Stage 2 Priority 0 Step 0.2 body-convergence — first verb. Both
interactive ``approve`` and coord ``coordinator_approve`` handler
bodies collapse into ``make_approve_handler()`` in
``turnstone/core/session_routes.py``. Each kind sets a
``SessionEndpointConfig`` on ``app.state`` carrying the kind-
specific policies (auth gate, manager lookup, tenant check, audit
prefix, not-found label) the lifted body consults at request time.
The two interactive URLs converge:
- ``POST /v1/api/workstreams/{ws_id}/approve`` (new, path-keyed)
reaches the lifted body directly via ``register_session_routes``.
- ``POST /v1/api/approve`` (legacy, body-keyed) keeps shipping;
``make_legacy_body_keyed_adapter`` peeks the body for ``ws_id``,
splices it into ``request.path_params``, and forwards to the same
lifted body. Frontend can keep using the legacy URL — no caller
churn.
Coord exposes only the path-keyed shape (its URLs were experimental
in 1.5.0aN; the URL-shape commit already removed the ``coordinator/``
prefix).
Tenant-check is split out from permission-gate so interactive's
``_require_ws_access`` (404 on cross-owner) and coord's
``_require_admin_coordinator`` (cluster-wide scope) coexist without
either kind triggering the wrong gate.
Coord-side test fixture (``test_coordinator_endpoints._make_client``)
swaps the imported ``coordinator_approve`` for the lifted handler
and seeds ``app.state.session_endpoint_config`` so the tests
exercise the same code path the live console does.
Net delta: ~−25 LOC for this verb on top of the SessionEndpointConfig
+ legacy-adapter scaffolding (~80 LOC paid once). Subsequent verb
lifts amortize against that scaffolding.
ruff + mypy + 4366 pytest pass. Live console smoke against the
unified URL returns 503 (no coord_mgr loaded in the smoke env) —
proves the lifted handler is reachable + the manager_lookup callable
fires correctly.
Verbs still kind-specific (deferred — bodies have substantive
behavior divergence, not just naming): ``send`` (Priority 1
worker dispatch), ``cancel`` (interactive forensics + force flag),
``close`` (interactive close-reason cap+redact+persist), ``open``
(interactive resume vs coord rehydrate), ``events`` (different SSE
replay shapes), ``create`` (interactive attachments vs coord
initial_message), ``list`` / ``saved`` (different response keys).
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.
