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Basic Information

fast-agent is a Python framework for rapidly creating, composing and running sophisticated AI Agents and Workflows that integrate with MCP (Model-Client-Provider) servers. It provides a declarative syntax to define agents, chains and other workflow constructs in simple files so prompts, configurations and version control are straightforward. The project includes end-to-end tested MCP feature support including Sampling and conversion of MCP tool results, and it natively supports multiple LLM backends such as Anthropic, OpenAI and Google as well as additional providers via TensorZero. Multimodal inputs (images and PDFs) are supported and agents can request human input during execution. The repository provides a CLI and examples, an interactive shell, and configuration conventions (fastagent.config.yaml and fastagent.secrets.yaml) to manage servers, models and secrets for local and remote MCP usage.

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App Details

Features
Declarative agent definition using @fast.agent and lightweight application boilerplate. Multiple workflow primitives including chain, parallel (fan-out/fan-in), evaluator-optimizer (generate-and-refine loops), router and orchestrator with configurable planning modes. Native multimodal support for images and PDFs and MCP tool result conversion to conversation messages. Sampling LLM support and per-server sampling configuration. Model selection and request parameters per agent, support for human-in-the-loop prompts, interactive shell for testing and tuning, quickstart generators and example applications. Uses configurable MCP Servers via fastagent.config.yaml, looks up fastagent.secrets.yaml recursively, and supports many backends (Anthropic, OpenAI, Google, Azure, Ollama, Deepseek, etc.) via built integrations and TensorZero.
Use Cases
fast-agent accelerates development of agent-based applications by reducing boilerplate and centralizing prompt, server and model configuration in simple files so teams can iterate quickly. The interactive shell and agent-level chat let developers tune prompts and diagnose behavior before deployment. Built workflow primitives enable composition of pipelines, parallel ensembles, automated routing and multi-agent orchestration, which simplifies building complex behaviors from smaller components. Multimodal and MCP tool result handling expands usable data types, and sampling plus multi-backend support makes it easier to test model‚ÜîMCP interactions. The framework also supports human input for mixed-initiative tasks and provides quickstart examples to bootstrap researcher, data analysis and workflow apps.

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