wehub-resource-sync
327 lines
17 KiB
Markdown
327 lines
17 KiB
Markdown
# Northwind Finance — CopilotKit v2 Banking Demo
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A customer-ready reference demo showing how to build a SaaS app with an embedded
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AI copilot on top of CopilotKit v2. The app — "Northwind Finance" — models a
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corporate banking dashboard where role-based users can view transactions,
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manage credit cards, and (for admins) manage team members. The copilot is
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wired into the same UI: it reads app context, calls typed tools to render
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generative UI, and asks the user to approve sensitive actions via
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human-in-the-loop.
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## Screenshots
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| ----------------------------------------------------------- | ---------------------------------------------- |
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While the officer demonstrates an action the copilot should learn from
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(approving a transaction, filing a policy exception), a soft violet vignette
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pulses around the canvas — the visible signal that the action is being
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recorded for the self-learning loop.
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## Running locally
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```bash
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export OPENAI_API_KEY=your-key
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pnpm install # from the repo root — this demo is a workspace package
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pnpm --filter demo-saas-copilot dev
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```
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Then open <http://localhost:3000>.
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The demo runs against the workspace versions of `@copilotkit/*` (see the root
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`pnpm-workspace.yaml`). The seed dataset lives in memory and resets every time
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the server restarts.
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## Memory & durable self-learning (Intelligence mode)
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By default the runtime is pure OSS: an SSE `CopilotRuntime` + `InMemoryAgentRunner`,
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with no external dependency. The agent runs locally against OpenAI and nothing is
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persisted. **This is the default and requires only `OPENAI_API_KEY`.**
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The runtime in `src/app/api/copilotkit/[[...slug]]/route.ts` is **env-gated**: when
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the three `INTELLIGENCE_*` vars below are all present it builds the runtime in
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Intelligence mode (`CopilotKitIntelligence` + `CopilotRuntime({ intelligence,
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identifyUser, licenseToken, … })`). The local `bankingAgent` still executes here,
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but it gains durable long-term memory — the `recall_memory` / `save_memory` MCP
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tools auto-attach from the memory-enabled Intelligence backend. If any of the three
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is unset, the demo falls back to the exact OSS path above.
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### What each mode recalls
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- **OSS (default):** the teach-a-workflow loop works _within a single
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conversation_. Start a **new** thread and the agent no longer knows the
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procedure; nothing persists across threads or restarts. (Expected — it's the
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signal that durable recall needs Intelligence mode.)
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- **Intelligence:** durable long-term memory across three flavours, all via
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`save_memory` / `recall_memory`:
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- **Demonstrated over-limit procedure** — saved as a `project`-scoped,
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`procedural` memory and recalled at the start of any later over-limit
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request. A **brand-new thread — or a different user on the same team** —
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recalls the procedure and completes the approval unaided. This is the
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durable cross-thread + cross-user proof (FOR-149).
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- **General facts / preferences (`user` scope)** — arbitrary personal facts
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persist cross-thread but stay per-person. Try _"remember my favorite food
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is sushi"_, then ask _"what's my favorite food?"_ in a **new** thread and
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the copilot recalls it.
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- **Team-shared facts (`project` scope)** — facts flagged for the whole team
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persist cross-user, so a teammate recalls them in their own threads.
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- **Secrets are never stored.** Passwords, API keys, tokens, and full card or
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SSN numbers are never written to memory.
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### 1. Start the memory-enabled stack (one command)
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A self-contained Intelligence stack (postgres + pgvector, redis, minio, a TEI
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embedder, and the `app-api` + realtime-gateway composite) is vendored as
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`docker-compose.yml`:
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**Recommended (one command, handles the embedder per-platform):**
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```bash
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cd examples/showcases/banking
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export INTELLIGENCE_REPO=/path/to/Intelligence # composite image build context + dev-license signer
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./run-demo.sh
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```
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`run-demo.sh` brings up the stack, picks the right embedder for your platform
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(native Metal TEI on Apple Silicon, the bundled docker `tei` on amd64/CI), mints
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a dev license if `.env` lacks one, then starts the Next.js dev server.
