Patrick Buckley c823156af5 refactor(mcp): consolidate per-server state into StaticServerState dataclass
Phase 0 of the OAuth-MCP RFC: prepare MCPClientManager for the per-(user,
server) session pool that lands in Phase 5, without changing static-path
behavior.

Two changes:

1. Hardening helpers _pre_close_streams and _tcp_probe rename their first
   parameter from `name` to `key`.  Type stays `str` for now; widening to
   `str | tuple[str, str]` happens in Phase 5 when callers actually pass
   tuples.  _safe_close_stack takes the stack directly and is unchanged.

2. The eleven parallel name-keyed dicts (_sessions, _per_server_stacks,
   _per_server_tools, _per_server_resources, _per_server_prompts,
   _supports_list_changed, _supports_resources, _supports_resource_list_changed,
   _supports_prompts, _supports_prompt_list_changed, _server_streams) are
   consolidated into _static_servers: dict[str, StaticServerState].  Server-
   level state (circuit breaker, notification debounce, last-error,
   db-managed, merged catalog maps, listener lists) stays on the manager,
   unchanged.

PoolEntryState is defined for Phase 5 use but no code instantiates it.  The
typed map declarations (dict[str, StaticServerState] vs dict[tuple[str, str],
PoolEntryState]) make accidental cross-keying lookups easier to catch.

PR #296 hardening preserved exactly:
- pre-close-streams atomic take-and-clear before stack teardown
- stale-session-and-stack guard at _connect_one top: both state.session and
  state.stack checked, cleared independently, entry preserved (not popped)
- transport-error session-eviction in dispatch sets state.session=None only,
  leaving stack/streams for the next connect-time guard sweep
- _safe_close_stack CancelledError suppression unchanged
- TCP probe before streamablehttp_client unchanged
- future.cancel() after TimeoutError in all sync bridges unchanged
- notification debounce stays manager-level (not migrated into the dataclass)

Refresh helpers (_refresh_server_tools/_resources/_prompts) snapshot
state.session into a local immediately after the None guard so concurrent
transport-error eviction during await cannot null the session reference
mid-call.

Tests: shared _seed_static_state helper in tests/conftest.py replaces eleven
direct dict mutations; new test_reconnect_preserves_static_state_identity
guards the entry-preservation invariant.  Pass count rises 5266 → 5267.

(cherry picked from commit be0950bb98)
2026-05-07 17:35:20 -07:00
2026-05-04 03:04:09 -07:00
2026-05-04 03:04:09 -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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