Round-1 ``/review`` apply-pass. Drops stale ``UserInterjection`` references from comments and docstrings that no longer describe the post-PR drain shape, asserts the two-stream invariant in the new queued-message persistence test, and pins the ``content.trim()`` + ``renderAssistantToolBatch`` invariants on coord-side so a future refactor can't silently regress the Qwen3 phantom-card fix or the chronological-order render fix. Deferred: * **bug-1** (back-to-back ``user`` row when ``user_feedback`` from the approval-prompt UI callback coexists with a queued-message drain). Reachable on strict OpenAI-compatible local templates (Anthropic and Anthropic-via-merge-consecutive collapse fine; vLLM-hosted Mistral / Llama enforcing role alternation can reject). The pre-PR splice guarded against this case by riding queued items inside the tool result envelope; that guard is what motivated the original UserInterjection design, so the fix lane needs a deliberate decision rather than a quick patch. Sleeping on it. * **q-1** (delete dead ``UserInterjection`` class + tests). Held for the bug-1 decision — if the chosen fix is to resume the splice for the ``user_feedback``+queue coexistence case, the advisory shape stays load-bearing. Class now carries a docstring note marking it retained-pending-decision so a passing reader doesn't grep for producers and assume it's actually dead. Apply-pass content: * ``q-2``: drop "queued user interjections" from the persistent- advisory parenthetical in ``send``'s tool-result loop comment; rewrite to point at ``_flush_queued_messages`` for the queue path. * ``q-3``: ``__init__`` channel-routing comment loses "and ``UserInterjection``" — only ``GuardAdvisory`` remains. * ``q-4``: ``_queue_tool_advisory`` docstring + the tool-error nudge comment lose the user-interjection mentions; the docstring also now describes the side-channel + ``_apply_reminders_for_provider`` splice path (the actual mechanism). * ``q-5``: ``AttachmentsNotQueueableError`` docstring rewritten to describe the post-PR ``_flush_queued_messages`` flow — the single-combined-turn ``\n\n``-join shape can't carry image / file blocks, and per-item separate user turns would expand the strict- template role-ordering surface that the post-batch drain already balances. * ``q-6``: the new ``test_queued_message_persists_as_user_row_after_tool_batch`` in ``test_session.py`` now asserts ``stream_idx == 2`` so a future regression where the post-batch flush runs but the send-loop short- circuits before the next iteration surfaces in CI rather than manual repro. * ``q-7``: ``test_coordinator_page.py`` gets two new string-grep pins mirroring the existing ``test_app_js.py`` shape — ``content.trim()`` on coord's assistant-replay branch and ``renderAssistantToolBatch`` for the hoisted helper that orders content card before tool batch. ## Test plan - [x] ``ruff check`` clean - [x] ``mypy turnstone/`` clean (189 source files) - [x] Affected test surface (``test_session.py`` + ``test_tool_advisory.py`` + ``test_app_js.py`` + ``test_coordinator_page.py``) — 240 passed
Turnstone
Multi-node AI orchestration platform. Deploy tool-using AI agents across a cluster of servers with direct HTTP routing, interactive interfaces, and enterprise governance.
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
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.