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**Manual (if you prefer raw compose):** the bundled `tei` is gated behind the
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`cpu-fallback` profile, so a bare `docker compose up` **skips it** (see the Apple
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Silicon note below). On amd64/CI, opt it in:
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```bash
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export INTELLIGENCE_REPO=/path/to/Intelligence
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docker compose --profile cpu-fallback up -d --wait # amd64/CI: bundled docker embedder
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pnpm dev
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```
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Host ports: app-api **7050**, gateway **7053**, postgres 7156, redis 7158, minio
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7160/7161, tei 7167. (The deps use a `715x` range so a bare `docker compose up`
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coexists with a developer's own Intelligence dev stack on `705x`.) Seeded org
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`casa-de-erlang`, key `cpk_sPRVSEED_seed0privat0longtoken00`, users
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`jordan-beamson` / `morgan-fluxx`. The team is exactly two members, each mapped
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1:1 to a seeded backend identity — **Alex Morgan (Admin) → `jordan-beamson`** and
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**Maya Chen (Assistant) → `morgan-fluxx`** (see `src/lib/intelligence/user-id.ts`).
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That 1:1 mapping is what makes cross-user memory scope demonstrable through the
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sidebar user switcher. `SL_ENABLED=true` + a reachable embedder are
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required for the `save_memory`/`recall_memory` MCP tools to attach — both are set
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on the `intelligence` service in `docker-compose.yml`.
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> **Apple Silicon:** the bundled `tei` image is amd64-only. Under emulation the
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> Candle/safetensors backend is unavailable, so TEI falls back to the ONNX/ORT
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> backend — which needs `onnx/model.onnx` files that `Qwen3-Embedding-0.6B` does
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> not publish (404), so the container **crash-loops**. `run-demo.sh` handles this
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> for you (native Metal TEI on `:7067`). To do it manually, run a native TEI on
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> the host and point the stack at it (the bundled `tei` is profile-gated, so a
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> bare `up` already skips it):
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>
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> ```bash
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> brew install text-embeddings-inference # one-time
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> text-embeddings-router --model-id Qwen/Qwen3-Embedding-0.6B --port 7067 --auto-truncate &
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> MEMORY_EMBEDDINGS_URL=http://host.docker.internal:7067 docker compose up -d --wait
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> ```
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>
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> Same TEI version (1.9.3) + model as the docker image → **byte-identical
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> embeddings**, and ~20× faster (Metal GPU vs CPU-under-emulation).
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### 2. Point the demo at the stack
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```bash
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cp .env.example .env # fill OPENAI_API_KEY; run `copilotkit license -n banking-demo`
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# .env (key lines):
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# INTELLIGENCE_API_URL=http://localhost:7050
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# INTELLIGENCE_GATEWAY_WS_URL=ws://localhost:7053
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# INTELLIGENCE_API_KEY=cpk_sPRVSEED_seed0privat0longtoken00
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# # INTELLIGENCE_USER_ID — leave UNPINNED for the interactive demo (see below)
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pnpm --filter demo-saas-copilot dev
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```
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The Next.js app needs only the three `INTELLIGENCE_*` vars (+ identity). The memory
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backend flags (`MEMORY_ENABLED`, `SL_ENABLED`, `MEMORY_EMBEDDINGS_URL`,
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`MEMORY_EMBEDDING_MODEL`) live on the `intelligence` service in `docker-compose.yml`.
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Leave `INTELLIGENCE_USER_ID` **unpinned** for the interactive demo: with it unset,
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the sidebar user switcher drives which backend identity (and therefore which memory
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scope) is active, so you can walk through cross-user isolation live. It is pinned to
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a single identity only for CI/e2e (see `playwright.config.ts`).
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### 3. The cross-thread payoff (FOR-149)
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With the stack up and project memory empty:
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1. **Thread A — teach.** Ask to approve an over-limit charge. The agent calls
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`recall_memory`, finds nothing, and offers to record. Demonstrate the policy
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exception on the dashboard, then click **Save workflow** — the agent calls
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`save_memory` (`scope:"project"`, `kind:"procedural"`).
