turnstone 0.2.1

Multi-node AI orchestration platform with tool use, agent routing, and cluster simulation.

Turnstone

Multi-node AI orchestration platform. Deploy tool-using AI agents across a cluster of servers, driven by message queues or interactive interfaces.

Named after the Ruddy Turnstone — a bird that flips rocks to expose what's hiding underneath.

What it does

Turnstone gives LLMs tools — shell, files, search, web, planning — and orchestrates multi-turn conversations where the model investigates, acts, and reports. It runs as:

• Interactive sessions — terminal CLI or browser UI with parallel workstreams
• Queue-driven agents — trigger workstreams via message queue, stream progress, approve or auto-approve tool use
• Multi-node clusters — generic work load-balances across nodes, directed work routes to a specific server
• Cluster dashboard — real-time view of all nodes, workstreams, and resource utilization
• Cluster simulator — test the stack at scale (up to 1000 nodes) without an LLM backend

External System → Message Queue → Bridge (per node) → Turnstone Server → LLM + Tools
                                      ↓
                                 Pub/Sub → Progress Events → External System
                                      ↓
                                 turnstone-console → Cluster Dashboard (browser)

Quickstart

Interactive (terminal)

pip install turnstone
turnstone --base-url http://localhost:8000/v1

Interactive (browser)

turnstone-server --port 8080 --base-url http://localhost:8000/v1

Queue-driven (programmatic)

pip install turnstone[mq]
turnstone-bridge --server-url http://localhost:8080 --redis-host localhost

from turnstone.mq import TurnstoneClient

with TurnstoneClient() as client:
    # Generic — any available node picks it up
    result = client.send_and_wait("Analyze the error logs", auto_approve=True)
    print(result.content)

    # Directed — must run on a specific server
    result = client.send_and_wait(
        "Check disk I/O on this server",
        target_node="server-12",
        auto_approve=True,
    )

Cluster dashboard

pip install turnstone[console]
turnstone-console --redis-host localhost --port 8090

Then open http://localhost:8090 for the cluster-wide dashboard.

Docker

cp .env.example .env  # edit LLM_BASE_URL, OPENAI_API_KEY, etc.
docker compose up     # starts redis + server + bridge + console

Console dashboard at http://localhost:8090. See docs/docker.md for configuration, scaling, and profiles.

Simulator

Test the multi-node stack at scale without an LLM backend:

docker compose --profile sim up redis console sim

Or standalone:

pip install turnstone[sim]
turnstone-sim --nodes 100 --scenario steady --duration 60 --mps 10

See docs/simulator.md for scenarios, CLI reference, and metrics.

All frontends connect to any OpenAI-compatible API (vLLM, NVIDIA NIM/NGC, llama.cpp, OpenAI, etc.) and auto-detect the model.

Architecture

turnstone/
├── core/              # UI-agnostic engine
│   ├── session.py     # ChatSession — multi-turn loop, tool dispatch, agents
│   ├── tools.py       # Tool definitions (auto-loaded from JSON)
│   ├── workstream.py  # WorkstreamManager — parallel independent sessions
│   ├── config.py      # Unified TOML config (~/.config/turnstone/config.toml)
│   ├── memory.py      # SQLite persistence (memories, conversations, FTS5)
│   ├── metrics.py     # Prometheus-compatible metrics collector
│   ├── edit.py        # File editing (fuzzy match, indentation)
│   ├── safety.py      # Path validation, sandbox checks
│   ├── sandbox.py     # Command sandboxing
│   └── web.py         # Web fetch/search helpers
├── mq/                # Message queue integration
│   ├── protocol.py    # Typed message dataclasses (JSON serialization)
│   ├── broker.py      # Abstract MessageBroker + RedisBroker
│   ├── bridge.py      # Bridge service (queue ↔ HTTP API, multi-node routing)
│   └── client.py      # TurnstoneClient — Python API for external systems
├── console/           # Cluster dashboard
│   ├── collector.py   # ClusterCollector — aggregates all nodes via Redis + HTTP
│   ├── server.py      # Dashboard HTTP server + SSE
│   └── static/        # Cluster dashboard web UI
├── tools/             # Tool schemas (one JSON file per tool)
├── ui/                # Frontend assets and terminal rendering
│   └── static/        # Web UI (HTML, CSS, JS)
├── sim/               # Cluster simulator
│   ├── cluster.py     # SimCluster — orchestrates N nodes + dispatchers
│   ├── node.py        # SimNode + SimWorkstream — protocol-compatible node
│   ├── engine.py      # LLM + tool execution simulation
│   ├── scenario.py    # 5 workload scenarios (steady, burst, node_failure, …)
│   ├── metrics.py     # Latency, throughput, utilization collection
│   └── cli.py         # CLI entry point (turnstone-sim)
├── cli.py             # Terminal frontend (+ /cluster commands for console)
├── server.py          # Web frontend (HTTP + SSE)
└── eval.py            # Evaluation and prompt optimization harness
docs/
├── architecture.md    # System architecture and threading model
├── api-reference.md   # Web server API and SSE event reference
├── console.md         # Cluster dashboard service (turnstone-console)
├── docker.md          # Docker Compose deployment and configuration
├── simulator.md       # Cluster simulator usage and scenarios
├── tools.md           # Tool schemas, execution pipeline, approval flow
└── eval.md            # Evaluation harness internals

