, reject) {
var script = document.createElement('script');
script.src = url;
script.async = true;
script.onload = function () {
resolve();
};
script.onerror = function () {
reject(new Error('Failed to load script: ' + url));
};
document.head.appendChild(script);
});
}
function ensureKatexLoaded() {
if (typeof renderMathInElement === 'function') return Promise.resolve(true);
if (katexLoadPromise) return katexLoadPromise;
katexLoadPromise = new Promise(function (resolve) {
var cssId = 'openfang-katex-css';
if (!document.getElementById(cssId)) {
var link = document.createElement('link');
link.id = cssId;
link.rel = 'stylesheet';
link.href = KATEX_CSS_URL;
document.head.appendChild(link);
}
loadScript(KATEX_JS_URL)
.then(function () {
return loadScript(KATEX_AUTORENDER_URL);
})
.then(function () {
resolve(typeof renderMathInElement === 'function');
})
.catch(function () {
katexLoadPromise = null;
resolve(false);
});
});
return katexLoadPromise;
}
// Render LaTeX math in the chat message container using KaTeX auto-render.
// Call this after new messages are inserted into the DOM.
function renderLatex(el) {
var target = el || document.getElementById('messages');
if (!target) return;
if (!hasLatexDelimiters(target.textContent || '')) return;
ensureKatexLoaded().then(function (ok) {
if (!ok || typeof renderMathInElement !== 'function') return;
try {
renderMathInElement(target, {
delimiters: [
{ left: '$$', right: '$$', display: true },
{ left: '\\[', right: '\\]', display: true },
{ left: '$', right: '$', display: false },
{ left: '\\(', right: '\\)', display: false },
],
throwOnError: false,
trust: false,
});
} catch (e) {
/* KaTeX render error — ignore gracefully */
}
});
}
crates/openfang-api/static/js/pages/agents.js
+813
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// OpenFang Agents Page — Multi-step spawn wizard, detail view with tabs, file editor, personality presets
'use strict';
/** Escape a string for use inside TOML triple-quoted strings ("""\n...\n""").
* Backslashes are escaped, and runs of 3+ consecutive double-quotes are
* broken up so the TOML parser never sees an unintended closing delimiter.
*/
function tomlMultilineEscape(s) {
return s.replace(/\\/g, '\\\\').replace(/"""/g, '""\\"');
}
/** Escape a string for use inside a TOML basic (single-line) string ("...").
* Backslashes, double-quotes, and common control chars are escaped.
*/
function tomlBasicEscape(s) {
return s.replace(/\\/g, '\\\\').replace(/"/g, '\\"').replace(/\n/g, '\\n').replace(/\r/g, '\\r').replace(/\t/g, '\\t');
}
function agentsPage() {
return {
tab: 'agents',
activeChatAgent: null,
// -- Agents state --
showSpawnModal: false,
showDetailModal: false,
detailAgent: null,
spawnMode: 'wizard',
spawning: false,
spawnToml: '',
filterState: 'all',
loading: true,
loadError: '',
spawnForm: {
name: '',
provider: 'groq',
model: 'llama-3.3-70b-versatile',
systemPrompt: 'You are a helpful assistant.',
profile: 'full',
caps: { memory_read: true, memory_write: true, network: false, shell: false, agent_spawn: false }
},
// -- Multi-step wizard state --
spawnProviders: [], // populated from /api/providers on wizard open
spawnProvidersLoading: false,
spawnStep: 1,
spawnIdentity: { emoji: '', color: '#FF5C00', archetype: '' },
selectedPreset: '',
soulContent: '',
emojiOptions: [
'\u{1F916}', '\u{1F4BB}', '\u{1F50D}', '\u{270D}\uFE0F', '\u{1F4CA}', '\u{1F6E0}\uFE0F',
'\u{1F4AC}', '\u{1F393}', '\u{1F310}', '\u{1F512}', '\u{26A1}', '\u{1F680}',
'\u{1F9EA}', '\u{1F3AF}', '\u{1F4D6}', '\u{1F9D1}\u200D\u{1F4BB}', '\u{1F4E7}', '\u{1F3E2}',
'\u{2764}\uFE0F', '\u{1F31F}', '\u{1F527}', '\u{1F4DD}', '\u{1F4A1}', '\u{1F3A8}'
],
archetypeOptions: ['Assistant', 'Researcher', 'Coder', 'Writer', 'DevOps', 'Support', 'Analyst', 'Custom'],
_personalityPresetsLoaded: false,
personalityPresets: [], // Loaded dynamically with i18n
// Load personality presets with i18n
loadPersonalityPresets: function() {
if (this._personalityPresetsLoaded) return;
var t = typeof window.t === 'function' ? window.t : function(s) { return s; };
this.personalityPresets = [
{ id: 'professional', label: t('presets.professional'), soul: t('presets.professional_soul') },
{ id: 'friendly', label: t('presets.friendly'), soul: t('presets.friendly_soul') },
{ id: 'technical', label: t('presets.technical'), soul: t('presets.technical_soul') },
{ id: 'creative', label: t('presets.creative'), soul: t('presets.creative_soul') },
{ id: 'concise', label: t('presets.concise'), soul: t('presets.concise_soul') },
{ id: 'mentor', label: t('presets.mentor'), soul: t('presets.mentor_soul') }
];
this._personalityPresetsLoaded = true;
},
// -- Detail modal tabs --
detailTab: 'info',
agentFiles: [],
editingFile: null,
fileContent: '',
fileSaving: false,
filesLoading: false,
configForm: {},
configSaving: false,
// -- Tool filters --
toolFilters: { tool_allowlist: [], tool_blocklist: [] },
toolFiltersLoading: false,
newAllowTool: '',
newBlockTool: '',
// -- Model switch --
editingModel: false,
newModelValue: '',
editingProvider: false,
newProviderValue: '',
modelSaving: false,
// -- Fallback chain --
editingFallback: false,
newFallbackValue: '',
// -- Templates state --
tplTemplates: [],
tplProviders: [],
tplLoading: false,
tplLoadError: '',
selectedCategory: 'All',
searchQuery: '',
builtinTemplates: [],
// Load templates from API
async init() {
await this.loadTemplates();
// Load personality presets with i18n
this.loadPersonalityPresets();
},
// ── Profile Descriptions (loaded dynamically with i18n) ──
_profileDescriptionsLoaded: false,
profileDescriptions: {},
loadProfileDescriptions: function() {
if (this._profileDescriptionsLoaded) return;
var t = typeof window.t === 'function' ? window.t : function(s) { return s; };
this.profileDescriptions = {
minimal: { label: t('agents.profile.minimal'), desc: t('agents.profile.minimal_desc') },
coding: { label: t('agents.profile.coding'), desc: t('agents.profile.coding_desc') },
research: { label: t('agents.profile.research'), desc: t('agents.profile.research_desc') },
messaging: { label: t('agents.profile.messaging'), desc: t('agents.profile.messaging_desc') },
automation: { label: t('agents.profile.automation'), desc: t('agents.profile.automation_desc') },
balanced: { label: t('agents.profile.balanced'), desc: t('agents.profile.balanced_desc') },
precise: { label: t('agents.profile.precise'), desc: t('agents.profile.precise_desc') },
creative: { label: t('agents.profile.creative'), desc: t('agents.profile.creative_desc') },
full: { label: t('agents.profile.full'), desc: t('agents.profile.full_desc') }
};
this._profileDescriptionsLoaded = true;
},
profileInfo: function(name) {
this.loadProfileDescriptions();
return this.profileDescriptions[name] || { label: name, desc: '' };
},
// ── Tool Preview in Spawn Modal ──
spawnProfiles: [],
spawnProfilesLoaded: false,
async loadSpawnProfiles() {
if (this.spawnProfilesLoaded) return;
try {
var data = await OpenFangAPI.get('/api/profiles');
this.spawnProfiles = data.profiles || [];
this.spawnProfilesLoaded = true;
} catch(e) { this.spawnProfiles = []; }
},
get selectedProfileTools() {
var pname = this.spawnForm.profile;
var match = this.spawnProfiles.find(function(p) { return p.name === pname; });
if (match && match.tools) return match.tools.slice(0, 15);
return [];
},
get agents() { return Alpine.store('app').agents; },
get filteredAgents() {
var f = this.filterState;
if (f === 'all') return this.agents;
return this.agents.filter(function(a) { return a.state.toLowerCase() === f; });
},
get runningCount() {
return this.agents.filter(function(a) { return a.state === 'Running'; }).length;
},
get stoppedCount() {
return this.agents.filter(function(a) { return a.state !== 'Running'; }).length;
},
// -- Templates computed --
get categories() {
var cats = { 'All': true };
this.builtinTemplates.forEach(function(t) { cats[t.category] = true; });
this.tplTemplates.forEach(function(t) { if (t.category) cats[t.category] = true; });
return Object.keys(cats);
},
get filteredBuiltins() {
var self = this;
return this.builtinTemplates.filter(function(t) {
if (self.selectedCategory !== 'All' && t.category !== self.selectedCategory) return false;
if (self.searchQuery) {
var q = self.searchQuery.toLowerCase();
if (t.name.toLowerCase().indexOf(q) === -1 &&
t.description.toLowerCase().indexOf(q) === -1) return false;
}
return true;
});
},
get filteredCustom() {
var self = this;
return this.tplTemplates.filter(function(t) {
if (self.searchQuery) {
var q = self.searchQuery.toLowerCase();
if ((t.name || '').toLowerCase().indexOf(q) === -1 &&
(t.description || '').toLowerCase().indexOf(q) === -1) return false;
}
return true;
});
},
isProviderConfigured(providerName) {
if (!providerName) return false;
var p = this.tplProviders.find(function(pr) { return pr.id === providerName; });
return p ? p.auth_status === 'configured' : false;
},
async init() {
var self = this;
this.loading = true;
this.loadError = '';
try {
await Alpine.store('app').refreshAgents();
await this.loadTemplates();
this.loadPersonalityPresets();
this.loadProfileDescriptions();
} catch(e) {
this.loadError = e.message || 'Could not load agents. Is the daemon running?';
}
this.loading = false;
// If a pending agent was set (e.g. from wizard or redirect), open chat inline
var store = Alpine.store('app');
if (store.pendingAgent) {
this.activeChatAgent = store.pendingAgent;
}
// Watch for future pendingAgent changes
this.$watch('$store.app.pendingAgent', function(agent) {
if (agent) {
self.activeChatAgent = agent;
}
});
},
async loadData() {
this.loading = true;
this.loadError = '';
try {
await Alpine.store('app').refreshAgents();
} catch(e) {
this.loadError = e.message || 'Could not load agents.';
}
this.loading = false;
},
async loadTemplates() {
this.tplLoading = true;
this.tplLoadError = '';
try {
var results = await Promise.all([
OpenFangAPI.get('/api/templates'),
OpenFangAPI.get('/api/providers').catch(function() { return { providers: [] }; })
]);
// Combine static and dynamic templates
this.builtinTemplates = [
{
name: 'General Assistant',
description: 'A versatile conversational agent that can help with everyday tasks, answer questions, and provide recommendations.',
category: 'General',
provider: 'default',
model: 'default',
profile: 'full',
system_prompt: 'You are a helpful, friendly assistant. Provide clear, accurate, and concise responses. Ask clarifying questions when needed.',
manifest_toml: 'name = "General Assistant"\ndescription = "A versatile conversational agent that can help with everyday tasks, answer questions, and provide recommendations."\nmodule = "builtin:chat"\nprofile = "full"\n\n[model]\nprovider = "default"\nmodel = "default"\nsystem_prompt = """\nYou are a helpful, friendly assistant. Provide clear, accurate, and concise responses. Ask clarifying questions when needed.\n"""'
},
{
name: 'Code Helper',
description: 'A programming-focused agent that writes, reviews, and debugs code across multiple languages.',
category: 'Development',
provider: 'default',
model: 'default',
profile: 'coding',
system_prompt: 'You are an expert programmer. Help users write clean, efficient code. Explain your reasoning. Follow best practices and conventions for the language being used.',
manifest_toml: 'name = "Code Helper"\ndescription = "A programming-focused agent that writes, reviews, and debugs code across multiple languages."\nmodule = "builtin:chat"\nprofile = "coding"\n\n[model]\nprovider = "default"\nmodel = "default"\nsystem_prompt = """\nYou are an expert programmer. Help users write clean, efficient code. Explain your reasoning. Follow best practices and conventions for the language being used.\n"""'
},
{
name: 'Researcher',
description: 'An analytical agent that breaks down complex topics, synthesizes information, and provides cited summaries.',
category: 'Research',
provider: 'default',
model: 'default',
profile: 'research',
system_prompt: 'You are a research analyst. Break down complex topics into clear explanations. Provide structured analysis with key findings. Cite sources when available.',
manifest_toml: 'name = "Researcher"\ndescription = "An analytical agent that breaks down complex topics, synthesizes information, and provides cited summaries."\nmodule = "builtin:chat"\nprofile = "research"\n\n[model]\nprovider = "default"\nmodel = "default"\nsystem_prompt = """\nYou are a research analyst. Break down complex topics into clear explanations. Provide structured analysis with key findings. Cite sources when available.\n"""'
},
{
name: 'Writer',
description: 'A creative writing agent that helps with drafting, editing, and improving written content of all kinds.',
category: 'Writing',
provider: 'default',
model: 'default',
profile: 'full',
system_prompt: 'You are a skilled writer and editor. Help users create polished content. Adapt your tone and style to match the intended audience. Offer constructive suggestions for improvement.',
manifest_toml: 'name = "Writer"\ndescription = "A creative writing agent that helps with drafting, editing, and improving written content of all kinds."\nmodule = "builtin:chat"\nprofile = "full"\n\n[model]\nprovider = "default"\nmodel = "default"\nsystem_prompt = """\nYou are a skilled writer and editor. Help users create polished content. Adapt your tone and style to match the intended audience. Offer constructive suggestions for improvement.\n"""'
},
{
name: 'Data Analyst',
description: 'A data-focused agent that helps analyze datasets, create queries, and interpret statistical results.',
category: 'Development',
provider: 'default',
model: 'default',
profile: 'coding',
system_prompt: 'You are a data analysis expert. Help users understand their data, write SQL/Python queries, and interpret results. Present findings clearly with actionable insights.',
manifest_toml: 'name = "Data Analyst"\ndescription = "A data-focused agent that helps analyze datasets, create queries, and interpret statistical results."\nmodule = "builtin:chat"\nprofile = "coding"\n\n[model]\nprovider = "default"\nmodel = "default"\nsystem_prompt = """\nYou are a data analysis expert. Help users understand their data, write SQL/Python queries, and interpret results. Present findings clearly with actionable insights.\n"""'
},
{
name: 'DevOps Engineer',
description: 'A systems-focused agent for CI/CD, infrastructure, Docker, and deployment troubleshooting.',
category: 'Development',
provider: 'default',
model: 'default',
profile: 'automation',
system_prompt: 'You are a DevOps engineer. Help with CI/CD pipelines, Docker, Kubernetes, infrastructure as code, and deployment. Prioritize reliability and security.',
manifest_toml: 'name = "DevOps Engineer"\ndescription = "A systems-focused agent for CI/CD, infrastructure, Docker, and deployment troubleshooting."\nmodule = "builtin:chat"\nprofile = "automation"\n\n[model]\nprovider = "default"\nmodel = "default"\nsystem_prompt = """\nYou are a DevOps engineer. Help with CI/CD pipelines, Docker, Kubernetes, infrastructure as code, and deployment. Prioritize reliability and security.\n"""'
},
...results[0].templates || []
];
this.tplProviders = results[1].providers || [];
} catch(e) {
this.builtinTemplates = [];
this.tplLoadError = e.message || 'Could not load templates.';
}
this.tplLoading = false;
},
chatWithAgent(agent) {
Alpine.store('app').pendingAgent = agent;
this.activeChatAgent = agent;
},
closeChat() {
this.activeChatAgent = null;
OpenFangAPI.wsDisconnect();
},
buildConfigForm(agent) {
var identity = (agent && agent.identity) || {};
return {
name: (agent && agent.name) || '',
system_prompt: (agent && agent.system_prompt) || '',
emoji: identity.emoji || '',
color: identity.color || '#FF5C00',
archetype: identity.archetype || '',
vibe: identity.vibe || ''
};
},
async showDetail(agent) {
this.detailTab = 'info';
this.agentFiles = [];
this.editingFile = null;
this.fileContent = '';
this.editingFallback = false;
this.newFallbackValue = '';
// Load the full detail payload before opening the modal so editable
// fields such as system_prompt and identity metadata are hydrated.
var detail = agent;
try {
var full = await OpenFangAPI.get('/api/agents/' + agent.id);
detail = Object.assign({}, agent, full, {
identity: Object.assign({}, (agent && agent.identity) || {}, (full && full.identity) || {})
});
} catch(e) { /* fall back to list payload */ }
this.detailAgent = detail;
this.detailAgent._fallbacks = detail.fallback_models || [];
this.configForm = this.buildConfigForm(detail);
this.showDetailModal = true;
},
killAgent(agent) {
var self = this;
OpenFangToast.confirm('Stop Agent', 'Stop agent "' + agent.name + '"? The agent will be shut down.', async function() {
try {
await OpenFangAPI.del('/api/agents/' + agent.id);
OpenFangToast.success('Agent "' + agent.name + '" stopped');
self.showDetailModal = false;
await Alpine.store('app').refreshAgents();
} catch(e) {
OpenFangToast.error('Failed to stop agent: ' + e.message);
}
});
},
// Issue #1163: uninstall an agent (kill + remove ~/.openfang/agents/<name>/).
