fast-agent vs agent-factory
Python tools for building AI agents and workflows compared
fast-agent is a Python framework and CLI for defining agents and composing them into workflows, while agent-factory generates executable Python workflows from natural-language task descriptions. The main difference is that fast-agent emphasizes hands-on agent development and workflow composition, whereas agent-factory automates initial workflow generation and supports evaluation.
fast-agent: Build and Run LLM Agents and Workflows
A Python framework and CLI for building, testing, and running LLM agents that use MCP tools and compose into workflows. It suits developers who want both interactive agent development and a programmable framework.
agent-factory: Generate Executable AI Agent Workflows
Agent Factory turns natural-language task descriptions into executable Python agent workflows, using MCP tools and the any-agent library. It is aimed at developers who want to create, evaluate, and run agents without hand-coding every workflow.
| fast-agent | agent-factory | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 3.9k | 111 |
| Forks | 446 | 13 |
| Last analyzed | Oct 7, 2026 | Oct 3, 2026 |
Key differences
- fast-agent lets developers define agents in Python and compose chains, routers, parallel workflows, and other patterns; agent-factory generates workflows from natural-language prompts.
- fast-agent includes an interactive, shell-enabled CLI and can run applications as an MCP server or through an ACP client; agent-factory focuses on generating runnable workflows with instructions and dependency files.
- fast-agent describes direct integration with Python function tools and a range of MCP capabilities; agent-factory identifies relevant tools and MCP servers for a task and builds on any-agent.
- fast-agent requires Python 3.12 or later, but below 3.15; agent-factory specifies Python 3.13 or later.
- Both projects use Python and Apache-2.0 licensing. fast-agent has 3.9k stars and 446 forks, compared with agent-factory's 111 stars and 13 forks.
- fast-agent's maintenance is described as active but concentrated in one contributor; agent-factory's data notes 33 open issues and recommends reviewing and evaluating generated code.
Choose fast-agent if you…
- need to define agents in Python and compose them into varied workflow patterns.
- want an interactive CLI for agent development, including shell access and model selection.
- need MCP capabilities or ways to expose an application through MCP or ACP.
Choose agent-factory if you…
- want to turn a natural-language task description into an executable Python workflow.
- prefer generated workflows that include operational instructions and dependency files.
- need to evaluate generated agents against automatic or manual criteria.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.