fast-agent vs openai-agents-js
Python and TypeScript frameworks for LLM agents compared
fast-agent and openai-agents-js both help developers build LLM agents that use tools and coordinate work across workflows. fast-agent focuses on Python-based agent configuration, MCP connectivity, and terminal use, while openai-agents-js provides a JavaScript and TypeScript SDK with sandbox and realtime voice workflows.
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.
openai-agents-js: Build Multi-Agent and Voice Workflows
A TypeScript SDK for building LLM agents that use tools, delegate work, and manage conversations. It also supports sandboxed tasks and low-latency voice agents, making it useful for teams building more than a single prompt-and-response flow.
| fast-agent | openai-agents-js | |
|---|---|---|
| Language | Python | TypeScript |
| License | Apache-2.0 | MIT |
| Stars | 3.9k | 3.9k |
| Forks | 446 | 987 |
| Last analyzed | Oct 7, 2026 | Oct 3, 2026 |
Key differences
- fast-agent is written in Python, while openai-agents-js is written in TypeScript.
- fast-agent supports native Anthropic, OpenAI, and Google providers, plus other providers through TensorZero; openai-agents-js is designed for use with OpenAI APIs and other providers.
- fast-agent offers workflow patterns such as chains, parallel fan-out, routers, and evaluator-optimizers; openai-agents-js emphasizes tools, handoffs, guardrails, sessions, and human involvement.
- fast-agent includes an interactive CLI and can run as an MCP server or through ACP clients; openai-agents-js supports sandboxed tasks and browser-based realtime voice interactions.
- fast-agent is licensed under Apache-2.0, while openai-agents-js is licensed under MIT.
- Both projects list 3.9k stars; fast-agent lists 447 forks and openai-agents-js lists 987 forks.
Choose fast-agent if you…
- need a Python framework and CLI for configuring and running agents.
- want to connect agents to MCP servers or compose them with varied workflow patterns.
- prefer interactive terminal use or running agent applications as MCP servers or through ACP clients.
Choose openai-agents-js if you…
- build agent features in a JavaScript or TypeScript application.
- need sandbox workspaces, browser-based realtime voice, or conversation sessions.
- want guardrails, human involvement, and tracing around agent runs.
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.