Multi-node routing

Each Turnstone server runs a bridge process. Bridges share a Redis instance for coordination:

Redis Key	Purpose
turnstone:inbound	Shared work queue — generic tasks, any node
turnstone:inbound:{node_id}	Per-node queue — directed tasks
turnstone:ws:{ws_id}	Workstream ownership — auto-routes follow-ups
turnstone:node:{node_id}	Node heartbeat + metadata for discovery
turnstone:events:{ws_id}	Per-workstream event pub/sub
turnstone:events:global	Global event pub/sub
turnstone:events:cluster	Cluster-wide state changes (for turnstone-console)

Routing rules:

1. Message has target_node → routes to that node's queue
2. Message has ws_id → looks up owner, routes to owning node
3. Neither → shared queue, next available bridge picks it up

Bridges BLPOP from their per-node queue (priority) then the shared queue. Directed work always takes precedence.

Tools

14 built-in tools, 2 agent tools:

Tool	Description	Auto-approved
bash	Execute shell commands
read_file	Read file contents	yes
write_file	Write/create files
edit_file	Fuzzy-match file editing
search	Search files by name/content	yes
math	Sandboxed Python evaluation
man	Read man pages	yes
web_fetch	Fetch URL content
web_search	Search via Tavily API
remember	Save persistent facts	yes
recall	Search memories and history	yes
forget	Remove a memory	yes
task	Spawn autonomous sub-agent
plan	Explore codebase, write .plan.md

Configuration

All entry points read ~/.config/turnstone/config.toml. CLI flags override config values.

[api]
base_url = "http://localhost:8000/v1"
api_key = ""
tavily_key = ""

[model]
name = ""              # empty = auto-detect
temperature = 0.5
reasoning_effort = "medium"

[tools]
timeout = 30
skip_permissions = false

[server]
host = "0.0.0.0"
port = 8080

[redis]
host = "localhost"
port = 6379
password = ""

[bridge]
server_url = "http://localhost:8080"
node_id = ""           # empty = hostname_xxxx

[console]
host = "0.0.0.0"
port = 8090
url = "http://localhost:8090"  # used by CLI /cluster commands
poll_interval = 10

Precedence: CLI args > environment variables > config.toml > defaults.

Workstreams

Parallel independent conversations, each with its own session and state:

Symbol	State	Meaning
·	idle	Waiting for input
◌	thinking	Model is generating
▸	running	Tool execution in progress
◆	attention	Waiting for approval
✖	error	Something went wrong

Idle workstreams are automatically cleaned up after 2 hours (configurable). In multi-node deployments, workstream ownership is tracked in Redis — follow-up messages auto-route to the owning node.

Monitoring

/metrics endpoint exposes Prometheus-format metrics:

• turnstone_tokens_total{direction} — prompt/completion token counters
• turnstone_tool_calls_total{tool} — per-tool invocation counts
• turnstone_workstream_context_ratio{ws_id} — per-workstream context utilization
• turnstone_http_request_duration_seconds — request latency histogram
• turnstone_workstreams_by_state{state} — workstream state gauges

Per-workstream metrics are labeled by ws_id (bounded to 10 max workstreams).

Requirements

• Python 3.11+
• An OpenAI-compatible API endpoint (vLLM, NVIDIA NIM, llama.cpp, etc.)
• Redis (for message queue bridge — pip install turnstone[mq])

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.