Oracle Agent Spec × Memory × CopilotKit

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:

Oracle Agent Spec — define the agent once as portable JSON, run it on LangGraph.
Oracle AI Database / Agent Memory — durable, cross-session memory via semantic search.
CopilotKit — the frontend chat layer, over the open AG-UI protocol.

Tell the concierge your travel preferences, come back in a brand-new session, and it still knows them — recalled from Oracle AI Database, not the current chat.

🌐 Try it live: hosted demo on Railway 📖 Full write-up: the cookbook recipe

How it works

text

Next.js + CopilotKit (V2) ──/api/copilotkit──▶ CopilotRuntime (HttpAgent)
                                                    │  AG-UI (SSE)
                                                    ▼
                                  Agent Spec JSON → ag_ui_agentspec (LangGraph)
                                     recall_memory · search_flights · book_flight (HITL ClientTool)
                                                    │  recall + persist
                                                    ▼
                                        oracleagentmemory → Oracle AI Database

The agent is defined once in Agent Spec (agent/concierge/agent.py) and run on LangGraph via the ag_ui_agentspec adapter. recall_memory pulls durable preferences from Oracle Agent Memory before planning; each turn is persisted so new preferences are extracted for next time, and a reconcile pass supersedes outdated facts so an updated preference wins on the next recall.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 with an HttpAgent, so the agent owns the LLM call.

Prerequisites

Python 3.12 (required — oracleagentmemory ships a cp312-only wheel), uv, Node.js 18+
Docker (for the local Oracle AI Database) or your own Oracle AI Database
OPENAI_API_KEY (defaults use OpenAI via litellm)

Heads-up: the frontend uses CopilotKit V2 prerelease builds so Agent Spec's human-in-the-loop renders, and the ag_ui_agentspec adapter is installed from the ag-ui repo (not PyPI). Both are pinned in the manifests.

Quickstart

1. Start Oracle AI Database (run from this directory)

bash

docker compose up -d
docker compose logs -f oracle-db   # wait for "DATABASE IS READY TO USE"
./db/setup-db.sh                   # create the cookbook DB user (idempotent)

First boot takes a few minutes. The container-registry.oracle.com/database/free image includes AI Vector Search, which oracleagentmemory uses for semantic recall.

2. Run the agent

bash

cd agent
cp .env.example .env          # add your OPENAI_API_KEY
uv sync
uv run uvicorn concierge.server:app --reload --port 8000

Health check: curl localhost:8000/health → {"status":"ok"}.

3. Run the frontend

bash

cd frontend
cp .env.local.example .env.local   # optional; defaults to localhost:8000/run
npm install
npm run dev

Open http://localhost:3000.

Try it

Tell it: "I'm vegetarian, I fly from SFO, and I prefer an aisle seat."
Click "+ New thread" in the left sidebar, then ask: "Find me a flight to Amsterdam."
It recalls your preferences from Oracle (home airport SFO, aisle seat, vegetarian meal) and surfaces flights like AMS-001 — KLM KL606, nonstop, $740 as clickable flight cards — driven by what it remembered, not what you said in this thread.

Book it: select a flight from the cards (or ask "Book me flight AMS-001 to Amsterdam"), then click Confirm & book on the confirmation card to get the boarding pass. book_flight is a CopilotKit ClientTool so the confirm→book step resolves in one agent run. Multi-turn follow-ups in the same thread work too, via a server-side workaround — see Notes below.

Tests

End-to-end Playwright tests drive the real chat UI against the live agent + Oracle AI Database and record video. See frontend/e2e/README.md:

bash

cd frontend && npm run test:e2e

Notes

User identity — defaults to a single demo-user. The Agent Spec × AG-UI adapter doesn't forward forwarded_props, so to scope memory per real user, set user_id from a ContextVar populated by a FastAPI dependency. See agent/concierge/tools.py.
Multi-turn & booking — book_flight is a CopilotKit ClientTool (useHumanInTheLoop), so the confirm→book step resolves inside a single agent run. Follow-up messages after a server-tool call would otherwise trip an upstream Agent Spec × AG-UI adapter bug (tool_call_id correlation); the cookbook works around it in agent/concierge/server.py by replacing the adapter's incremental message merge with a full-history replace each turn, so multi-turn conversations work end-to-end. The "+ New thread" flow above just proves recall is user-scoped — a fresh thread still remembers you. See docs/known-issues/agentspec-multiturn-toolcall-correlation.md.
Models — set CHAT_MODEL, MEMORY_LLM_MODEL, EMBEDDING_MODEL in agent/.env.