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).
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.
