Patrick Buckley 4d89efa7b8 fix(mcp): registry-liveness for cooled entries, dead-grant convergence, revoke interlock
Fix round for the review of the #836 catalog-retention change
(14 findings: 9 correctness, 5 cleanup):

- Cooled retention now requires the server to still exist in the pool
  registries (_retain_cooled — ONE policy shared by the TTL skip and
  the close path): an admin delete/disable/rename/auth-flip drops the
  ghost catalog within one eviction tick. Pre-#836 the idle TTL
  bounded such ghosts to ~10 minutes; retention made them immortal,
  including a disabled server that stayed dispatchable and duplicate
  tool names after a flip to static.
- A dispatch that learns the grant is durably GONE (token row missing
  or refresh permanently rejected — the mcp_consent_required class)
  drops that (user, server) catalog, so a disconnect made on another
  node converges here at first touch instead of re-offering revoked
  tools behind a consent card. Re-consent restores the tools through
  the existing consent-completion single-server prime.
- Revocation drops serialize against an in-flight connect via the
  entry's open_lock (_drop_catalog_locked): an unserialized drop was
  republished (resurrected) by the connect's completing discovery,
  with nothing left to ever clear it.
- The LRU pass counts closes incrementally and the TTL pass checks a
  once-per-tick listener snapshot instead of scanning the listener
  registry per entry under its lock.
- bound_token (a plaintext bearer) is cleared whenever the session is
  dropped — it is dead on a session-less entry, and cooling otherwise
  retained it for the life of the user's sessions.
- Per-user status falls back to the cooled catalog for its counts and
  reports the idle pool separately (user_pools_idle): cooled is the
  steady state now, and the warm-only view said '0 tools' for a
  catalog the same user's chat was actively offered.
- Session construction re-reads the merged tool lists after listener
  registration, closing the read-then-register window that missed a
  concurrent drop's only notification.
- Dedup: one retention policy, one warm predicate, one rebuild+notify
  sequence (was three copies), and the drop-catalog path now layers
  on _evict_session instead of copying its prologue.

Known limits, deliberately deferred: shared-workstream participants
who are not the acting user still lose their catalogs at TTL (not a
regression — the next send re-primes), and the pre-existing
orphaned-lock race on full-drop is unchanged.
2026-07-13 21:13:42 -07:00
2026-07-05 01:57:54 -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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