lagent vs openai-agents-js
Python and TypeScript frameworks for building LLM agents
lagent and openai-agents-js both help developers build LLM agents that use tools, manage conversation state, and coordinate work. lagent focuses on composable Python components and synchronous or asynchronous workflows, while openai-agents-js offers a JavaScript and TypeScript SDK with features for sandboxed tasks, guardrails, tracing, and realtime voice.
lagent: Build LLM Agents with a PyTorch-Inspired Framework
InternLM’s Lagent is a lightweight Python framework for building LLM-based agents, using a PyTorch-inspired design for composing agents and message passing. It supports synchronous and asynchronous workflows, tools, memory, and multi-agent applications.
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.
| lagent | openai-agents-js | |
|---|---|---|
| Language | Python | TypeScript |
| License | Apache-2.0 | MIT |
| Stars | 2.3k | 3.9k |
| Forks | 248 | 987 |
| Last analyzed | Oct 15, 2026 | Oct 3, 2026 |
Key differences
- lagent is written in Python; openai-agents-js is written in TypeScript and supports JavaScript applications.
- lagent emphasizes configurable components for messages, memory, parsing, and action execution; openai-agents-js emphasizes agents with tools, guardrails, and handoffs.
- lagent describes synchronous and asynchronous components and multi-agent workflows; openai-agents-js also supports sandbox workspaces and browser-based realtime voice interactions.
- lagent uses the Apache-2.0 license; openai-agents-js uses the MIT license.
- lagent's documented examples include code execution, browsing, and iterative writing; openai-agents-js also describes tracing, human involvement, and validation guardrails.
- openai-agents-js documents a Node.js 22 or later environment, with Deno and Bun also supported; lagent's considerations focus on configuring model-serving backends and tools for the chosen workflow.
Choose lagent if you…
- need a Python framework with composable agent, memory, parser, and action components.
- want synchronous or asynchronous interfaces for tool-using or multi-agent workflows.
- plan to prototype workflows such as code execution, browsing, or writer-and-critic loops.
Choose openai-agents-js if you…
- build agent features in JavaScript or TypeScript applications.
- need sandbox workspaces, browser-based realtime voice, or tracing for agent runs.
- want guardrails or human involvement in agent workflows, and can meet the documented runtime requirements.
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.