fast-agent is a Python framework for rapidly building and testing multi-agent systems (MAS) and complex workflows. It simplifies agent definition, chaining, parallel execution, and routing, supporting both Anthropic and OpenAI models with multi-modal capabilities. The framework targets developers building sophisticated AI applications who need a structured yet flexible way to compose and manage agent interactions.

How It Works

fast-agent utilizes a declarative syntax for defining agents and workflows, abstracting away much of the boilerplate. It leverages MCP (Message Communication Protocol) servers for inter-agent communication and tool integration. Key features include chaining agents sequentially, running them in parallel with fan-out/fan-in patterns, and using LLMs for routing and orchestration. This approach allows for modular development and easy experimentation with different agent compositions and LLM backends.

Quick Start & Requirements

• Install with uv pip install fast-agent-mcp.
• Run fast-agent setup to create example files.
• Execute agents with uv run agent.py [--model=<model_name>].
• Documentation: https://fast-agent.ai
• Examples: https://github.com/evalstate/fast-agent-docs

Highlighted Details

• End-to-end tested MCP Feature support, including sampling.
• Multi-modal support for Images and PDFs via Prompts, Resources, and MCP Tool Call results.
• Built-in support for interactive debugging and testing of agents and workflows.
• Evaluator-Optimizer pattern for iterative content refinement.

Maintenance & Community

The project is open to contributions and feedback. A roadmap and detailed contribution guidelines are planned.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

Windows users may require specific configuration changes for Filesystem and Docker MCP Servers. The project is actively under development, with contribution guidelines and roadmap details forthcoming.