Adds the server-side foundation for SSE reconnect-with-replay (PR-D in issue #540's sequencing): a per-ws monotonic ring buffer that holds the last N events for replay against a client's `Last-Event-ID` header (or `?last_event_id=N` query-param fallback for manual reconnect paths that can't set custom headers). Per-ws lane (SessionUIBase + make_events_handler): - `_event_buffer` deque (cap 2000, env-overridable via `TURNSTONE_SSE_EVENT_BUFFER_MAX`) holds (event_id, event_dict) tuples; `maxlen` evicts the oldest automatically. - Existing `_ws_inflight_seq` renamed to `_event_id` and lifted to live alongside the listeners — one monotonic counter drives both the new replay slice AND the existing `_seq`/`snap_seq` snapshot dedup (byte-identical contract on token events). - `_enqueue` now stamps every event with `_event_id` (and `_seq` on `content`/`reasoning` token events) under `_listeners_lock`, so the buffer append + listener fan-out + new listener registration are all atomic against each other. - New `register_listener_with_replay` returns (queue, replay_events, status, lost_count, earliest_id) where status ∈ {replay_ok, truncated}. `make_events_handler` reads `Last-Event-ID` (header or query), branches three ways (fresh / replay_ok / truncated), and emits the SSE `id:` field on every event sourced from the buffer. On `replay_ok` the in-progress snapshot is skipped (the buffered events already cover it); on `truncated` an explicit envelope precedes the fresh-style recovery path. - Every events stream emits a jittered `retry:` in [2500, 4500] ms on first yield so 6-pane reconnects don't lockstep on EventSource's default ~3 s interval. Global lane (server.py / _global_fanout_thread / global_events_sse): - Parallel buffer + counter on `app.state.global_event_buffer` and `app.state.global_event_id_holder`; fanout thread stamps each event with `_event_id` and appends to the buffer under `global_listeners_lock`. `global_events_sse` branches on `Last-Event-ID` with the same three shapes. Tests: - 16 new tests in `tests/test_sse_reconnect_replay.py` cover the ring buffer semantics (empty-listeners hold, last_event_id slicing, truncation, atomic registration), the counter invariants (monotonic under concurrent writers, no skip on queue.Full, persists across turn boundaries, cross-thread consistency), and the handler branching (retry on first yield, id: on buffered events, snapshot-skip on replay_ok, envelope on truncated, query-param fallback, malformed header → fresh). - Existing `tests/test_session_ui_base.py` updated for the `_ws_inflight_seq` → `_event_id` rename and the new `_event_id` field on enqueued events. Backward-compat: all consumers that don't send `Last-Event-ID` (today's browser, Python SDK, TypeScript SDK, channel adapter) see behaviour identical to pre-PR — the server change is purely additive on the request side.
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.
