# Agora Conversational AI — Agent Handoff Recipe (Python)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](./LICENSE)
[![Python](https://img.shields.io/badge/python-%3E%3D3.10-blue)](https://www.python.org/)
[![Bun](https://img.shields.io/badge/bun-latest-black)](https://bun.sh/)

The **agent-handoff** recipe in the Agora Conversational AI recipes family. It
demonstrates a **3-persona Travel Concierge** that transitions automatically
between **Triage → Booking → Trip Support** as the conversation progresses:

- **Triage** — greets the user and determines destination intent.
- **Booking** — presents deterministic flight options and books the chosen one.
- **Trip Support** — manages the confirmed trip (show, change, or cancel).

The active persona is derived on every turn from the user's intent keywords and
the contents of a local SQLite itinerary database — there is no session id and
no stored persona field. The trip persists across restarts (SQLite). Agora cloud
never sees a `tool_call`; the persona logic lives entirely inside `server/src/llm.py`,
mounted at `/llm` in the same backend process.

STT (Deepgram nova-3) and TTS (MiniMax) stay Agora-managed. This repo ships a
**zero-key mock** LLM endpoint so the full pipeline runs immediately without an
LLM API key.

## Prerequisites

- [Python 3.10+](https://www.python.org/)
- [Bun](https://bun.sh/)
- [Agora CLI](https://github.com/AgoraIO/cli) — makes generating an App ID + App Certificate easy
- [ngrok](https://ngrok.com/) — the backend must be publicly reachable so Agora cloud can call `/llm`

## Run It

```bash
# 1. Install + create the server Python venv
bun run setup

# 2. Add Agora credentials (CLI), or edit server/.env.local by hand
agora login
agora project use <your-project>          # select which project to use
agora project env write server/.env.local # writes App ID/Certificate

# 3. Expose the backend publicly (Agora cloud calls /llm/chat/completions)
ngrok http 8000

# 4. Add the tunnel URL to server/.env.local
#    CUSTOM_LLM_URL=https://<your-tunnel>.ngrok-free.dev/llm/chat/completions

# 5. Run the backend and web
bun run dev
```

Open [http://localhost:3000](http://localhost:3000) → **Start Conversation** → speak.

Try: "I want to fly to Paris" → "book the morning one" → "what's my itinerary"
→ "cancel my trip".

### Working from a clone

`bun run setup` creates the server Python venv and installs web dependencies.
`bun run dev` brings up the backend and web. You still need Agora credentials in
`server/.env.local` and a public `CUSTOM_LLM_URL` before a conversation can connect.

Services:

- Frontend — http://localhost:3000
- Backend — http://localhost:8000 (also serves `/llm`)
- API docs — http://localhost:8000/docs

## Deploy

Deploy `web` (Next.js) and `server` (a single publicly reachable FastAPI
backend). The concierge LLM endpoint is mounted at `/llm` in the same process, so
Agora cloud reaches it at `<public-url>/llm/chat/completions`. Set
`AGENT_BACKEND_URL` in the web deployment so Next rewrites reach the backend.

A single-process Docker image is published to
`ghcr.io/AgoraIO-Conversational-AI/recipe-agent-handoff` on `v*` tags. It
bundles the agent backend and the mock LLM endpoint in one process on port 8000.
Point `CUSTOM_LLM_URL` at `<public-url>/llm/chat/completions`.

> **Co-public caveat:** the server :8000 is now the public endpoint Agora calls
> (`/llm`), so the token endpoints are co-public; the App Certificate is only
> used in-memory to mint tokens (never on the wire); add auth/rate-limiting
> before a real deployment.

## Environment variables

Backend env file: [`server/.env.example`](server/.env.example).

| Variable | Required | Default | Notes |
| --- | :---: | :---: | --- |
| `AGORA_APP_ID` | ✅ | — | Agora Console → Project → App ID |
| `AGORA_APP_CERTIFICATE` | ✅ | — | Agora Console → Project → App Certificate (server only) |
| `CUSTOM_LLM_URL` | ✅ | — | **Public** chat-completions URL of your mounted `/llm` endpoint (`<tunnel>/llm/chat/completions`). Agora cloud calls it; cannot be `localhost`. |
| `CUSTOM_LLM_API_KEY` | ✅ | `any-key-here` | Forwarded by Agora cloud as `Authorization: Bearer`. Required by the `CustomLLM` vendor. |
| `CUSTOM_LLM_MODEL` |  | `handoff-mock` | Model name passed to your endpoint |
| `AGENT_GREETING` |  | built-in | Optional opening line override |
| `PORT` |  | `8000` | Agent backend port |
| `ITINERARY_DB_PATH` |  | `itinerary.db` | SQLite file the concierge LLM stores the booked trip in. Set to `/tmp/itinerary.db` in Docker. |
| `AGENT_BACKEND_URL` (web deploy) | ✅ | — | Required in a deployed `web` app when proxying to the backend |

