Patrick Buckley 33d16d19ce fix(renderer): handle LaTeX-style \(...\) and \[...\] math delimiters (#425)
* 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).
2026-04-27 12:19:19 -07:00
2026-04-23 18:46:15 -07:00
2026-04-27 07:50:27 -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 console — multi-workstream AI orchestration with mermaid diagrams

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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