Patrick Buckley 77dc24e42d fix(coord): three regressions on PR 447 inline tool-batch refactor
Three regressions reported during operator harness shakedown, all
landed by the inline tool-batch refactor in caa07e6:

1. ``stripAnsi`` ReferenceError on every ``tool_result``.
   ``_appendResultToRow`` called ``stripAnsi(output || "")`` but the
   helper only existed in ``ui/static/app.js`` — coord.js never
   imported or defined it.  The thrown ReferenceError propagated up
   through ``appendToolResult``, aborting the SSE handler before
   ``loadTasksDebounced()`` could fire, AND the result block never
   appended to the row, AND history replay's tool-message loop
   bailed out at the first orphan-tool-result.  Three reported
   bugs (tasks pane stops auto-refreshing, tool output missing in
   the modal, reload only rebuilds the conversation up to the first
   tool result), one root cause.

   Fix: hoist a local ``stripAnsi`` mirroring the interactive UI's
   regex.  Keep it local rather than centralised — coord and
   interactive tool-output paths have different rendering
   strategies, and the interactive helper isn't on the shared
   module surface today.

2. JSON tool output rendered as a single unreadable line.  Coord
   tool surfaces (``list_nodes``, ``tasks``, ``spawn_workstream``,
   ...) emit JSON by default, and ``textContent = stripAnsi(raw)``
   showed the whole envelope on one line.  The parent
   ``.coord-tool-row-result`` already has ``white-space: pre-wrap``
   so a ``JSON.stringify(parsed, null, 2)`` body lays out as
   intended without a nested ``<pre>``.  Non-JSON / unparseable
   output falls through to the raw cleaned string.

3. Header tier badge stuck on ``⚙ heuristic`` after the LLM judge
   landed an upgraded verdict.  ``_pickBatchTier(items)`` ran once
   at batch-creation time; later ``intent_verdict`` SSE events
   updated the per-row chip via ``_appendVerdictLineTo`` but never
   refreshed the head.

   Fix: persist the verdict's tier on ``row.dataset.verdictTier``
   (+ ``verdictModel`` when set), add ``_refreshBatchTier(batch)``
   that scans the rows and computes the cross-row best tier (LLM
   beats heuristic), and call it from ``_appendVerdictLineTo``
   whenever a row writes a verdict.  ``_pickBatchTier`` gets the
   same prefer-LLM scan so the initial render is consistent.  The
   ``intent_verdict`` cache entry tags ``tier: "llm"`` so a late
   verdict landing on a previously heuristic-only row escalates
   the badge correctly.

No Python touched; node --check on coordinator.js clean.
2026-04-28 20:54:42 -07:00
2026-04-28 00:32:01 -07:00
2026-04-28 00:32:01 -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 console — multi-workstream AI orchestration with mermaid diagrams

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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