Patrick Buckley 2d174cd71c perf(webui): bound the transcript — containment, block flow, windowing
The remaining steady-state cost after the wedge-proofing pass was
structural: every row of an unbounded transcript participated in every
layout, and full re-renders rebuilt all of it.

CSS pair (shared scroller + the ui/static duplicate):
- The messages scroller is BLOCK flow, not a column flexbox — flex
  relayouts all items when the streaming row's height changes, O(rows)
  per token; block flow dirties only the tail. The old per-child
  flex-shrink pin (and the min-height:auto squish hazard it suppressed)
  goes with it; inter-row rhythm moves to a sibling margin.
- overflow-anchor: none — the pane owns bottom pinning, and native
  anchoring kept re-selecting an anchor inside the innerHTML-replaced
  live bubble every frame.
- content-visibility: auto with contain-intrinsic-size: auto estimates
  on .msg (80px) and .conv-batch (200px) rows, exempting the last two
  children so the live tail never toggles skip-state mid-stream. The
  `auto` keyword memoizes each row's rendered size, keeping scrollHeight
  and the bottom pin stable once painted.

Transcript windowing (interactive pane):
- Full re-renders paint the most recent 300 messages, cut FORWARD to a
  user-turn boundary so an assistant tool_calls message is never split
  from the tool results that anchor to it. Hidden content sits behind a
  "Load earlier messages" pager; each click grows the window a step and
  refetches, restoring the scroll anchor by scrollHeight delta (the
  rAF pin re-checks the near-bottom flag at fire time, so no
  suppression is needed). Rewind/edit turn math is tail-relative and
  unaffected — pinned as such.
- Live appends are bounded at the idle edge: past 900 rendered rows the
  oldest rows are trimmed (again to a turn boundary), only while pinned
  to the bottom — a scrolled-up user is reading the rows a trim would
  remove. Trimmed content stays in /history and returns through the
  pager; detached agent-card entries are swept.

The perf page gains ?window= (and the runner --perf-extra) so windowing
and containment effects can be isolated.

Measured (n=3000 history + 20-turn storm; baseline -> previous branch
-> this change): full replay 1060ms -> 238ms -> 28ms windowed / 107ms
with the window grown to the full transcript; longtasks during the run
6/1080ms -> 4/495ms -> none windowed / 4/205ms unwindowed; per-turn
live-storm cost at n=3000 now equals n=300 (~220ms harness floor) even
with all 25k nodes live — the transcript-size tax is gone. Known
degraded-mode cost: the chunk path at a fully-grown window measures
~1017ms vs the 833ms floor; the shipped windowed config sits at the
floor.
2026-07-02 00:33:35 -07:00
2026-07-01 21:07:52 -07:00
2026-07-01 21:07:52 -07:00

Turnstone

CI PyPI Python License Discord

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 Eval harness for prompt/tool optimization
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)

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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