uninstallAgent(agent) {
var self = this;
OpenFangToast.confirm(
'Uninstall Agent',
'Uninstall agent "' + agent.name + '"? This stops the agent AND deletes its files from your workspace. This cannot be undone.',
async function() {
try {
var res = await OpenFangAPI.del('/api/agents/' + agent.id + '/uninstall');
var msg = 'Agent "' + agent.name + '" uninstalled';
if (res && res.dir_removed === false) {
msg += ' (no on-disk files found)';
}
OpenFangToast.success(msg);
self.showDetailModal = false;
await Alpine.store('app').refreshAgents();
} catch(e) {
OpenFangToast.error('Failed to uninstall agent: ' + e.message);
}
}
);
},
killAllAgents() {
var list = this.filteredAgents;
if (!list.length) return;
OpenFangToast.confirm('Stop All Agents', 'Stop ' + list.length + ' agent(s)? All agents will be shut down.', async function() {
var errors = [];
for (var i = 0; i < list.length; i++) {
try {
await OpenFangAPI.del('/api/agents/' + list[i].id);
} catch(e) { errors.push(list[i].name + ': ' + e.message); }
}
await Alpine.store('app').refreshAgents();
if (errors.length) {
OpenFangToast.error('Some agents failed to stop: ' + errors.join(', '));
} else {
OpenFangToast.success(list.length + ' agent(s) stopped');
}
});
},
// ── Multi-step wizard navigation ──
async openSpawnWizard() {
this.showSpawnModal = true;
this.spawnStep = 1;
this.spawnMode = 'wizard';
this.spawnIdentity = { emoji: '', color: '#FF5C00', archetype: '' };
this.selectedPreset = '';
this.soulContent = '';
this.spawnForm.name = '';
this.spawnForm.provider = 'default';
this.spawnForm.model = 'default';
this.spawnForm.systemPrompt = 'You are a helpful assistant.';
this.spawnForm.profile = 'full';
// Fetch status defaults and dynamic provider list concurrently
this.spawnProvidersLoading = true;
try {
var results = await Promise.all([
OpenFangAPI.get('/api/status').catch(function() { return {}; }),
OpenFangAPI.get('/api/providers').catch(function() { return { providers: [] }; })
]);
var status = results[0];
var provData = results[1];
if (status.default_provider) this.spawnForm.provider = status.default_provider;
if (status.default_model) this.spawnForm.model = status.default_model;
this.spawnProviders = provData.providers || [];
} catch(e) {
this.spawnProviders = [];
}
this.spawnProvidersLoading = false;
},
nextStep() {
if (this.spawnStep === 1 && !this.spawnForm.name.trim()) {
OpenFangToast.warn('Please enter an agent name');
return;
}
if (this.spawnStep < 5) this.spawnStep++;
},
prevStep() {
if (this.spawnStep > 1) this.spawnStep--;
},
selectPreset(preset) {
this.selectedPreset = preset.id;
this.soulContent = preset.soul;
},
generateToml() {
var f = this.spawnForm;
var si = this.spawnIdentity;
var lines = [
'name = "' + tomlBasicEscape(f.name) + '"',
'module = "builtin:chat"'
];
if (f.profile && f.profile !== 'custom') {
lines.push('profile = "' + f.profile + '"');
}
lines.push('', '[model]');
lines.push('provider = "' + f.provider + '"');
lines.push('model = "' + f.model + '"');
lines.push('system_prompt = """\n' + tomlMultilineEscape(f.systemPrompt) + '\n"""');
if (f.profile === 'custom') {
lines.push('', '[capabilities]');
if (f.caps.memory_read) lines.push('memory_read = ["*"]');
if (f.caps.memory_write) lines.push('memory_write = ["self.*"]');
if (f.caps.network) lines.push('network = ["*"]');
if (f.caps.shell) lines.push('shell = ["*"]');
if (f.caps.agent_spawn) lines.push('agent_spawn = true');
}
return lines.join('\n');
},
async setMode(agent, mode) {
try {
await OpenFangAPI.put('/api/agents/' + agent.id + '/mode', { mode: mode });
agent.mode = mode;
OpenFangToast.success('Mode set to ' + mode);
await Alpine.store('app').refreshAgents();
} catch(e) {
OpenFangToast.error('Failed to set mode: ' + e.message);
}
},
async spawnAgent() {
this.spawning = true;
var toml = this.spawnMode === 'wizard' ? this.generateToml() : this.spawnToml;
if (!toml.trim()) {
this.spawning = false;
OpenFangToast.warn('Manifest is empty \u2014 enter agent config first');
return;
}
try {
var res = await OpenFangAPI.post('/api/agents', { manifest_toml: toml });
if (res.agent_id) {
// Post-spawn: update identity + write SOUL.md if personality preset selected
var patchBody = {};
if (this.spawnIdentity.emoji) patchBody.emoji = this.spawnIdentity.emoji;
if (this.spawnIdentity.color) patchBody.color = this.spawnIdentity.color;
if (this.spawnIdentity.archetype) patchBody.archetype = this.spawnIdentity.archetype;
if (this.selectedPreset) patchBody.vibe = this.selectedPreset;
if (Object.keys(patchBody).length) {
OpenFangAPI.patch('/api/agents/' + res.agent_id + '/config', patchBody).catch(function(e) { console.warn('Post-spawn config patch failed:', e.message); });
}
if (this.soulContent.trim()) {
OpenFangAPI.put('/api/agents/' + res.agent_id + '/files/SOUL.md', { content: '# Soul\n' + this.soulContent }).catch(function(e) { console.warn('SOUL.md write failed:', e.message); });
}
this.showSpawnModal = false;
this.spawnForm.name = '';
this.spawnToml = '';
this.spawnStep = 1;
OpenFangToast.success('Agent "' + (res.name || 'new') + '" spawned');
await Alpine.store('app').refreshAgents();
this.chatWithAgent({ id: res.agent_id, name: res.name, model_provider: '?', model_name: '?' });
} else {
OpenFangToast.error('Spawn failed: ' + (res.error || 'Unknown error'));
}
} catch(e) {
OpenFangToast.error('Failed to spawn agent: ' + e.message);
}
this.spawning = false;
},
// ── Detail modal: Files tab ──
async loadAgentFiles() {
if (!this.detailAgent) return;
this.filesLoading = true;
try {
var data = await OpenFangAPI.get('/api/agents/' + this.detailAgent.id + '/files');
this.agentFiles = data.files || [];
} catch(e) {
this.agentFiles = [];
OpenFangToast.error('Failed to load files: ' + e.message);
}
this.filesLoading = false;
},
async openFile(file) {
if (!file.exists) {
// Create with empty content
this.editingFile = file.name;
this.fileContent = '';
return;
}
try {
var data = await OpenFangAPI.get('/api/agents/' + this.detailAgent.id + '/files/' + encodeURIComponent(file.name));
this.editingFile = file.name;
this.fileContent = data.content || '';
} catch(e) {
OpenFangToast.error('Failed to read file: ' + e.message);
}
},
async saveFile() {
if (!this.editingFile || !this.detailAgent) return;
this.fileSaving = true;
try {
await OpenFangAPI.put('/api/agents/' + this.detailAgent.id + '/files/' + encodeURIComponent(this.editingFile), { content: this.fileContent });
OpenFangToast.success(this.editingFile + ' saved');
await this.loadAgentFiles();
} catch(e) {
OpenFangToast.error('Failed to save file: ' + e.message);
}
this.fileSaving = false;
},
closeFileEditor() {
this.editingFile = null;
this.fileContent = '';
},
// ── Detail modal: Config tab ──
async saveConfig() {
if (!this.detailAgent) return;
this.configSaving = true;
try {
await OpenFangAPI.patch('/api/agents/' + this.detailAgent.id + '/config', this.configForm);
OpenFangToast.success('Config updated');
await Alpine.store('app').refreshAgents();
} catch(e) {
OpenFangToast.error('Failed to save config: ' + e.message);
}
this.configSaving = false;
},
// ── Clone agent ──
async cloneAgent(agent) {
var newName = (agent.name || 'agent') + '-copy';
try {
var res = await OpenFangAPI.post('/api/agents/' + agent.id + '/clone', { new_name: newName });
if (res.agent_id) {
OpenFangToast.success('Cloned as "' + res.name + '"');
await Alpine.store('app').refreshAgents();
this.showDetailModal = false;
}
} catch(e) {
OpenFangToast.error('Clone failed: ' + e.message);
}
},
// -- Template methods --
async spawnFromTemplate(template) {
try {
var manifestToml = template.manifest_toml;
if (!manifestToml) {
// If template doesn't have manifest_toml, fetch it from the API
var data = await OpenFangAPI.get('/api/templates/' + encodeURIComponent(template.name));
manifestToml = data.manifest_toml;
}
if (manifestToml) {
var res = await OpenFangAPI.post('/api/agents', { manifest_toml: manifestToml });
if (res.agent_id) {
OpenFangToast.success('Agent "' + (res.name || template.name) + '" spawned from template');
await Alpine.store('app').refreshAgents();
this.chatWithAgent({ id: res.agent_id, name: res.name || template.name, model_provider: '?', model_name: '?' });
}
}
} catch(e) {
OpenFangToast.error('Failed to spawn from template: ' + e.message);
}
},
// ── Clear agent history ──
async clearHistory(agent) {
var self = this;
OpenFangToast.confirm('Clear History', 'Clear all conversation history for "' + agent.name + '"? This cannot be undone.', async function() {
try {
await OpenFangAPI.del('/api/agents/' + agent.id + '/history');
OpenFangToast.success('History cleared for "' + agent.name + '"');
} catch(e) {
OpenFangToast.error('Failed to clear history: ' + e.message);
}
});
},
// ── Model switch ──
async changeModel() {
if (!this.detailAgent || !this.newModelValue.trim()) return;
this.modelSaving = true;
try {
var resp = await OpenFangAPI.put('/api/agents/' + this.detailAgent.id + '/model', { model: this.newModelValue.trim() });
var providerInfo = (resp && resp.provider) ? ' (provider: ' + resp.provider + ')' : '';
OpenFangToast.success('Model changed' + providerInfo + ' (memory reset)');
this.editingModel = false;
await Alpine.store('app').refreshAgents();
// Refresh detailAgent
var agents = Alpine.store('app').agents;
for (var i = 0; i < agents.length; i++) {
if (agents[i].id === this.detailAgent.id) { this.detailAgent = agents[i]; break; }
}
} catch(e) {
OpenFangToast.error('Failed to change model: ' + e.message);
}
this.modelSaving = false;
},
// ── Provider switch ──
async changeProvider() {
if (!this.detailAgent || !this.newProviderValue.trim()) return;
this.modelSaving = true;
try {
var combined = this.newProviderValue.trim() + '/' + this.detailAgent.model_name;
var resp = await OpenFangAPI.put('/api/agents/' + this.detailAgent.id + '/model', { model: combined });
OpenFangToast.success('Provider changed to ' + (resp && resp.provider ? resp.provider : this.newProviderValue.trim()));
this.editingProvider = false;
await Alpine.store('app').refreshAgents();
var agents = Alpine.store('app').agents;
for (var i = 0; i < agents.length; i++) {
if (agents[i].id === this.detailAgent.id) { this.detailAgent = agents[i]; break; }
}
} catch(e) {
OpenFangToast.error('Failed to change provider: ' + e.message);
}
this.modelSaving = false;
},
// ── Fallback model chain ──
async addFallback() {
if (!this.detailAgent || !this.newFallbackValue.trim()) return;
var parts = this.newFallbackValue.trim().split('/');
var provider = parts.length > 1 ? parts[0] : this.detailAgent.model_provider;
var model = parts.length > 1 ? parts.slice(1).join('/') : parts[0];
if (!this.detailAgent._fallbacks) this.detailAgent._fallbacks = [];
this.detailAgent._fallbacks.push({ provider: provider, model: model });
try {
await OpenFangAPI.patch('/api/agents/' + this.detailAgent.id + '/config', {
fallback_models: this.detailAgent._fallbacks
});
OpenFangToast.success('Fallback added: ' + provider + '/' + model);
} catch(e) {
OpenFangToast.error('Failed to save fallbacks: ' + e.message);
this.detailAgent._fallbacks.pop();
}
this.editingFallback = false;
this.newFallbackValue = '';
},
async removeFallback(idx) {
if (!this.detailAgent || !this.detailAgent._fallbacks) return;
var removed = this.detailAgent._fallbacks.splice(idx, 1);
try {
await OpenFangAPI.patch('/api/agents/' + this.detailAgent.id + '/config', {
fallback_models: this.detailAgent._fallbacks
});
OpenFangToast.success('Fallback removed');
} catch(e) {
OpenFangToast.error('Failed to save fallbacks: ' + e.message);
this.detailAgent._fallbacks.splice(idx, 0, removed[0]);
}
},
// ── Tool filters ──
async loadToolFilters() {
if (!this.detailAgent) return;
this.toolFiltersLoading = true;
try {
this.toolFilters = await OpenFangAPI.get('/api/agents/' + this.detailAgent.id + '/tools');
} catch(e) {
this.toolFilters = { tool_allowlist: [], tool_blocklist: [] };
}
this.toolFiltersLoading = false;
},
addAllowTool() {
var t = this.newAllowTool.trim();
if (t && this.toolFilters.tool_allowlist.indexOf(t) === -1) {
this.toolFilters.tool_allowlist.push(t);
this.newAllowTool = '';
this.saveToolFilters();
}
},
removeAllowTool(tool) {
this.toolFilters.tool_allowlist = this.toolFilters.tool_allowlist.filter(function(t) { return t !== tool; });
this.saveToolFilters();
},
addBlockTool() {
var t = this.newBlockTool.trim();
if (t && this.toolFilters.tool_blocklist.indexOf(t) === -1) {
this.toolFilters.tool_blocklist.push(t);
this.newBlockTool = '';
this.saveToolFilters();
}
},
removeBlockTool(tool) {
this.toolFilters.tool_blocklist = this.toolFilters.tool_blocklist.filter(function(t) { return t !== tool; });
this.saveToolFilters();
},
async saveToolFilters() {
if (!this.detailAgent) return;
try {
await OpenFangAPI.put('/api/agents/' + this.detailAgent.id + '/tools', this.toolFilters);
} catch(e) {
OpenFangToast.error('Failed to update tool filters: ' + e.message);
}
},
async spawnBuiltin(t) {
var toml = 'name = "' + tomlBasicEscape(t.name) + '"\n';
toml += 'description = "' + tomlBasicEscape(t.description) + '"\n';
toml += 'module = "builtin:chat"\n';
toml += 'profile = "' + t.profile + '"\n\n';
toml += '[model]\nprovider = "' + t.provider + '"\nmodel = "' + t.model + '"\n';
toml += 'system_prompt = """\n' + tomlMultilineEscape(t.system_prompt) + '\n"""\n';
try {
var res = await OpenFangAPI.post('/api/agents', { manifest_toml: toml });
if (res.agent_id) {
OpenFangToast.success('Agent "' + t.name + '" spawned');
await Alpine.store('app').refreshAgents();
this.chatWithAgent({ id: res.agent_id, name: t.name, model_provider: t.provider, model_name: t.model });
}
} catch(e) {
OpenFangToast.error('Failed to spawn agent: ' + e.message);
}
}
};
}
crates/openfang-api/static/js/pages/approvals.js
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// OpenFang Approvals Page — Execution approval queue for sensitive agent actions
'use strict';
function approvalsPage() {
return {
approvals: [],
filterStatus: 'all',
loading: true,
loadError: '',
refreshTimer: null,
init() {
var self = this;
this.loadData();
this.refreshTimer = setInterval(function() {
self.loadData();
}, 5000);
},
destroy() {
if (this.refreshTimer) {
clearInterval(this.refreshTimer);
this.refreshTimer = null;
}
},
get filtered() {
var f = this.filterStatus;
if (f === 'all') return this.approvals;
return this.approvals.filter(function(a) { return a.status === f; });
},
get pendingCount() {
return this.approvals.filter(function(a) { return a.status === 'pending'; }).length;
},
async loadData() {
this.loading = true;
this.loadError = '';
try {
var data = await OpenFangAPI.get('/api/approvals');
this.approvals = data.approvals || [];
} catch(e) {
this.loadError = e.message || 'Could not load approvals.';
}
this.loading = false;
},
async approve(id) {
try {
await OpenFangAPI.post('/api/approvals/' + id + '/approve', {});
OpenFangToast.success('Approved');
await this.loadData();
} catch(e) {
OpenFangToast.error(e.message);
}
},
async reject(id) {
var self = this;
OpenFangToast.confirm('Reject Action', 'Are you sure you want to reject this action?', async function() {
try {
await OpenFangAPI.post('/api/approvals/' + id + '/reject', {});
OpenFangToast.success('Rejected');
await self.loadData();
} catch(e) {
OpenFangToast.error(e.message);
}
});
},
timeAgo(dateStr) {
if (!dateStr) return '';
var d = new Date(dateStr);
var secs = Math.floor((Date.now() - d.getTime()) / 1000);
if (secs < 60) return secs + 's ago';
if (secs < 3600) return Math.floor(secs / 60) + 'm ago';
if (secs < 86400) return Math.floor(secs / 3600) + 'h ago';
return Math.floor(secs / 86400) + 'd ago';
}
};
}
crates/openfang-api/static/js/pages/channels.js
+309
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// OpenFang Channels Page — OpenClaw-style setup UX with QR code support
'use strict';
function channelsPage() {
return {
allChannels: [],
categoryFilter: 'all',
searchQuery: '',
setupModal: null,
configuring: false,
testing: {},
formValues: {},
showAdvanced: false,
showBusinessApi: false,
loading: true,
loadError: '',
pollTimer: null,
// Setup flow step tracking
setupStep: 1, // 1=Configure, 2=Verify, 3=Ready
testPassed: false,
// WhatsApp QR state
qr: {
loading: false,
available: false,
dataUrl: '',
sessionId: '',
message: '',
help: '',
connected: false,
expired: false,
error: ''
},
qrPollTimer: null,
categories: [
{ key: 'all', label: 'All' },
{ key: 'messaging', label: 'Messaging' },
{ key: 'social', label: 'Social' },
{ key: 'enterprise', label: 'Enterprise' },
{ key: 'developer', label: 'Developer' },
{ key: 'notifications', label: 'Notifications' }
],
get filteredChannels() {
var self = this;
return this.allChannels.filter(function(ch) {
if (self.categoryFilter !== 'all' && ch.category !== self.categoryFilter) return false;
if (self.searchQuery) {
var q = self.searchQuery.toLowerCase();
return ch.name.toLowerCase().indexOf(q) !== -1 ||
ch.display_name.toLowerCase().indexOf(q) !== -1 ||
ch.description.toLowerCase().indexOf(q) !== -1;
}
return true;
});
},
get configuredCount() {
return this.allChannels.filter(function(ch) { return ch.configured; }).length;
},
categoryCount(cat) {
var all = this.allChannels.filter(function(ch) { return cat === 'all' || ch.category === cat; });
var configured = all.filter(function(ch) { return ch.configured; });
return configured.length + '/' + all.length;
},
basicFields() {
if (!this.setupModal || !this.setupModal.fields) return [];
return this.setupModal.fields.filter(function(f) { return !f.advanced; });
},
advancedFields() {
if (!this.setupModal || !this.setupModal.fields) return [];
return this.setupModal.fields.filter(function(f) { return f.advanced; });
},
hasAdvanced() {
return this.advancedFields().length > 0;
},
isQrChannel() {
return this.setupModal && this.setupModal.setup_type === 'qr';
},
async loadChannels() {
this.loading = true;
this.loadError = '';
try {
var data = await OpenFangAPI.get('/api/channels');
this.allChannels = (data.channels || []).map(function(ch) {
ch.connected = ch.configured && ch.has_token;
return ch;
});
} catch(e) {
this.loadError = e.message || 'Could not load channels.';
}
this.loading = false;
this.startPolling();
},
async loadData() { return this.loadChannels(); },
startPolling() {
var self = this;
if (this.pollTimer) clearInterval(this.pollTimer);
this.pollTimer = setInterval(function() { self.refreshStatus(); }, 15000);
},
async refreshStatus() {
try {
var data = await OpenFangAPI.get('/api/channels');
var byName = {};
(data.channels || []).forEach(function(ch) { byName[ch.name] = ch; });
this.allChannels.forEach(function(c) {
var fresh = byName[c.name];
if (fresh) {
c.configured = fresh.configured;
c.has_token = fresh.has_token;
c.connected = fresh.configured && fresh.has_token;
c.fields = fresh.fields;
}
});
} catch(e) { console.warn('Channel refresh failed:', e.message); }
},
statusBadge(ch) {
if (!ch.configured) return { text: 'Not Configured', cls: 'badge-muted' };
if (!ch.has_token) return { text: 'Missing Token', cls: 'badge-warn' };
if (ch.connected) return { text: 'Ready', cls: 'badge-success' };
return { text: 'Configured', cls: 'badge-info' };
},
difficultyClass(d) {
if (d === 'Easy') return 'difficulty-easy';
if (d === 'Hard') return 'difficulty-hard';
return 'difficulty-medium';
},
openSetup(ch) {
this.setupModal = ch;
// Pre-populate form values from saved config (non-secret fields).
