Patrick Buckley a7cab83dd1 fix(mcp): address pre-push review findings
A max-effort review of the branch before pushing surfaced six defects, several
introduced by this branch's own commits. All fixed:

[0]+[3] oauth priming (refined). Fully non-destructive priming never cleared a
genuinely-revoked grant — the dead token stayed "consented", its tools never
entered the catalog, and (bug) the PERMANENT branch returned before arming the
cooldown, so every session re-hit the AS with a dead refresh token. Root cause:
invalid_grant (PERMANENT) is a RELIABLE dead-grant signal (RFC 6749 §5.2), so
deferring its revoke was net-harmful. Renamed the flag revoke_on_dead_grant ->
revoke_ambiguous_escalation: priming now revokes genuinely-dead grants (permanent
/ expired-no-refresh) so the catalog isn't stranded cold behind a phantom token,
and defers ONLY the sustained-UNCLASSIFIABLE (ambiguous) escalation to lazy
dispatch — the case the "don't revoke an unused server's grant on a
misclassification" concern actually applies to. The cooldown is armed before the
ambiguous path, so the deferred case can't hammer the AS either.

[1] server.py. _public_server_status (operator refresh/reconnect endpoints)
didn't forward the new scope, so after per-user scoping every warm oauth_user
server rendered disconnected/empty there. Now passes aggregate=True (operator /
approve-scoped cluster view, matching the admin console).

[5] _is_dead_transport. The widened httpx.TimeoutException swept in
httpx.PoolTimeout — pool saturation, NOT a dead connection — so transient load
would evict a healthy session and trip the shared breaker for all users.
Narrowed to Connect/Read/WriteTimeout (kept NetworkError, RemoteProtocolError).

[8] _is_dead_transport. The exact-message "session terminated" fallback still
fired on a healthy session-owning server's protocol error with that message. The
SDK-synthesized code 32600 is the only deterministic signal (the message is
application-controlled), so match the code ALONE and drop the message fallback.

[11] cleanup. The dead-transport except block was triplicated across
call_tool_sync / read_resource_sync / get_prompt_sync — the exact drift this
branch had to repair. Extracted _record_and_evict_on_dead_transport.

Tests updated/added: prime revokes-permanent / defers-ambiguous (drives the real
resolver both ways); PoolTimeout-is-not-dead; exact-"Session terminated"-message
stays alive; _public_server_status aggregate. 836 test_mcp_* green, ruff + mypy
clean.
2026-06-30 19:30:20 -07:00
2026-06-28 04:12:06 -07:00
2026-06-28 04:12:06 -07:00

Turnstone

CI PyPI Python License Discord

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 hypothesis →

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 Eval harness for prompt/tool optimization
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)

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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