CARGO-AI

How it works

From an idea to a tool
you can run again.

Cargo-AI puts inputs, structured results, and action rules in a readable definition. Run it directly or turn it into a native command-line tool.

  1. Define the agent.

    Describe the inputs, the structured result you need, and the actions that can follow. Your coding assistant can help turn that request into a JSON definition you can read and review.

  2. Run and inspect.

    Choose a supported model profile and run the definition locally. Cargo-AI checks the returned structure against your schema before running matching actions. Review the output and refine the workflow.

  3. Add your own tools.

    Connect project-owned Rust tools to files, APIs, or business systems. Keep calculations, validation, and other precise operations in code you can test.

  4. Package and share.

    Keep agents, tools, and assets together in a project. Run the definition directly, or hatch it into a native executable with generated Rust source you can inspect and retain.

Keep the important choices explicit.

Your definition makes the model's role and the available follow-up actions reviewable. Schema validation checks the structure of a result; your tools can add business rules and review steps appropriate to the work.

Choose supported hosted or local models through connection profiles. Different tasks can use different models, while the workflow and tools remain yours to maintain. When you use a hosted model, its inputs are sent to that provider.

Built on Rust. Open to inspection.

Cargo-AI is MIT-licensed open source, built on Rust and Cargo. Inspect the foundation, keep generated source, and create tools that fit your needs. Connected models and services have their own terms and capabilities.

Explore the full technical documentation on GitHub for definitions, providers, tools, and release notes.

Build your first agent