AI Developer Tools

Millwright

Millwright is an open-source, self-hosted LLM router that routes AI requests to the lowest-cost model providers based on policy, preserving prompt caches and controlling spend.

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Millwright

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What is Millwright?

Millwright is an open-source LLM router that directs AI requests to the cheapest appropriate model provider based on policy, preserving prompt cache affinity and controlling spend. It runs between your AI applications and model providers, accepting OpenAI Chat Completions and Anthropic Messages, and routing to OpenAI-compatible APIs, Anthropic, or Amazon Bedrock.

Millwright vs Similar AI Tools

Pricing ModelFreeFreeFreeFree
Free Credits
Key Features
  • Policy-controlled routing with task and risk headers
  • Cache-aware concurrency maintaining independent affinity lanes for cheap, mid, and frontier roles
  • Spend tracking and cost analysis via ledger, CLI commands, and management APIs
  • Seven breakable boxes covering OWASP Agentic Top-10 vulnerabilities
  • Three guided simulations for cascading failures, human-agent trust, and rogue agents
  • Network-isolated Docker containers for safe execution
  • Code-to-runtime reasoning across cloud, Git, and Kubernetes
  • Action-gate enforces read-only policy on every API call
  • Sandboxed JavaScript execution for concurrent research
  • Append-only, SHA-256-addressed event history through Jaybase
  • AES-256-GCM encryption for stored node payloads
  • Unified RBAC for ledger, notes, snapshots, and audit reads
Pros
  • Cost control by using cheapest appropriate models
  • Preserves prompt caches across requests
  • Covers full OWASP Agentic Top-10 in a realistic manner
  • Docker isolation prevents accidental damage
  • Read-only by construction prevents accidental writes
  • Evidence-backed verification cross-checks every finding
  • Opinionated and secure accounting CLI with immutable audit trail
  • Designed for AI agent integration with JSON output
Cons
  • Requires self-hosting and setup
  • Limited to supported protocols (OpenAI, Anthropic, Bedrock)
  • Requires Docker and technical setup
  • Not for production use; only for lab environments
  • Requires an LLM API key, incurring token costs
  • Limited to read-only operations, cannot remediate
  • Pre-1.0, limited feature set
  • No native QuickBooks import (agents must normalize data)
Best For
  • Teams managing LLM costs
  • Developers needing policy-based routing
  • Security researchers focusing on AI agent vulnerabilities
  • Developers building MCP-based applications
  • Security engineers
  • DevOps teams
  • Small teams needing secure, auditable accounting with AI agent support
  • Developers integrating automated bookkeeping workflows

How to use Millwright?

  1. 1git clone https://github.com/Northwood-Systems/millwright.git
  2. 2cd millwright
  3. 3cargo install --path . --locked
  4. 4millwright init
  5. 5millwright serve
  6. 6Alternatively, for a local demo:
  7. 7make compose-up
  8. 8curl http://localhost:8080/v1/chat/completions -H 'Authorization: Bearer sk-millwright-local-demo' -H 'Content-Type: application/json' -d '{"model":"millwright-fast","messages":[{"role":"user","content":"Hello"}]}'

Millwright Key Features

  • Policy-controlled routing with task and risk headers
  • Cache-aware concurrency maintaining independent affinity lanes for cheap, mid, and frontier roles
  • Spend tracking and cost analysis via ledger, CLI commands, and management APIs
  • Provider and protocol support for OpenAI Chat Completions, Anthropic Messages, and Amazon Bedrock
  • Self-hosting as a single Rust binary with Docker support; provider credentials stay on your infrastructure

Millwright Use Cases

  • Reduce LLM costs by routing requests to the cheapest model that meets policy requirements
  • Maintain prompt cache reuse across a session by preserving provider/model affinity
  • Switch between providers without changing application code
  • Inspect and audit routing decisions with response headers and ledger entries

Millwright Pricing & Free Credits

Millwright currently operates on a Free model.

This tool is completely free to use

Self-Hosted

Free

Open-source under Apache-2.0 license; no usage limits.

Millwright Pros & Cons

Pros

  • Cost control by using cheapest appropriate models
  • Preserves prompt caches across requests
  • Provider-agnostic; easy to switch without code changes
  • Fully self-hosted; data and credentials remain on your infrastructure
  • Inspectable routing decisions with detailed logging

Cons

  • Requires self-hosting and setup
  • Limited to supported protocols (OpenAI, Anthropic, Bedrock)
  • Not an agent orchestrator; no prompt inspection or rewriting

What is Millwright best for?

  • Teams managing LLM costs
  • Developers needing policy-based routing
  • Enterprises requiring self-hosted privacy

Millwright FAQ

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