var vals = {};
if (ch.fields) {
ch.fields.forEach(function(f) {
if (f.value !== undefined && f.value !== null && f.type !== 'secret') {
vals[f.key] = String(f.value);
}
});
}
this.formValues = vals;
this.showAdvanced = false;
this.showBusinessApi = false;
this.setupStep = ch.configured ? 3 : 1;
this.testPassed = !!ch.configured;
this.resetQR();
// Auto-start QR flow for QR-type channels
if (ch.setup_type === 'qr') {
this.startQR();
}
},
// ── QR Code Flow (WhatsApp Web style) ──────────────────────────
resetQR() {
this.qr = {
loading: false, available: false, dataUrl: '', sessionId: '',
message: '', help: '', connected: false, expired: false, error: ''
};
if (this.qrPollTimer) { clearInterval(this.qrPollTimer); this.qrPollTimer = null; }
},
async startQR() {
this.qr.loading = true;
this.qr.error = '';
this.qr.connected = false;
this.qr.expired = false;
try {
var result = await OpenFangAPI.post('/api/channels/whatsapp/qr/start', {});
this.qr.available = result.available || false;
this.qr.dataUrl = result.qr_data_url || '';
this.qr.sessionId = result.session_id || '';
this.qr.message = result.message || '';
this.qr.help = result.help || '';
this.qr.connected = result.connected || false;
if (this.qr.available && this.qr.dataUrl && !this.qr.connected) {
this.pollQR();
}
if (this.qr.connected) {
OpenFangToast.success('WhatsApp connected!');
await this.refreshStatus();
}
} catch(e) {
this.qr.error = e.message || 'Could not start QR login';
}
this.qr.loading = false;
},
pollQR() {
var self = this;
if (this.qrPollTimer) clearInterval(this.qrPollTimer);
this.qrPollTimer = setInterval(async function() {
try {
var result = await OpenFangAPI.get('/api/channels/whatsapp/qr/status?session_id=' + encodeURIComponent(self.qr.sessionId));
if (result.connected) {
clearInterval(self.qrPollTimer);
self.qrPollTimer = null;
self.qr.connected = true;
self.qr.message = result.message || 'Connected!';
OpenFangToast.success('WhatsApp linked successfully!');
await self.refreshStatus();
} else if (result.expired) {
clearInterval(self.qrPollTimer);
self.qrPollTimer = null;
self.qr.expired = true;
self.qr.message = 'QR code expired. Click to generate a new one.';
} else {
self.qr.message = result.message || 'Waiting for scan...';
}
} catch(e) { /* silent retry */ }
}, 3000);
},
// ── Standard Form Flow ─────────────────────────────────────────
async saveChannel() {
if (!this.setupModal) return;
var name = this.setupModal.name;
this.configuring = true;
try {
await OpenFangAPI.post('/api/channels/' + name + '/configure', {
fields: this.formValues
});
this.setupStep = 2;
// Auto-test after save
try {
var testResult = await OpenFangAPI.post('/api/channels/' + name + '/test', {});
if (testResult.status === 'ok') {
this.testPassed = true;
this.setupStep = 3;
OpenFangToast.success(this.setupModal.display_name + ' activated!');
} else {
OpenFangToast.success(this.setupModal.display_name + ' saved. ' + (testResult.message || ''));
}
} catch(te) {
OpenFangToast.success(this.setupModal.display_name + ' saved. Test to verify connection.');
}
await this.refreshStatus();
} catch(e) {
OpenFangToast.error('Failed: ' + (e.message || 'Unknown error'));
}
this.configuring = false;
},
async removeChannel() {
if (!this.setupModal) return;
var name = this.setupModal.name;
var displayName = this.setupModal.display_name;
var self = this;
OpenFangToast.confirm('Remove Channel', 'Remove ' + displayName + ' configuration? This will deactivate the channel.', async function() {
try {
await OpenFangAPI.delete('/api/channels/' + name + '/configure');
OpenFangToast.success(displayName + ' removed and deactivated.');
await self.refreshStatus();
self.setupModal = null;
} catch(e) {
OpenFangToast.error('Failed: ' + (e.message || 'Unknown error'));
}
});
},
async testChannel() {
if (!this.setupModal) return;
var name = this.setupModal.name;
this.testing[name] = true;
try {
var result = await OpenFangAPI.post('/api/channels/' + name + '/test', {});
if (result.status === 'ok') {
this.testPassed = true;
this.setupStep = 3;
OpenFangToast.success(result.message);
} else {
OpenFangToast.error(result.message);
}
} catch(e) {
OpenFangToast.error('Test failed: ' + (e.message || 'Unknown error'));
}
this.testing[name] = false;
},
async copyConfig(ch) {
var tpl = ch ? ch.config_template : (this.setupModal ? this.setupModal.config_template : '');
if (!tpl) return;
try {
await navigator.clipboard.writeText(tpl);
OpenFangToast.success('Copied to clipboard');
} catch(e) {
OpenFangToast.error('Copy failed');
}
},
destroy() {
if (this.pollTimer) { clearInterval(this.pollTimer); this.pollTimer = null; }
if (this.qrPollTimer) { clearInterval(this.qrPollTimer); this.qrPollTimer = null; }
}
};
}
crates/openfang-api/static/js/pages/chat.js
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crates/openfang-api/static/js/pages/comms.js
+201
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// OpenFang Comms Page — Agent topology & inter-agent communication feed
'use strict';
function commsPage() {
return {
topology: { nodes: [], edges: [] },
events: [],
loading: true,
loadError: '',
sseSource: null,
showSendModal: false,
showTaskModal: false,
sendFrom: '',
sendTo: '',
sendMsg: '',
sendLoading: false,
taskTitle: '',
taskDesc: '',
taskAssign: '',
taskLoading: false,
async loadData() {
this.loading = true;
this.loadError = '';
try {
var results = await Promise.all([
OpenFangAPI.get('/api/comms/topology'),
OpenFangAPI.get('/api/comms/events?limit=200')
]);
this.topology = results[0] || { nodes: [], edges: [] };
this.events = results[1] || [];
this.startSSE();
} catch(e) {
this.loadError = e.message || 'Could not load comms data.';
}
this.loading = false;
},
startSSE() {
if (this.sseSource) this.sseSource.close();
var self = this;
var url = '/api/comms/events/stream';
if (OpenFangAPI.apiKey) url += '?token=' + encodeURIComponent(OpenFangAPI.apiKey);
this.sseSource = new EventSource(url);
this.sseSource.onmessage = function(ev) {
if (ev.data === 'ping') return;
try {
var event = JSON.parse(ev.data);
self.events.unshift(event);
if (self.events.length > 200) self.events.length = 200;
// Refresh topology on spawn/terminate events
if (event.kind === 'agent_spawned' || event.kind === 'agent_terminated') {
self.refreshTopology();
}
} catch(e) { /* ignore parse errors */ }
};
},
stopSSE() {
if (this.sseSource) {
this.sseSource.close();
this.sseSource = null;
}
},
async refreshTopology() {
try {
this.topology = await OpenFangAPI.get('/api/comms/topology');
} catch(e) { /* silent */ }
},
rootNodes() {
var childIds = {};
var self = this;
this.topology.edges.forEach(function(e) {
if (e.kind === 'parent_child') childIds[e.to] = true;
});
return this.topology.nodes.filter(function(n) { return !childIds[n.id]; });
},
childrenOf(id) {
var childIds = {};
this.topology.edges.forEach(function(e) {
if (e.kind === 'parent_child' && e.from === id) childIds[e.to] = true;
});
return this.topology.nodes.filter(function(n) { return childIds[n.id]; });
},
peersOf(id) {
var peerIds = {};
this.topology.edges.forEach(function(e) {
if (e.kind === 'peer') {
if (e.from === id) peerIds[e.to] = true;
if (e.to === id) peerIds[e.from] = true;
}
});
return this.topology.nodes.filter(function(n) { return peerIds[n.id]; });
},
stateBadgeClass(state) {
switch(state) {
case 'Running': return 'badge badge-success';
case 'Suspended': return 'badge badge-warning';
case 'Terminated': case 'Crashed': return 'badge badge-danger';
default: return 'badge badge-dim';
}
},
eventBadgeClass(kind) {
switch(kind) {
case 'agent_message': return 'badge badge-info';
case 'agent_spawned': return 'badge badge-success';
case 'agent_terminated': return 'badge badge-danger';
case 'task_posted': return 'badge badge-warning';
case 'task_claimed': return 'badge badge-info';
case 'task_completed': return 'badge badge-success';
default: return 'badge badge-dim';
}
},
eventIcon(kind) {
switch(kind) {
case 'agent_message': return '\u2709';
case 'agent_spawned': return '+';
case 'agent_terminated': return '\u2715';
case 'task_posted': return '\u2691';
case 'task_claimed': return '\u2690';
case 'task_completed': return '\u2713';
default: return '\u2022';
}
},
eventLabel(kind) {
switch(kind) {
case 'agent_message': return 'Message';
case 'agent_spawned': return 'Spawned';
case 'agent_terminated': return 'Terminated';
case 'task_posted': return 'Task Posted';
case 'task_claimed': return 'Task Claimed';
case 'task_completed': return 'Task Done';
default: return kind;
}
},
timeAgo(dateStr) {
if (!dateStr) return '';
var d = new Date(dateStr);
var secs = Math.floor((Date.now() - d.getTime()) / 1000);
if (secs < 60) return secs + 's ago';
if (secs < 3600) return Math.floor(secs / 60) + 'm ago';
if (secs < 86400) return Math.floor(secs / 3600) + 'h ago';
return Math.floor(secs / 86400) + 'd ago';
},
openSendModal() {
this.sendFrom = '';
this.sendTo = '';
this.sendMsg = '';
this.showSendModal = true;
},
async submitSend() {
if (!this.sendFrom || !this.sendTo || !this.sendMsg.trim()) return;
this.sendLoading = true;
try {
await OpenFangAPI.post('/api/comms/send', {
from_agent_id: this.sendFrom,
to_agent_id: this.sendTo,
message: this.sendMsg
});
OpenFangToast.success('Message sent');
this.showSendModal = false;
} catch(e) {
OpenFangToast.error(e.message || 'Send failed');
}
this.sendLoading = false;
},
openTaskModal() {
this.taskTitle = '';
this.taskDesc = '';
this.taskAssign = '';
this.showTaskModal = true;
},
async submitTask() {
if (!this.taskTitle.trim()) return;
this.taskLoading = true;
try {
var body = { title: this.taskTitle, description: this.taskDesc };
if (this.taskAssign) body.assigned_to = this.taskAssign;
await OpenFangAPI.post('/api/comms/task', body);
OpenFangToast.success('Task posted');
this.showTaskModal = false;
} catch(e) {
OpenFangToast.error(e.message || 'Task failed');
}
this.taskLoading = false;
}
};
}
crates/openfang-api/static/js/pages/hands.js
+962
View File
@@ -0,0 +1,962 @@
// OpenFang Hands Page — curated autonomous capability packages
'use strict';
function handsPage() {
return {
tab: 'available',
hands: [],
instances: [],
loading: true,
activeLoading: false,
loadError: '',
activatingId: null,
activateResult: null,
detailHand: null,
settingsValues: {},
_toastTimer: null,
browserViewer: null,
browserViewerOpen: false,
_browserPollTimer: null,
// ── Trader Dashboard State ────────────────────────────────────────────
dashboardOpen: false,
dashboardLoading: false,
dashboardData: null,
_dashboardInst: null,
_chartEquity: null,
_chartPnl: null,
_chartRadar: null,
// ── Setup Wizard State ──────────────────────────────────────────────
setupWizard: null,
setupStep: 1,
setupLoading: false,
setupChecking: false,
clipboardMsg: null,
_clipboardTimer: null,
detectedPlatform: 'linux',
installPlatforms: {},
apiKeyInputs: {},
async loadData() {
this.loading = true;
this.loadError = '';
try {
var data = await OpenFangAPI.get('/api/hands');
this.hands = data.hands || [];
} catch(e) {
this.hands = [];
this.loadError = e.message || 'Could not load hands.';
}
this.loading = false;
},
async loadActive() {
this.activeLoading = true;
try {
var data = await OpenFangAPI.get('/api/hands/active');
this.instances = (data.instances || []).map(function(i) {
i._stats = null;
return i;
});
} catch(e) {
this.instances = [];
}
this.activeLoading = false;
},
getHandIcon(handId) {
for (var i = 0; i < this.hands.length; i++) {
if (this.hands[i].id === handId) return this.hands[i].icon;
}
return '\u{1F91A}';
},
async showDetail(handId) {
try {
var data = await OpenFangAPI.get('/api/hands/' + handId);
this.detailHand = data;
} catch(e) {
for (var i = 0; i < this.hands.length; i++) {
if (this.hands[i].id === handId) {
this.detailHand = this.hands[i];
break;
}
}
}
},
// ── Setup Wizard ────────────────────────────────────────────────────
async activate(handId) {
this.openSetupWizard(handId);
},
async openSetupWizard(handId) {
this.setupLoading = true;
this.setupWizard = null;
try {
var data = await OpenFangAPI.get('/api/hands/' + handId);
// Pre-populate settings defaults
this.settingsValues = {};
if (data.settings && data.settings.length > 0) {
for (var i = 0; i < data.settings.length; i++) {
var s = data.settings[i];
this.settingsValues[s.key] = s.default || '';
}
}
// Detect platform from server response, fallback to client-side
if (data.server_platform) {
this.detectedPlatform = data.server_platform;
} else {
this._detectClientPlatform();
}
// Initialize per-requirement platform selections and API key inputs
this.installPlatforms = {};
this.apiKeyInputs = {};
if (data.requirements) {
for (var j = 0; j < data.requirements.length; j++) {
this.installPlatforms[data.requirements[j].key] = this.detectedPlatform;
if (data.requirements[j].type === 'ApiKey') {
this.apiKeyInputs[data.requirements[j].key] = '';
}
}
}
// Initialize optional instance name (for multi-instance hands).
data.instanceName = '';
this.setupWizard = data;
// Skip deps step if no requirements
var hasReqs = data.requirements && data.requirements.length > 0;
this.setupStep = hasReqs ? 1 : 2;
} catch(e) {
this.showToast('Could not load hand details: ' + (e.message || 'unknown error'));
}
this.setupLoading = false;
},
_detectClientPlatform() {
var ua = (navigator.userAgent || '').toLowerCase();
if (ua.indexOf('mac') !== -1) {
this.detectedPlatform = 'macos';
} else if (ua.indexOf('win') !== -1) {
this.detectedPlatform = 'windows';
} else {
this.detectedPlatform = 'linux';
}
},
// ── Auto-Install Dependencies ───────────────────────────────────
installProgress: null, // null = idle, object = { status, current, total, results, error }
async installDeps() {
if (!this.setupWizard) return;
var handId = this.setupWizard.id;
var missing = (this.setupWizard.requirements || []).filter(function(r) { return !r.satisfied; });
if (missing.length === 0) {
this.showToast('All dependencies already installed!');
return;
}
this.installProgress = {
status: 'installing',
current: 0,
total: missing.length,
currentLabel: missing[0] ? missing[0].label : '',
results: [],
error: null
};
try {
var data = await OpenFangAPI.post('/api/hands/' + handId + '/install-deps', {});
var results = data.results || [];
this.installProgress.results = results;
this.installProgress.current = results.length;
this.installProgress.status = 'done';
// Update requirements from server response
if (data.requirements && this.setupWizard.requirements) {
for (var i = 0; i < this.setupWizard.requirements.length; i++) {
var existing = this.setupWizard.requirements[i];
for (var j = 0; j < data.requirements.length; j++) {
if (data.requirements[j].key === existing.key) {
existing.satisfied = data.requirements[j].satisfied;
break;
}
}
}
this.setupWizard.requirements_met = data.requirements_met;
}
var installed = results.filter(function(r) { return r.status === 'installed' || r.status === 'already_installed'; }).length;
var failed = results.filter(function(r) { return r.status === 'error' || r.status === 'timeout'; }).length;
if (data.requirements_met) {
this.showToast('All dependencies installed successfully!');
// Auto-advance to step 2 after a short delay
var self = this;
setTimeout(function() {
self.installProgress = null;
self.setupNextStep();
}, 1500);
} else if (failed > 0) {
this.installProgress.error = failed + ' dependency(ies) failed to install. Check the details below.';
}
} catch(e) {
this.installProgress = {
status: 'error',
current: 0,
total: missing.length,
currentLabel: '',
results: [],
error: e.message || 'Installation request failed'
};
}
},
getInstallResultIcon(status) {
if (status === 'installed' || status === 'already_installed') return '\u2713';
if (status === 'error' || status === 'timeout') return '\u2717';
return '\u2022';
},
getInstallResultClass(status) {
if (status === 'installed' || status === 'already_installed') return 'dep-met';
if (status === 'error' || status === 'timeout') return 'dep-missing';
return '';
},
async recheckDeps() {
if (!this.setupWizard) return;
this.setupChecking = true;
try {
var data = await OpenFangAPI.post('/api/hands/' + this.setupWizard.id + '/check-deps', {});
if (data.requirements && this.setupWizard.requirements) {
for (var i = 0; i < this.setupWizard.requirements.length; i++) {
var existing = this.setupWizard.requirements[i];
for (var j = 0; j < data.requirements.length; j++) {
if (data.requirements[j].key === existing.key) {
existing.satisfied = data.requirements[j].satisfied;
break;
}
}
}
this.setupWizard.requirements_met = data.requirements_met;
}
if (data.requirements_met) {
this.showToast('All dependencies satisfied!');
}
} catch(e) {
this.showToast('Check failed: ' + (e.message || 'unknown'));
}
this.setupChecking = false;
},
getInstallCmd(req) {
if (!req || !req.install) return null;
var inst = req.install;
var plat = this.installPlatforms[req.key] || this.detectedPlatform;
if (plat === 'macos' && inst.macos) return inst.macos;
if (plat === 'windows' && inst.windows) return inst.windows;
if (plat === 'linux') {
return inst.linux_apt || inst.linux_dnf || inst.linux_pacman || inst.pip || null;
}
return inst.pip || inst.macos || inst.windows || inst.linux_apt || null;
},
getLinuxVariant(req) {
if (!req || !req.install) return null;
var inst = req.install;
var plat = this.installPlatforms[req.key] || this.detectedPlatform;
if (plat !== 'linux') return null;
// Return all available Linux variants
var variants = [];
if (inst.linux_apt) variants.push({ label: 'apt', cmd: inst.linux_apt });
if (inst.linux_dnf) variants.push({ label: 'dnf', cmd: inst.linux_dnf });
if (inst.linux_pacman) variants.push({ label: 'pacman', cmd: inst.linux_pacman });
if (inst.pip) variants.push({ label: 'pip', cmd: inst.pip });
return variants.length > 1 ? variants : null;
},
copyToClipboard(text) {
var self = this;
navigator.clipboard.writeText(text).then(function() {
self.clipboardMsg = text;
if (self._clipboardTimer) clearTimeout(self._clipboardTimer);
self._clipboardTimer = setTimeout(function() { self.clipboardMsg = null; }, 2000);
});
},
get setupReqsMet() {
if (!this.setupWizard || !this.setupWizard.requirements) return 0;
var count = 0;
for (var i = 0; i < this.setupWizard.requirements.length; i++) {
var req = this.setupWizard.requirements[i];
if (req.satisfied) { count++; continue; }
// Count API key reqs as met if user entered a value
if (req.type === 'ApiKey' && this.apiKeyInputs[req.key] && this.apiKeyInputs[req.key].trim() !== '') count++;
}
return count;
},
get setupReqsTotal() {
if (!this.setupWizard || !this.setupWizard.requirements) return 0;
return this.setupWizard.requirements.length;
},
get setupAllReqsMet() {
if (!this.setupWizard || !this.setupWizard.requirements) return false;
if (this.setupReqsTotal === 0) return false;
for (var i = 0; i < this.setupWizard.requirements.length; i++) {
var req = this.setupWizard.requirements[i];
if (req.satisfied) continue;
// API key reqs are satisfied if the user entered a value in the input
if (req.type === 'ApiKey' && this.apiKeyInputs[req.key] && this.apiKeyInputs[req.key].trim() !== '') continue;
return false;
}
return true;
},
getSettingKeyForReq(req) {
// Find the matching setting key for an API key requirement.
// Convention: setting key is the lowercase version of the requirement key.
