Turnstone

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?

$ℋ : s_{n+1} ~ T(s_n) for n < τ_H — the whole controlled loop: π lowers state to context, M_W proposes a readout, γ authorizes it, Q_E acts on the world, ρ verifies and folds back$

ℋ :  s_{n+1} ~ T(s_n)   for n < τ_H

the primer → · the formalism →

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.

Repository branches

Beginning with 1.8.0, stable releases and next-release development use distinct branches:

Branch	Role	Pull requests
`main`	Current stable release and install source	Fixes that should ship on the current stable line
`dev`	Next release integration and GitHub default	Features, refactors, and development-only fixes
`stable/X.Y`	Maintained prior release line	Fixes for that supported older minor

GitHub defaults to dev, so new pull requests and ordinary source clones start on the development line. Stable-source install paths explicitly select main. There is no independently writable stable/X.Y mirror for the minor currently on main. Before main advances to the next stable minor, its current tip is preserved as that minor's maintenance branch. Maintainers then merge fixes forward (stable/X.Y → main → dev) so the branches retain shared ancestry without recurring cherry-picks.

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

The browser's Default / Compact control saves your transcript preference in that browser. Compact hides reasoning text and folds completed successful tool details. Both views show a Reasoning indicator with elapsed seconds while the model prepares its answer. The clock starts when the browser observes that phase and continues as reasoning tokens arrive; reconnecting to an existing phase starts a new clock if its start was not observed. Completed history has no estimated reasoning duration.

Turnstone system architecture

Quickstart

pip install turnstone

# Terminal REPL
turnstone --base-url http://localhost:8000/v1

# Browser UI — define models in the console Models tab, or in
# ~/.config/turnstone/config.toml:
#   [models.local]
#   base_url = "http://localhost:8000/v1"
#   model = "qwen3-32b"
turnstone-server --port 8080

# 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

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 stable source and run it yourself:

git clone --branch main https://github.com/turnstonelabs/turnstone
cd turnstone
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.

For optional SSO, per-user MCP OAuth, or model gateway authentication, the installer and both stacks support one shared authentication config. The default local-login setup needs no additional configuration.

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, docs/mcp-registry.md for MCP configuration, and MCP authentication for auth choices and remote Docker/GitHub OAuth setup.

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
MCP authentication / OAuth	docs/mcp-oauth.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.

Release files for turnstone 1.8.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for turnstone 1.8.3
File	Size	Uploaded
turnstone-1.8.3.tar.gz	8.5 MB	Sep 8, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for turnstone 1.8.3
File	Interpreter	ABI	Platform	Reset
turnstone-1.8.3-py3-none-any.whl 5.3 MB Sep 8, 2026	Python 3	none	any	Details

Total release size: 13.7 MB

Release files / turnstone-1.8.3.tar.gz

Download URL	turnstone-1.8.3.tar.gz
Size	8.5 MB
Tags	Source
SHA-256 checksum How to use checksums	`d1dde09cc262804d8915f9167e0d3c634440196116517d4d27c33fc33d3a13b0`
BLAKE2b-256 checksum How to use checksums	`c0863353d1dbaf5c1cdc13af6b74e6aadf71e6927bd6ce259fd3442660e41f0b`
Upload date	Sep 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/turnstonelabs/turnstone
Publishing commit: github.com/turnstonelabs/turnstone/tree/3f7bf635b09143d0d09694ef6a9039325aa3af2a

Workflow

Publishing configuration: github.com/turnstonelabs/turnstone/blob/3f7bf635b09143d0d09694ef6a9039325aa3af2a/.github/workflows/publish.yml
Publishing logs: github.com/turnstonelabs/turnstone/actions/runs/34184881248/attempts/1

Artifact

Subject: turnstone-1.8.3.tar.gz
SHA-256 checksum: d1dde09cc262804d8915f9167e0d3c634440196116517d4d27c33fc33d3a13b0 Verifying attestations

Release files / turnstone-1.8.3-py3-none-any.whl

Download URL	turnstone-1.8.3-py3-none-any.whl
Size	5.3 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`6bdd8c681db5cdec79180b3f0da90e77b69bc16183cb80d08a614c7d2d529577`
BLAKE2b-256 checksum How to use checksums	`98a23bcff01e529673ce99d4a5fa48969d1a78a2441d4065557a4a821b9f901b`
Upload date	Sep 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/turnstonelabs/turnstone
Publishing commit: github.com/turnstonelabs/turnstone/tree/3f7bf635b09143d0d09694ef6a9039325aa3af2a

Workflow

Publishing configuration: github.com/turnstonelabs/turnstone/blob/3f7bf635b09143d0d09694ef6a9039325aa3af2a/.github/workflows/publish.yml
Publishing logs: github.com/turnstonelabs/turnstone/actions/runs/34184881248/attempts/1

Artifact

Subject: turnstone-1.8.3-py3-none-any.whl
SHA-256 checksum: 6bdd8c681db5cdec79180b3f0da90e77b69bc16183cb80d08a614c7d2d529577 Verifying attestations