* fix(renderer): handle LaTeX-style \(...\) and \[...\] math delimiters The browser renderer at turnstone/shared_static/renderer.js only recognized TeX-style $...$ / $$...$$ delimiters. Most modern LLMs (GPT-5 / o-series, Claude with reasoning effort) emit LaTeX-style \(...\) for inline math and \[...\] for display by default — those slipped through as raw text in the coord + interactive WebUIs, making KaTeX appear "broken when nested inside a markdown block" (actually broken everywhere, the surrounding markdown just made the failure noticeable). Added a second pass for each delimiter style alongside the existing $...$ / $$...$$ patterns. Both styles now feed the same mathBlocks / inlineMaths placeholder pipeline so all the existing nested-block handling (lists, blockquotes, tables, bold, headings, details, post-render KaTeX markup) Just Works. Edge cases verified by the new test_renderer_js.py harness: - \(...\) inside inline code stays literal - \(...\) inside fenced code blocks stays literal - Solo \[ with no closing \] doesn't trigger spurious math - Markdown links [text](url) untouched (regex uses \[ \], not [ ]) - Mixed TeX + LaTeX delimiters in one message both render The harness drives renderer.js through Node via vm.runInThisContext with stubbed document/katex globals — first JS-side regression guard for the renderer; previously it had no test coverage at all. * fix(renderer): apply Copilot feedback on PR #425 Three review items from Copilot: 1. Display-math sentinel could leak through inline-code spans. The original ordering ran $$...$$ / \[...\] extraction BEFORE inline code, so a backtick span around math (e.g. `$$x$$` or `\[x\]`) had its delimiters consumed by the math regex and replaced with \x00MB…\x00. Inline code then captured the sentinel; restore order put MB after IC, leaving the null-byte placeholder visible inside the rendered <code>. Reorder: inline code first, then display math, then inline math. Code spans now seal their content before any math regex sees it. The reverse edge case (math containing backticks, e.g. \verb|`x`|) is much rarer and KaTeX rejects \verb anyway. 2. Inline LaTeX-style \(...\) regex used [\s\S]+? which allowed newlines, so an unterminated \( on one line would eat the next paragraph until it found a closing \). Aligned with the existing $...$ behavior by switching to [^\n]+? — display math (\[...\] / $$...$$) stays multi-line by design. 3. tests/test_renderer_js.py was guarded with a node-availability skip, but CI's test + test-postgres jobs didn't explicitly install Node, so the suite would have silently no-op'd if the runner image dropped Node. Added actions/setup-node@v5 to both jobs. Four new regression tests cover the leak (both delimiter styles inside backticks must stay literal) and the cross-paragraph span (both \(...\) and $...$ must not eat newlines).
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.