if (!this.setupWizard || !this.setupWizard.settings) return null;
var lowerKey = req.key.toLowerCase();
for (var i = 0; i < this.setupWizard.settings.length; i++) {
if (this.setupWizard.settings[i].key === lowerKey) return lowerKey;
}
// Fallback: try matching by check_value lowercased
if (req.check_value) {
var lowerCheck = req.check_value.toLowerCase();
for (var j = 0; j < this.setupWizard.settings.length; j++) {
if (this.setupWizard.settings[j].key === lowerCheck) return lowerCheck;
}
}
return null;
},
get setupHasReqs() {
return this.setupReqsTotal > 0;
},
get setupHasSettings() {
return this.setupWizard && this.setupWizard.settings && this.setupWizard.settings.length > 0;
},
setupNextStep() {
// When leaving step 1, sync API key inputs into settings values
if (this.setupStep === 1) {
this._syncApiKeysToSettings();
}
if (this.setupStep === 1 && this.setupHasSettings) {
this.setupStep = 2;
} else if (this.setupStep === 1) {
this.setupStep = 3;
} else if (this.setupStep === 2) {
this.setupStep = 3;
}
},
_syncApiKeysToSettings() {
if (!this.setupWizard || !this.setupWizard.requirements) return;
for (var i = 0; i < this.setupWizard.requirements.length; i++) {
var req = this.setupWizard.requirements[i];
if (req.type === 'ApiKey' && this.apiKeyInputs[req.key] && this.apiKeyInputs[req.key].trim() !== '') {
var settingKey = this.getSettingKeyForReq(req);
if (settingKey) {
this.settingsValues[settingKey] = this.apiKeyInputs[req.key].trim();
}
}
}
},
setupPrevStep() {
if (this.setupStep === 3 && this.setupHasSettings) {
this.setupStep = 2;
} else if (this.setupStep === 3) {
this.setupStep = this.setupHasReqs ? 1 : 2;
} else if (this.setupStep === 2 && this.setupHasReqs) {
this.setupStep = 1;
}
},
closeSetupWizard() {
this.setupWizard = null;
this.setupStep = 1;
this.setupLoading = false;
this.setupChecking = false;
this.clipboardMsg = null;
this.installPlatforms = {};
this.apiKeyInputs = {};
},
async launchHand() {
if (!this.setupWizard) return;
var handId = this.setupWizard.id;
// Sync API key inputs from step 1 into settings values
if (this.setupWizard.requirements) {
for (var i = 0; i < this.setupWizard.requirements.length; i++) {
var req = this.setupWizard.requirements[i];
if (req.type === 'ApiKey' && this.apiKeyInputs[req.key] && this.apiKeyInputs[req.key].trim() !== '') {
var settingKey = this.getSettingKeyForReq(req);
if (settingKey) {
this.settingsValues[settingKey] = this.apiKeyInputs[req.key].trim();
}
}
}
}
var config = {};
for (var key in this.settingsValues) {
config[key] = this.settingsValues[key];
}
this.activatingId = handId;
try {
var payload = { config: config };
var name = (this.setupWizard.instanceName || '').trim();
if (name) {
payload.instance_name = name;
}
var data = await OpenFangAPI.post('/api/hands/' + handId + '/activate', payload);
var label = data.instance_name || data.agent_name || data.instance_id;
this.showToast('Hand "' + handId + '" activated as ' + label);
this.closeSetupWizard();
await this.loadActive();
this.tab = 'active';
} catch(e) {
this.showToast('Activation failed: ' + (e.message || 'unknown error'));
}
this.activatingId = null;
},
selectOption(settingKey, value) {
this.settingsValues[settingKey] = value;
},
getSettingDisplayValue(setting) {
var val = this.settingsValues[setting.key] || setting.default || '';
if (setting.setting_type === 'toggle') {
return val === 'true' ? 'Enabled' : 'Disabled';
}
if (setting.setting_type === 'select' && setting.options) {
for (var i = 0; i < setting.options.length; i++) {
if (setting.options[i].value === val) return setting.options[i].label;
}
}
return val || '-';
},
// ── Existing methods ────────────────────────────────────────────────
async pauseHand(inst) {
try {
await OpenFangAPI.post('/api/hands/instances/' + inst.instance_id + '/pause', {});
inst.status = 'Paused';
} catch(e) {
this.showToast('Pause failed: ' + (e.message || 'unknown error'));
}
},
async resumeHand(inst) {
try {
await OpenFangAPI.post('/api/hands/instances/' + inst.instance_id + '/resume', {});
inst.status = 'Active';
} catch(e) {
this.showToast('Resume failed: ' + (e.message || 'unknown error'));
}
},
async deactivate(inst) {
var self = this;
var handName = inst.agent_name || inst.hand_id;
OpenFangToast.confirm('Deactivate Hand', 'Deactivate hand "' + handName + '"? This will kill its agent.', async function() {
try {
await OpenFangAPI.delete('/api/hands/instances/' + inst.instance_id);
self.instances = self.instances.filter(function(i) { return i.instance_id !== inst.instance_id; });
OpenFangToast.success('Hand deactivated.');
} catch(e) {
OpenFangToast.error('Deactivation failed: ' + (e.message || 'unknown error'));
}
});
},
async loadStats(inst) {
try {
var data = await OpenFangAPI.get('/api/hands/instances/' + inst.instance_id + '/stats');
inst._stats = data.metrics || {};
} catch(e) {
inst._stats = { 'Error': { value: e.message || 'Could not load stats', format: 'text' } };
}
},
formatMetric(m) {
if (!m || m.value === null || m.value === undefined) return '-';
if (m.format === 'duration') {
var secs = parseInt(m.value, 10);
if (isNaN(secs)) return String(m.value);
var h = Math.floor(secs / 3600);
var min = Math.floor((secs % 3600) / 60);
var s = secs % 60;
if (h > 0) return h + 'h ' + min + 'm';
if (min > 0) return min + 'm ' + s + 's';
return s + 's';
}
if (m.format === 'number') {
var n = parseFloat(m.value);
if (isNaN(n)) return String(m.value);
return n.toLocaleString();
}
return String(m.value);
},
showToast(msg) {
var self = this;
this.activateResult = msg;
if (this._toastTimer) clearTimeout(this._toastTimer);
this._toastTimer = setTimeout(function() { self.activateResult = null; }, 4000);
},
// ── Browser Viewer ───────────────────────────────────────────────────
isBrowserHand(inst) {
return inst.hand_id === 'browser';
},
async openBrowserViewer(inst) {
this.browserViewer = {
instance_id: inst.instance_id,
hand_id: inst.hand_id,
agent_name: inst.agent_name,
url: '',
title: '',
screenshot: '',
content: '',
loading: true,
error: ''
};
this.browserViewerOpen = true;
await this.refreshBrowserView();
this.startBrowserPolling();
},
async refreshBrowserView() {
if (!this.browserViewer) return;
var id = this.browserViewer.instance_id;
try {
var data = await OpenFangAPI.get('/api/hands/instances/' + id + '/browser');
if (data.active) {
this.browserViewer.url = data.url || '';
this.browserViewer.title = data.title || '';
this.browserViewer.screenshot = data.screenshot_base64 || '';
this.browserViewer.content = data.content || '';
this.browserViewer.error = '';
} else {
this.browserViewer.error = 'No active browser session';
this.browserViewer.screenshot = '';
}
} catch(e) {
this.browserViewer.error = e.message || 'Could not load browser state';
}
this.browserViewer.loading = false;
},
startBrowserPolling() {
var self = this;
this.stopBrowserPolling();
this._browserPollTimer = setInterval(function() {
if (self.browserViewerOpen) {
self.refreshBrowserView();
} else {
self.stopBrowserPolling();
}
}, 3000);
},
stopBrowserPolling() {
if (this._browserPollTimer) {
clearInterval(this._browserPollTimer);
this._browserPollTimer = null;
}
},
closeBrowserViewer() {
this.stopBrowserPolling();
this.browserViewerOpen = false;
this.browserViewer = null;
},
// ── Trader Dashboard ──────────────────────────────────────────────────
isTraderHand(inst) {
return inst.hand_id === 'trader';
},
async openDashboard(inst) {
this._dashboardInst = inst;
this.dashboardOpen = true;
this.dashboardLoading = true;
this.dashboardData = null;
await this._fetchDashboardData(inst);
this.dashboardLoading = false;
// Render charts after DOM update
var self = this;
setTimeout(function() { self._renderCharts(); }, 60);
},
async refreshDashboard() {
if (!this._dashboardInst) return;
this.dashboardLoading = true;
await this._fetchDashboardData(this._dashboardInst);
this.dashboardLoading = false;
var self = this;
setTimeout(function() { self._renderCharts(); }, 60);
},
closeDashboard() {
this.dashboardOpen = false;
this._destroyCharts();
this.dashboardData = null;
this._dashboardInst = null;
},
async _fetchDashboardData(inst) {
var data = {
agent_name: inst.agent_name || inst.hand_id,
portfolio_value: null,
total_pnl: null,
win_rate: null,
sharpe_ratio: null,
max_drawdown: null,
trades_count: null,
equity_curve: [],
daily_pnl: [],
watchlist_heatmap: [],
signal_radar: null,
recent_trades: []
};
// Fetch basic stats from the hand stats endpoint
try {
var stats = await OpenFangAPI.get('/api/hands/instances/' + inst.instance_id + '/stats');
var m = stats.metrics || {};
if (m['Portfolio Value']) data.portfolio_value = this._metricVal(m['Portfolio Value']);
if (m['Total P&L']) data.total_pnl = this._metricVal(m['Total P&L']);
if (m['Win Rate']) data.win_rate = this._metricVal(m['Win Rate']);
if (m['Sharpe Ratio']) data.sharpe_ratio = this._metricVal(m['Sharpe Ratio']);
if (m['Max Drawdown']) data.max_drawdown = this._metricVal(m['Max Drawdown']);
if (m['Trades Executed']) data.trades_count = this._metricVal(m['Trades Executed']);
} catch(e) {
// Stats endpoint might fail — continue with KV data
}
// Fetch rich chart data from agent memory KV
var agentId = inst.agent_id || 'shared';
var kvKeys = [
'trader_hand_equity_curve',
'trader_hand_daily_pnl',
'trader_hand_watchlist_heatmap',
'trader_hand_signal_radar',
'trader_hand_recent_trades',
'trader_hand_portfolio_value',
'trader_hand_total_pnl',
'trader_hand_win_rate',
'trader_hand_sharpe_ratio',
'trader_hand_max_drawdown',
'trader_hand_trades_count'
];
for (var i = 0; i < kvKeys.length; i++) {
try {
var resp = await OpenFangAPI.get('/api/memory/agents/' + agentId + '/kv/' + kvKeys[i]);
if (resp && resp.value !== null && resp.value !== undefined) {
var val = resp.value;
this._applyKvToData(data, kvKeys[i], val);
}
} catch(e) {
// Key might not exist yet — that's fine
}
}
this.dashboardData = data;
},
_metricVal(metric) {
if (!metric) return null;
var v = metric.value;
if (v === null || v === undefined) return null;
// Values come as JSON values — could be string, number, etc.
if (typeof v === 'string') return v;
return String(v);
},
_applyKvToData(data, key, val) {
// Values from KV can be strings (JSON-encoded) or already parsed
var parsed = val;
if (typeof val === 'string') {
try { parsed = JSON.parse(val); } catch(e) { parsed = val; }
}
switch(key) {
case 'trader_hand_portfolio_value':
if (!data.portfolio_value) data.portfolio_value = String(parsed);
break;
case 'trader_hand_total_pnl':
if (!data.total_pnl) data.total_pnl = String(parsed);
break;
case 'trader_hand_win_rate':
if (!data.win_rate) data.win_rate = String(parsed);
break;
case 'trader_hand_sharpe_ratio':
if (!data.sharpe_ratio) data.sharpe_ratio = String(parsed);
break;
case 'trader_hand_max_drawdown':
if (!data.max_drawdown) data.max_drawdown = String(parsed);
break;
case 'trader_hand_trades_count':
if (!data.trades_count) data.trades_count = String(parsed);
break;
case 'trader_hand_equity_curve':
if (Array.isArray(parsed)) data.equity_curve = parsed;
break;
case 'trader_hand_daily_pnl':
if (Array.isArray(parsed)) data.daily_pnl = parsed;
break;
case 'trader_hand_watchlist_heatmap':
if (Array.isArray(parsed)) data.watchlist_heatmap = parsed;
break;
case 'trader_hand_signal_radar':
if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) data.signal_radar = parsed;
break;
case 'trader_hand_recent_trades':
if (Array.isArray(parsed)) data.recent_trades = parsed;
break;
}
},
_destroyCharts() {
if (this._chartEquity) { this._chartEquity.destroy(); this._chartEquity = null; }
if (this._chartPnl) { this._chartPnl.destroy(); this._chartPnl = null; }
if (this._chartRadar) { this._chartRadar.destroy(); this._chartRadar = null; }
},
_renderCharts() {
if (typeof Chart === 'undefined') return;
this._destroyCharts();
if (!this.dashboardData) return;
var d = this.dashboardData;
// Detect theme
var isDark = document.documentElement.getAttribute('data-theme') === 'dark' ||
(!document.documentElement.getAttribute('data-theme') && window.matchMedia('(prefers-color-scheme: dark)').matches);
var gridColor = isDark ? 'rgba(255,255,255,0.08)' : 'rgba(0,0,0,0.08)';
var textColor = isDark ? '#8A8380' : '#6B6560';
var accentColor = '#FF5C00';
var successColor = isDark ? '#4ADE80' : '#22C55E';
var errorColor = '#EF4444';
// ── Equity Curve ──
if (d.equity_curve && d.equity_curve.length > 0) {
var eqCanvas = document.getElementById('traderEquityChart');
if (eqCanvas) {
var labels = [];
var values = [];
for (var i = 0; i < d.equity_curve.length; i++) {
labels.push(d.equity_curve[i].date || '');
values.push(parseFloat(d.equity_curve[i].value) || 0);
}
// Determine gradient
var eqCtx = eqCanvas.getContext('2d');
var gradient = eqCtx.createLinearGradient(0, 0, 0, eqCanvas.parentElement.clientHeight || 180);
gradient.addColorStop(0, isDark ? 'rgba(255, 92, 0, 0.25)' : 'rgba(255, 92, 0, 0.15)');
gradient.addColorStop(1, 'rgba(255, 92, 0, 0)');
this._chartEquity = new Chart(eqCtx, {
type: 'line',
data: {
labels: labels,
datasets: [{
data: values,
borderColor: accentColor,
backgroundColor: gradient,
borderWidth: 2,
fill: true,
tension: 0.3,
pointRadius: d.equity_curve.length > 20 ? 0 : 3,
pointHoverRadius: 5,
pointBackgroundColor: accentColor
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
interaction: { mode: 'index', intersect: false },
plugins: {
legend: { display: false },
tooltip: {
backgroundColor: isDark ? '#1a1a1a' : '#fff',
titleColor: textColor,
bodyColor: isDark ? '#e0e0e0' : '#333',
borderColor: gridColor,
borderWidth: 1,
padding: 10,
callbacks: {
label: function(ctx) {
return '$' + ctx.parsed.y.toLocaleString(undefined, {minimumFractionDigits: 2, maximumFractionDigits: 2});
}
}
}
},
scales: {
x: {
grid: { color: gridColor },
ticks: { color: textColor, maxTicksLimit: 8, font: { size: 10 } }
},
y: {
grid: { color: gridColor },
ticks: {
color: textColor,
font: { size: 10 },
callback: function(v) { return '$' + v.toLocaleString(); }
}
}
}
}
});
}
}
// ── Daily P&L Bar Chart ──
if (d.daily_pnl && d.daily_pnl.length > 0) {
var pnlCanvas = document.getElementById('traderPnlChart');
if (pnlCanvas) {
var pnlLabels = [];
var pnlValues = [];
var pnlColors = [];
for (var j = 0; j < d.daily_pnl.length; j++) {
pnlLabels.push(d.daily_pnl[j].date || '');
var pnlVal = parseFloat(d.daily_pnl[j].pnl) || 0;
pnlValues.push(pnlVal);
pnlColors.push(pnlVal >= 0 ? successColor : errorColor);
}
this._chartPnl = new Chart(pnlCanvas.getContext('2d'), {
type: 'bar',
data: {
labels: pnlLabels,
datasets: [{
data: pnlValues,
backgroundColor: pnlColors,
borderRadius: 3,
borderSkipped: false
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
plugins: {
legend: { display: false },
tooltip: {
backgroundColor: isDark ? '#1a1a1a' : '#fff',
titleColor: textColor,
bodyColor: isDark ? '#e0e0e0' : '#333',
borderColor: gridColor,
borderWidth: 1,
padding: 10,
callbacks: {
label: function(ctx) {
var v = ctx.parsed.y;
return (v >= 0 ? '+$' : '-$') + Math.abs(v).toLocaleString(undefined, {minimumFractionDigits: 2, maximumFractionDigits: 2});
}
}
}
},
scales: {
x: {
grid: { display: false },
ticks: { color: textColor, maxTicksLimit: 7, font: { size: 10 } }
},
y: {
grid: { color: gridColor },
ticks: {
color: textColor,
font: { size: 10 },
callback: function(v) {
return (v >= 0 ? '+$' : '-$') + Math.abs(v).toLocaleString();
}
}
}
}
}
});
}
}
// ── Signal Radar Chart ──
if (d.signal_radar) {
var radarCanvas = document.getElementById('traderRadarChart');
if (radarCanvas) {
var radarLabels = [];
var radarValues = [];
var keys = ['technical', 'fundamental', 'sentiment', 'macro'];
var displayLabels = ['Technical', 'Fundamental', 'Sentiment', 'Macro'];
for (var k = 0; k < keys.length; k++) {
radarLabels.push(displayLabels[k]);
radarValues.push(parseFloat(d.signal_radar[keys[k]]) || 0);
}
this._chartRadar = new Chart(radarCanvas.getContext('2d'), {
type: 'radar',
data: {
labels: radarLabels,
datasets: [{
data: radarValues,
borderColor: accentColor,
backgroundColor: isDark ? 'rgba(255, 92, 0, 0.2)' : 'rgba(255, 92, 0, 0.12)',
borderWidth: 2,
pointBackgroundColor: accentColor,
pointRadius: 4,
pointHoverRadius: 6
}]
},
options: {
responsive: true,
maintainAspectRatio: true,
plugins: {
legend: { display: false },
tooltip: {
backgroundColor: isDark ? '#1a1a1a' : '#fff',
titleColor: textColor,
bodyColor: isDark ? '#e0e0e0' : '#333',
borderColor: gridColor,
borderWidth: 1,
padding: 10,
callbacks: {
label: function(ctx) { return ctx.parsed.r + '/100'; }
}
}
},
scales: {
r: {
min: 0,
max: 100,
beginAtZero: true,
grid: { color: gridColor },
angleLines: { color: gridColor },
pointLabels: {
color: textColor,
font: { size: 11, weight: '600' }
},
ticks: {
color: textColor,
backdropColor: 'transparent',
stepSize: 25,
font: { size: 9 }
}
}
}
}
});
}
}
}
};
}
crates/openfang-api/static/js/pages/logs.js
+255
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// OpenFang Logs Page — Real-time log viewer (SSE streaming + polling fallback) + Audit Trail tab
'use strict';
function logsPage() {
return {
tab: 'live',
// -- Live logs state --
entries: [],
levelFilter: '',
textFilter: '',
autoRefresh: true,
hovering: false,
loading: true,
loadError: '',
_pollTimer: null,
// -- SSE streaming state --
_eventSource: null,
streamConnected: false,
streamPaused: false,
// -- Audit state --
auditEntries: [],
tipHash: '',
chainValid: null,
filterAction: '',
auditLoading: false,
auditLoadError: '',
startStreaming: function() {
var self = this;
if (this._eventSource) { this._eventSource.close(); this._eventSource = null; }
var url = '/api/logs/stream';
var sep = '?';
var token = OpenFangAPI.getToken();
if (token) { url += sep + 'token=' + encodeURIComponent(token); sep = '&'; }
try {
this._eventSource = new EventSource(url);
} catch(e) {
// EventSource not supported or blocked; fall back to polling
this.streamConnected = false;
this.startPolling();
return;
}
this._eventSource.onopen = function() {
self.streamConnected = true;
self.loading = false;
self.loadError = '';
};
this._eventSource.onmessage = function(event) {
if (self.streamPaused) return;
try {
var entry = JSON.parse(event.data);
// Avoid duplicate entries by checking seq
var dominated = false;
for (var i = 0; i < self.entries.length; i++) {
if (self.entries[i].seq === entry.seq) { dominated = true; break; }
}
if (!dominated) {
self.entries.push(entry);
// Cap at 500 entries (remove oldest)
if (self.entries.length > 500) {
self.entries.splice(0, self.entries.length - 500);
}
// Auto-scroll to bottom
if (self.autoRefresh && !self.hovering) {
self.$nextTick(function() {
var el = document.getElementById('log-container');
if (el) el.scrollTop = el.scrollHeight;
});
}
}
} catch(e) {
// Ignore parse errors (heartbeat comments are not delivered to onmessage)
}
};
this._eventSource.onerror = function() {
self.streamConnected = false;
if (self._eventSource) {
self._eventSource.close();
self._eventSource = null;
}
// Fall back to polling
self.startPolling();
};
},
startPolling: function() {
var self = this;
this.streamConnected = false;
this.fetchLogs();
if (this._pollTimer) clearInterval(this._pollTimer);
this._pollTimer = setInterval(function() {
if (self.autoRefresh && !self.hovering && self.tab === 'live' && !self.streamPaused) {
self.fetchLogs();
}
}, 2000);
},
async fetchLogs() {
if (this.loading) this.loadError = '';
try {
var data = await OpenFangAPI.get('/api/audit/recent?n=200');
this.entries = data.entries || [];
if (this.autoRefresh && !this.hovering) {
this.$nextTick(function() {
var el = document.getElementById('log-container');
if (el) el.scrollTop = el.scrollHeight;
});
}
if (this.loading) this.loading = false;
} catch(e) {
if (this.loading) {
this.loadError = e.message || 'Could not load logs.';
this.loading = false;
}
}
},
async loadData() {
this.loading = true;
return this.fetchLogs();
},
togglePause: function() {
this.streamPaused = !this.streamPaused;
if (!this.streamPaused && this.streamConnected) {
// Resume: scroll to bottom
var self = this;
this.$nextTick(function() {
var el = document.getElementById('log-container');
if (el) el.scrollTop = el.scrollHeight;
});
}
},
clearLogs: function() {
this.entries = [];
},
classifyLevel: function(action) {
if (!action) return 'info';
var a = action.toLowerCase();
if (a.indexOf('error') !== -1 || a.indexOf('fail') !== -1 || a.indexOf('crash') !== -1) return 'error';
if (a.indexOf('warn') !== -1 || a.indexOf('deny') !== -1 || a.indexOf('block') !== -1) return 'warn';
return 'info';
},
get filteredEntries() {
var self = this;
var levelF = this.levelFilter;
