wehub-resource-sync
118 lines
5.4 KiB
Markdown
118 lines
5.4 KiB
Markdown
# Oracle Agent Spec × Memory × CopilotKit
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A personal **travel concierge** that shows how to use three things together — it searches flights, renders generative UI (flight cards, boarding-pass ticket), and remembers you across sessions:
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- **Oracle Agent Spec** — define the agent once as portable JSON, run it on LangGraph.
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- **Oracle AI Database / Agent Memory** — durable, cross-session memory via semantic search.
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- **CopilotKit** — the frontend chat layer, over the open [AG-UI](https://docs.ag-ui.com/) protocol.
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Tell the concierge your travel preferences, come back in a brand-new session, and
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it still knows them — recalled from Oracle AI Database, not the current chat.
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> 🌐 Try it live: [hosted demo on Railway](https://showcase-oracle-agent-memory-production.up.railway.app)
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> 📖 Full write-up: [the cookbook recipe](../../../showcase/shell-docs/src/content/docs/cookbook/oracle-agent-spec-memory.mdx)
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## How it works
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```text
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Next.js + CopilotKit (V2) ──/api/copilotkit──▶ CopilotRuntime (HttpAgent)
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│ AG-UI (SSE)
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▼
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Agent Spec JSON → ag_ui_agentspec (LangGraph)
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recall_memory · search_flights · book_flight (HITL ClientTool)
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│ recall + persist
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▼
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oracleagentmemory → Oracle AI Database
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```
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The agent is **defined once** in Agent Spec (`agent/concierge/agent.py`) and run on
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LangGraph via the `ag_ui_agentspec` adapter. `recall_memory` pulls durable
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preferences from Oracle Agent Memory before planning; each turn is persisted so new
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preferences are extracted for next time, and a reconcile pass supersedes outdated facts so an updated preference wins on the next recall. CopilotKit consumes the AG-UI endpoint
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with an `HttpAgent`, so the agent owns the LLM call.
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## Prerequisites
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- **Python 3.12** (required — `oracleagentmemory` ships a cp312-only wheel),
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[`uv`](https://docs.astral.sh/uv/), Node.js 18+
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- Docker (for the local Oracle AI Database) or your own Oracle AI Database
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- `OPENAI_API_KEY` (defaults use OpenAI via litellm)
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> **Heads-up:** the frontend uses CopilotKit **V2 prerelease** builds so Agent
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> Spec's human-in-the-loop renders, and the `ag_ui_agentspec` adapter is installed
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> from the `ag-ui` repo (not PyPI). Both are pinned in the manifests.
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## Quickstart
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### 1. Start Oracle AI Database (run from this directory)
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```bash
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docker compose up -d
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docker compose logs -f oracle-db # wait for "DATABASE IS READY TO USE"
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./db/setup-db.sh # create the cookbook DB user (idempotent)
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```
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First boot takes a few minutes. The `container-registry.oracle.com/database/free`
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image includes AI Vector Search, which `oracleagentmemory` uses for semantic recall.
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### 2. Run the agent
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```bash
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cd agent
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cp .env.example .env # add your OPENAI_API_KEY
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uv sync
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uv run uvicorn concierge.server:app --reload --port 8000
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```
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Health check: `curl localhost:8000/health` → `{"status":"ok"}`.
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### 3. Run the frontend
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```bash
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cd frontend
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cp .env.local.example .env.local # optional; defaults to localhost:8000/run
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npm install
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npm run dev
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```
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Open http://localhost:3000.
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## Try it
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1. Tell it: _"I'm vegetarian, I fly from SFO, and I prefer an aisle seat."_
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2. Click **"+ New thread"** in the left sidebar, then ask: _"Find me a flight to Amsterdam."_
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3. It recalls your preferences from Oracle (home airport SFO, aisle seat, vegetarian meal)
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and surfaces flights like **AMS-001 — KLM KL606, nonstop, $740** as clickable flight
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cards — driven by what it remembered, not what you said in this thread.
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**Book it:** select a flight from the cards (or ask _"Book me flight AMS-001 to Amsterdam"_),
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then click **Confirm & book** on the confirmation card to get the boarding pass.
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`book_flight` is a CopilotKit **ClientTool** so the confirm→book step resolves in one agent run.
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Multi-turn follow-ups in the same thread work too, via a server-side workaround — see Notes below.
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## Tests
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End-to-end Playwright tests drive the real chat UI against the live agent + Oracle
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AI Database and record video. See [`frontend/e2e/README.md`](frontend/e2e/README.md):
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```bash
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cd frontend && npm run test:e2e
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```
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## Notes
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- **User identity** — defaults to a single `demo-user`. The Agent Spec × AG-UI
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adapter doesn't forward `forwarded_props`, so to scope memory per real user, set
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`user_id` from a ContextVar populated by a FastAPI dependency. See
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`agent/concierge/tools.py`.
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- **Multi-turn & booking** — `book_flight` is a CopilotKit **ClientTool**
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(`useHumanInTheLoop`), so the confirm→book step resolves inside a single agent run.
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Follow-up messages after a server-tool call would otherwise trip an upstream Agent
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Spec × AG-UI adapter bug (`tool_call_id` correlation); the cookbook works around it in
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`agent/concierge/server.py` by replacing the adapter's incremental message merge with a
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full-history replace each turn, so multi-turn conversations work end-to-end. The
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**"+ New thread"** flow above just proves recall is user-scoped — a fresh thread still
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remembers you. See
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[`docs/known-issues/agentspec-multiturn-toolcall-correlation.md`](docs/known-issues/agentspec-multiturn-toolcall-correlation.md).
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- **Models** — set `CHAT_MODEL`, `MEMORY_LLM_MODEL`, `EMBEDDING_MODEL` in `agent/.env`.
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