Patrick Buckley b6391d1f90 fix(model-turn): one sampling-knob assignment scheme — alias > config > model definition > omit
Round-2 review fixes. The round-1 de-pinning collided with
ConfigStore.get's default-on-miss semantics: the registry defaults
(temperature 1.0, effort "medium") were manufactured onto every
store-backed lane's wire, making the documented "unset -> omit"
terminal unreachable. Unset is now representable end to end, and one
scheme governs every lane: per-model alias value > operator-stored
global setting > in-code model definition (effort only: caps
declaration) > field omitted, inference engine's default rules.

- settings_registry: model.temperature default None, model.reasoning_effort
  default "" — the registered defaults ARE the unset sentinels, so the
  admin UI and the wire agree. Admin webux renders nullable floats blank
  ("(inherit model default)") and maps blank-save to reset; the "" effort
  choice reads "(inherit)".
- model_turn: resolve_temperature_setting/resolve_effort_setting are the
  ONE pair of operator-rung resolvers, shared by resolve_lane, both
  session factories, and the /model switch (the 4th-copy mirror is gone;
  the switch no longer leaks the previous model's override on store-less
  sessions). The caps rung moved out of the lane into model_turn's
  effective computation, below a new request-shaped default_reasoning_effort
  parameter (utility + output guard pass "low": budget coherence with
  their small token caps, not sampling policy — any operator or
  model-definition value beats it). The hidden "medium" terminal is gone.
- providers: Protocol + all adapters take reasoning_effort: str | None =
  None (the Protocol-signature "medium" was the same manufactured pin one
  layer down); ModelCapabilities.default_reasoning_effort defaults "" —
  commercial rows all declare theirs explicitly, so only local lanes and
  Anthropic change, both to match their real serving defaults (Anthropic
  manual-thinking models no longer get implicit thinking-on-medium).
  reasoning_template_kwargs distinguishes unset (inject nothing; template
  default rules) from the explicit "none" off-switch. apply_temperature
  skips temperature unless reasoning is EXPLICITLY off on none-declaring
  models (unset leaves the server default in charge, possibly reasoning-on).
- session: ctor takes temperature: float | None / reasoning_effort:
  str | None = None; _save_config/resume round-trip unset as "" (the
  str(None) era guarded); _run_agent relays session temperature AND
  effort on the same-alias fall-through only (a task alias's configured
  knobs stay reachable in both directions).
- optimizer: the five meta lanes are decoupled from --temperature/
  --reasoning-effort (test-model knobs, per their documented meaning);
  registry-less meta lanes omit both fields.
- cli: --temperature/--reasoning-effort default unset and fall through
  the model config instead of pinning 0.5/"medium" for every CLI session.
- cleanup from the review's below-cap findings: dead resolve_server_type
  deleted (tests re-pointed at _server_type_of), stale ChatSession
  comments in _openai_responses fixed, _store_get_or_none extracted,
  eval system-turn conversion hoisted out of the per-turn loop, dead
  _provider_extra_params patch removed, test_perception uses the shared
  mock_completion_result, effort_ladder uses apply_capability_overrides
  instead of a SimpleNamespace fake config.

Wire goldens regenerated: the only drift is the manufactured "medium"
effort vanishing from unset-effort requests (Responses reasoning.effort,
Chat/Google reasoning_effort, Anthropic output_config.effort) — pure
removals, no additions. Ladder tests now fake ConfigStore with the REAL
get() semantics (registry default on miss) so a forgiving fake can't
mask this class of bug again.
2026-07-13 08:48:27 -07:00
2026-07-05 01:57:54 -07:00

Turnstone

CI PyPI Python License Discord Sponsor

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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