Patrick Buckley 1873e7a758 feat(reasoning): persist reasoning text on history payload (Phase 1)
Surface stored Anthropic thinking blocks on /history responses so
refreshing the page rehydrates the reasoning bubble. Wire payloads
unchanged. Per-model operator knobs added to model_definitions for
both UI rehydration and (Phase 2) wire-build replay.

Why now: reasoning is already round-tripped via _provider_content for
Anthropic-with-thinking turns, but never surfaces on the history wire,
so a tab reload showed only the final answer with no rationale.
Operators also have no per-model lever to opt out of UI display or to
opt in to replay-to-model on subsequent calls.

What this change does

* Migration 052 adds two boolean columns to model_definitions:
  persist_reasoning (default 1) controls UI rehydration; replay_
  reasoning_to_model (default 0) reserved for Phase 2's wire-build
  shape filter. Mirrors the enabled column pattern (NOT NULL +
  integer server_default).
* LLMProvider Protocol gains extract_reasoning_text(provider_blocks)
  with concrete impls on AnthropicProvider (walks type=='thinking'
  blocks, joins with newline, caps at 64 KiB) and no-op stubs on
  OpenAIChatCompletionsProvider + OpenAIResponsesProvider. Google
  inherits the no-op via OpenAIChat. Phase 3 will wire the OpenAI
  Responses extractor once include=['reasoning.encrypted_content']
  is requested.
* turnstone.core.history_decoration gains a structural dispatcher
  extract_reasoning_text_from_provider_content keyed off the first
  block's type field (Anthropic 'thinking' / OpenAI Responses
  'reasoning' / Gemini 'thought' are non-overlapping by API design).
  Both history surfaces use it: _build_history calls the dispatcher
  directly (the SSE-replay path builds entry dicts from scratch),
  and the lifted make_history_handler runs the list-helper variant
  in the existing to_thread block.
* make_history_handler resolves persist_reasoning via three tiers:
  live session -> workstream_config.model_alias (the same key
  SessionManager uses to rehydrate the original model after process
  restart) -> conservative True default. Operator flag-flip takes
  effect uniformly on both warm and cold workstreams.
* Frontend: app.js replayHistory and coordinator.js role==='assistant'
  branch each call the existing reasoning-bubble construction (for
  app.js, the document.createElement pattern from the live SSE
  handler; for coord, the appendMsg('reasoning') helper) when
  msg.reasoning is non-empty. Reasoning bubbles render before the
  content bubble, matching live SSE order.
* Admin UI: two checkboxes ('Persist reasoning', 'Replay reasoning
  to model') in the model edit modal, plus override-pill display in
  the model row when set to non-default values.

What is intentionally out of scope

* Phase 2 -- ANTHROPIC_VALID_BLOCK_TYPES shape filter at
  _anthropic.py:312-316, _convert_messages replay_reasoning_to_model
  parameter, thinking-strip branch, _msg_text_chars token-calibration
  extension. The replay flag is stored but not consumed on the wire.
* Phase 3 -- OpenAI Responses include=['reasoning.encrypted_content'],
  Gemini include_thoughts spike, ModelCapabilities.supports_
  reasoning_replay.
* Phase 4 -- Local-model / chat-template reasoning persistence
  (session.py:3486 reasoning_parts accumulator).

Tests

* AnthropicProvider.extract_reasoning_text -- 13 unit tests covering
  None / empty / mixed / multi-block / cap / malformed / non-list
  inputs plus other-provider no-op verification (real provider
  instances, no mocks).
* extract_reasoning_for_history -- 10 dispatcher tests including
  block-type discriminator routing (thinking vs reasoning vs
  unknown), strip-when-flag-false, empty / non-dict guards, and
  cross-role isolation.
* _build_history -- 6 boundary tests through the real Anthropic
  extractor with stub sessions, including the registry-lookup
  failure default-True branch.
* make_history_handler -- 5 round-trip tests through real storage:
  the storage layer's reconstruct_messages decodes provider_data
  into _provider_content, and the helper extracts through the real
  AnthropicProvider. Includes the live-session flag honoring path,
  the cold-workstream workstream_config lookup path, and the
  no-alias default-True fallback path.
* Audit-log discipline -- 4 structural mock-and-assert tests that
  capture every Logger.info / warning / error call across the
  pipeline (extractor, dispatcher, list-helper, _build_history)
  and assert no captured payload contains a marker reasoning string.
* model_definitions storage -- 6 round-trip tests: default flags,
  explicit create with both flags, individual update of each flag,
  and list-includes-flags assertion.
* model_registry -- 4 tests: dataclass defaults, dataclass with
  explicit flags, DB-row-mapping with both flags, and pre-052
  legacy-row default-fallback.

Edge cases pinned by the test suite

* Pre-052 DB rows missing the new columns degrade to dataclass
  defaults (test_db_reasoning_flags_default_when_absent).
* Live session in memory has its flag honored (test_history_handler_
  with_persist_flag_false_via_live_session).
* Cold workstream resolves the flag via workstream_config +
  app.state.registry (test_history_handler_cold_workstream_resolves_
  via_workstream_config) -- this closes the gap where a process
  restart would have silently un-honored an operator flag-flip.
* Cold workstream without persisted model_alias falls through to
  default True (test_history_handler_cold_workstream_no_alias_
  defaults_true).
* Foreign / unknown / missing block types degrade silently to no
  reasoning field rather than misroute or crash.

Lint + test gate

* ruff check + ruff format -- clean.
* mypy -- no issues across all 191 source files.
* pytest -m 'not live' -- 6030 passed (3 deselected).
2026-05-09 02:45:13 -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 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.

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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