Patrick Buckley 98d4be8ffe fix(watch): deliver terminal fires instead of dropping them silently
WatchRunner._poll_watch committed active=False to the row BEFORE
calling _dispatch_result for a terminal fire, and the dispatch closure
registered by ChatSession.set_watch_runner enqueued each reminder with
a valid_until=is_watch_active predicate that re-read the row at drain
time. Since the runner already flipped active to 0, the predicate
returned False for every dispatched fire and NudgeQueue.drain silently
dropped the entry — the model never saw a watch result. Then a
subsequent action=cancel call hit list_watches_for_ws (filters
active==1), the now-inactive row was invisible, and the cancel
returned 'Watch "X" not found.' regardless of whether the watch had
actually run.

Reorder _poll_watch to dispatch before the row write, drop the
valid_until predicate from the watch closure (its only effect was the
bug above), and add a _terminal_dispatched guard on the runner so a
transient storage failure between dispatch and row-write doesn't
re-fire the reminder on the next tick. Add WatchRunner.forget_terminal_dispatched
and call it from the cancel path so an out-of-band deactivate (next_poll='')
doesn't leak the watch_id from the runner's pending-retry set indefinitely.

Cancel-by-name now routes through a new find_watch_by_name storage
method that ignores the active filter and prefers active rows over
newer-inactive same-name siblings. The session.py cancel branch
distinguishes 'already completed (auto-cancelled)' from 'not found'
so the model can tell apart 'this watch ran and finished' from
'no such watch.' Consolidate the two byte-identical _escape_like
/ _escape_ilike helpers in the storage backends into a single
turnstone.core.storage._utils.escape_like and apply it to the new
find_watch_by_name LIKE pattern so a model-supplied watch name
containing % or _ can't redirect a cancel to a sibling watch.

NudgeQueue.drain previously dropped predicate-failed entries without
logging anything, which is what hid this bug for so long. Drain now
emits nudge_queue.predicate_dropped: info for reason=predicate_false
(the normal lifecycle case — idle_children when every active child
finished between enqueue and drain), warning with exc_info for
reason=predicate_raised (a misbehaving predicate).

Tests: new test_poll_watch_terminal_fire_survives_drain (parametrized
stop_on_fired + max_polls_reached) drives the real WatchRunner._poll_watch
against a real tmp_db row and confirmed to fail against pristine main.
test_poll_watch_retry_deactivate_after_update_watch_failure exercises
the _terminal_dispatched retry-deactivate branch end to end.
test_cancel_clears_pending_terminal_dispatched_entry covers the cancel-
path leak case. test_find_by_name_prefers_active_over_newer_inactive
catches the ordering regression. test_find_by_name_treats_percent_as_literal
+ test_find_by_name_treats_underscore_as_literal pin the LIKE escape.
2026-05-14 15:13:00 -07:00
2026-05-13 15:47:12 -07:00
2026-05-13 15:47:12 -07:00
2026-05-13 15:47:31 -07:00

Turnstone

CI PyPI Python License

Multi-node AI orchestration platform. Deploy tool-using AI agents across a cluster of servers with direct HTTP routing, interactive interfaces, and enterprise governance.

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.

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.

  • Interactive sessions — terminal CLI or browser UI with parallel workstreams
  • Cluster dashboard — real-time view of all nodes and workstreams with console routing proxy
  • Intent validation — LLM judge evaluates every tool call with risk assessments and evidence
  • Governance — RBAC, OIDC SSO, tool policies, skills, usage tracking, audit logs
  • Multi-provider — OpenAI-compatible APIs (vLLM, llama.cpp, NIM), Anthropic Messages API, and Google Gemini
  • MCP support — external tool servers with native deferred loading (Anthropic/OpenAI) or BM25 fallback

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
pip install turnstone[console]
turnstone-console --port 8090

For PostgreSQL (recommended for production):

pip install turnstone[postgres]
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

cp .env.example .env  # edit LLM_BASE_URL, OPENAI_API_KEY, etc.
docker compose --profile production up

See QUICKSTART.md for the bootstrap wizard and docs/docker.md for Docker configuration and profiles.

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-bootstrap LLM-guided setup wizard

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: PostgreSQL (pip install turnstone[postgres]), Anthropic (pip install turnstone[anthropic])
  • Git LFS for cloning (diagram PNGs)

License

Business Source License 1.1 — free for all use except hosting as a managed service. Converts to Apache 2.0 on 2030-03-01.

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