var textF = this.textFilter.toLowerCase();
return this.entries.filter(function(e) {
if (levelF && self.classifyLevel(e.action) !== levelF) return false;
if (textF) {
var haystack = ((e.action || '') + ' ' + (e.detail || '') + ' ' + (e.agent_id || '')).toLowerCase();
if (haystack.indexOf(textF) === -1) return false;
}
return true;
});
},
get connectionLabel() {
if (this.streamPaused) return 'Paused';
if (this.streamConnected) return 'Live';
if (this._pollTimer) return 'Polling';
return 'Disconnected';
},
get connectionClass() {
if (this.streamPaused) return 'paused';
if (this.streamConnected) return 'live';
if (this._pollTimer) return 'polling';
return 'disconnected';
},
exportLogs: function() {
var lines = this.filteredEntries.map(function(e) {
return new Date(e.timestamp).toISOString() + ' [' + e.action + '] ' + (e.detail || '');
});
var blob = new Blob([lines.join('\n')], { type: 'text/plain' });
var url = URL.createObjectURL(blob);
var a = document.createElement('a');
a.href = url;
a.download = 'openfang-logs-' + new Date().toISOString().slice(0, 10) + '.txt';
a.click();
URL.revokeObjectURL(url);
},
// -- Audit methods --
get filteredAuditEntries() {
var self = this;
if (!self.filterAction) return self.auditEntries;
return self.auditEntries.filter(function(e) { return e.action === self.filterAction; });
},
async loadAudit() {
this.auditLoading = true;
this.auditLoadError = '';
try {
var data = await OpenFangAPI.get('/api/audit/recent?n=200');
this.auditEntries = data.entries || [];
this.tipHash = data.tip_hash || '';
} catch(e) {
this.auditEntries = [];
this.auditLoadError = e.message || 'Could not load audit log.';
}
this.auditLoading = false;
},
auditAgentName: function(agentId) {
if (!agentId) return '-';
var agents = Alpine.store('app').agents || [];
var agent = agents.find(function(a) { return a.id === agentId; });
return agent ? agent.name : agentId.substring(0, 8) + '...';
},
friendlyAction: function(action) {
if (!action) return 'Unknown';
var map = {
'AgentSpawn': 'Agent Created', 'AgentKill': 'Agent Stopped', 'AgentTerminated': 'Agent Stopped',
'ToolInvoke': 'Tool Used', 'ToolResult': 'Tool Completed', 'AgentMessage': 'Message',
'NetworkAccess': 'Network Access', 'ShellExec': 'Shell Command', 'FileAccess': 'File Access',
'MemoryAccess': 'Memory Access', 'AuthAttempt': 'Login Attempt', 'AuthSuccess': 'Login Success',
'AuthFailure': 'Login Failed', 'CapabilityDenied': 'Permission Denied', 'RateLimited': 'Rate Limited'
};
return map[action] || action.replace(/([A-Z])/g, ' $1').trim();
},
async verifyChain() {
try {
var data = await OpenFangAPI.get('/api/audit/verify');
this.chainValid = data.valid === true;
if (this.chainValid) {
OpenFangToast.success('Audit chain verified — ' + (data.entries || 0) + ' entries valid');
} else {
OpenFangToast.error('Audit chain broken!');
}
} catch(e) {
this.chainValid = false;
OpenFangToast.error('Chain verification failed: ' + e.message);
}
},
destroy: function() {
if (this._eventSource) { this._eventSource.close(); this._eventSource = null; }
if (this._pollTimer) { clearInterval(this._pollTimer); this._pollTimer = null; }
}
};
}
crates/openfang-api/static/js/pages/overview.js
+292
View File
@@ -0,0 +1,292 @@
// OpenFang Overview Dashboard — Landing page with system stats + provider status
'use strict';
function overviewPage() {
return {
health: {},
status: {},
usageSummary: {},
recentAudit: [],
channels: [],
providers: [],
mcpServers: [],
skillCount: 0,
loading: true,
loadError: '',
refreshTimer: null,
lastRefresh: null,
async loadOverview() {
this.loading = true;
this.loadError = '';
try {
await Promise.all([
this.loadHealth(),
this.loadStatus(),
this.loadUsage(),
this.loadAudit(),
this.loadChannels(),
this.loadProviders(),
this.loadMcpServers(),
this.loadSkills()
]);
this.lastRefresh = Date.now();
} catch(e) {
this.loadError = e.message || 'Could not load overview data.';
}
this.loading = false;
},
async loadData() { return this.loadOverview(); },
// Silent background refresh (no loading spinner)
async silentRefresh() {
try {
await Promise.all([
this.loadHealth(),
this.loadStatus(),
this.loadUsage(),
this.loadAudit(),
this.loadChannels(),
this.loadProviders(),
this.loadMcpServers(),
this.loadSkills()
]);
this.lastRefresh = Date.now();
} catch(e) { /* silent */ }
},
startAutoRefresh() {
this.stopAutoRefresh();
this.refreshTimer = setInterval(() => this.silentRefresh(), 30000);
},
stopAutoRefresh() {
if (this.refreshTimer) {
clearInterval(this.refreshTimer);
this.refreshTimer = null;
}
},
async loadHealth() {
try {
this.health = await OpenFangAPI.get('/api/health');
} catch(e) { this.health = { status: 'unreachable' }; }
},
async loadStatus() {
try {
this.status = await OpenFangAPI.get('/api/status');
} catch(e) { this.status = {}; throw e; }
},
async loadUsage() {
try {
var data = await OpenFangAPI.get('/api/usage');
var agents = data.agents || [];
var totalTokens = 0;
var totalTools = 0;
var totalCost = 0;
agents.forEach(function(a) {
totalTokens += (a.total_tokens || 0);
totalTools += (a.tool_calls || 0);
totalCost += (a.cost_usd || 0);
});
this.usageSummary = {
total_tokens: totalTokens,
total_tools: totalTools,
total_cost: totalCost,
agent_count: agents.length
};
} catch(e) {
this.usageSummary = { total_tokens: 0, total_tools: 0, total_cost: 0, agent_count: 0 };
}
},
async loadAudit() {
try {
var data = await OpenFangAPI.get('/api/audit/recent?n=8');
this.recentAudit = data.entries || [];
} catch(e) { this.recentAudit = []; }
},
async loadChannels() {
try {
var data = await OpenFangAPI.get('/api/channels');
this.channels = (data.channels || []).filter(function(ch) { return ch.has_token; });
} catch(e) { this.channels = []; }
},
async loadProviders() {
try {
var data = await OpenFangAPI.get('/api/providers');
this.providers = data.providers || [];
} catch(e) { this.providers = []; }
},
async loadMcpServers() {
try {
var data = await OpenFangAPI.get('/api/mcp/servers');
this.mcpServers = data.servers || [];
} catch(e) { this.mcpServers = []; }
},
async loadSkills() {
try {
var data = await OpenFangAPI.get('/api/skills');
this.skillCount = (data.skills || []).length;
} catch(e) { this.skillCount = 0; }
},
get configuredProviders() {
return this.providers.filter(function(p) { return p.auth_status === 'configured'; });
},
get unconfiguredProviders() {
return this.providers.filter(function(p) { return p.auth_status === 'not_set' || p.auth_status === 'missing'; });
},
get connectedMcp() {
return this.mcpServers.filter(function(s) { return s.status === 'connected'; });
},
// Provider health badge color
providerBadgeClass(p) {
if (p.auth_status === 'configured') {
if (p.health === 'cooldown' || p.health === 'open') return 'badge-warn';
return 'badge-success';
}
if (p.auth_status === 'not_set' || p.auth_status === 'missing') return 'badge-muted';
return 'badge-dim';
},
// Provider health tooltip
providerTooltip(p) {
if (p.health === 'cooldown') return p.display_name + ' \u2014 cooling down (rate limited)';
if (p.health === 'open') return p.display_name + ' \u2014 circuit breaker open';
if (p.auth_status === 'configured') return p.display_name + ' \u2014 ready';
return p.display_name + ' \u2014 not configured';
},
// Audit action badge color
actionBadgeClass(action) {
if (!action) return 'badge-dim';
if (action === 'AgentSpawn' || action === 'AuthSuccess') return 'badge-success';
if (action === 'AgentKill' || action === 'AgentTerminated' || action === 'AuthFailure' || action === 'CapabilityDenied') return 'badge-error';
if (action === 'RateLimited' || action === 'ToolInvoke') return 'badge-warn';
return 'badge-created';
},
// ── Setup Checklist ──
checklistDismissed: localStorage.getItem('of-checklist-dismissed') === 'true',
get setupChecklist() {
return [
{ key: 'provider', label: 'Configure an LLM provider', done: this.configuredProviders.length > 0, action: '#settings' },
{ key: 'agent', label: 'Create your first agent', done: (Alpine.store('app').agents || []).length > 0, action: '#agents' },
{ key: 'chat', label: 'Send your first message', done: localStorage.getItem('of-first-msg') === 'true', action: '#chat' },
{ key: 'channel', label: 'Connect a messaging channel', done: this.channels.length > 0, action: '#channels' },
{ key: 'skill', label: 'Browse or install a skill', done: localStorage.getItem('of-skill-browsed') === 'true', action: '#skills' }
];
},
get setupProgress() {
var done = this.setupChecklist.filter(function(item) { return item.done; }).length;
return (done / 5) * 100;
},
get setupDoneCount() {
return this.setupChecklist.filter(function(item) { return item.done; }).length;
},
dismissChecklist() {
this.checklistDismissed = true;
localStorage.setItem('of-checklist-dismissed', 'true');
},
formatUptime(secs) {
if (!secs) return '-';
var d = Math.floor(secs / 86400);
var h = Math.floor((secs % 86400) / 3600);
var m = Math.floor((secs % 3600) / 60);
if (d > 0) return d + 'd ' + h + 'h';
if (h > 0) return h + 'h ' + m + 'm';
return m + 'm';
},
formatNumber(n) {
if (!n) return '0';
if (n >= 1000000) return (n / 1000000).toFixed(1) + 'M';
if (n >= 1000) return (n / 1000).toFixed(1) + 'K';
return String(n);
},
formatCost(n) {
if (!n || n === 0) return '$0.00';
if (n < 0.01) return '<$0.01';
return '$' + n.toFixed(2);
},
// Relative time formatting ("2m ago", "1h ago", "just now")
timeAgo(timestamp) {
if (!timestamp) return '';
var now = Date.now();
var ts = new Date(timestamp).getTime();
var diff = Math.floor((now - ts) / 1000);
if (diff < 10) return 'just now';
if (diff < 60) return diff + 's ago';
if (diff < 3600) return Math.floor(diff / 60) + 'm ago';
if (diff < 86400) return Math.floor(diff / 3600) + 'h ago';
return Math.floor(diff / 86400) + 'd ago';
},
// Map raw audit action names to user-friendly labels
friendlyAction(action) {
if (!action) return 'Unknown';
var map = {
'AgentSpawn': 'Agent Created',
'AgentKill': 'Agent Stopped',
'AgentTerminated': 'Agent Stopped',
'ToolInvoke': 'Tool Used',
'ToolResult': 'Tool Completed',
'MessageReceived': 'Message In',
'MessageSent': 'Response Sent',
'SessionReset': 'Session Reset',
'SessionCompact': 'Compacted',
'ModelSwitch': 'Model Changed',
'AuthAttempt': 'Login Attempt',
'AuthSuccess': 'Login OK',
'AuthFailure': 'Login Failed',
'CapabilityDenied': 'Denied',
'RateLimited': 'Rate Limited',
'WorkflowRun': 'Workflow Run',
'TriggerFired': 'Trigger Fired',
'SkillInstalled': 'Skill Installed',
'McpConnected': 'MCP Connected'
};
return map[action] || action.replace(/([A-Z])/g, ' $1').trim();
},
// Audit action icon (small inline SVG)
actionIcon(action) {
if (!action) return '';
var icons = {
'AgentSpawn': '<svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><circle cx="12" cy="12" r="10"/><path d="M12 8v8M8 12h8"/></svg>',
'AgentKill': '<svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><circle cx="12" cy="12" r="10"/><path d="M15 9l-6 6M9 9l6 6"/></svg>',
'AgentTerminated': '<svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><circle cx="12" cy="12" r="10"/><path d="M15 9l-6 6M9 9l6 6"/></svg>',
'ToolInvoke': '<svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M14.7 6.3a1 1 0 0 0 0 1.4l1.6 1.6a1 1 0 0 0 1.4 0l3.77-3.77a6 6 0 0 1-7.94 7.94l-6.91 6.91a2.12 2.12 0 0 1-3-3l6.91-6.91a6 6 0 0 1 7.94-7.94l-3.76 3.76z"/></svg>',
'MessageReceived': '<svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z"/></svg>',
'MessageSent': '<svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M22 2L11 13M22 2l-7 20-4-9-9-4 20-7z"/></svg>'
};
return icons[action] || '<svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><circle cx="12" cy="12" r="10"/></svg>';
},
// Resolve agent UUID to name if possible
agentName(agentId) {
if (!agentId) return '-';
var agents = Alpine.store('app').agents || [];
var agent = agents.find(function(a) { return a.id === agentId; });
return agent ? agent.name : agentId.substring(0, 8) + '\u2026';
}
};
}
crates/openfang-api/static/js/pages/runtime.js
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// Runtime page — system overview and provider status
document.addEventListener('alpine:init', function() {
Alpine.data('runtimePage', function() {
return {
loading: true,
uptime: '-',
agentCount: 0,
version: '-',
defaultModel: '-',
platform: '-',
arch: '-',
apiListen: '-',
homeDir: '-',
logLevel: '-',
networkEnabled: false,
providers: [],
async loadData() {
this.loading = true;
try {
var results = await Promise.all([
OpenFangAPI.get('/api/status'),
OpenFangAPI.get('/api/version'),
OpenFangAPI.get('/api/providers'),
OpenFangAPI.get('/api/agents')
]);
var status = results[0];
var ver = results[1];
var prov = results[2];
var agents = results[3];
this.version = ver.version || '-';
this.platform = ver.platform || '-';
this.arch = ver.arch || '-';
this.agentCount = Array.isArray(agents) ? agents.length : 0;
this.defaultModel = status.default_model || '-';
this.apiListen = status.api_listen || status.listen || '-';
this.homeDir = status.home_dir || '-';
this.logLevel = status.log_level || '-';
this.networkEnabled = !!status.network_enabled;
// Compute uptime from uptime_seconds
var diff = status.uptime_seconds || 0;
if (diff < 60) this.uptime = diff + 's';
else if (diff < 3600) this.uptime = Math.floor(diff / 60) + 'm ' + (diff % 60) + 's';
else if (diff < 86400) this.uptime = Math.floor(diff / 3600) + 'h ' + Math.floor((diff % 3600) / 60) + 'm';
else this.uptime = Math.floor(diff / 86400) + 'd ' + Math.floor((diff % 86400) / 3600) + 'h';
this.providers = (prov.providers || []).filter(function(p) {
return p.auth_status === 'Configured' || p.reachable || p.is_local;
});
} catch(e) {
console.error('Runtime load error:', e);
}
this.loading = false;
}
};
});
});
crates/openfang-api/static/js/pages/scheduler.js
+649
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// OpenFang Scheduler Page — Cron job management + event triggers unified view
'use strict';
function schedulerPage() {
return {
tab: 'jobs',
// -- Scheduled Jobs state --
jobs: [],
loading: true,
loadError: '',
// -- Event Triggers state --
triggers: [],
trigLoading: false,
trigLoadError: '',
// -- Run History state --
history: [],
historyLoading: false,
// -- Create Job form --
showCreateForm: false,
newJob: {
name: '',
cron: '',
agent_id: '',
message: '',
enabled: true,
delivery_targets: []
},
creating: false,
// -- Run Now state --
runningJobId: '',
// -- Delivery targets picker (create modal) --
showTargetPicker: false,
pickerType: 'channel',
draftTarget: null,
// -- Expanded job / delivery log state --
expandedJobId: '',
deliveryLog: { targets: [], entries: [] },
deliveryLogLoading: false,
deliveryLogError: '',
// -- Edit targets state (per-existing-job) --
editingTargetsJobId: '',
editingTargets: [],
savingTargets: false,
// -- Available channel types (populated from /api/channels) --
channelTypes: [],
// Cron presets
cronPresets: [
{ label: 'Every minute', cron: '* * * * *' },
{ label: 'Every 5 minutes', cron: '*/5 * * * *' },
{ label: 'Every 15 minutes', cron: '*/15 * * * *' },
{ label: 'Every 30 minutes', cron: '*/30 * * * *' },
{ label: 'Every hour', cron: '0 * * * *' },
{ label: 'Every 6 hours', cron: '0 */6 * * *' },
{ label: 'Daily at midnight', cron: '0 0 * * *' },
{ label: 'Daily at 9am', cron: '0 9 * * *' },
{ label: 'Weekdays at 9am', cron: '0 9 * * 1-5' },
{ label: 'Every Monday 9am', cron: '0 9 * * 1' },
{ label: 'First of month', cron: '0 0 1 * *' }
],
// ── Lifecycle ──
async loadData() {
this.loading = true;
this.loadError = '';
try {
await this.loadJobs();
// Channels are optional — failure is non-fatal for the scheduler page.
this.loadChannelTypes();
} catch(e) {
this.loadError = e.message || 'Could not load scheduler data.';
}
this.loading = false;
},
async loadChannelTypes() {
try {
var data = await OpenFangAPI.get('/api/channels');
// /api/channels returns an array of channel descriptors; pull names.
var list = Array.isArray(data) ? data : (data && data.channels) || [];
var names = [];
for (var i = 0; i < list.length; i++) {
var ch = list[i];
var name = ch && (ch.name || ch.display_name || ch.channel_type);
if (name && names.indexOf(name) === -1) names.push(name);
}
this.channelTypes = names;
} catch(e) {
// Fall through silently — the form uses a plain input as fallback.
this.channelTypes = [];
}
},
async loadJobs() {
var data = await OpenFangAPI.get('/api/cron/jobs');
var raw = data.jobs || [];
// Normalize cron API response to flat fields the UI expects
this.jobs = raw.map(function(j) {
var cron = '';
if (j.schedule) {
if (j.schedule.kind === 'cron') cron = j.schedule.expr || '';
else if (j.schedule.kind === 'every') cron = 'every ' + j.schedule.every_secs + 's';
else if (j.schedule.kind === 'at') cron = 'at ' + (j.schedule.at || '');
}
return {
id: j.id,
name: j.name,
cron: cron,
agent_id: j.agent_id,
message: j.action ? j.action.message || '' : '',
enabled: j.enabled,
last_run: j.last_run,
next_run: j.next_run,
delivery: j.delivery ? j.delivery.kind || '' : '',
delivery_targets: Array.isArray(j.delivery_targets) ? j.delivery_targets : [],
created_at: j.created_at
};
});
},
async loadTriggers() {
this.trigLoading = true;
this.trigLoadError = '';
try {
var data = await OpenFangAPI.get('/api/triggers');
this.triggers = Array.isArray(data) ? data : [];
} catch(e) {
this.triggers = [];
this.trigLoadError = e.message || 'Could not load triggers.';
}
this.trigLoading = false;
},
async loadHistory() {
this.historyLoading = true;
try {
var historyItems = [];
var jobs = this.jobs || [];
for (var i = 0; i < jobs.length; i++) {
var job = jobs[i];
if (job.last_run) {
historyItems.push({
timestamp: job.last_run,
name: job.name || '(unnamed)',
type: 'schedule',
status: 'completed',
run_count: 0
});
}
}
var triggers = this.triggers || [];
for (var j = 0; j < triggers.length; j++) {
var t = triggers[j];
if (t.fire_count > 0) {
historyItems.push({
timestamp: t.created_at,
name: 'Trigger: ' + this.triggerType(t.pattern),
type: 'trigger',
status: 'fired',
run_count: t.fire_count
});
}
}
historyItems.sort(function(a, b) {
return new Date(b.timestamp).getTime() - new Date(a.timestamp).getTime();
});
this.history = historyItems;
} catch(e) {
this.history = [];
}
this.historyLoading = false;
},
// ── Job CRUD ──
async createJob() {
if (!this.newJob.name.trim()) {
OpenFangToast.warn('Please enter a job name');
return;
}
if (!this.newJob.cron.trim()) {
OpenFangToast.warn('Please enter a cron expression');
return;
}
this.creating = true;
try {
var jobName = this.newJob.name;
var body = {
agent_id: this.newJob.agent_id,
name: this.newJob.name,
schedule: { kind: 'cron', expr: this.newJob.cron },
action: { kind: 'agent_turn', message: this.newJob.message || 'Scheduled task: ' + this.newJob.name },
delivery: { kind: 'last_channel' },
enabled: this.newJob.enabled
};
if (this.newJob.delivery_targets && this.newJob.delivery_targets.length) {
body.delivery_targets = this.newJob.delivery_targets.map(this.sanitizeTarget);
}
await OpenFangAPI.post('/api/cron/jobs', body);
this.showCreateForm = false;
this.newJob = { name: '', cron: '', agent_id: '', message: '', enabled: true, delivery_targets: [] };
OpenFangToast.success('Schedule "' + jobName + '" created');
await this.loadJobs();
} catch(e) {
OpenFangToast.error('Failed to create schedule: ' + (e.message || e));
}
this.creating = false;
},
async toggleJob(job) {
try {
var newState = !job.enabled;
await OpenFangAPI.put('/api/cron/jobs/' + job.id + '/enable', { enabled: newState });
job.enabled = newState;
OpenFangToast.success('Schedule ' + (newState ? 'enabled' : 'paused'));
} catch(e) {
OpenFangToast.error('Failed to toggle schedule: ' + (e.message || e));
}
},
deleteJob(job) {
var self = this;
var jobName = job.name || job.id;
OpenFangToast.confirm('Delete Schedule', 'Delete "' + jobName + '"? This cannot be undone.', async function() {
try {
await OpenFangAPI.del('/api/cron/jobs/' + job.id);
self.jobs = self.jobs.filter(function(j) { return j.id !== job.id; });
OpenFangToast.success('Schedule "' + jobName + '" deleted');
} catch(e) {
OpenFangToast.error('Failed to delete schedule: ' + (e.message || e));
}
});
},
async runNow(job) {
this.runningJobId = job.id;
try {
var result = await OpenFangAPI.post('/api/cron/jobs/' + job.id + '/run', {});
if (result.status === 'triggered' || result.status === 'completed') {
OpenFangToast.success('Job "' + (job.name || 'job') + '" triggered');
// Don't update job.last_run here — the job runs asynchronously in the
// background. The real last_run is set by the server on completion and
// will appear on the next data refresh.
