Hold Slayer's logs are shipped to Loki by the host's Alloy agent, which
reads container stdout. Text lines arrive there as an opaque blob:
filtering on a status code meant regex over a formatted string. This adds
LOG_FORMAT=json (default "text", so local dev stays readable) rendering
one JSON object per line.
Two parts were less obvious than a format= argument would suggest, and
both are why this is a module rather than a basicConfig tweak:
Uvicorn attaches its own handlers to `uvicorn` and `uvicorn.access` with
propagate=False, so configuring only the root logger would have left the
access log — the highest-volume, most useful stream — as colourised text
next to our JSON. configure_logging clears those handlers and re-enables
propagation, and is called both at import (for startup config checks) and
in lifespan (uvicorn configures itself after importing the app). The
__main__ path passes log_config=None so uvicorn never applies its own.
The access record's payload lives in record.args as a 5-tuple, not in the
message. Formatting it would throw the structure away and force Loki to
parse it back out, so the tuple is unpacked into real fields and
status_code is emitted as a number for range filtering.
Also drops uvicorn's `color_message` extra, an ANSI-coloured duplicate of
the message that generic extra-promotion would otherwise copy into every
startup line — the same unreadable-in-Grafana problem recently fixed for
the lab's Asterisk logs.
Verified against a real uvicorn server: 39/39 lines valid JSON, zero ANSI
escapes, no duplicates, access lines structured with correct status codes;
text mode unchanged. Thread name is included off the main thread, since
"which execution context logged this" is the first question when debugging
across the asyncio/Sippy/PJSUA2 boundary. SecretStr extras stay masked.
README Phase 4 item ticked; LOG_FORMAT and the previously-undocumented
LOG_LEVEL added to the config table and .env.example.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The gateway can now hear. Verified end to end against the Asterisk lab:
speech classifies as live_human, hold music as music, the speech→music
transition tracks the dialplan, and DTMF reaches a real IVR (Asterisk logged
"caller pressed 1 -> accounts" and branched). RTP stats show 0% packet loss.
PJSUAEngine implements the existing SIPEngine interface, so the gateway,
call manager and hold-slayer service are unchanged. It is selected with
SIP_ENGINE=pjsua2; the default stays "sippy" while this is proven, and the
mock remains opt-in as before.
Why a new engine rather than fixing the old path: PJSUA2 exposes no
standalone RTP media object, so it will not surface media for a dialog it
does not own. Owning the dialog is the price of owning the media. Sippy keeps
the SBC roles it is good at — device registration, routing, leg bridging —
and SippyEngine remains fully functional for signalling; it simply cannot
carry media, which its media branch now says plainly instead of calling a
method that could never work.
The safety invariants are untouched. This engine is reachable only through
gateway.make_call, which refuses emergency numbers and enforces the
concurrency cap before any SIP action. No new dial path was introduced.
MediaPipeline.add_remote_stream(host, port) is replaced by
attach_call_media(stream_id, audio_media), called from onCallMediaState —
the one place PJSUA2 hands out RTP-backed media. Taps requested before media
comes up are attached when it does, so the classifier never misses the start
of a call.
Three crash/lifetime bugs found by running against the real bindings, none of
which any unit test would have caught:
- pj.Call and pj.Account objects finalised after libDestroy() abort the
process on a native assertion, exactly as media ports do. Both are now
dropped and collected before the pipeline destroys the endpoint.
- PJSUA2 keeps delivering callbacks during interpreter teardown, when module
globals may already be cleared. The callbacks alias what they need locally
and swallow everything: a raise there escapes into C++ and takes the worker
thread with it.
- hangup() only queues the BYE, so shutdown deleted the account with a call
still active and left the far end on an unclosed dialog. stop() now waits
briefly for the teardown to complete.
Threading follows the established rule: PJSUA2 worker threads reach the loop
only through _post_from_pj → run_coroutine_threadsafe, and any thread PJSUA2
did not create registers itself before touching a PJSUA2 object.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Add comprehensive rule documentation for AI-assisted development covering
authentication surfaces, outbound-call safety invariants, and other project
conventions to guide Claude's understanding of critical system behaviors.
Engine mode is now explicit: USE_MOCK_SIP=true is the only way to get
the mock engine; an unconfigured trunk fails startup with guidance
instead of silently degrading. Root-caused why the engine always ran
mock: nested pydantic-settings never read .env (no env_file on the
sub-settings classes) — all 8 now declare it.
/health stops lying: reports engine mode (sippy|mock), a live DB
SELECT 1, trunk registration state with reason, and TTS/STT
availability from their last real request; "healthy" now requires
ready + db + sippy + registered trunk.
Error policy: leaf services (tts/transcription/llm_client) raise and
track availability; call-loop callers catch, publish EventType.ERROR
naming the failed service, and apply an explicit fallback. Persistence
writes get one bounded 3x exponential retry, then an ERROR log — no
more silent data loss.
Event bus: a full subscriber queue drops its oldest event (counted)
instead of silently evicting the subscription; subscribe(replay_last=N)
delivers the advertised history replay, used by /ws/events (25).
Receptionist correctness: a matched TAKE_MESSAGE rule beats the LLM;
voicemail polls for early hangup and stops/transcribes/hangs up in
finally; RecordingSession finally keeps its leg_ids so taps detach.
Dead code removed: models/contact.py + Contact table, dtmf_buffer,
transcribe_stream stub, SMS stub in notification.py.
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>
Adds read-only access to persisted call records for the dashboard
and implements a client for the Rhema text-to-speech service.
- api/call_history.py: New router providing paged call lists
and detailed call records with transcript metadata.
- services/tts.py: Async client for OpenAI-compatible TTS
endpoints (Rhema/Kokoro) used for call-flow steps.
- Simplified .env.example to use localhost SPEACHES_URL
- Removed unused prod_url from SpeachesSettings config
- Added dashboard node_modules and build dirs to .gitignore
- Streamlines local development setup
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
