Patrick Buckley a164d61552 fix(renderer): contain markdown sentinel-forgery and recursive-frame content loss
The markdown renderer protects structural blocks with in-band NUL-framed
sentinels (chr(0)+tag+index+chr(0)). escapeHtml preserves U+0000, so
model/tool text could forge sentinels, and recursively-rendered <details>
bodies re-rendered against fresh block arrays and lost their content. This
lands the ordered containment fixes from the render-containment brief.

Fixes (each pinned in tests/test_renderer_js.py; all NUL-sensitive cases also
confirmed in real headless Chrome, which drops a U+0000 token the node harness
preserves):

- B1/B2/B3 — forged sentinels: strip U+0000 at the TOP-LEVEL render entry only
  (_fnDepth === 0). renderer.js is the sole NUL producer and every restore
  regex is NUL-framed, so removing NUL closes every forgery path (block
  duplication/relocation, out-of-range "undefined", cross-container injection)
  while generated sentinels in recursive frames survive. Only NUL is stripped,
  so a code fence still shows pasted control bytes (ESC/FF/VT/DEL) verbatim.
- B4 — blockquote-in-fence (the common one): the code-fence pass now runs
  before the line-based blockquote pass. Its open matches at line start after
  optional indent and an optional list marker (`- `, `1. `), and re-emits that
  indent+marker before the sentinel so the fence keeps its document position
  (a nested-list item stays nested; a fence continuing a footnote definition
  keeps the indent its continuation scan needs). A blockquoted fence (`> ```)
  is not matched (`>` is neither indent nor a list marker), so the blockquote
  pass extracts that `> ` run and its recursion renders the fence. A `> ` line
  inside a plain fence stays literal.
- B5 — <details> open anchored to line start (^[ \t]*), so a `<details>`
  mentioned mid-line inside inline code no longer starts a block.
- NEW-1 — recursive-frame content loss: <details> extraction runs AFTER fence
  protection and restores a fenced body from a saved raw-source array
  (codeBlockRaw) back to raw markdown before the recursive render, so
  code-in-details renders in-frame instead of restoring to "undefined". Running
  after fence also means a </details> shown as example code inside a fence
  can't close the block early, and a <details> shown inside a fence stays
  literal — no offset-based fence-awareness needed. Inline-code/math in footnote
  definitions render via the restore round-trip the undefined-guard enables
  (documented at the append site).
- NEW-3 — code blocks gained the <p>SENTINEL</p> unwrap variant DT/BQ/MB/TB
  already had, removing a stray empty <p> before a standalone <pre>. The CB
  unwrap is whitespace-tolerant so an indented own-line fence (whose indent the
  fence pass re-emits) also doesn't leave a stray <p>.
- Defense-in-depth: every restore callback returns the matched sentinel
  (inert; the browser drops the NUL) instead of the array's `undefined`.

Non-obvious decisions:
- Control chars are authored as literal \xNN hex escapes (byte-verified: only
  \uXXXX decodes to raw bytes in this toolchain; \xNN matches the file's
  existing \x00 sentinel convention).
- Open anchors allow arbitrary leading indent (the fence open also allows a
  list marker), not CommonMark's ^ {0,3}: the renderer has no indented-code
  fallback, so preserving the prior behaviour of matching indented/list-nested
  fences beats CommonMark strictness, while still excluding `> ``` and mid-line
  forms.
- codeBlockRaw (the <details> raw-fence array) and the restore callbacks are
  factored through a _restorer(arr) helper; codeBlockRaw is only populated when
  the text contains a <details> tag (its sole reader).
- The entry strip is depth-0-only on purpose: an unconditional strip would
  shred the generated sentinels recursive frames carry, foreclosing NEW-1.

Negative-tested (reverted the production line, confirmed the pin fails):
- fence anchor: unanchored swallows a blockquoted fence.
- NEW-1 codeBlockRaw restore: without it, code inside <details> is lost.

