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Patrick Buckley
3143965e00
A freshly-installed console with no model rows in the DB at boot caught the ``ValueError`` from ``load_model_registry()`` in the lifespan and skipped the entire coord subsystem build, leaving ``coord_mgr`` ``None``. ``_refresh_coord_registry`` then bailed out at ``existing is None`` rather than building the subsystem on first model add — operators had to restart the console after configuring their first model in the admin panel for the "Coordinator subsystem not initialized" banner to clear. Extract the lifespan's coord build into a reusable ``_bootstrap_coord_subsystem`` and add ``_maybe_bootstrap_coord_subsystem`` that runs as an ``asyncio.to_thread`` follow-on after every admin model-CRUD endpoint (create/update/delete/reload). The helper: - fast-paths to a no-op when ``coord_mgr`` is already set; - guards concurrent first-install attempts with ``_COORD_BOOTSTRAP_LOCK`` + double-checked re-test inside the lock; - pre-computes config-derived integers BEFORE any thread starts so ``int(config_store.get(...))`` failures don't strand a started ``StateWriter`` daemon; - stamps ``coord_state_writer`` to ``app.state`` immediately after ``.start()`` so the new ``_teardown_partial_coord_subsystem`` can shut it down on a partial failure (no thread leaks across retries); - atomically commits ``coord_registry`` + clears ``coord_registry_error`` as the final step so callers can rely on the invariant ``coord_registry`` is set iff ``coord_mgr`` is set; - replaces the stale boot-time "no model definitions" message with a builder-failure-specific diagnosis (carrying ``type(exc).__name__``) on construction failure so the dashboard's 503 banner reflects the actual cause. Both the lifespan path and the runtime-bootstrap path now route through the same helper and the same teardown on failure. Tests: 12 new tests covering the helper-level wiring (idempotent fast-path, missing-prereq parametrised over ``config_store`` / ``collector`` / ``console_metrics``, no-rows error recording, builder failure error replacement, partial-state teardown), the endpoint integration, the deterministic concurrent-call lock test (uses an instrumented lock wrapper that signals when a second acquirer arrives, so the test fails fast on slow CI rather than depending on a wall-clock sleep), and a real-builder end-to-end case constructing a working ``SessionManager`` against a real ``ConfigStore`` + real ``ClusterCollector``.
Turnstone
Multi-node AI orchestration platform. Deploy tool-using AI agents across a cluster of servers with direct HTTP routing, interactive interfaces, and enterprise governance.
Named after the Ruddy Turnstone (Arenaria interpres) — a shorebird that flips stones to discover what's hiding underneath.
Release Tracks
| Track | Install | Docker | Description |
|---|---|---|---|
| Stable | pip install turnstone |
ghcr.io/turnstonelabs/turnstone:stable |
Production-grade. Bugfixes only. |
| Experimental | pip install turnstone --pre |
ghcr.io/turnstonelabs/turnstone:experimental |
New features. May have rough edges. |
See docs/releasing.md for the full release process.
What it does
Turnstone gives LLMs tools — shell, files, search, web, planning — and orchestrates multi-turn conversations where the model investigates, acts, and reports.
- Interactive sessions — terminal CLI or browser UI with parallel workstreams
- Cluster dashboard — real-time view of all nodes and workstreams with console routing proxy
- Intent validation — LLM judge evaluates every tool call with risk assessments and evidence
- Governance — RBAC, OIDC SSO, tool policies, skills, usage tracking, audit logs
- Multi-provider — OpenAI-compatible APIs (vLLM, llama.cpp, NIM), Anthropic Messages API, and Google Gemini
- MCP support — external tool servers with native deferred loading (Anthropic/OpenAI) or BM25 fallback
Quickstart
pip install turnstone
# Terminal REPL
turnstone --base-url http://localhost:8000/v1
# Browser UI
turnstone-server --port 8080 --base-url http://localhost:8000/v1
# Cluster dashboard
pip install turnstone[console]
turnstone-console --port 8090
For PostgreSQL (recommended for production):
pip install turnstone[postgres]
export TURNSTONE_DB_BACKEND=postgresql
export TURNSTONE_DB_URL="postgresql+psycopg://user:pass@localhost:5432/turnstone"
turnstone-server --port 8080 --base-url http://localhost:8000/v1
Docker
cp .env.example .env # edit LLM_BASE_URL, OPENAI_API_KEY, etc.
docker compose --profile production up
See QUICKSTART.md for the bootstrap wizard and docs/docker.md for Docker configuration and profiles.
Programmatic (SDK)
from turnstone.sdk import TurnstoneServer
with TurnstoneServer("http://localhost:8080", token="tok_xxx") as client:
ws = client.create_workstream(name="demo")
result = client.send_and_wait("Analyze the error logs", ws.ws_id, auto_approve=True)
print(result.content)
Tools
Built-in tools for shell, files, search, web, memory, notifications, and autonomous sub-agents — plus external tools via MCP with native deferred loading. See docs/tools.md for the full reference and docs/mcp-registry.md for MCP configuration.
Architecture
Single-node: Client → Server (direct HTTP + SSE). No external dependencies beyond the database.
Multi-node: Client → Console (rendezvous routing proxy) → Server nodes. The console picks the target node for each workstream via rendezvous (HRW) hashing over the live service registry — pure function of (ws_id, live_nodes), no stored bucket state, deterministic across readers. A node join or drop only re-routes the keys that score highest on the affected node.
| Component | Purpose |
|---|---|
turnstone |
Terminal CLI (REPL) |
turnstone-server |
Web UI + REST API + SSE events |
turnstone-console |
Cluster dashboard + routing proxy + admin panel |
turnstone-channel |
Channel gateway (Discord and Slack adapters) |
turnstone-admin |
User/token management CLI |
turnstone-eval |
Eval harness for prompt/tool optimization |
turnstone-bootstrap |
LLM-guided setup wizard |
Diagrams
UML diagrams in docs/diagrams/:
| Diagram | Description |
|---|---|
| System Context | Components and external dependencies |
| Package Structure | Python modules and dependency graph |
| Core Engine | SessionUI, ChatSession, LLMProvider |
| Conversation Turn | Message lifecycle through the engine |
| Tool Pipeline | Prepare / approve / execute |
| Workstream States | State machine transitions |
| Console Data Flow | Dashboard data collection |
| Deployment | Docker Compose topology |
| Auth | JWT, scopes, login flows |
| Channels | Discord / Slack adapters + routing |
| Judge | Intent validation pipeline |
| OIDC | SSO authorization code flow |
Documentation
| Topic | Link |
|---|---|
| Configuration reference | docs/settings.md |
| API reference | docs/api-reference.md |
| Docker deployment | docs/docker.md |
| Intent validation (judge) | docs/judge.md |
| Governance & RBAC | docs/governance.md |
| OIDC SSO | docs/oidc.md |
| TLS / mTLS | docs/tls.md |
| Channel integrations | docs/channels.md |
| Console dashboard | docs/console.md |
| Eval harness | docs/eval.md |
| Tools reference | docs/tools.md |
| MCP integration | docs/mcp-registry.md |
Requirements
- Python 3.11+
- An OpenAI-compatible API endpoint, Anthropic API key, or Google Gemini API key
- Optional: PostgreSQL (
pip install turnstone[postgres]), Anthropic (pip install turnstone[anthropic]) - Git LFS for cloning (diagram PNGs)
License
Business Source License 1.1 — free for all use except hosting as a managed service. Converts to Apache 2.0 on 2030-03-01.