Patrick Buckley 961a2017dc fix(streaming): delete the retry window's shared slots and gate the send epilogue
Fourth review round. The recurring defect family — cross-frame session
slots racing an orphanable window — is removed structurally instead of
gated again:

- The wire-fold slot is deleted. The fold the stream was actually
  created from rides the returned message dict on the underscore lane
  (like _provider_content) and is popped at the single calibration site
  before commit, so a superseding generation can never alias it and
  there is nothing left to clear. Plain-dict test fakes fall through the
  pop to the frame-local fold.
- The stream-provider slot is demoted to a creation-time handoff
  register: _try_stream stamps it, _stream_response copies it into a
  frame-local immediately after each create returns, and only that
  local feeds the retry gate. The fatal formatter returns to the
  consistent PRIMARY identity triple — pairing a fallback's provider
  name with the primary's base_url and alias sent operators to debug
  the wrong backend; stamping the full producing identity is #964.
- send()'s epilogue is generation-gated: a superseded thread's escaped
  death no longer records a fatal error over the healthy successor turn
  (error banner, buffer-wiping error-state drain, wrong last_error for
  the coord), and a Ctrl-C on an orphan no longer mutates history.
- The terminal arm discards as well as finalizes. Keeping the buffers
  bought nothing — the fatal path's error-state drain wipes them on
  every server lane — and the skipped discard let a mid-consumption
  overflow recovered by compact-and-retry concatenate the dead
  attempt's text with the recovered answer in the idle payload. Pinned
  with real-buffer tests for the overflow-recovery and orphan-epilogue
  paths.
- stream.post_finish_blip regains usage_captured, tracked by
  transport_guarded from the chunks it forwards, restoring
  missing-spend attribution on both lanes.
- TerminalUI.on_thinking_start is idempotent at the callee (a live
  spinner is stopped before being replaced), removing the caller-side
  stop-first dance and the leak the next unaware call site would have
  reintroduced.
- The think-tag vocabulary in _strip_reasoning and the title lane is
  derived from ThinkTagSplitter, closing the drift channel that would
  leak raw reasoning into compaction summaries and titles.
- on_stream_discarded's docstring states the true pending-batch
  semantics (defensive drop; the shipped sequence flushes via the
  preceding stream_end), and the live-suite recording fake gains the
  protocol method.
2026-08-04 04:53:17 -07:00
2026-07-29 22:49:25 -07:00
2026-08-03 11:38:31 -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?

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

 :  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.

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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