Patrick Buckley eb89ddab1e fix(metacog): cleanup batch — share watch-key constant, sanitize metadata, drop tombstones
Closes round-1 review findings q-2 (minor), q-5 (minor), q-6 (nit), q-7
(nit), sec-1 (nit), perf-4 (nit).

* **q-5:** Export ``_WATCH_REMINDER_OPTIONAL_KEYS`` from
  ``turnstone/core/watch.py`` and import in the dispatch closure
  (session.py) and the replay filter (server.py:_build_history).  The
  three-place duplication of the literal tuple
  ``("watch_name", "command", "poll_count", "max_polls", "is_final")``
  is gone; future field adds touch one constant.

* **sec-1:** Run ``sanitize_payload`` over string-typed metadata fields
  (``watch_name`` / ``command``) before they enter the queue.  Today's
  consumers all use ``textContent``, but the asymmetry — sanitised
  ``text`` alongside unsanitised metadata — would survive forever in
  DB rows and resurface if a future consumer used a non-textContent
  sink (aria-label, copy-to-clipboard, markdown render).

* **q-7:** Drop the per-iteration ``isinstance(reminder, dict)`` from
  the dispatch closure's metadata comprehension.  By the time the
  block runs, ``text = reminder.get("text", "") if isinstance(...)``
  + the ``if not sanitized: return`` guard above already established
  ``reminder`` is a non-empty dict.

* **q-2:** Strip tombstone-style references — "post-#482", "post-#484",
  "Step 7 of the watch-card UX plan", "Post-Step-7 dispatch surface",
  and the brittle line-anchor "session.py:2685-2686" — across
  ``session.py``, ``test_session.py``, ``test_watch.py``,
  ``test_watch_dispatch.py``, ``test_watch_integration.py``.  Comment
  intent preserved; historical anchors gone.

* **q-6:** Drop the ``del source`` line in ``cli.py``'s
  ``on_user_reminder``; the parallel ``on_tool_reminder`` ignores
  ``tool_call_id`` without ``del`` and the comment alone is enough.

* **perf-4:** Document the SQLite ``render_as_batch=True`` recreate
  cost in migration 050's docstring — first deployment after upgrade
  copies the conversations table twice (one per ``add_column``).
  PostgreSQL is unaffected.

5734 non-live tests pass; ruff + mypy clean.

(cherry picked from commit 7e35050b68)
2026-05-07 17:35:22 -07:00

Turnstone

CI PyPI Python License

Multi-node AI orchestration platform. Deploy tool-using AI agents across a cluster of servers with direct HTTP routing, interactive interfaces, and enterprise governance.

Turnstone coordinator — parallel tool batches with judge-graded approval and child workstream tracking

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

Turnstone system architecture

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.

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