fast-agent is an agent framework that lets you build, code, and evaluate LLM-powered agents with comprehensive support for Model Control Protocol (MCP), Agent Control Protocol (ACP), and Agent-to-Agent (A2A) communication.
The tool addresses the need to quickly prototype and deploy sophisticated agents by providing a declarative syntax for composing prompts and connecting MCP servers. It operates as a CLI-first platform with an optional interactive terminal interface, streaming responses through rich formatting without requiring full-screen terminal UIs. The framework handles the complexity of agent orchestration by managing skills, connecting to external services, and supporting shell command execution directly within agent workflows.
fast-agent suits developers building coding agents, development toolkits, evaluation platforms, or automation workflows who want to move quickly from concept to deployment. It works well for teams needing multimodal agent capabilities with vision and PDF support, structured outputs, and the ability to test against multiple LLM providers. The framework supports Anthropic, OpenAI, and Google natively, alongside Azure, Ollama, Deepseek, and dozens of other providers through TensorZero. If you need to inspect HTTP transport usage for compliance or reliability verification, the tool's streamable transport diagnostics are distinctive. The simple declarative approach means you focus on prompt composition and server integration rather than boilerplate agent infrastructure.
The project maintains active development with passing continuous integration checks and responsive issue tracking. Development prioritizes end-to-end tested MCP feature completeness, including advanced capabilities like sampling and elicitations that distinguish it from earlier agent frameworks. The team has recently added agent skills, MCP applications, shell mode, advanced transport diagnostics, and OAuth support with keyring-based secret management, indicating sustained investment in both core functionality and operational concerns like secure credential handling.