} else {
OpenFangToast.error('Run failed: ' + (result.error || 'Unknown error'));
}
} catch(e) {
OpenFangToast.error('Run failed: ' + (e.message || e));
}
this.runningJobId = '';
},
// ── Delivery target editing (create modal) ──
openTargetPicker() {
this.pickerType = 'channel';
this.draftTarget = this.blankTarget('channel');
this.showTargetPicker = true;
},
cancelTargetPicker() {
this.showTargetPicker = false;
this.draftTarget = null;
},
onPickerTypeChange() {
this.draftTarget = this.blankTarget(this.pickerType);
},
blankTarget(type) {
if (type === 'channel') {
return { type: 'channel', channel_type: '', recipient: '' };
}
if (type === 'webhook') {
return { type: 'webhook', url: '', auth_header: '' };
}
if (type === 'local_file') {
return { type: 'local_file', path: '', append: false };
}
if (type === 'email') {
return { type: 'email', to: '', subject_template: '' };
}
return null;
},
addDraftTarget() {
var err = this.validateTarget(this.draftTarget);
if (err) {
OpenFangToast.warn(err);
return;
}
if (!Array.isArray(this.newJob.delivery_targets)) this.newJob.delivery_targets = [];
this.newJob.delivery_targets.push(this.sanitizeTarget(this.draftTarget));
this.showTargetPicker = false;
this.draftTarget = null;
},
removeTarget(idx) {
if (!Array.isArray(this.newJob.delivery_targets)) return;
this.newJob.delivery_targets.splice(idx, 1);
},
validateTarget(t) {
if (!t || !t.type) return 'Pick a target type';
if (t.type === 'channel') {
if (!t.channel_type || !t.channel_type.trim()) return 'Channel type is required';
if (!t.recipient || !t.recipient.trim()) return 'Recipient is required';
} else if (t.type === 'webhook') {
if (!t.url || !t.url.trim()) return 'Webhook URL is required';
if (t.url.indexOf('http://') !== 0 && t.url.indexOf('https://') !== 0) {
return 'Webhook URL must start with http:// or https://';
}
} else if (t.type === 'local_file') {
if (!t.path || !t.path.trim()) return 'File path is required';
} else if (t.type === 'email') {
if (!t.to || !t.to.trim()) return 'Recipient email is required';
}
return null;
},
// Strip empty-string optional fields so serde accepts the payload cleanly.
sanitizeTarget(t) {
if (!t) return null;
var out = { type: t.type };
if (t.type === 'channel') {
out.channel_type = (t.channel_type || '').trim();
out.recipient = (t.recipient || '').trim();
} else if (t.type === 'webhook') {
out.url = (t.url || '').trim();
if (t.auth_header && t.auth_header.trim()) out.auth_header = t.auth_header.trim();
} else if (t.type === 'local_file') {
out.path = (t.path || '').trim();
out.append = !!t.append;
} else if (t.type === 'email') {
out.to = (t.to || '').trim();
if (t.subject_template && t.subject_template.trim()) {
out.subject_template = t.subject_template.trim();
}
}
return out;
},
// ── Chip rendering helpers ──
targetChipLabel(t) {
if (!t || !t.type) return '?';
if (t.type === 'channel') return 'CHANNEL: ' + (t.channel_type || '?');
if (t.type === 'webhook') return 'WEBHOOK';
if (t.type === 'local_file') return 'FILE: ' + this.truncate(t.path || '', 28);
if (t.type === 'email') return 'EMAIL: ' + this.truncate(t.to || '', 24);
return t.type.toUpperCase();
},
targetChipClass(t) {
if (!t || !t.type) return 'badge-dim';
if (t.type === 'channel') return 'badge-info';
if (t.type === 'webhook') return 'badge-created';
if (t.type === 'local_file') return 'badge-muted';
if (t.type === 'email') return 'badge-warn';
return 'badge-dim';
},
targetSummary(t) {
if (!t) return '';
if (t.type === 'channel') return (t.channel_type || '?') + ' -> ' + (t.recipient || '?');
if (t.type === 'webhook') return t.url || '(no url)';
if (t.type === 'local_file') return (t.append ? 'append ' : 'overwrite ') + (t.path || '');
if (t.type === 'email') {
var base = t.to || '';
if (t.subject_template) base += ' · subject: ' + t.subject_template;
return base;
}
return JSON.stringify(t);
},
// ── Expand row / delivery log ──
async toggleExpand(job) {
if (this.expandedJobId === job.id) {
this.expandedJobId = '';
return;
}
this.expandedJobId = job.id;
this.deliveryLog = { targets: [], entries: [] };
this.deliveryLogError = '';
this.deliveryLogLoading = true;
try {
var data = await OpenFangAPI.get('/api/schedules/' + job.id + '/delivery-log');
this.deliveryLog = {
targets: Array.isArray(data.targets) ? data.targets : [],
entries: Array.isArray(data.entries) ? data.entries : []
};
} catch(e) {
this.deliveryLogError = e.message || 'Could not load delivery log.';
}
this.deliveryLogLoading = false;
},
// ── Edit targets on existing job ──
startEditTargets(job) {
this.editingTargetsJobId = job.id;
// Clone so cancel doesn't mutate the loaded list.
this.editingTargets = (job.delivery_targets || []).map(function(t) {
return JSON.parse(JSON.stringify(t));
});
this.pickerType = 'channel';
this.draftTarget = null;
this.showTargetPicker = false;
},
cancelEditTargets() {
this.editingTargetsJobId = '';
this.editingTargets = [];
this.draftTarget = null;
this.showTargetPicker = false;
},
addEditTarget() {
this.pickerType = 'channel';
this.draftTarget = this.blankTarget('channel');
this.showTargetPicker = true;
},
addDraftTargetToEdit() {
var err = this.validateTarget(this.draftTarget);
if (err) {
OpenFangToast.warn(err);
return;
}
this.editingTargets.push(this.sanitizeTarget(this.draftTarget));
this.showTargetPicker = false;
this.draftTarget = null;
},
removeEditTarget(idx) {
this.editingTargets.splice(idx, 1);
},
async saveEditTargets() {
if (!this.editingTargetsJobId) return;
this.savingTargets = true;
try {
var clean = this.editingTargets.map(this.sanitizeTarget);
await OpenFangAPI.put('/api/schedules/' + this.editingTargetsJobId, {
delivery_targets: clean
});
OpenFangToast.success('Delivery targets updated');
this.cancelEditTargets();
await this.loadJobs();
} catch(e) {
OpenFangToast.error('Failed to update targets: ' + (e.message || e));
}
this.savingTargets = false;
},
// ── Trigger helpers ──
triggerType(pattern) {
if (!pattern) return 'unknown';
if (typeof pattern === 'string') return pattern;
var keys = Object.keys(pattern);
if (keys.length === 0) return 'unknown';
var key = keys[0];
var names = {
lifecycle: 'Lifecycle',
agent_spawned: 'Agent Spawned',
agent_terminated: 'Agent Terminated',
system: 'System',
system_keyword: 'System Keyword',
memory_update: 'Memory Update',
memory_key_pattern: 'Memory Key',
all: 'All Events',
content_match: 'Content Match'
};
return names[key] || key.replace(/_/g, ' ');
},
async toggleTrigger(trigger) {
try {
var newState = !trigger.enabled;
await OpenFangAPI.put('/api/triggers/' + trigger.id, { enabled: newState });
trigger.enabled = newState;
OpenFangToast.success('Trigger ' + (newState ? 'enabled' : 'disabled'));
} catch(e) {
OpenFangToast.error('Failed to toggle trigger: ' + (e.message || e));
}
},
deleteTrigger(trigger) {
var self = this;
OpenFangToast.confirm('Delete Trigger', 'Delete this trigger? This cannot be undone.', async function() {
try {
await OpenFangAPI.del('/api/triggers/' + trigger.id);
self.triggers = self.triggers.filter(function(t) { return t.id !== trigger.id; });
OpenFangToast.success('Trigger deleted');
} catch(e) {
OpenFangToast.error('Failed to delete trigger: ' + (e.message || e));
}
});
},
// ── Utility ──
get availableAgents() {
return Alpine.store('app').agents || [];
},
agentName(agentId) {
if (!agentId) return '(any)';
var agents = this.availableAgents;
for (var i = 0; i < agents.length; i++) {
if (agents[i].id === agentId) return agents[i].name;
}
if (agentId.length > 12) return agentId.substring(0, 8) + '...';
return agentId;
},
describeCron(expr) {
if (!expr) return '';
// Handle non-cron schedule descriptions
if (expr.indexOf('every ') === 0) return expr;
if (expr.indexOf('at ') === 0) return 'One-time: ' + expr.substring(3);
var map = {
'* * * * *': 'Every minute',
'*/2 * * * *': 'Every 2 minutes',
'*/5 * * * *': 'Every 5 minutes',
'*/10 * * * *': 'Every 10 minutes',
'*/15 * * * *': 'Every 15 minutes',
'*/30 * * * *': 'Every 30 minutes',
'0 * * * *': 'Every hour',
'0 */2 * * *': 'Every 2 hours',
'0 */4 * * *': 'Every 4 hours',
'0 */6 * * *': 'Every 6 hours',
'0 */12 * * *': 'Every 12 hours',
'0 0 * * *': 'Daily at midnight',
'0 6 * * *': 'Daily at 6:00 AM',
'0 9 * * *': 'Daily at 9:00 AM',
'0 12 * * *': 'Daily at noon',
'0 18 * * *': 'Daily at 6:00 PM',
'0 9 * * 1-5': 'Weekdays at 9:00 AM',
'0 9 * * 1': 'Mondays at 9:00 AM',
'0 0 * * 0': 'Sundays at midnight',
'0 0 1 * *': '1st of every month',
'0 0 * * 1': 'Mondays at midnight'
};
if (map[expr]) return map[expr];
var parts = expr.split(' ');
if (parts.length !== 5) return expr;
var min = parts[0];
var hour = parts[1];
var dom = parts[2];
var mon = parts[3];
var dow = parts[4];
if (min.indexOf('*/') === 0 && hour === '*' && dom === '*' && mon === '*' && dow === '*') {
return 'Every ' + min.substring(2) + ' minutes';
}
if (min === '0' && hour.indexOf('*/') === 0 && dom === '*' && mon === '*' && dow === '*') {
return 'Every ' + hour.substring(2) + ' hours';
}
var dowNames = { '0': 'Sun', '1': 'Mon', '2': 'Tue', '3': 'Wed', '4': 'Thu', '5': 'Fri', '6': 'Sat', '7': 'Sun',
'1-5': 'Weekdays', '0,6': 'Weekends', '6,0': 'Weekends' };
if (dom === '*' && mon === '*' && min.match(/^\d+$/) && hour.match(/^\d+$/)) {
var h = parseInt(hour, 10);
var m = parseInt(min, 10);
var ampm = h >= 12 ? 'PM' : 'AM';
var h12 = h === 0 ? 12 : (h > 12 ? h - 12 : h);
var mStr = m < 10 ? '0' + m : '' + m;
var timeStr = h12 + ':' + mStr + ' ' + ampm;
if (dow === '*') return 'Daily at ' + timeStr;
var dowLabel = dowNames[dow] || ('DoW ' + dow);
return dowLabel + ' at ' + timeStr;
}
return expr;
},
applyCronPreset(preset) {
this.newJob.cron = preset.cron;
},
formatTime(ts) {
if (!ts) return '-';
try {
var d = new Date(ts);
if (isNaN(d.getTime())) return '-';
return d.toLocaleString();
} catch(e) { return '-'; }
},
relativeTime(ts) {
if (!ts) return 'never';
try {
var diff = Date.now() - new Date(ts).getTime();
if (isNaN(diff)) return 'never';
if (diff < 0) {
// Future time
var absDiff = Math.abs(diff);
if (absDiff < 60000) return 'in <1m';
if (absDiff < 3600000) return 'in ' + Math.floor(absDiff / 60000) + 'm';
if (absDiff < 86400000) return 'in ' + Math.floor(absDiff / 3600000) + 'h';
return 'in ' + Math.floor(absDiff / 86400000) + 'd';
}
if (diff < 60000) return 'just now';
if (diff < 3600000) return Math.floor(diff / 60000) + 'm ago';
if (diff < 86400000) return Math.floor(diff / 3600000) + 'h ago';
return Math.floor(diff / 86400000) + 'd ago';
} catch(e) { return 'never'; }
},
truncate(s, n) {
if (!s) return '';
if (s.length <= n) return s;
return s.substring(0, n - 1) + '…';
},
jobCount() {
var enabled = 0;
for (var i = 0; i < this.jobs.length; i++) {
if (this.jobs[i].enabled) enabled++;
}
return enabled;
},
triggerCount() {
var enabled = 0;
for (var i = 0; i < this.triggers.length; i++) {
if (this.triggers[i].enabled) enabled++;
}
return enabled;
}
};
}
crates/openfang-api/static/js/pages/sessions.js
+157
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@@ -0,0 +1,157 @@
// OpenFang Sessions Page — Session listing + Memory tab
'use strict';
function sessionsPage() {
return {
tab: 'sessions',
// -- Sessions state --
sessions: [],
searchFilter: '',
loading: true,
loadError: '',
// -- Memory state --
memAgentId: '',
kvPairs: [],
showAdd: false,
newKey: '',
newValue: '""',
editingKey: null,
editingValue: '',
memLoading: false,
memLoadError: '',
// -- Sessions methods --
async loadSessions() {
this.loading = true;
this.loadError = '';
try {
var data = await OpenFangAPI.get('/api/sessions');
var sessions = data.sessions || [];
var agents = Alpine.store('app').agents;
var agentMap = {};
agents.forEach(function(a) { agentMap[a.id] = a.name; });
sessions.forEach(function(s) {
s.agent_name = agentMap[s.agent_id] || '';
});
this.sessions = sessions;
} catch(e) {
this.sessions = [];
this.loadError = e.message || 'Could not load sessions.';
}
this.loading = false;
},
async loadData() { return this.loadSessions(); },
get filteredSessions() {
var f = this.searchFilter.toLowerCase();
if (!f) return this.sessions;
return this.sessions.filter(function(s) {
return (s.agent_name || '').toLowerCase().indexOf(f) !== -1 ||
(s.agent_id || '').toLowerCase().indexOf(f) !== -1;
});
},
openInChat(session) {
var agents = Alpine.store('app').agents;
var agent = agents.find(function(a) { return a.id === session.agent_id; });
if (agent) {
Alpine.store('app').pendingAgent = agent;
}
location.hash = 'agents';
},
deleteSession(sessionId) {
var self = this;
var t = window.i18n ? window.i18n.t.bind(window.i18n) : function(k) { return k; };
OpenFangToast.confirm(
t('sessions.delete_session') || 'Delete Session',
t('sessions.delete_confirm') || 'This will permanently remove the session and its messages.',
async function() {
try {
await OpenFangAPI.del('/api/sessions/' + sessionId);
self.sessions = self.sessions.filter(function(s) { return s.session_id !== sessionId; });
OpenFangToast.success('Session deleted');
} catch(e) {
OpenFangToast.error('Failed to delete session: ' + e.message);
}
}
);
},
// -- Memory methods --
async loadKv() {
if (!this.memAgentId) { this.kvPairs = []; return; }
this.memLoading = true;
this.memLoadError = '';
try {
var data = await OpenFangAPI.get('/api/memory/agents/' + this.memAgentId + '/kv');
this.kvPairs = data.kv_pairs || [];
} catch(e) {
this.kvPairs = [];
this.memLoadError = e.message || 'Could not load memory data.';
}
this.memLoading = false;
},
async addKey() {
if (!this.memAgentId || !this.newKey.trim()) return;
var value;
try { value = JSON.parse(this.newValue); } catch(e) { value = this.newValue; }
try {
await OpenFangAPI.put('/api/memory/agents/' + this.memAgentId + '/kv/' + encodeURIComponent(this.newKey), { value: value });
this.showAdd = false;
OpenFangToast.success('Key "' + this.newKey + '" saved');
this.newKey = '';
this.newValue = '""';
await this.loadKv();
} catch(e) {
OpenFangToast.error('Failed to save key: ' + e.message);
}
},
deleteKey(key) {
var self = this;
var t = window.i18n ? window.i18n.t.bind(window.i18n) : function(k) { return k; };
OpenFangToast.confirm(
t('sessions.delete_key') || 'Delete Key',
(t('sessions.delete_key_confirm') || 'Delete key') + ' "' + key + '"? This cannot be undone.',
async function() {
try {
await OpenFangAPI.del('/api/memory/agents/' + self.memAgentId + '/kv/' + encodeURIComponent(key));
OpenFangToast.success('Key "' + key + '" deleted');
await self.loadKv();
} catch(e) {
OpenFangToast.error('Failed to delete key: ' + e.message);
}
}
);
},
startEdit(kv) {
this.editingKey = kv.key;
this.editingValue = typeof kv.value === 'object' ? JSON.stringify(kv.value, null, 2) : String(kv.value);
},
cancelEdit() {
this.editingKey = null;
this.editingValue = '';
},
async saveEdit() {
if (!this.editingKey || !this.memAgentId) return;
var value;
try { value = JSON.parse(this.editingValue); } catch(e) { value = this.editingValue; }
try {
await OpenFangAPI.put('/api/memory/agents/' + this.memAgentId + '/kv/' + encodeURIComponent(this.editingKey), { value: value });
OpenFangToast.success('Key "' + this.editingKey + '" updated');
this.editingKey = null;
this.editingValue = '';
await this.loadKv();
} catch(e) {
OpenFangToast.error('Failed to save: ' + e.message);
}
}
};
}
crates/openfang-api/static/js/pages/settings.js
+819
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@@ -0,0 +1,819 @@
// OpenFang Settings Page — Provider Hub, Model Catalog, Config, Tools + Security, Network, Migration tabs
'use strict';
function settingsPage() {
return {
tab: 'providers',
sysInfo: {},
usageData: [],
tools: [],
config: {},
providers: [],
models: [],
toolSearch: '',
modelSearch: '',
modelProviderFilter: '',
modelTierFilter: '',
showCustomModelForm: false,
customModelId: '',
customModelProvider: 'openrouter',
customModelContext: 128000,
customModelMaxOutput: 8192,
customModelStatus: '',
providerKeyInputs: {},
providerUrlInputs: {},
providerUrlSaving: {},
providerTesting: {},
providerTestResults: {},
providerSearch: '',
providerStatusFilter: '',
providerCategoryFilter: '',
copilotOAuth: { polling: false, userCode: '', verificationUri: '', pollId: '', interval: 5 },
customProviderName: '',
customProviderUrl: '',
customProviderKey: '',
customProviderStatus: '',
addingCustomProvider: false,
loading: true,
loadError: '',
// -- Dynamic config state --
configSchema: null,
configValues: {},
configDirty: {},
configSaving: {},
// -- Security state --
securityData: null,
secLoading: false,
verifyingChain: false,
chainResult: null,
coreFeatures: [
{
name: 'Path Traversal Prevention', key: 'path_traversal',
description: 'Blocks directory escape attacks (../) in all file operations. Two-phase validation: syntactic rejection of path components, then canonicalization to normalize symlinks.',
threat: 'Directory escape, privilege escalation via symlinks',
impl: 'host_functions.rs — safe_resolve_path() + safe_resolve_parent()'
},
{
name: 'SSRF Protection', key: 'ssrf_protection',
description: 'Blocks outbound requests to private IPs, localhost, and cloud metadata endpoints (AWS/GCP/Azure). Validates DNS resolution results to defeat rebinding attacks.',
threat: 'Internal network reconnaissance, cloud credential theft',
impl: 'host_functions.rs — is_ssrf_target() + is_private_ip()'
},
{
name: 'Capability-Based Access Control', key: 'capability_system',
description: 'Deny-by-default permission system. Every agent operation (file I/O, network, shell, memory, spawn) requires an explicit capability grant in the manifest.',
threat: 'Unauthorized resource access, sandbox escape',
impl: 'host_functions.rs — check_capability() on every host function'
},
{
name: 'Privilege Escalation Prevention', key: 'privilege_escalation_prevention',
description: 'When a parent agent spawns a child, the kernel enforces child capabilities are a subset of parent capabilities. No agent can grant rights it does not have.',
threat: 'Capability escalation through agent spawning chains',
impl: 'kernel_handle.rs — spawn_agent_checked()'
},
{
name: 'Subprocess Environment Isolation', key: 'subprocess_isolation',
description: 'Child processes (shell tools) inherit only a safe allow-list of environment variables. API keys, database passwords, and secrets are never leaked to subprocesses.',
threat: 'Secret exfiltration via child process environment',
impl: 'subprocess_sandbox.rs — env_clear() + SAFE_ENV_VARS'
},
{
name: 'Security Headers', key: 'security_headers',
description: 'Every HTTP response includes CSP, X-Frame-Options: DENY, X-Content-Type-Options: nosniff, Referrer-Policy, and X-XSS-Protection headers.',
threat: 'XSS, clickjacking, MIME sniffing, content injection',
impl: 'middleware.rs — security_headers()'
},
{
name: 'Wire Protocol Authentication', key: 'wire_hmac_auth',
description: 'Agent-to-agent OFP connections use HMAC-SHA256 mutual authentication with nonce-based handshake and constant-time signature comparison (subtle crate).',
threat: 'Man-in-the-middle attacks on mesh network',
impl: 'peer.rs — hmac_sign() + hmac_verify()'
},
{
name: 'Request ID Tracking', key: 'request_id_tracking',
description: 'Every API request receives a unique UUID (x-request-id header) and is logged with method, path, status code, and latency for full traceability.',
threat: 'Untraceable actions, forensic blind spots',
impl: 'middleware.rs — request_logging()'
}
],
configurableFeatures: [
{
name: 'API Rate Limiting', key: 'rate_limiter',
description: 'GCRA (Generic Cell Rate Algorithm) with cost-aware tokens. Different endpoints cost different amounts — spawning an agent costs 50 tokens, health check costs 1.',
configHint: 'Hard-coded: 500 tokens/minute per IP. Edit rate_limiter.rs to tune.',
valueKey: 'rate_limiter'
},
{
name: 'WebSocket Connection Limits', key: 'websocket_limits',