Deferred (called out per the brief):
- B6/NEW-4 bidi controls (U+202A–202E, U+2066–2069, U+200E/F) still pass
  through unescaped; they are not C0 so the entry strip misses them. Left to a
  follow-up — stripping risks corrupting legitimate RTL text and <bdi>
  isolation is involved for a string renderer.
2026-07-07 23:03:47 -07:00
2026-07-05 01:57:54 -07:00
2026-07-05 02:03:18 -07:00
2026-07-06 18:20:14 -07:00

Turnstone

CI PyPI Python License Discord Sponsor

Self-hosted, local-first orchestration for tool-using AI agents. Give LLMs real tools — shell, files, search, web — and run them across your own cluster with direct HTTP routing and interactive interfaces. Your code, your models, your data stay on hardware you control: no telemetry, no phone-home.

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.

What is a harness?

 :  s_{n+1} ~ T(s_n)   for n < τ*,    T = ρ ∘ (M_W ∘ π, E)

the hypothesis →

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.

  • Local-first & private — runs entirely on hardware you control, with no telemetry and no phone-home. Point it at local models (vLLM, llama.cpp) or commercial APIs you hold the keys to — your prompts and data never transit a third party you didn't choose.
  • Bring your own models — OpenAI-compatible APIs (vLLM, llama.cpp, NIM), the Anthropic Messages API, and Google Gemini, mixed freely per role
  • Interactive sessions — terminal CLI or browser UI with parallel workstreams
  • Cluster dashboard — real-time view of every node and workstream, with a rendezvous routing proxy
  • Intent validation — an LLM judge (your model) grades every tool call with a risk assessment and evidence before it runs
  • MCP support — external tool servers with native deferred loading (Anthropic/OpenAI) or BM25 fallback
  • Team controls when you need them — optional RBAC, SSO, tool policies, and audit logs, all stored in your own database

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
turnstone-console --port 8090

For PostgreSQL (recommended for production):

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

One-line install — autodetects Ubuntu/Debian, Fedora/RHEL, Arch, and WSL, installs git + Docker if missing, generates secrets, and starts the stack:

curl -fsSL https://raw.githubusercontent.com/turnstonelabs/turnstone/main/run.sh | bash

Or, if you already have Docker, clone the repo and run it yourself:

docker compose up

That builds one image and brings up a full local cluster — PostgreSQL, console, Caddy, channel gateway, and 10 server nodes — with no .env required (it ships with insecure dev defaults). Open the dashboard at https://localhost:8443 (Caddy serves it over TLS with its own local CA — trust it once). Nodes boot without an LLM; add model backends from the console UI.

For production (released images from ghcr.io, real secrets required), use the bundled stack: docker compose -f turnstone/deploy/compose.yaml up.

See QUICKSTART.md for the install + troubleshooting walkthrough and docs/docker.md for Docker configuration.

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 Headless measurement — scores tool-use against expected actions
turnstone-optimizer Prompt/tool optimizer (UCB self-modify loop over the eval substrate)
turnstone-doctor LLM-backed cluster diagnostics

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: Discord / Slack channel integrations (pip install turnstone[discord,slack])
  • Git LFS for cloning (diagram PNGs)

Support

Turnstone is free, Apache-2.0, and self-hosted — no paid tier, no telemetry, no upsell. If it saves you time or you'd like to help keep development moving, you can sponsor the project:

❤ Sponsor Turnstone → · one-off via PayPal

Sponsorship is entirely optional and funds maintenance, new features, and infrastructure. Prefer to contribute in other ways? Filing issues, improving docs, and pull requests help just as much.

Community

Questions, ideas, or want to show what you're building? Join us on Discord: discord.gg/Nh3bWMacaq.

License

Apache License 2.0, as of version 1.6.0. Versions 1.5.x and earlier remain under the Business Source License 1.1 they shipped with.

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