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2. **Thread B — recall.** Open a **new** thread and ask to approve a _different_
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over-limit charge. The agent calls `recall_memory`, gets the procedure, files
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the exception with the learned code, and approves — **with no recording offer.**
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3. **Different persona.** Switch user (sidebar avatar) in a fresh thread and repeat
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— same unaided success, proving `project`-scope cross-user recall.
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### 4. The memory-scope isolation demo (two personas)
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The team is exactly **Alex Morgan (Admin)** and **Maya Chen (Assistant)**, mapped
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1:1 to the seeded `jordan-beamson` / `morgan-fluxx` backend identities. With
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`INTELLIGENCE_USER_ID` unpinned, the sidebar user switcher (bottom-left avatar)
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selects which identity is live, so scope isolation is visible end-to-end:
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1. **Personal fact — save (as Alex).** Ask _"remember my favorite food is
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sushi."_ The copilot confirms it saved (`user` scope).
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2. **Personal fact — recall (as Alex, new thread).** Open a **new** thread and
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ask _"what's my favorite food?"_ — the copilot recalls **sushi** (cross-thread,
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same person).
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3. **Personal fact — isolated (switch to Maya, fresh thread).** Switch the sidebar
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user to Maya, open a fresh thread, and ask _"what's my favorite food?"_ — the
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copilot **does NOT know it.** `user`-scope memory is per-person.
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4. **Team fact — crosses users.** Back as Alex, say _"keep in mind, for the whole
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team: our fiscal year ends in March."_ Switch to Maya and ask _"when does our
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fiscal year end?"_ — the copilot recalls **March.** `project`-scope memory
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crosses users on the same team.
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To replay the "fails first" beat, forget the saved procedure between runs
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(`DELETE http://localhost:7050/api/memories/:id`, or via the agent's
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`forget_memory` tool). Per-run reset for a repeatable public demo is a separate
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follow-up (user-scope memory, periodic DB reset, or a dashboard control).
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### Testing
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- **Deterministic E2E (CI gate):** `pnpm --filter demo-saas-copilot test:self-learning`
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runs `e2e/memory-learning.spec.ts`. The agent's LLM is served by
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[`@copilotkit/aimock`](https://github.com/CopilotKit/aimock) (fixtured
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`save_memory`/`recall_memory` tool calls) while the **real** local memory backend
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persists + recalls — so the full teach→save→fresh-thread-recall→unlock flow is
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deterministic. It asserts the fresh thread completes the unlock from recalled
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memory and never offers to record.
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- **Real-LLM drift smoke (manual, non-gating):**
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`node scripts/memory-drift-smoke.mjs` seeds the procedure via REST, then drives a
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fresh-thread over-limit request against a real OpenAI key and asserts the live
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model still emits `recall_memory` (the autonomous recall-first moment). aimock
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fixtures replay a fixed decision and cannot catch _behavioral drift_ after a
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prompt edit — this can. The save half is HITL-gated (not headless), so verify it
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via the manual walkthrough above + the aimock E2E. Run after editing the prompt or
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teach tools.
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### Advanced mode — Glass Engine
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Glass Engine is a docked inspector (desktop-only) that exposes the copilot's
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internals. It is gated twice:
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- **Availability (deployment):** set `GLASS_ENGINE_AVAILABLE=true` to expose the
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left-rail telescope toggle. Leave it unset on public deployments — Glass Engine
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is then absent entirely and the `/api/memories*` routes return 404. (FDE/sales/
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conference deployments set it; one image, per-deployment env.)
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- **Activation (presenter):** when available, the telescope toggles the pane on/off
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per session (persisted in localStorage), so you can reveal it case-by-case during
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a talk.
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Tabs:
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- **Timeline** — every AG-UI protocol event of each run, live (works in any mode).
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- **Memory** — durable memory recall + semantic search (Intelligence mode only).
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The "Recalled memories" list is top-k semantic recall, not a full enumeration.
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- **Learning** — the over-limit teach→save→recall procedure and live recall
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activity (Intelligence mode only).
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In OSS mode (no `INTELLIGENCE_*`) the Memory and Learning tabs show a "Requires
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Intelligence mode" hint.
