- Tracker: add GET /v1/tracker-nodes/<model> returning nodes registered
with tracker_mode=true whose shard_start matches the model's first layer
- Node: StubNodeServer and TorchNodeServer accept tracker_mode/tracker_url;
when tracker_mode=True (or auto-detected via shard_start==0 for Torch),
/v1/chat/completions is served alongside /forward
- TorchNodeServer: full pipeline implementation — encode_prompt → route
selection via tracker → binary forward through remaining hops → decode
- Gateway: _handle_chat_completions checks _get_tracker_nodes() first and
proxies round-robin to tracker-nodes; falls back to existing direct
pipeline when none found (preserves all US-005 backward compat)
- CLI: --tracker-mode and --tracker-url flags added to meshnet-node start
- Test: two stub tracker-nodes + two mid-shard nodes for gpt2; 10 requests;
round-robin 5/5 split verified; all OpenAI-format responses validated
- All 78 tests pass
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- GET /v1/health, GET /v1/models, POST /v1/chat/completions (streaming + non-streaming)
- OpenAI SDK, LangChain ChatOpenAI, and SSE streaming integration tests
- Tracker-backed GET /v1/models endpoint
- OpenAI-format errors for unavailable model (503) and pipeline failures
- Malformed JSON body handled with 400 instead of crash
- Test deps (openai, langchain-openai) declared in root pyproject dev extras
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
