* fix(coord): close gaps an operator's harness shakedown surfaced
Operator-driven shakedown of the coordinator tool surface flagged
five issues; this commit addresses all of them plus the review
findings against the initial fix.
1. Cancelled-mid-stream partial assistant content now carries a
"[generation cancelled before completion]" marker. Without it,
``inspect_workstream`` / ``wait_for_workstream`` callers and the
next coord-LLM turn read the truncated text as a complete answer.
``_cancelled_partial_msg`` no longer ships ``_provider_content``
(Anthropic would otherwise read that lane verbatim and bypass the
marker; partial tool_use blocks could also leak through).
2. ``spawn_workstream`` / ``spawn_batch`` no longer surface the
routing-proxy ``status`` field (always HTTP 200 on the success
path). The tool description claimed it was "lifecycle state at
creation"; code that did ``if result["status"] == "idle"``
silently never matched. Lifecycle state lives on the workstream
row — ``inspect_workstream`` is the read. Tool JSON descriptions
plus docs/coordinator-skills.md and docs/bulk-endpoints.md
examples updated to match.
3. ``inspect_workstream`` not-found error string is bare ("workstream
not found"); the structured ``ws_id`` field carries the queried
id. Pre-fix the error STRING echoed the id back at the caller
who just sent it — redundant and out of step with the rest of the
surface. Cross-tenant + missing rows still return the same shape,
preserving the existence-leak guarantee.
4. ``tasks(...)`` is now rejected when called in a parallel tool
batch. The prior shape relied on a docstring warning ("a list
paralleled with writes can reflect pre-write state") that put
cognitive overhead on every model invocation; turning the silent
footgun into an explicit error means the model only thinks about
the rule the moment it actually breaks it. Warning dropped from
the tasks tool description. ``_PARALLEL_INCOMPATIBLE_TOOLS``
constant in session.py is the extension point for any future
tool with the same read-after-write hazard.
Plus the multi-stage code review's findings against the initial
fix (q-1 / q-2 docs drift, q-3 idiom, q-4 keys-assertion, q-5
duplicate guard) — all addressed in the same pass.
Tests: 4752 pass, +6 net since the pre-fix baseline. Ruff + mypy
clean. Three new tests pin the parallel-batch-rejection behaviour
on tasks (rejected when batched, runs alone, sibling tools
unaffected); existing cancel + spawn + inspect tests updated to
match the new shape.
* fix(coord): close two copilot review gaps on PR 444
Copilot review on PR 444 flagged two follow-ups:
1. Empty-content cancel divergence — when ``GenerationCancelled``
races BEFORE the first content token, the prior shape skipped
``save_message`` and only appended an empty-content msg in
memory. In-memory and storage diverged: a rehydrate would see
nothing in storage but the session would carry an empty
assistant turn. Both branches now persist; on the empty-content
shape the marker becomes the entire message
("[generation cancelled before completion]") so storage matches
the in-memory history.
2. Test stub cleanup — three new tests injected ``ui.approve_tools``
via ad-hoc ``lambda + type: ignore[attr-defined]``. Replaced
with a permissive ``approve_tools`` method on ``_StubUI`` so the
stub matches the SessionUI surface the dispatcher actually
reads. Tests that exercise approval pathways can still override
per-instance.
Tests: 4752 pass. Ruff + mypy clean.
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.