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## Architecture at a glance
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ Frontend (Next.js 16, React 19, Tailwind v4) │
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│ CopilotKitProvider + CopilotPopup (@copilotkit/react-core/v2) │
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│ ├── useAgentContext → share user / page state with agent │
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│ ├── useFrontendTool → generative UI (showTransactions) │
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│ └── useHumanInTheLoop → approval flows (addNewCard, …) │
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└─────────────────────────────┬───────────────────────────────────┘
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│ AG-UI over SSE
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▼
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┌─────────────────────────────────────────────────────────────────┐
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│ Runtime (Hono, same Next process) │
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│ src/app/api/copilotkit/[[...slug]]/route.ts │
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│ BuiltInAgent + CopilotRuntime + createCopilotHonoHandler │
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│ (from @copilotkit/runtime/v2) │
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│ env-gated: OSS SSE + InMemoryAgentRunner by default; │
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│ CopilotKitIntelligence when INTELLIGENCE_* env is set │
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│ (see "Self-learning backend" below) │
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└─────────────────────────────┬───────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────────────┐
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│ Data layer │
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│ src/data/seed.json → seed cards, team, policies, txns │
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│ src/lib/store.ts → typed, in-memory store (resets) │
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│ src/app/api/v1/* → REST surface │
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│ (cards, transactions, │
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│ users, policies) │
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│ src/lib/identity.ts → Northwind branding strings │
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└─────────────────────────────────────────────────────────────────┘
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```
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## Key features and where to find them
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### App-wide context for the copilot
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`src/components/copilot-context.tsx` shares the current user and the current
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page with the agent via `useAgentContext`, so the LLM can adapt its responses
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to the logged-in role and the route the user is on. The Northwind brand and
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assistant greeting are centralized in `src/lib/identity.ts`.
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Switch between users from the bottom-left avatar in the sidebar to see how
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role (Admin vs Assistant) changes what the copilot will agree to do.
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### Generative UI — `showTransactions`
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The cards landing page at `src/app/page.tsx` registers
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`useFrontendTool({ name: "showTransactions", render })`. When you ask the
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copilot something like _"Show me transactions for my card ending 4242"_, the
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LLM calls the tool and the rendered list IS the answer — there is no
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follow-up paragraph restating the data.
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### Human-in-the-loop — `addNewCard` and `navigateToPageAndPerform`
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- `useHumanInTheLoop({ name: "addNewCard", render })` in `src/app/page.tsx`
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shows the "add card" confirmation card directly in chat; the user clicks
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Approve / Cancel and the result is sent back to the agent. The team page
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(`src/app/team/page.tsx`) uses the same pattern for removing a member and
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changing a member's role or team (inviting a member is a UI-only dialog
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flow, not an agent tool).
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- `useHumanInTheLoop({ name: "navigateToPageAndPerform" })` in
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`src/components/copilot-context.tsx` is the cross-page fallback: if the user
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asks for an operation that lives on another page (e.g. "change my Visa PIN"
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from the team page), the copilot asks for permission to navigate, then
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redirects with an `?operation=…` query param so the destination page can
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open the right dialog.
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### Role-based behaviour
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Authorization is communicated to the agent through `useAgentContext` rather
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than enforced on the LLM by prompt alone. The REST handlers in
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`src/app/api/v1/*` enforce the same rules on the server side, so a curious
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user (or a hallucinating model) cannot bypass them.
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## Backend & data
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- All read/write goes through `src/lib/store.ts`, which exposes typed helpers
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— readers like `cards()`, `team()`, `policies()`, `transactions()` and
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mutators like `findCard`, `updateCardPin`, `assignPolicyToCard`,
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`updateTransaction` — over an in-memory copy of `src/data/seed.json`.
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- The REST endpoints under `src/app/api/v1/*` (cards, transactions, users,
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policies) are thin handlers around the store and are what the UI uses.
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- There is no database. State resets on every server restart — this keeps the
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demo deterministic for screenshots, e2e tests, and customer walkthroughs.
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## Tests
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End-to-end Playwright smoke tests live under `e2e/` and can be run with:
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```bash
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pnpm --filter demo-saas-copilot test:e2e
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```
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