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chore: import upstream snapshot with attribution
2026-07-13 13:00:43 +08:00

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# DeepTutor — Agent-Native Architecture
## Overview
DeepTutor is an **agent-native** intelligent learning companion organized
around a two-layer plugin model — single-shot **Tools** invoked by the
LLM, and multi-stage **Capabilities** that take over a turn — exposed
through three entry points: CLI, WebSocket API, and Python SDK.
## Architecture
```
Entry Points: CLI (Typer) | WebSocket /api/v1/ws | Python SDK
↓ ↓ ↓
┌─────────────────────────────────────────────────┐
│ ChatOrchestrator │
│ routes UnifiedContext → selected Capability │
│ (defaults to `chat`) │
└──────────┬──────────────┬───────────────────────┘
│ │
┌──────────▼──┐ ┌────────▼──────────┐
│ ToolRegistry │ │ CapabilityRegistry │
│ (Level 1) │ │ (Level 2) │
└──────────────┘ └────────────────────┘
```
All capabilities emit on a shared `StreamBus`; the orchestrator fans
events out to consumers. Runtime settings live in
`data/user/settings/*.json` — project-root `.env` files are intentionally
ignored.
### Level 1 — Tools
Single-function tools the LLM picks on demand. Four user-toggleable tools
surface in `/settings/tools`:
| Tool | Description |
| -------------- | --------------------------------------------- |
| `brainstorm` | Breadth-first idea exploration with rationale |
| `web_search` | Web search with citations |
| `paper_search` | arXiv preprint search |
| `reason` | Dedicated deep-reasoning LLM call |
The rest are **context-gated**: the chat capability auto-mounts them from
`ToolMountFlags` (presence of a KB, attachments, sandbox availability, …), and
any of them can also be force-enabled via `--tool`. Auto-mounted set: `rag`,
`read_source`, `read_memory`, `write_memory`, `read_skill`, `load_tools`,
`exec`, `code_execution` (sandboxed Python: NL intent → code → run),
`list_notebook`, `write_note`, `web_fetch`, `github`, `cron`,
`ask_user` (pauses the turn and resumes with the user's reply), plus the
mastery-path tools. `geogebra_analysis` is parked under
`COMING_SOON_TOOL_TYPES`.
### Level 2 — Capabilities
Multi-stage pipelines that own the turn:
| Capability | Stages |
| ---------------- | ----------------------------------------------------- |
| `chat` | exploring → responding (single agentic loop, default) |
| `mastery_path` | responding (Guided Learning — chat loop + mastery tools, gated per topic type) |
| `deep_solve` | planning → reasoning → writing |
| `deep_question` | ideation → generation |
| `deep_research` | rephrasing → decomposing → researching → reporting |
| `visualize` | analyzing → generating → reviewing (SVG / Chart.js / Mermaid / HTML; or routes to Manim sub-stages via `render_type`) |
| `math_animator` | concept_analysis → concept_design → code_generation → code_retry → summary → render_output |
All capabilities converge on `emit_capability_result()` in
`deeptutor/capabilities/_shared.py` so every turn emits the same envelope
(response payload + `cost_summary` from `UsageTracker`). Status copy and
prompts are i18n'd via `capabilities/prompts/{en,zh}/<name>.yaml`.
## CLI Usage
```bash
# Install
pip install deeptutor # Full app (CLI + Web/API + packaged Web assets)
pip install deeptutor-cli # CLI-only
# Run any capability
deeptutor run chat "Explain Fourier transform"
deeptutor run deep_solve "Solve x^2=4" -t rag --kb my-kb
deeptutor run visualize "Animate sine wave" --config render_mode=manim_video
# Interactive REPL
deeptutor chat
# (inside the REPL: /regenerate or /retry re-runs the last user message)
# Partners (IM-connected companions)
deeptutor partner list
# Knowledge bases, memory, server
deeptutor kb list
deeptutor kb create my-kb --doc textbook.pdf
deeptutor memory show
deeptutor serve --port 8001 # API server only
deeptutor start # backend + frontend together
```
## Key Files
| Path | Purpose |
| ------------------------------------------ | ------------------------------------ |
| `deeptutor/runtime/orchestrator.py` | `ChatOrchestrator` — unified entry |
| `deeptutor/runtime/launcher.py` | Backend + frontend lifecycle / port discovery |
| `deeptutor/runtime/registry/` | Tool + Capability registries |
| `deeptutor/runtime/bootstrap/builtin_capabilities.py` | Built-in capability class paths |
| `deeptutor/services/config/runtime_settings.py` | JSON settings + process-env overrides |
| `deeptutor/core/stream.py`, `stream_bus.py` | StreamEvent protocol + async fan-out |
| `deeptutor/core/tool_protocol.py` | `BaseTool` + `ToolDefinition` |
| `deeptutor/core/capability_protocol.py` | `BaseCapability` + `CapabilityManifest` |
| `deeptutor/core/context.py` | `UnifiedContext` dataclass |
| `deeptutor/tools/builtin/__init__.py` | All built-in tool wrappers |
| `deeptutor/capabilities/` | Built-in capability implementations |
| `deeptutor/app.py` | `DeepTutorApp` — Python SDK facade |
| `deeptutor_cli/main.py` | Typer CLI entry point |
| `deeptutor/api/routers/unified_ws.py` | Unified WebSocket endpoint |
## Dependency Layers
Public install paths and source extras are defined in `pyproject.toml`.
Requirements files mirror the same dependency groups for Docker/CI installs.
```
pip install deeptutor — Full app (CLI + Web/API + packaged Web assets)
pip install deeptutor-cli — CLI-only (LLM + RAG + providers + document parsing)
pip install -e . — Source install for development
Source extras (.[ extra ], defined in pyproject.toml):
.[cli] — CLI-only dependency set
.[server] — Web/API server dependencies
.[partners] — Partner channel SDKs + MCP client (legacy alias: .[tutorbot])
.[matrix] — Matrix channel for Partners (matrix-nio; needs libolm)
.[matrix-e2e] — Matrix with end-to-end encryption (matrix-nio[e2e])
.[math-animator] — Manim addon (powers `visualize` Manim renders + `deeptutor run math_animator`)
.[dev] — Test / lint tooling
.[all] — Everything above
```