description: 'Per-IP connection cap prevents connection exhaustion. Idle timeout closes abandoned connections. Message rate limiting prevents flooding.',
configHint: 'Hard-coded: 5 connections/IP, 30min idle timeout, 64KB max message. Edit ws.rs to tune.',
valueKey: 'websocket_limits'
},
{
name: 'WASM Dual Metering', key: 'wasm_sandbox',
description: 'WASM modules run with two independent resource limits: fuel metering (CPU instruction count) and epoch interruption (wall-clock timeout with watchdog thread).',
configHint: 'Default: 1M fuel units, 30s timeout. Configurable per-agent via SandboxConfig.',
valueKey: 'wasm_sandbox'
},
{
name: 'Bearer Token Authentication', key: 'auth',
description: 'All non-health endpoints require Authorization: Bearer header. When no API key is configured, all requests are restricted to localhost only.',
configHint: 'Set api_key in ~/.openfang/config.toml for remote access. Empty = localhost only.',
valueKey: 'auth'
}
],
monitoringFeatures: [
{
name: 'Merkle Audit Trail', key: 'audit_trail',
description: 'Every security-critical action is appended to an immutable, tamper-evident log. Each entry is cryptographically linked to the previous via SHA-256 hash chain.',
configHint: 'Always active. Verify chain integrity from the Audit Log page.',
valueKey: 'audit_trail'
},
{
name: 'Information Flow Taint Tracking', key: 'taint_tracking',
description: 'Labels data by provenance (ExternalNetwork, UserInput, PII, Secret, UntrustedAgent) and blocks unsafe flows: external data cannot reach shell_exec, secrets cannot reach network.',
configHint: 'Always active. Prevents data flow attacks automatically.',
valueKey: 'taint_tracking'
},
{
name: 'Ed25519 Manifest Signing', key: 'manifest_signing',
description: 'Agent manifests can be cryptographically signed with Ed25519. Verify manifest integrity before loading to prevent supply chain tampering.',
configHint: 'Available for use. Sign manifests with ed25519-dalek for verification.',
valueKey: 'manifest_signing'
}
],
// -- Peers state --
peers: [],
peersLoading: false,
peersLoadError: '',
_peerPollTimer: null,
// -- Migration state --
migStep: 'intro',
detecting: false,
scanning: false,
migrating: false,
sourcePath: '',
targetPath: '',
scanResult: null,
migResult: null,
// -- Settings load --
async loadSettings() {
this.loading = true;
this.loadError = '';
try {
await Promise.all([
this.loadSysInfo(),
this.loadUsage(),
this.loadTools(),
this.loadConfig(),
this.loadProviders(),
this.loadModels()
]);
} catch(e) {
this.loadError = e.message || 'Could not load settings.';
}
this.loading = false;
},
async loadData() { return this.loadSettings(); },
async loadSysInfo() {
try {
var ver = await OpenFangAPI.get('/api/version');
var status = await OpenFangAPI.get('/api/status');
this.sysInfo = {
version: ver.version || '-',
platform: ver.platform || '-',
arch: ver.arch || '-',
uptime_seconds: status.uptime_seconds || 0,
agent_count: status.agent_count || 0,
default_provider: status.default_provider || '-',
default_model: status.default_model || '-'
};
} catch(e) { throw e; }
},
async loadUsage() {
try {
var data = await OpenFangAPI.get('/api/usage');
this.usageData = data.agents || [];
} catch(e) { this.usageData = []; }
},
async loadTools() {
try {
var data = await OpenFangAPI.get('/api/tools');
this.tools = data.tools || [];
} catch(e) { this.tools = []; }
},
async loadConfig() {
try {
this.config = await OpenFangAPI.get('/api/config');
} catch(e) { this.config = {}; }
},
async loadProviders() {
try {
var data = await OpenFangAPI.get('/api/providers');
this.providers = data.providers || [];
for (var i = 0; i < this.providers.length; i++) {
var p = this.providers[i];
if (p.is_local) {
if (!this.providerUrlInputs[p.id]) {
this.providerUrlInputs[p.id] = p.base_url || '';
}
if (this.providerUrlSaving[p.id] === undefined) {
this.providerUrlSaving[p.id] = false;
}
}
}
} catch(e) { this.providers = []; }
},
async loadModels() {
try {
var data = await OpenFangAPI.get('/api/models');
this.models = data.models || [];
} catch(e) { this.models = []; }
},
async addCustomModel() {
var id = this.customModelId.trim();
if (!id) return;
this.customModelStatus = 'Adding...';
try {
await OpenFangAPI.post('/api/models/custom', {
id: id,
provider: this.customModelProvider || 'openrouter',
context_window: this.customModelContext || 128000,
max_output_tokens: this.customModelMaxOutput || 8192,
});
this.customModelStatus = 'Added!';
this.customModelId = '';
this.showCustomModelForm = false;
await this.loadModels();
} catch(e) {
this.customModelStatus = 'Error: ' + (e.message || 'Failed');
}
},
async deleteCustomModel(modelId) {
if (!confirm('Delete custom model "' + modelId + '"?')) return;
try {
await OpenFangAPI.del('/api/models/custom/' + encodeURIComponent(modelId));
OpenFangToast.success('Model deleted');
await this.loadModels();
} catch(e) {
OpenFangToast.error('Failed to delete: ' + (e.message || 'Unknown error'));
}
},
async loadConfigSchema() {
try {
var results = await Promise.all([
OpenFangAPI.get('/api/config/schema').catch(function() { return {}; }),
OpenFangAPI.get('/api/config')
]);
this.configSchema = results[0].sections || null;
this.configValues = results[1] || {};
} catch(e) { /* silent */ }
},
isConfigDirty(section, field) {
return this.configDirty[section + '.' + field] === true;
},
markConfigDirty(section, field) {
this.configDirty[section + '.' + field] = true;
},
async saveConfigField(section, field, value) {
var key = section + '.' + field;
// Root-level fields (api_key, api_listen, log_level) use just the field name
var sectionMeta = this.configSchema && this.configSchema[section];
var path = (sectionMeta && sectionMeta.root_level) ? field : key;
this.configSaving[key] = true;
try {
await OpenFangAPI.post('/api/config/set', { path: path, value: value });
this.configDirty[key] = false;
OpenFangToast.success('Saved ' + field);
} catch(e) {
OpenFangToast.error('Failed to save: ' + e.message);
}
this.configSaving[key] = false;
},
get filteredTools() {
var q = this.toolSearch.toLowerCase().trim();
if (!q) return this.tools;
return this.tools.filter(function(t) {
return t.name.toLowerCase().indexOf(q) !== -1 ||
(t.description || '').toLowerCase().indexOf(q) !== -1;
});
},
get filteredModels() {
var self = this;
return this.models.filter(function(m) {
if (self.modelProviderFilter && m.provider !== self.modelProviderFilter) return false;
if (self.modelTierFilter && m.tier !== self.modelTierFilter) return false;
if (self.modelSearch) {
var q = self.modelSearch.toLowerCase();
if (m.id.toLowerCase().indexOf(q) === -1 &&
(m.display_name || '').toLowerCase().indexOf(q) === -1) return false;
}
return true;
});
},
get uniqueProviderNames() {
var seen = {};
this.models.forEach(function(m) { seen[m.provider] = true; });
return Object.keys(seen).sort();
},
/// Coarse category for a provider used to group the Providers tab.
/// Returns: 'frontier' | 'oss' | 'local' | 'aggregator' | 'regional' | 'other'.
providerCategory(p) {
if (!p) return 'other';
if (p.is_local || p.key_required === false) return 'local';
var id = (p.id || '').toLowerCase();
var FRONTIER = ['anthropic','openai','gemini','google','xai','bedrock','azure','vertex'];
var OSS = ['groq','together','fireworks','cerebras','sambanova','deepseek','mistral','perplexity','cohere','ai21','huggingface','replicate','nvidia','venice','novita','chutes'];
var AGG = ['openrouter','litellm','github-copilot','claude-code'];
var REGIONAL = ['qwen','minimax','zhipu','zai','moonshot','qianfan','volcengine','kimi'];
if (FRONTIER.indexOf(id) !== -1) return 'frontier';
if (REGIONAL.indexOf(id) !== -1) return 'regional';
if (AGG.indexOf(id) !== -1) return 'aggregator';
if (OSS.indexOf(id) !== -1) return 'oss';
return 'other';
},
providerCategoryLabel(cat) {
switch (cat) {
case 'frontier': return 'Frontier (Anthropic, OpenAI, Google, xAI, Bedrock)';
case 'oss': return 'Open-Weight Hosts (Groq, Together, Fireworks, DeepSeek, etc.)';
case 'aggregator': return 'Aggregators & Gateways (OpenRouter, GitHub Copilot)';
case 'regional': return 'Regional / China (Qwen, Zhipu, Moonshot, MiniMax)';
case 'local': return 'Local / Self-Hosted (Ollama, vLLM, LM Studio, Lemonade)';
default: return 'Other Providers';
}
},
/// Stable category order for grouped rendering.
get providerCategoriesOrdered() {
return ['frontier', 'oss', 'aggregator', 'regional', 'local', 'other'];
},
/// Returns filter-matched providers grouped by category, preserving order.
/// Each entry: { category, label, items: [...] }. Empty groups are omitted.
get providersGrouped() {
var self = this;
var filtered = this.filteredProviders;
var by = {};
filtered.forEach(function(p) {
var c = self.providerCategory(p);
if (!by[c]) by[c] = [];
by[c].push(p);
});
// Sort each group: configured first, then alphabetical
Object.keys(by).forEach(function(c) {
by[c].sort(function(a, b) {
var ac = a.auth_status === 'configured' ? 0 : 1;
var bc = b.auth_status === 'configured' ? 0 : 1;
if (ac !== bc) return ac - bc;
return (a.display_name || a.id).localeCompare(b.display_name || b.id);
});
});
var out = [];
this.providerCategoriesOrdered.forEach(function(c) {
if (by[c] && by[c].length) {
out.push({ category: c, label: self.providerCategoryLabel(c), items: by[c] });
}
});
return out;
},
get filteredProviders() {
var self = this;
return this.providers.filter(function(p) {
if (self.providerStatusFilter === 'configured' && p.auth_status !== 'configured') return false;
if (self.providerStatusFilter === 'unconfigured' && p.auth_status === 'configured') return false;
if (self.providerCategoryFilter && self.providerCategory(p) !== self.providerCategoryFilter) return false;
if (self.providerSearch) {
var q = self.providerSearch.toLowerCase();
if ((p.display_name || '').toLowerCase().indexOf(q) === -1 &&
(p.id || '').toLowerCase().indexOf(q) === -1 &&
(p.api_key_env || '').toLowerCase().indexOf(q) === -1) return false;
}
return true;
});
},
get configuredProviderCount() {
return this.providers.filter(function(p) { return p.auth_status === 'configured'; }).length;
},
clearProviderFilters() {
this.providerSearch = '';
this.providerStatusFilter = '';
this.providerCategoryFilter = '';
},
get uniqueTiers() {
var seen = {};
this.models.forEach(function(m) { if (m.tier) seen[m.tier] = true; });
return Object.keys(seen).sort();
},
providerAuthClass(p) {
if (p.auth_status === 'configured') return 'auth-configured';
if (p.auth_status === 'not_set' || p.auth_status === 'missing') return 'auth-not-set';
return 'auth-no-key';
},
providerAuthText(p) {
if (p.auth_status === 'configured') return 'Configured';
if (p.auth_status === 'not_set' || p.auth_status === 'missing') {
if (p.id === 'claude-code') return 'Not Installed';
return 'Not Set';
}
return 'No Key Needed';
},
providerCardClass(p) {
if (p.auth_status === 'configured') return 'configured';
if (p.auth_status === 'not_set' || p.auth_status === 'missing') return 'not-configured';
return 'no-key';
},
tierBadgeClass(tier) {
if (!tier) return '';
var t = tier.toLowerCase();
if (t === 'frontier') return 'tier-frontier';
if (t === 'smart') return 'tier-smart';
if (t === 'balanced') return 'tier-balanced';
if (t === 'fast') return 'tier-fast';
return '';
},
formatCost(cost) {
if (!cost && cost !== 0) return '-';
return '$' + cost.toFixed(4);
},
formatContext(ctx) {
if (!ctx) return '-';
if (ctx >= 1000000) return (ctx / 1000000).toFixed(1) + 'M';
if (ctx >= 1000) return Math.round(ctx / 1000) + 'K';
return String(ctx);
},
formatUptime(secs) {
if (!secs) return '-';
var h = Math.floor(secs / 3600);
var m = Math.floor((secs % 3600) / 60);
var s = secs % 60;
if (h > 0) return h + 'h ' + m + 'm';
if (m > 0) return m + 'm ' + s + 's';
return s + 's';
},
async saveProviderKey(provider) {
var key = this.providerKeyInputs[provider.id];
if (!key || !key.trim()) { OpenFangToast.error('Please enter an API key'); return; }
try {
var resp = await OpenFangAPI.post('/api/providers/' + encodeURIComponent(provider.id) + '/key', { key: key.trim() });
if (resp && resp.switched_default) {
OpenFangToast.warning(resp.message || 'Default provider was switched to ' + provider.display_name);
} else {
OpenFangToast.success('API key saved for ' + provider.display_name);
}
this.providerKeyInputs[provider.id] = '';
await this.loadProviders();
await this.loadModels();
} catch(e) {
OpenFangToast.error('Failed to save key: ' + e.message);
}
},
async removeProviderKey(provider) {
try {
await OpenFangAPI.del('/api/providers/' + encodeURIComponent(provider.id) + '/key');
OpenFangToast.success('API key removed for ' + provider.display_name);
await this.loadProviders();
await this.loadModels();
} catch(e) {
OpenFangToast.error('Failed to remove key: ' + e.message);
}
},
async startCopilotOAuth() {
this.copilotOAuth.polling = true;
this.copilotOAuth.userCode = '';
try {
var resp = await OpenFangAPI.post('/api/providers/github-copilot/oauth/start', {});
this.copilotOAuth.userCode = resp.user_code;
this.copilotOAuth.verificationUri = resp.verification_uri;
this.copilotOAuth.pollId = resp.poll_id;
this.copilotOAuth.interval = resp.interval || 5;
window.open(resp.verification_uri, '_blank');
this.pollCopilotOAuth();
} catch(e) {
OpenFangToast.error('Failed to start Copilot login: ' + e.message);
this.copilotOAuth.polling = false;
}
},
pollCopilotOAuth() {
var self = this;
setTimeout(async function() {
if (!self.copilotOAuth.pollId) return;
try {
var resp = await OpenFangAPI.get('/api/providers/github-copilot/oauth/poll/' + self.copilotOAuth.pollId);
if (resp.status === 'complete') {
OpenFangToast.success('GitHub Copilot authenticated successfully!');
self.copilotOAuth = { polling: false, userCode: '', verificationUri: '', pollId: '', interval: 5 };
await self.loadProviders();
await self.loadModels();
} else if (resp.status === 'pending') {
if (resp.interval) self.copilotOAuth.interval = resp.interval;
self.pollCopilotOAuth();
} else if (resp.status === 'expired') {
OpenFangToast.error('Device code expired. Please try again.');
self.copilotOAuth = { polling: false, userCode: '', verificationUri: '', pollId: '', interval: 5 };
} else if (resp.status === 'denied') {
OpenFangToast.error('Access denied by user.');
self.copilotOAuth = { polling: false, userCode: '', verificationUri: '', pollId: '', interval: 5 };
} else {
OpenFangToast.error('OAuth error: ' + (resp.error || resp.status));
self.copilotOAuth = { polling: false, userCode: '', verificationUri: '', pollId: '', interval: 5 };
}
} catch(e) {
OpenFangToast.error('Poll error: ' + e.message);
self.copilotOAuth = { polling: false, userCode: '', verificationUri: '', pollId: '', interval: 5 };
}
}, self.copilotOAuth.interval * 1000);
},
async testProvider(provider) {
this.providerTesting[provider.id] = true;
this.providerTestResults[provider.id] = null;
try {
var result = await OpenFangAPI.post('/api/providers/' + encodeURIComponent(provider.id) + '/test', {});
this.providerTestResults[provider.id] = result;
if (result.status === 'ok') {
OpenFangToast.success(provider.display_name + ' connected (' + (result.latency_ms || '?') + 'ms)');
} else {
OpenFangToast.error(provider.display_name + ': ' + (result.error || 'Connection failed'));
}
} catch(e) {
this.providerTestResults[provider.id] = { status: 'error', error: e.message };
OpenFangToast.error('Test failed: ' + e.message);
}
this.providerTesting[provider.id] = false;
},
async saveProviderUrl(provider) {
var url = this.providerUrlInputs[provider.id];
if (!url || !url.trim()) { OpenFangToast.error('Please enter a base URL'); return; }
url = url.trim();
if (url.indexOf('http://') !== 0 && url.indexOf('https://') !== 0) {
OpenFangToast.error('URL must start with http:// or https://'); return;
}
this.providerUrlSaving[provider.id] = true;
try {
var result = await OpenFangAPI.put('/api/providers/' + encodeURIComponent(provider.id) + '/url', { base_url: url });
if (result.reachable) {
OpenFangToast.success(provider.display_name + ' URL saved &mdash; reachable (' + (result.latency_ms || '?') + 'ms)');
} else {
OpenFangToast.warning(provider.display_name + ' URL saved but not reachable');
}
await this.loadProviders();
} catch(e) {
OpenFangToast.error('Failed to save URL: ' + e.message);
}
this.providerUrlSaving[provider.id] = false;
},
async addCustomProvider() {
var name = this.customProviderName.trim().toLowerCase().replace(/[^a-z0-9-]/g, '-').replace(/-+/g, '-');
if (!name) { OpenFangToast.error('Please enter a provider name'); return; }
var url = this.customProviderUrl.trim();
if (!url) { OpenFangToast.error('Please enter a base URL'); return; }
if (url.indexOf('http://') !== 0 && url.indexOf('https://') !== 0) {
OpenFangToast.error('URL must start with http:// or https://'); return;
}
this.addingCustomProvider = true;
this.customProviderStatus = '';
try {
var result = await OpenFangAPI.put('/api/providers/' + encodeURIComponent(name) + '/url', { base_url: url });
if (this.customProviderKey.trim()) {
await OpenFangAPI.post('/api/providers/' + encodeURIComponent(name) + '/key', { key: this.customProviderKey.trim() });
}
this.customProviderName = '';
this.customProviderUrl = '';
this.customProviderKey = '';
this.customProviderStatus = '';
OpenFangToast.success('Provider "' + name + '" added' + (result.reachable ? ' (reachable)' : ' (not reachable yet)'));
await this.loadProviders();
} catch(e) {
this.customProviderStatus = 'Error: ' + (e.message || 'Failed');
OpenFangToast.error('Failed to add provider: ' + e.message);
}
this.addingCustomProvider = false;
},
// -- Security methods --
async loadSecurity() {
this.secLoading = true;
try {
this.securityData = await OpenFangAPI.get('/api/security');
} catch(e) {
this.securityData = null;
}
this.secLoading = false;
},
isActive(key) {
if (!this.securityData) return true;
var core = this.securityData.core_protections || {};
if (core[key] !== undefined) return core[key];
return true;
},
getConfigValue(key) {
if (!this.securityData) return null;
var cfg = this.securityData.configurable || {};
return cfg[key] || null;
},
getMonitoringValue(key) {
if (!this.securityData) return null;
var mon = this.securityData.monitoring || {};
return mon[key] || null;
},
formatConfigValue(feature) {
var val = this.getConfigValue(feature.valueKey);
if (!val) return feature.configHint;
switch (feature.valueKey) {
case 'rate_limiter':
return 'Algorithm: ' + (val.algorithm || 'GCRA') + ' | ' + (val.tokens_per_minute || 500) + ' tokens/min per IP';
case 'websocket_limits':
return 'Max ' + (val.max_per_ip || 5) + ' conn/IP | ' + Math.round((val.idle_timeout_secs || 1800) / 60) + 'min idle timeout | ' + Math.round((val.max_message_size || 65536) / 1024) + 'KB max msg';
case 'wasm_sandbox':
return 'Fuel: ' + (val.fuel_metering ? 'ON' : 'OFF') + ' | Epoch: ' + (val.epoch_interruption ? 'ON' : 'OFF') + ' | Timeout: ' + (val.default_timeout_secs || 30) + 's';
case 'auth':
return 'Mode: ' + (val.mode || 'unknown') + (val.api_key_set ? ' (key configured)' : ' (no key set)');
default:
return feature.configHint;
}
},
formatMonitoringValue(feature) {
var val = this.getMonitoringValue(feature.valueKey);
if (!val) return feature.configHint;
switch (feature.valueKey) {
case 'audit_trail':
return (val.enabled ? 'Active' : 'Disabled') + ' | ' + (val.algorithm || 'SHA-256') + ' | ' + (val.entry_count || 0) + ' entries logged';
case 'taint_tracking':
var labels = val.tracked_labels || [];
return (val.enabled ? 'Active' : 'Disabled') + ' | Tracking: ' + labels.join(', ');
case 'manifest_signing':
return 'Algorithm: ' + (val.algorithm || 'Ed25519') + ' | ' + (val.available ? 'Available' : 'Not available');
default:
return feature.configHint;
}
},
async verifyAuditChain() {
this.verifyingChain = true;
this.chainResult = null;
try {
var res = await OpenFangAPI.get('/api/audit/verify');
this.chainResult = res;
} catch(e) {
