An Asterisk instance that answers calls, plays an IVR, holds with music and
connects a "human", so the gateway has something real to dial that is not the
PSTN: no charges, no strangers, no E911 exposure.
Asterisk rather than Kamailio because the unproven risks are media risks.
Kamailio is a proxy — it routes signalling and answers nothing, so it would
forward the INVITE and find nobody home. Asterisk is a B2BUA: it answers,
plays prompts and collects DTMF, which is the hold-slayer scenario itself.
Kamailio remains the better model for trunk registration/digest auth later.
No application changes are needed to use it. SIP_TRUNK_HOST is just an
address, so the production code path runs unmodified — there is no test-only
branch anywhere in the gateway. It also means safety is structural: while the
trunk points at the lab there is no route to the PSTN at all, an absence of
route rather than a policy that could be misconfigured.
Nine scenarios (1001-1008 plus an echo test) cover the baseline call, the
IVR/DTMF path, hold-then-human, long hold, busy, no-answer, remote hangup and
silence.
The image ships no sound files, so sounds/generate.py synthesises three
fixtures from fixed seeds — byte-identical on every run, which is what makes
a classifier regression distinguishable from noise. Verified against
AudioClassifier: music→MUSIC 0.85, speech→LIVE_HUMAN 0.75,
silence→SILENCE 1.00. The speech formants deliberately avoid the DTMF bands;
the first version landed on a valid pair and classified as a keypress.
Anonymous inbound calls are refused, and endpoint matching is by source
address — Asterisk's default matches the From-header domain, which Hold
Slayer populates from its SIP bind address (0.0.0.0 on a wildcard bind).
Generated audio and the rendered per-host configs are gitignored: the former
is reproducible from a fixed seed, the latter carry a host-specific IP and
the lab password.
Known limit, documented in the README: MediaPipeline.create_tap is a stub, so
the classifier receives no audio on a live call. RTP flows and Asterisk plays
audio, but the tap is never fed — the fixture results above were measured by
feeding the classifier directly. This blocks scenarios 1002/1003/1004.
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 deployment and validation plan documenting a staged
bring-up approach that gates each layer on its predecessor and defers
PSTN testing until everything else is proven.
Update .env.example to reflect current configuration:
- Replace static API_TOKEN with Casdoor SSO + owner-minted PAT auth
- Add Rhema TTS settings with port-collision warning
- Add AI Receptionist settings for inbound calls
- Reconcile GATEWAY_SIP_PORT to 5060 and default SIP domain
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>
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
