The call-flow learner finally gets fed: exploration mode records its
IVR discoveries on the call (ActiveCall.exploration_steps) instead of
throwing them away, persistence stores them in the call record's
metadata, and the rebuilt learn_call_flow MCP tool turns a completed
exploration call into a stored flow via CallFlowLearner — correct
constructor (llm_client from get_llm, heuristic labels when the LLM
is unavailable), build for a new number, merge/refine when a flow
already exists. save_learned_flow/update_flow_from_model keep the
CallFlow↔row mapping in call_persistence.
Test gaps closed: tests/test_learner.py (discoveries→linked steps,
exploration persistence, learn-then-refine through the in-memory MCP
client, no-data and unknown-call answers) and tests/test_websocket.py
(4401 without token, trunk-status-then-replay on connect, per-call
stream filtering).
Docs aligned to code: README (15 tools incl. learn_call_flow, HTTP
not SSE, Python 3.12+, PostgreSQL+Alembic — no SQLite fallback, media
pipeline marked stub-mode until pjsua2 installed, Alembic and honest
/health checked off); docs/mcp-server.md rewritten against the actual
tool surface (hangup not end_call, real params, 3 real resources,
/mcp/ streamable HTTP + bearer auth); architecture/development/
configuration drift fixed.
pyproject: pruned never-imported deps (websockets, librosa,
soundfile, python-multipart).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The gateway was the composition root, device registry, inbound-call
policy, and call-operations service in one class, with core↔services
circular imports papered over by function-local imports, wiring done
by assigning private attributes, and MCP tools duplicating REST query
logic against their own sessions.
Composition:
- main.py's lifespan now builds every service and wires them by
constructor/registration. gateway.from_config() is gone; core/ no
longer imports services/ anywhere — the cycle is dead.
- Inbound-call policy moved to ReceptionistService.on_inbound_call
(routing evaluation, reject/answer, screening dispatch); wired as
the engine's on_incoming_call by the lifespan. Receptionist deps
(tts/transcription/recording/routing) are constructor-injected —
no more gateway._tts reach-through or importing hold_slayer's
private _get_llm (now services.llm_client.get_llm, shared).
- Hold-slayer launch goes through a mode-handler registry
(register_mode_handler); the gateway no longer knows the service's
type. CallManager takes on_call_ended in its constructor.
- build_sip_engine() is a pure function taking explicit callbacks.
- api/routing.py uses the routing service from app.state via a
proper dependency instead of gateway._routing.
Shared data layer:
- db.session_scope() is the one session convention (get_db wraps it).
- services/call_persistence.py gains the query/write functions and
the single StoredCallFlow→CallFlow mapper; api/call_flows.py,
api/call_history.py, and the six DB-touching MCP tools are thin
wrappers over them — the two surfaces can't drift.
- legs_for_call() replaces the three private _call_legs scans
(gateway transfer/hangup, REST dtmf, MCP dtmf).
7 new tests (mode-handler launch, on_call_ended hook, receptionist
inbound answer/reject, call-flow CRUD round-trip and history routes
against real SQLite through the shared layer). aiosqlite added to dev
deps for that.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The MCP server was created but never mounted — no client could reach
it. Mount it at /mcp/ over streamable HTTP with a combined lifespan,
resolving the gateway lazily so mounting happens at app construction.
Security and safety for the agent surface:
- One static API_TOKEN (SecretStr) enforced across REST (dependency),
WebSocket (query param/header before accept), and MCP
(StaticTokenVerifier). Startup refuses tokenless non-loopback binds.
- Emergency numbers (911/9911/112) always refused on make_call, plus a
MAX_CONCURRENT_CALLS cap; ValueError surfaces as 400/ToolError.
- Safe defaults: debug off, no credential in default DATABASE_URL,
SIP/LLM/TTS secrets as SecretStr.
Cleanups:
- Delete broken learn_call_flow tool (wrong ctor args, nonexistent
method) and the never-fed CallAnalytics service; keep
call_flow_learner for proper wiring later.
- Trim dial_plan to what is actually used (emergency guard, extension
allocation); delete the unreferenced matcher/normaliser.
- Register call_history before calls so /api/calls/history is no
longer shadowed by /api/calls/{call_id}.
- fastmcp pinned >=3.0 (http_app + StaticTokenVerifier).
New tests: MCP in-memory client (tool surface, lazy gateway, emergency
refusal, call cap) and API security (401 paths, route order, mount).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Complete project scaffolding and core implementation of an AI-powered
telephony system that calls companies, navigates IVR menus, waits on
hold, and transfers to the user when a human answers.
Key components:
- FastAPI server with REST API, WebSocket, and MCP (SSE) interfaces
- SIP/VoIP call management via PJSUA2 with RTP audio streaming
- LLM-powered IVR navigation using OpenAI/Anthropic with tool calling
- Hold detection service combining audio analysis and silence detection
- Real-time STT (Whisper/Deepgram) and TTS (OpenAI/Piper) pipelines
- Call recording with per-channel and mixed audio capture
- Event bus (asyncio pub/sub) for real-time client updates
- Web dashboard with live call monitoring
- SQLite persistence via SQLAlchemy with call history and analytics
- Notification support (email, SMS, webhook, desktop)
- Docker Compose deployment with Opal VoIP and Opal Media containers
- Comprehensive test suite with unit, integration, and E2E tests
- Simplified .gitignore and full project documentation in README
