Started as the SIP_ENGINE row flagged in the last commit. Cross-checking the
tables against config.py mechanically (rather than by eye) turned up more,
including two entries that were actively wrong.
Corrections:
- GATEWAY_RTP_PORT_MIN/MAX and GATEWAY_HOST are documented in
configuration.md but do not exist — no code reads them and they are absent
from .env.example. Setting them today does nothing. Replaced with the real
GATEWAY_SIP_ fields (host/port/domain).
- GATEWAY_SIP_PORT was documented as 5080 in two places; the code and
.env.example both say 5060.
- DATABASE_URL was documented with a SQLite default. There is none, and
startup exits if it is unset.
Additions — every env var the models accept is now documented somewhere
(verified bidirectionally: nothing in the models undocumented, nothing
documented that the models reject):
- Server section: HOST, PORT, DEBUG, LOG_LEVEL, LOG_FORMAT
- Safety section: MAX_CONCURRENT_CALLS, USE_MOCK_SIP, SIP_ENGINE
- Receptionist section (configuration.md had none, though seven vars exist)
Structural staleness, from the PR #8 media-plane work:
- core/pjsua_engine.py was absent from the component list and file tree; so
were dial_plan.py (the emergency guard) and sip_engine.py.
- architecture.md's banner still read "media plane in transition". The engine
landed; it is now two selectable engines with the audio consequence stated.
- Tech Stack described "single-process async architecture" — the
simplification CLAUDE.md explicitly calls out. Now points at the threading
model, since there are three execution contexts.
- The Asterisk lab shipped in PR #8 with its own README but nothing linked to
it. Linked from the test section and both doc indexes.
- CLAUDE.md's "no structured JSON logging" gap is closed; test count was 146
across 16 files, now 189 across 19. The other listed gaps (no /metrics, no
rate limiting, no health-probe log filter) were re-verified and still hold.
Deliberately not hardcoding a test count in the README — that is the same
staleness this commit is clearing up. All internal links and anchors verified
to resolve; 189 tests pass; lint unchanged at its 216 baseline.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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>
Update deployment validation plan to reflect Phase 1b completion — pjsua2
built from pjproject 2.17 on caliban without sudo. Document the non-obvious
RPATH/patchelf step, the status() method name correction, and two remaining
caveats (not captured by pip install, Docker still runs stub media).
Update README to point at the new docs/pjsua2-build.md and clarify stub-mode
behavior.
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.
The SvelteKit build was always made for the root (no base path); it
now mounts at / — registered last so /api/v1, /ws, /health, and /mcp
match first — and the JSON root endpoint is gone (its info lives in
/health and gateway_status). REST routers move from /api/* to
/api/v1/*; /ws and /health stay put; /mcp/ unchanged. Dashboard API
client, tests, README, and docs updated; dashboard rebuilt (build/ is
gitignored).
Verified live: / serves the UI, /api/v1 answers 200/401, the old
/api paths 404, MCP still lists 15 tools.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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 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
