openai/openai-agents-python alternatives

Choose when you want a Python agent harness tightly integrated with LangChain's ecosystem and prebuilt tools.

The batteries-included agent harness.

Choose when you need Google's code-first Python agent toolkit with built-in evaluation and deployment features.

An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

Choose when you want a Python/TypeScript SDK for building and controlling agent harnesses with any model or cloud.

Build an agent harness and control it end-to-end. Open-source SDK for production AI agents in Python & TypeScript - any model, any cloud.

Choose when you want a Python multi-agent framework with .NET support and enterprise orchestration capabilities.

A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.

Choose when you want a Python multi-agent harness with Apache-2.0 licensing and a focus on production control.

Multi-Agent Harness for Production AI

Choose when you want a meta-harness to orchestrate multiple existing agent harnesses without rewriting your own agents.

Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device.

Choose when you need a Java enterprise agent workflow platform with commercial-friendly licensing.

Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents.

Choose when you need a Java framework for building distributed, long-running production agents.

Build distributed, production-grade, long-running agents.

Choose when you need a TypeScript platform for creating and managing agents with integration across multiple models.

Evo AI is an open-source platform for creating and managing AI agents, enabling integration with different AI models and services.

Choose when you prefer a Rust-core agent framework with a Python wrapper for higher performance and safety.

GraphBit is the world’s first enterprise-grade Agentic AI framework, built on a Rust core with a Python wrapper for unmatched speed, security, and scalability. It enables reliable multi-agent workflows with minimal CPU and memory usage, making it production-ready for real-world enterprise environments.

Choose when you want a Go agent framework with MCP tool discovery and event-driven multi-agent workflows.

Open-source Agentic AI framework in Go for building, orchestrating, and deploying intelligent agents. LLM-agnostic, event-driven, with multi-agent workflows, MCP tool discovery, and production-grade observability.

Choose when you want graph-orchestrated agent loops with Dify DSL transpilation, built on LangGraph.

Graph-Orchestrated Agent Loop — a production-grade framework on LangGraph. Combine workflow graphs and agent loops, transpile Dify DSL to runnable code, swap wire protocols (Dify/OpenAI).

Choose when you need a TypeScript agent framework with an engineering platform for building and evaluating agents.

AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework

Choose when you want graph-based orchestration with explicit state management instead of handoff-based agent workflows.

Build resilient agents.

Choose when you want a TypeScript agent harness focused on runtime execution and working agents in production.

The open-source agent harness - the runtime layer that turns an LLM into a working agent.

Choose when you want a C# SDK for integrating LLMs into apps with plugins and planners, not specifically multi-agent workflows.

Integrate cutting-edge LLM technology quickly and easily into your apps

Choose when you want a Python agent framework centered on Model Context Protocol and simple workflow patterns.

Build effective agents using Model Context Protocol and simple workflow patterns

Choose when you need a memory-first agent harness specialized for deep research rather than general multi-agent workflows.

The memory-first, self-improving agent harness built on EverOS, with MiroThinker-powered deep research and reasoning.

Choose when you want a lightweight self-hosted personal AI agent with WebUI, memory, and MCP rather than a general multi-agent framework.

Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat apps

Choose when you want a ready-to-use personal AI agent that evolves with usage, not a builder SDK.

The agent that grows with you

Choose when you want an out-of-the-box assistant with planning, tools, and self-evolution instead of building your own.

Open-source super AI assistant & Agent Harness. Plans tasks, runs tools and skills, self-evolves with memory and knowledge. Multi-model, multi-channel. Lightweight, extensible, one-line install. (formerly chatgpt-on-wechat)

Choose when you want a personal AI assistant in Go with computer-use and browser-use capabilities.

⚡️next-generation personal AI assistant powered by LLM, RAG and agent loops, supporting computer-use, browser-use and coding agent, demo: https://demo.openagentai.org

Choose when you want OpenClaw's personal assistant functionality in Go with multi-tenant isolation.

GoClaw - GoClaw is OpenClaw rebuilt in Go — with multi-tenant isolation, 5-layer security, and native concurrency. Deploy AI agent teams at scale without compromising on safety.