Patrick Buckley 3dd0e196fe refactor(coord): hygiene pass on coord_registry refresh — async + selective teardown + test cleanup
Hygiene follow-ups from the multi-stage code review on #453.

perf-1 — sync helper called from async route handlers
``_refresh_coord_registry`` runs two sync DB reads and a registry reload
that takes ``_client_lock``; calling it directly from an async handler
held the event loop for the duration.  All four call sites now
``await asyncio.to_thread(_refresh_coord_registry, ...)``, matching the
pattern from commit ``1f7d6ad`` (offloaded ``tenant_check``).

perf-3 — ModelRegistry.reload() tore down all clients unconditionally
The reload always closed every cached client and provider, even when
the changed fields (``model``, ``temperature``, ``context_window``)
didn't touch the connection target.  Now selective: clients drop only
when alias removed or ``(base_url, api_key, provider)`` differs;
providers drop only when alias removed or ``provider`` string differs.
Keeps connection pools warm across the common admin-edit case where
only metadata changed.  Two new ``test_model_registry`` cases lock the
keep-warm vs drop-on-change behaviour, and the existing
``test_reload_clears_clients`` was updated (it asserted the old
overly-aggressive contract) into
``test_reload_keeps_clients_when_connection_target_unchanged``.

q-5 — helper rename
``_refresh_console_coord_registry`` → ``_refresh_coord_registry``.  The
``console_`` prefix was redundant given the function lives in
``turnstone/console/server.py`` and sibling helpers there
(``_notify_nodes_model_reload``, ``_publish_config_change``,
``_collect_model_status``) all omit it.

q-1 — shared test middleware
``tests/test_admin_model_registry_refresh`` now imports the
header-driven ``_AuthMiddleware`` from ``tests/_coord_test_helpers``
and sets default ``X-Test-User`` / ``X-Test-Perms`` headers on the
``TestClient``.  The local hardcoded variant duplicated infrastructure
the helper module exists to centralise.

q-3 — multi-alias test registry
``_make_registry`` extracted a ``_make_config`` helper and gained an
``extras={alias: model}`` param so multi-alias scenarios stop
hand-building ``ModelConfig`` literals.
``test_delete_endpoint_refreshes_registry`` now uses the helper.

310 tests pass across the related coordinator + model surfaces.
2026-04-29 20:20:38 -07:00
2026-04-29 00:16:57 -07:00
2026-04-29 00:16:57 -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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