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>
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 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>
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