this.chainResult = { valid: false, error: e.message };
}
this.verifyingChain = false;
},
// -- Peers methods --
async loadPeers() {
this.peersLoading = true;
this.peersLoadError = '';
try {
var data = await OpenFangAPI.get('/api/peers');
this.peers = (data.peers || []).map(function(p) {
return {
node_id: p.node_id,
node_name: p.node_name,
address: p.address,
state: p.state,
agent_count: (p.agents || []).length,
protocol_version: p.protocol_version || 1
};
});
} catch(e) {
this.peers = [];
this.peersLoadError = e.message || 'Could not load peers.';
}
this.peersLoading = false;
},
startPeerPolling() {
var self = this;
this.stopPeerPolling();
this._peerPollTimer = setInterval(async function() {
if (self.tab !== 'network') { self.stopPeerPolling(); return; }
try {
var data = await OpenFangAPI.get('/api/peers');
self.peers = (data.peers || []).map(function(p) {
return {
node_id: p.node_id,
node_name: p.node_name,
address: p.address,
state: p.state,
agent_count: (p.agents || []).length,
protocol_version: p.protocol_version || 1
};
});
} catch(e) { /* silent */ }
}, 15000);
},
stopPeerPolling() {
if (this._peerPollTimer) { clearInterval(this._peerPollTimer); this._peerPollTimer = null; }
},
// -- Migration methods --
async autoDetect() {
this.detecting = true;
try {
var data = await OpenFangAPI.get('/api/migrate/detect');
if (data.detected && data.scan) {
this.sourcePath = data.path;
this.scanResult = data.scan;
this.migStep = 'preview';
} else {
this.migStep = 'not_found';
}
} catch(e) {
this.migStep = 'not_found';
}
this.detecting = false;
},
async scanPath() {
if (!this.sourcePath) return;
this.scanning = true;
try {
var data = await OpenFangAPI.post('/api/migrate/scan', { path: this.sourcePath });
if (data.error) {
OpenFangToast.error('Scan error: ' + data.error);
this.scanning = false;
return;
}
this.scanResult = data;
this.migStep = 'preview';
} catch(e) {
OpenFangToast.error('Scan failed: ' + e.message);
}
this.scanning = false;
},
async runMigration(dryRun) {
this.migrating = true;
try {
var target = this.targetPath;
if (!target) target = '';
var data = await OpenFangAPI.post('/api/migrate', {
source: 'openclaw',
source_dir: this.sourcePath || (this.scanResult ? this.scanResult.path : ''),
target_dir: target,
dry_run: dryRun
});
this.migResult = data;
this.migStep = 'result';
} catch(e) {
this.migResult = { status: 'failed', error: e.message };
this.migStep = 'result';
}
this.migrating = false;
},
destroy() {
this.stopPeerPolling();
}
};
}
crates/openfang-api/static/js/pages/skills.js
+507
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@@ -0,0 +1,507 @@
// OpenFang Skills Page — OpenClaw/ClawHub ecosystem + local skills + MCP servers
'use strict';
function skillsPage() {
return {
tab: 'installed',
skills: [],
loading: true,
loadError: '',
// ClawHub state
clawhubSearch: '',
clawhubResults: [],
clawhubBrowseResults: [],
clawhubLoading: false,
clawhubError: '',
clawhubSort: 'trending',
clawhubNextCursor: null,
installingSlug: null,
installResult: null,
_searchTimer: null,
_browseCache: {}, // { key: { ts, data } } client-side 60s cache
_searchCache: {},
// Skill detail modal
skillDetail: null,
detailLoading: false,
showSkillCode: false,
skillCode: '',
skillCodeFilename: '',
skillCodeLoading: false,
// Skill config modal (local skill configuration from SKILL.md frontmatter)
configSkill: null, // skill object whose config is being edited
configDeclared: {}, // { var_name: { description, env, default, required } }
configResolved: {}, // { var_name: { value, source, is_secret } }
configDraft: {}, // { var_name: user-edited string value }
configRevealed: {}, // { var_name: bool } — toggle password reveal per row
configLoading: false,
configSaving: false,
configError: '',
// MCP servers
mcpServers: [],
mcpLoading: false,
// Category definitions from the OpenClaw ecosystem (loaded from i18n)
get categories() {
var t = window.i18n ? window.i18n.t.bind(window.i18n) : function(k) { return k; };
return [
{ id: 'coding', name: t('skills.cat_coding') || 'Coding & IDEs' },
{ id: 'git', name: t('skills.cat_git') || 'Git & GitHub' },
{ id: 'web', name: t('skills.cat_frontend') || 'Web & Frontend' },
{ id: 'devops', name: t('skills.cat_devops') || 'DevOps & Cloud' },
{ id: 'browser', name: t('skills.cat_browser') || 'Browser & Automation' },
{ id: 'search', name: t('skills.cat_search') || 'Search & Research' },
{ id: 'ai', name: t('skills.cat_ai') || 'AI & ML' },
{ id: 'data', name: t('skills.cat_data') || 'Data & Analytics' },
{ id: 'productivity', name: t('skills.cat_productivity') || 'Productivity' },
{ id: 'communication', name: t('skills.cat_communication') || 'Communication' },
{ id: 'media', name: t('skills.cat_media') || 'Media & Streaming' },
{ id: 'notes', name: t('skills.cat_notes') || 'Notes & PKM' },
{ id: 'security', name: t('skills.cat_security') || 'Security' },
{ id: 'cli', name: t('skills.cat_cli') || 'CLI Utilities' },
{ id: 'marketing', name: t('skills.cat_marketing') || 'Marketing & Sales' },
{ id: 'finance', name: t('skills.cat_finance') || 'Finance' },
{ id: 'smart-home', name: t('skills.cat_smarthome') || 'Smart Home & IoT' },
{ id: 'docs', name: t('skills.cat_docs') || 'Documentation' },
];
},
runtimeBadge: function(rt) {
var r = (rt || '').toLowerCase();
if (r === 'python' || r === 'py') return { text: 'PY', cls: 'runtime-badge-py' };
if (r === 'node' || r === 'nodejs' || r === 'js' || r === 'javascript') return { text: 'JS', cls: 'runtime-badge-js' };
if (r === 'wasm' || r === 'webassembly') return { text: 'WASM', cls: 'runtime-badge-wasm' };
if (r === 'prompt_only' || r === 'prompt' || r === 'promptonly') return { text: 'PROMPT', cls: 'runtime-badge-prompt' };
return { text: r.toUpperCase().substring(0, 4), cls: 'runtime-badge-prompt' };
},
sourceBadge: function(source) {
if (!source) return { text: 'Local', cls: 'badge-dim' };
switch (source.type) {
case 'clawhub': return { text: 'ClawHub', cls: 'badge-info' };
case 'openclaw': return { text: 'OpenClaw', cls: 'badge-info' };
case 'bundled': return { text: 'Built-in', cls: 'badge-success' };
default: return { text: 'Local', cls: 'badge-dim' };
}
},
formatDownloads: function(n) {
if (!n) return '0';
if (n >= 1000000) return (n / 1000000).toFixed(1) + 'M';
if (n >= 1000) return (n / 1000).toFixed(1) + 'K';
return n.toString();
},
async loadSkills() {
this.loading = true;
this.loadError = '';
try {
var data = await OpenFangAPI.get('/api/skills');
this.skills = (data.skills || []).map(function(s) {
return {
name: s.name,
description: s.description || '',
version: s.version || '',
author: s.author || '',
runtime: s.runtime || 'unknown',
tools_count: s.tools_count || 0,
tags: s.tags || [],
enabled: s.enabled !== false,
source: s.source || { type: 'local' },
has_prompt_context: !!s.has_prompt_context,
config_declared_count: s.config_declared_count || 0
};
});
} catch(e) {
this.skills = [];
this.loadError = e.message || 'Could not load skills.';
}
this.loading = false;
},
// ── Skill config editing ────────────────────────────────────────────
async openSkillConfig(skill) {
this.configSkill = skill;
this.configDeclared = {};
this.configResolved = {};
this.configDraft = {};
this.configRevealed = {};
this.configError = '';
this.configLoading = true;
try {
var data = await OpenFangAPI.get('/api/skills/' + encodeURIComponent(skill.name) + '/config');
this.configDeclared = data.declared || {};
this.configResolved = data.resolved || {};
// Pre-populate draft values only for vars the user has already
// overridden — never copy redacted responses back into inputs or
// they'd be re-saved as "****redacted****" strings.
var names = Object.keys(this.configDeclared);
for (var i = 0; i < names.length; i++) {
var n = names[i];
var res = this.configResolved[n] || {};
if (res.source === 'user' && !res.is_secret) {
this.configDraft[n] = res.value == null ? '' : String(res.value);
} else {
this.configDraft[n] = '';
}
}
} catch(e) {
this.configError = e.message || 'Failed to load skill config.';
}
this.configLoading = false;
},
closeSkillConfig() {
this.configSkill = null;
this.configDeclared = {};
this.configResolved = {};
this.configDraft = {};
this.configRevealed = {};
this.configError = '';
},
configRowInvalid(name) {
// A required var is invalid iff the user hasn't entered anything AND
// no env/default resolves it. Source from the server tells us where
// the current value came from; if it's "unresolved" and the draft is
// blank, the save would write an empty string over the required var.
var decl = this.configDeclared[name] || {};
if (!decl.required) return false;
var draft = (this.configDraft[name] || '').trim();
if (draft) return false;
var res = this.configResolved[name] || {};
if (res.source === 'env' || res.source === 'default') return false;
// If the user has an existing secret override we don't want to force
// them to re-type it — treat that as "currently resolved".
if (res.source === 'user') return false;
return true;
},
hasInvalidConfig() {
var names = Object.keys(this.configDeclared);
for (var i = 0; i < names.length; i++) {
if (this.configRowInvalid(names[i])) return true;
}
return false;
},
toggleReveal(name) {
this.configRevealed[name] = !this.configRevealed[name];
},
sourceBadgeClass(source) {
switch (source) {
case 'user': return 'badge-success';
case 'env': return 'badge-info';
case 'default': return 'badge-dim';
default: return 'badge-danger';
}
},
sourceBadgeLabel(res) {
if (!res) return 'unresolved';
switch (res.source) {
case 'user': return 'user override';
case 'env': return 'env' + ((this.configDeclared[res.__name] && this.configDeclared[res.__name].env) ? ':' + this.configDeclared[res.__name].env : '');
case 'default': return 'default';
default: return 'unresolved';
}
},
async saveSkillConfig() {
if (!this.configSkill) return;
if (this.hasInvalidConfig()) {
OpenFangToast.error('Fill in all required variables before saving.');
return;
}
this.configSaving = true;
this.configError = '';
// Only PUT values the user actually typed. Empty strings are dropped
// so we don't silently clobber an env/default with "".
var payload = {};
var names = Object.keys(this.configDeclared);
for (var i = 0; i < names.length; i++) {
var n = names[i];
var v = (this.configDraft[n] || '').trim();
if (v.length > 0) payload[n] = v;
}
try {
await OpenFangAPI.put('/api/skills/' + encodeURIComponent(this.configSkill.name) + '/config', { values: payload });
OpenFangToast.success('Saved, reloading agents\u2026');
// Refresh the modal contents so the new source/value shows up.
var refreshed = this.configSkill;
await this.loadSkills();
this.closeSkillConfig();
// Find the possibly-refreshed skill object and reopen.
var self = this;
var updated = this.skills.find(function(s) { return s.name === refreshed.name; });
if (updated) await self.openSkillConfig(updated);
} catch(e) {
this.configError = e.message || 'Save failed.';
OpenFangToast.error('Save failed: ' + (e.message || 'unknown error'));
}
this.configSaving = false;
},
async resetSkillConfigVar(name) {
if (!this.configSkill) return;
var decl = this.configDeclared[name] || {};
var res = this.configResolved[name] || {};
// If the server is already reporting a non-user source there's nothing
// to remove; just clear the draft so the input disappears.
if (res.source !== 'user') {
this.configDraft[name] = '';
return;
}
try {
await OpenFangAPI.del('/api/skills/' + encodeURIComponent(this.configSkill.name) + '/config/' + encodeURIComponent(name));
OpenFangToast.success('Reset ' + name);
// Refresh modal state from server.
var data = await OpenFangAPI.get('/api/skills/' + encodeURIComponent(this.configSkill.name) + '/config');
this.configResolved = data.resolved || {};
this.configDraft[name] = '';
} catch(e) {
var msg = e.message || 'Reset failed';
if (msg.indexOf('required') !== -1 || msg.indexOf('409') !== -1) {
OpenFangToast.error('Cannot reset: ' + decl.description + ' is required with no fallback.');
} else {
OpenFangToast.error('Reset failed: ' + msg);
}
}
},
get configDeclaredNames() {
return Object.keys(this.configDeclared).sort();
},
async loadData() {
await this.loadSkills();
},
// Debounced search — fires 350ms after user stops typing
onSearchInput() {
if (this._searchTimer) clearTimeout(this._searchTimer);
var q = this.clawhubSearch.trim();
if (!q) {
this.clawhubResults = [];
this.clawhubError = '';
return;
}
var self = this;
this._searchTimer = setTimeout(function() { self.searchClawHub(); }, 350);
},
// ClawHub search
async searchClawHub() {
if (!this.clawhubSearch.trim()) {
this.clawhubResults = [];
return;
}
this.clawhubLoading = true;
this.clawhubError = '';
try {
var data = await OpenFangAPI.get('/api/clawhub/search?q=' + encodeURIComponent(this.clawhubSearch.trim()) + '&limit=20');
this.clawhubResults = data.items || [];
if (data.error) this.clawhubError = data.error;
} catch(e) {
this.clawhubResults = [];
this.clawhubError = e.message || 'Search failed';
}
this.clawhubLoading = false;
},
// Clear search and go back to browse
clearSearch() {
this.clawhubSearch = '';
this.clawhubResults = [];
this.clawhubError = '';
if (this._searchTimer) clearTimeout(this._searchTimer);
},
// ClawHub browse by sort (with 60s client-side cache)
async browseClawHub(sort) {
this.clawhubSort = sort || 'trending';
var ckey = 'browse:' + this.clawhubSort;
var cached = this._browseCache[ckey];
if (cached && (Date.now() - cached.ts) < 60000) {
this.clawhubBrowseResults = cached.data.items || [];
this.clawhubNextCursor = cached.data.next_cursor || null;
return;
}
this.clawhubLoading = true;
this.clawhubError = '';
this.clawhubNextCursor = null;
try {
var data = await OpenFangAPI.get('/api/clawhub/browse?sort=' + this.clawhubSort + '&limit=20');
this.clawhubBrowseResults = data.items || [];
this.clawhubNextCursor = data.next_cursor || null;
if (data.error) this.clawhubError = data.error;
this._browseCache[ckey] = { ts: Date.now(), data: data };
} catch(e) {
this.clawhubBrowseResults = [];
this.clawhubError = e.message || 'Browse failed';
}
this.clawhubLoading = false;
},
// ClawHub load more results
async loadMoreClawHub() {
if (!this.clawhubNextCursor || this.clawhubLoading) return;
this.clawhubLoading = true;
try {
var data = await OpenFangAPI.get('/api/clawhub/browse?sort=' + this.clawhubSort + '&limit=20&cursor=' + encodeURIComponent(this.clawhubNextCursor));
this.clawhubBrowseResults = this.clawhubBrowseResults.concat(data.items || []);
this.clawhubNextCursor = data.next_cursor || null;
} catch(e) {
// silently fail on load more
}
this.clawhubLoading = false;
},
// Show skill detail
async showSkillDetail(slug) {
this.detailLoading = true;
this.skillDetail = null;
this.installResult = null;
try {
var data = await OpenFangAPI.get('/api/clawhub/skill/' + encodeURIComponent(slug));
this.skillDetail = data;
} catch(e) {
OpenFangToast.error('Failed to load skill details');
}
this.detailLoading = false;
},
closeDetail() {
this.skillDetail = null;
this.installResult = null;
this.showSkillCode = false;
this.skillCode = '';
this.skillCodeFilename = '';
},
async viewSkillCode(slug) {
if (this.showSkillCode) {
this.showSkillCode = false;
return;
}
this.skillCodeLoading = true;
try {
var data = await OpenFangAPI.get('/api/clawhub/skill/' + encodeURIComponent(slug) + '/code');
this.skillCode = data.code || '';
this.skillCodeFilename = data.filename || 'source';
this.showSkillCode = true;
} catch(e) {
OpenFangToast.error('Could not load skill source code');
}
this.skillCodeLoading = false;
},
// Install from ClawHub
async installFromClawHub(slug) {
this.installingSlug = slug;
this.installResult = null;
try {
var data = await OpenFangAPI.post('/api/clawhub/install', { slug: slug });
this.installResult = data;
if (data.warnings && data.warnings.length > 0) {
OpenFangToast.success('Skill "' + data.name + '" installed with ' + data.warnings.length + ' warning(s)');
} else {
OpenFangToast.success('Skill "' + data.name + '" installed successfully');
}
// Update installed state in detail modal if open
if (this.skillDetail && this.skillDetail.slug === slug) {
this.skillDetail.installed = true;
}
await this.loadSkills();
} catch(e) {
var msg = e.message || 'Install failed';
if (msg.includes('already_installed')) {
OpenFangToast.error('Skill is already installed');
} else if (msg.includes('SecurityBlocked')) {
OpenFangToast.error('Skill blocked by security scan');
} else {
OpenFangToast.error('Install failed: ' + msg);
}
}
this.installingSlug = null;
},
// Uninstall
uninstallSkill: function(name) {
var self = this;
var t = window.i18n ? window.i18n.t.bind(window.i18n) : function(k) { return k; };
OpenFangToast.confirm(
t('skills.uninstall_skill') || 'Uninstall Skill',
t('skills.uninstall_confirm') + ' "' + name + '"? This cannot be undone.',
async function() {
try {
await OpenFangAPI.post('/api/skills/uninstall', { name: name });
OpenFangToast.success('Skill "' + name + '" uninstalled');
await self.loadSkills();
} catch(e) {
OpenFangToast.error('Failed to uninstall skill: ' + e.message);
}
}
);
},
// Create prompt-only skill
async createDemoSkill(skill) {
try {
await OpenFangAPI.post('/api/skills/create', {
name: skill.name,
description: skill.description,
runtime: 'prompt_only',
prompt_context: skill.prompt_context || skill.description
});
OpenFangToast.success('Skill "' + skill.name + '" created');
this.tab = 'installed';
await this.loadSkills();
} catch(e) {
OpenFangToast.error('Failed to create skill: ' + e.message);
}
},
// Load MCP servers
async loadMcpServers() {
this.mcpLoading = true;
try {
var data = await OpenFangAPI.get('/api/mcp/servers');
this.mcpServers = data;
} catch(e) {
this.mcpServers = { configured: [], connected: [], total_configured: 0, total_connected: 0 };
}
this.mcpLoading = false;
},
// Category search on ClawHub
searchCategory: function(cat) {
this.clawhubSearch = cat.name;
this.searchClawHub();
},
// Quick start skills (prompt-only, zero deps)
quickStartSkills: [
{ name: 'code-review-guide', description: 'Adds code review best practices and checklist to agent context.', prompt_context: 'You are an expert code reviewer. When reviewing code:\n1. Check for bugs and logic errors\n2. Evaluate code style and readability\n3. Look for security vulnerabilities\n4. Suggest performance improvements\n5. Verify error handling\n6. Check test coverage' },
{ name: 'writing-style', description: 'Configurable writing style guide for content generation.', prompt_context: 'Follow these writing guidelines:\n- Use clear, concise language\n- Prefer active voice over passive voice\n- Keep paragraphs short (3-4 sentences)\n- Use bullet points for lists\n- Maintain consistent tone throughout' },
{ name: 'api-design', description: 'REST API design patterns and conventions.', prompt_context: 'When designing REST APIs:\n- Use nouns for resources, not verbs\n- Use HTTP methods correctly (GET, POST, PUT, DELETE)\n- Return appropriate status codes\n- Use pagination for list endpoints\n- Version your API\n- Document all endpoints' },
{ name: 'security-checklist', description: 'OWASP-aligned security review checklist.', prompt_context: 'Security review checklist (OWASP aligned):\n- Input validation on all user inputs\n- Output encoding to prevent XSS\n- Parameterized queries to prevent SQL injection\n- Authentication and session management\n- Access control checks\n- CSRF protection\n- Security headers\n- Error handling without information leakage' },
],
// Check if skill is installed by slug
isSkillInstalled: function(slug) {
return this.skills.some(function(s) {
return s.source && s.source.type === 'clawhub' && s.source.slug === slug;
});
},
// Check if skill is installed by name
isSkillInstalledByName: function(name) {
return this.skills.some(function(s) { return s.name === name; });
},
};
}

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