Patrick Buckley 7085c24530 refactor(skills): unify single-row lookup + allow coord-side load
Two changes that share the same kind-scoping touch point.

Lookup unification (closes the bypass Copilot flagged on _exec_skills_load):
- New ChatSession._lookup_visible_skill(name) — single source of truth for
  "find me a skill by name, if it's visible to this session". Combines
  storage.get_prompt_template_by_name with the kind filter in one call;
  returns None for both the missing-row and out-of-kind cases so callers
  don't have to branch on the reason.
- _exec_skills_get refactored from inline two-step to one helper call.
- _exec_skills_load refactored from the unscoped memory.get_skill_by_name
  to the new helper — the kind-scoping bypass it had (interactive could
  load a kind=coordinator skill by name) goes away by construction
  because the unscoped path no longer exists on the model-tool surface.
- memory.get_skill_by_name stays available for admin / sub-agent /
  rehydrate paths that need full-catalog visibility — those are
  deliberate cross-kind callers, not bypass surfaces. Storage exceptions
  now propagate from _lookup_visible_skill by design (distinct from the
  legacy swallow-and-return-None) so the operator gets a clear signal on
  DB outage rather than a misleading "not found".

Coord-side load support:
- _prepare_skills_load no longer rejects coordinator sessions. Parity
  with the admin / HTTP create path that already accepts a `skill` body
  field on kind=coordinator workstreams — what the operator can do at
  create time, the model can now do on its own session. Visibility is
  still kind-scoped via _lookup_visible_skill at exec (a coord can only
  load {coordinator, any}-tagged skills; interactive can only load
  {interactive, any}), matching what `find` / `get` enforce.

The kind-scoping itself is queued for a separate cleanup PR: the marker
turned out to be a discoverability hint that never gated runtime
capability, and the combinatorial complexity (every new model-tool /
HTTP path needs kind awareness) isn't worth the squeeze at this team
size. Follow-up issue to land.

Test coverage:
- TestLookupVisibleSkill — 5 cases: visible / cross-kind / missing /
  storage-unavailable / kind=any-on-both-surfaces.
- TestExecSkillsLoadKindScoping — kind-rejection from both directions
  (interactive→coord-only, coord→interactive-only), disabled-skill
  caller-side gate, and the two new positive coord-load cases (coord
  loads kind=coordinator and kind=any).
- Removed test_load_on_coord_session_errors (the rejection it pinned
  is gone).

Plus the /review-suggested doc fixes that came with the unification:
- Comment in _exec_skills_load now correctly attributes the disabled
  collapse to the caller's enabled check rather than implying the
  helper handles it.
- _lookup_visible_skill docstring documents the deliberate
  exception-propagation behavior.
2026-05-22 16:25:52 -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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