## Commands

```bash
bun run setup            # install web deps + create server/ venv
bun run dev              # run backend (:8000, serves /llm) + web (:3000)

bun run doctor           # prerequisite check (no creds needed)
bun run doctor:local     # + .env.local + credentials + CUSTOM_LLM_URL checks

bun run verify           # web-only gate (no Agora creds needed)
bun run verify:local     # full local gate: backend compile + smoke tests + web build
bun run clean            # remove venvs and build artifacts
```

Tests run standalone (no Agora cloud needed): `pytest` in `server/`, plus
`bun run verify` in `web/`. CI runs them on Linux/macOS/Windows × Python 3.10 & 3.13.

## Architecture

```
Browser (localhost:3000)
  │  fetch /api/*
  ▼
Next.js  ──rewrite──▶  Agent backend  (server/, localhost:8000)
                          │  starts agent session (CustomLLM vendor)
                          ▼
                       Agora ConvoAI Cloud
                          │  POST <CUSTOM_LLM_URL>   (Authorization: Bearer)
                          ▼
                       Concierge LLM endpoint  (mounted at /llm in server/, localhost:8000)
                          ▲  public via ngrok tunnel
                          │  derives persona, runs FSM, streams reply
                          │  reads/writes SQLite itinerary.db
```

See [ARCHITECTURE.md](./ARCHITECTURE.md) for full detail.

## Repo Map

- `web/` — Next.js frontend (:3000); RTC/RTM lifecycle and UI.
- `server/` — FastAPI agent backend (:8000); Agora tokens + agent lifecycle, `CustomLLM` vendor, and the `/llm` endpoint mounted at the same port.
  - `server/src/llm.py` — OpenAI-compatible mock `/chat/completions` handler; 3-persona handoff FSM (Triage → Booking → Trip Support) over SQLite itinerary; no Agora deps.
- `ARCHITECTURE.md` — system shape and component boundaries.
- `AGENTS.md` — guide for coding agents working in this repo.

## What You Get

- A **Next.js** web client (:3000) that drives the RTC/RTM lifecycle and only
  ever calls `/api/*`.
- A **FastAPI** agent backend (:8000) that owns Agora token generation and the
  agent session lifecycle.
- The `/api/get_config` · `/api/startAgent` · `/api/stopAgent` contract between
  the web client and the backend (Next rewrites, no Route Handlers).
- A **3-persona handoff** — Triage → Booking → Trip Support — with persona derived
  at every turn from intent keywords + SQLite itinerary DB state.
- Deterministic flight options (Paris, Tokyo, Rome) and `_match_choice` for slot
  selection ("the morning one", "the cheapest").
- **SQLite + recall**: the booked itinerary persists across restarts.
- A **zero-key mock** LLM endpoint so the full pipeline runs with no LLM API key.

## How It Works

1. The browser calls `/api/get_config`; the backend mints an Agora token.
2. The browser joins the RTC channel, then calls `/api/startAgent`; the backend
   starts a session using the `CustomLLM` vendor pointed at `CUSTOM_LLM_URL`.
3. The user speaks. Agora runs STT (Deepgram nova-3), then sends the transcript
   to your `/llm` endpoint as `POST /chat/completions`.
4. `run_agent_turn()` calls `derive_persona()` — if a booking exists in SQLite
   the persona is `trip_support`; if the text contains booking keywords it is
   `booking`; otherwise `triage`. The function then dispatches to the right
   handler and streams only the final spoken reply in OpenAI SSE format.
5. Agora runs TTS (MiniMax) and plays it back. The persona transition is
   invisible to Agora cloud.
6. `/api/stopAgent` ends the session.

### Replacing the mock

Edit `server/src/llm.py`. The key surface area is `derive_persona()`,
`run_agent_turn()`, and the handler functions (`search_trips`, `book_trip`,
`get_itinerary`, `cancel_booking`, `modify_booking`). The endpoint must keep the
OpenAI streaming `/chat/completions` contract.

## Troubleshooting

| Problem | Fix |
| --- | --- |
| Agent starts but never speaks | `CUSTOM_LLM_URL` is not public or omits `/llm/chat/completions`. Use your ngrok URL. |
| `doctor:local` warns about localhost | Replace the local URL with your public tunnel URL. |
| Local calls fail under a global proxy | Configure your proxy to send `127.0.0.1` and `localhost` DIRECT. |
| `Missing server/venv` during verify | Run `bun run setup`. |

## More Docs

- [ARCHITECTURE.md](./ARCHITECTURE.md)
- [AGENTS.md](./AGENTS.md)

## License

Released under the [MIT License](./LICENSE).