Patrick Buckley 53f11454ad fix(mcp): review round 3 — cap static catalogs, atomic removal, unify the list_changed protocol twins
- Static resource/prompt catalogs are now size-capped at connect
  discovery AND on every refresh (mirrors the pool twins and the static
  tools path): a misbehaving server's push ran uncapped through the new
  spawned refresh path and could balloon the shared node's merged
  catalogs on every notification.
- remove_server_sync mutates NOTHING outside the per-name lock: the
  up-front config pop meant a removal cancelled while parked (behind
  the push-refresh runners that now share this lock) left a
  half-removed server — config gone, session and published catalogs
  alive, no driver able to reconnect or cleanly re-remove. A timed-out
  removal is now honestly retryable.
- _refresh_all's DISCONNECTED branch busy-skips too (parking inside
  _ensure_static_connected burned the pass's 30s budget on one
  mid-reconnect server), and a busy-skip on either branch ARMS the
  health-tick retry — an operator-requested refresh can no longer be
  silently dropped with output indistinguishable from 'no changes'.
- reconnect_sync drops the session before queueing on the lock (FORCE
  semantics already rebuilt live sessions): parked push runners bail
  at their session gate instead of serializing up to one 30s list call
  per kind ahead of the operator's recovery action. Residual: one
  mid-list holder can still precede the 45s attempt; a timed-out
  reconnect is honest and retryable.
- _refresh_server's supersede check gains the session arm: a spawned
  retry/post-reconnect pass racing an eviction skipped instead of
  manufacturing a false 'not connected' error pill (and a re-arm loop)
  for a self-healing condition.
- The list_changed protocol twins are UNIFIED (Closes #842): the
  admission half (_admit_list_changed) and the runner half
  (_run_list_changed_refresh) each exist once as plain parametrized
  methods — values and small closures, no factory layer (mcp v2 drops
  the factory pattern; the two thin message_handler closures remain
  only as SDK-v1 bindings). The one true asymmetry — coalesce-marker
  ownership on the superseded path — is a documented boolean: pool
  markers are only ever cleared by their runner; static markers are
  cleared by remove_server_sync, so a present marker belongs to the
  re-added generation. Both runners keep their names and signatures;
  the notification suites pass unchanged.
- Cleanups: per-kind staleness rechecks stripped from the static
  refreshers (unreachable under the lock discipline — the MUST-hold-
  lock contract is documented instead); _run_hl (5th run-on-loop copy)
  replaced at 44 call sites; _poll_until centralizes the live-test
  wait loops; docs no longer describe the periodic refresh tier
  removed in eb2a119d.

Refs #839
2026-07-14 11:39:25 -07:00
2026-07-05 01:57:54 -07:00
2026-07-13 22:41:36 -07:00
2026-07-13 22:41:36 -07:00

Turnstone

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Self-hosted, local-first orchestration for tool-using AI agents. Give LLMs real tools — shell, files, search, web — and run them across your own cluster with direct HTTP routing and interactive interfaces. Your code, your models, your data stay on hardware you control: no telemetry, no phone-home.

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.

What is a harness?

 :  s_{n+1} ~ T(s_n)   for n < τ*,    T = ρ ∘ (M_W ∘ π, E)

the primer →

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.

  • Local-first & private — runs entirely on hardware you control, with no telemetry and no phone-home. Point it at local models (vLLM, llama.cpp) or commercial APIs you hold the keys to — your prompts and data never transit a third party you didn't choose.
  • Bring your own models — OpenAI-compatible APIs (vLLM, llama.cpp, NIM), the Anthropic Messages API, and Google Gemini, mixed freely per role
  • Interactive sessions — terminal CLI or browser UI with parallel workstreams
  • Cluster dashboard — real-time view of every node and workstream, with a rendezvous routing proxy
  • Intent validation — an LLM judge (your model) grades every tool call with a risk assessment and evidence before it runs
  • MCP support — external tool servers with native deferred loading (Anthropic/OpenAI) or BM25 fallback
  • Team controls when you need them — optional RBAC, SSO, tool policies, and audit logs, all stored in your own database

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
turnstone-console --port 8090

For PostgreSQL (recommended for production):

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

One-line install — autodetects Ubuntu/Debian, Fedora/RHEL, Arch, and WSL, installs git + Docker if missing, generates secrets, and starts the stack:

curl -fsSL https://raw.githubusercontent.com/turnstonelabs/turnstone/main/run.sh | bash

Or, if you already have Docker, clone the repo and run it yourself:

docker compose up

That builds one image and brings up a full local cluster — PostgreSQL, console, Caddy, channel gateway, and 10 server nodes — with no .env required (it ships with insecure dev defaults). Open the dashboard at https://localhost:8443 (Caddy serves it over TLS with its own local CA — trust it once). Nodes boot without an LLM; add model backends from the console UI.

For production (released images from ghcr.io, real secrets required), use the bundled stack: docker compose -f turnstone/deploy/compose.yaml up.

See QUICKSTART.md for the install + troubleshooting walkthrough and docs/docker.md for Docker configuration.

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 Headless measurement — scores tool-use against expected actions
turnstone-optimizer Prompt/tool optimizer (UCB self-modify loop over the eval substrate)
turnstone-doctor LLM-backed cluster diagnostics

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: Discord / Slack channel integrations (pip install turnstone[discord,slack])
  • Git LFS for cloning (diagram PNGs)

Support

Turnstone is free, Apache-2.0, and self-hosted — no paid tier, no telemetry, no upsell. If it saves you time or you'd like to help keep development moving, you can sponsor the project:

❤ Sponsor Turnstone → · one-off via PayPal

Sponsorship is entirely optional and funds maintenance, new features, and infrastructure. Prefer to contribute in other ways? Filing issues, improving docs, and pull requests help just as much.

Community

Questions, ideas, or want to show what you're building? Join us on Discord: discord.gg/Nh3bWMacaq.

License

Apache License 2.0, as of version 1.6.0. Versions 1.5.x and earlier remain under the Business Source License 1.1 they shipped with.

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