Openmirai

OpenMirai

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> Decentralize your AI agents. One binary. Any LLM. Your machine. Your rules.

OpenMirai is a Rust-native engine that runs agentic workflows defined as simple YAML graphs. No cloud lock-in, no heavy runtime, no vendor handcuffs — the engine runs wherever you do: your laptop, your server, your edge. A single portable binary with zero runtime dependencies.

A drop-in alternative to LangGraph, CrewAI, and Google ADK — without tying your agents to someone else's cloud.

50 built-in tools · 7 LLM providers · 712 tests · MIT license

Why decentralized?

Most agent platforms run their runtime, on their cloud, against their preferred model. OpenMirai inverts that:

• Runs anywhere — one self-contained binary, zero runtime dependencies. Your machine, your server, your edge.
• Any LLM — 7 providers today, local Ollama included. No vendor lock-in.
• Your data stays yours — agents execute where you put them; nothing phones home.
• Embeddable — drop the engine into any app via CLI, HTTP, or as a Rust crate.

> Decentralizing AI agents means taking power back from closed platforms and handing it to whoever builds.

Quick Start

# Build from source
cargo build --release

# Run an agent
./target/release/mirai run examples/hello-world.yaml

# With a specific provider
./target/release/mirai run examples/hello-world.yaml --provider claude --api-key $ANTHROPIC_API_KEY

# Start the HTTP server
./target/release/mirai serve --port 3000

How It Works

An agent is a YAML file. The engine executes it.

name: hello-world
version: v1
graph:
  nodes:
    - id: start
      tool_type: trigger/manual
    - id: think
      tool_type: ai/llm_call
      config:
        prompt: "Answer the user's question concisely."
    - id: respond
      tool_type: output/response
  edges:
    - source: start
      target: think
    - source: think
      target: respond

mirai run hello.yaml --input '{"query": "What is Rust?"}'

Architecture

Agent  = YAML config  (portable, versionable, language-agnostic)
Engine = Rust binary   (FFI, WASM, CLI — 712 tests)
Host   = Your app      (Python, Swift, Go, JavaScript — anything)

The agent defines what to do. The engine decides how to run it.

What's different

| | OpenMirai | LangGraph / CrewAI | Google ADK | |---|---|---|---| | Runtime | Single binary, zero deps | Python runtime + deps | Python runtime + deps | | Run on your own machine/edge | ✅ first-class | ⚠️ needs Python env | ⚠️ GCP-oriented | | LLM choice | 7 providers, local-first | Provider-agnostic | Gemini-first | | Agent format | Portable YAML | Python code | Python code | | Embed in any app | CLI · HTTP · Rust crate | Python library | Python library | | License | MIT | MIT | Apache-2.0 |

The point isn't "more features" — it's where and how it runs: yours, portable, and not chained to a cloud.

Built-in Tools

| Category | Tools | |----------|-------| | Trigger | webhook, manual, schedule, event, heartbeat | | AI | llmcall, embeddings, transcribe, claudecode | | Logic | condition, switch, loop, merge, wait, humaninput, deadline | | Data | dbread, dbwrite, entityquery, entityupsert, storageread, storagewrite, vaultread, vaultwrite, webscrape, htmltomarkdown, ragsearch | | Filesystem | readfile, writefile, editfile, globfiles, grepfiles, listdir, tree, copy, move, delete, mkdir, fileinfo | | System | bash, processlist, sandboxexec | | Git | status, diff, log, commit | | Output | response | | Agent | run_agent | | MCP | call | | State | memory |

LLM Providers

| Provider | Config | |----------|--------| | Ollama (default) | Local, no API key needed | | Claude | --provider claude --api-key $ANTHROPIC_API_KEY | | OpenAI | --provider openai --api-key $OPENAI_API_KEY | | Gemini | --provider gemini --api-key $GOOGLE_API_KEY | | Groq | --provider groq --api-key $GROQ_API_KEY | | NVIDIA NIM | --provider nvidia --api-key $NVIDIA_API_KEY | | OpenRouter | --provider openrouter --api-key $OPENROUTER_API_KEY |

Python SDK

pip install openmirai

from openmirai import Engine, Agent

engine = Engine(provider="ollama")
agent = Agent.from_file("my-agent.yaml")
result = engine.run(agent, input={"query": "hello"})
print(result.output)

Project Structure

engine/        Core library (openmirai-engine crate)
cli/           CLI binary (mirai)
sdks/python/   Python SDK (openmirai)
examples/      Ready-to-run agent examples
docs/          Technical documentation

Key Features

• Graph execution with conditional branching, fan-out/fan-in, and retry with backoff
• Input/output contracts — typed validation with defaults, required fields, and clear error messages
• Structured output — JSON schema enforcement on LLM responses with auto-retry
• Soul system — give agents personality via SOUL.md files
• Universe — multi-agent routing with keyword, round-robin, or LLM-based strategies
• Energy tracking — metered cost accounting per operation
• Hook system — 7 interception points for execution control
• Checkpoint/resume — pause and resume agent execution
• SSE streaming — real-time execution events via Server-Sent Events
• Security scanner — prompt injection detection with configurable sensitivity
• MCP support — Model Context Protocol for external tool servers
• Sub-agents — compose agents that call other agents (max depth 3)

HTTP Server

# Start with auth (recommended for production)
MIRAI_API_KEY=your-secret mirai serve --port 3000

# Or without auth (development only)
mirai serve --port 3000

API endpoints: /api/v1/agents, /api/v1/graphs, /api/v1/sessions, /api/v1/tools, /health

Contributing

OpenMirai is open source and built in the open. Contributions are welcome — new tools, LLM providers, docs, examples, bug fixes.

• Start with [CONTRIBUTING.md](CONTRIBUTING.md)
• Pick up a good first issue
• Read [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) for the system map
• Be kind — see our [Code of Conduct](CODEOFCONDUCT.md)

License

MIT

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Gabo-TheCreator
• Source: Gabo-TheCreator/openmirai
• License: MIT
• Homepage: https://datamirai.com

Install and usage instructions live in the source repository linked above.