AI agent framework / MCP
Build effective AI agents using Model Context Protocol and simple, composable workflow patterns.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Python/TS SDK for building agent harnesses; choose if you need polyglot support and full runtime control.
Build an agent harness and control it end-to-end. Open-source SDK for production AI agents in Python & TypeScript - any model, any cloud.
Java framework for building distributed, long-running agents; choose for Java-based production systems.
Build distributed, production-grade, long-running agents.
TypeScript agent harness runtime; choose if you prefer TypeScript over Python and a runtime-focused layer.
The open-source agent harness - the runtime layer that turns an LLM into a working agent.
Rust-core agent framework with Python wrapper; choose for high-performance or security-critical multi-agent workloads.
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.
OpenAI's lightweight multi-agent framework; choose if you prefer OpenAI-style function calling over MCP.
A lightweight, powerful framework for multi-agent workflows
Microsoft's LLM integration SDK; choose for C# or enterprise Microsoft stack.
Integrate cutting-edge LLM technology quickly and easily into your apps
Microsoft's agent framework for Python/.NET; choose for multi-language Microsoft agent development.
A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.
Production multi-agent harness; choose for a hardened harness focused on production AI workloads.
Multi-Agent Harness for Production AI
Go agent framework with MCP support; choose for Go-based agents using MCP.
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.
Graph-based agent orchestration; choose for complex stateful workflows instead of simple patterns.
Build resilient agents.
Batteries-included harness from LangChain; choose if you want prebuilt tools and LangChain ecosystem.
The batteries-included agent harness.
Google's code-first agent toolkit; choose for Google's approach with built-in evaluation/deployment.
An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
TypeScript AI agent framework; choose for TypeScript development with an integrated platform.
AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework
Java enterprise agentic workflow platform; choose for Java enterprise deployments with workflow orchestration.
Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents.
Meta-harness that orchestrates existing agents (Claude Code, Codex); choose if you manage multiple agent harnesses.
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.
Memory-first agent harness with self-improvement; choose if you need strong memory capabilities.
The Harness of Harnesses: a trusted, persistent, self-evolving multi-agent ecosystem for all-domain collaboration.
Google's Go agent toolkit; choose for Go development with ADK.
An open-source, code-first Go toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
Event-driven multi-agent framework; choose for event-driven architectures and data integration.
An event-driven framework designed to build and orchestrate multi-agent AI systems. It enables seamless integration of AI agents with real-world data sources and systems, facilitating complex, multi-step workflows.
Role-based autonomous agent framework; choose if you like the crew/role-playing paradigm.
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
Framework for building/running/managing agent platforms; choose if you need full platform lifecycle management.
Build, run, and manage agent platforms.
Low-code AI workforce framework; choose for quick deployment of self-improving agents.
PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous self-improving agents that research, plan, code, and execute tasks. Deployed in 5 lines of code with built-in memory, RAG, and support for 100+ LLMs.
Enterprise multi-agent orchestration framework; choose for large-scale enterprise agent systems.
The Enterprise-Grade Multi-Agent Orchestration Framework. Website: https://swarms.ai
Open-source AgentOS (formerly AutoGen); choose for a mature conversation-driven agent framework.
AG2 (formerly AutoGen): The Open-Source AgentOS.Join us at: https://discord.gg/sNGSwQME3x
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Previous-generation agent platform with no recent pushes; choose if you need a simple, legacy agent management tool.
Evo AI is an open-source platform for creating and managing AI agents, enabling integration with different AI models and services.
These projects were analysed and named mcp-agent among their alternatives. The relationship is not symmetric — how mcp-agent rates them is a separate judgement, made when mcp-agent is analysed in its own right.
calls mcp-agent “choose it if you want MCP as the core protocol for agent workflow patterns.”
Build resilient agents.
calls mcp-agent “MCP-centric agent builder with simple workflow patterns; choose it if you standardize on Model Context Protocol.”
The batteries-included agent harness.
calls mcp-agent “Choose when you want a Python agent framework centered on Model Context Protocol and simple workflow patterns.”
A lightweight, powerful framework for multi-agent workflows
calls mcp-agent “choose if you standardize on Model Context Protocol and want an MCP-native agent framework.”
Integrate cutting-edge LLM technology quickly and easily into your apps
calls mcp-agent “MCP-first agent framework; choose if you want agent tool integrations standardised on Model Context Protocol.”
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
calls mcp-agent “MCP-centric workflow patterns; choose for an MCP-native stack.”
An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
calls mcp-agent “choose if you prefer a lightweight MCP-protocol-based workflow engine over graph orchestration.”
A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.
calls mcp-agent “choose it when you need agents built specifically around the Model Context Protocol.”
Multi-Agent Harness for Production AI
calls mcp-agent “Python framework focused on MCP-based agents with simple workflow patterns; choose if you standardize on Model Context Protocol.”
Agentic AI Framework for Java Developers
calls mcp-agent “MCP-centric Python agent workflow; choose if you want to standardize on Model Context Protocol.”
AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework
calls mcp-agent “when you want an MCP-centric agent framework with simple workflow patterns.”
Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents.
calls mcp-agent “if you want an MCP-first Python agent framework.”
An open-source, code-first Go toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
calls mcp-agent “choose when you want a Python framework focused on MCP-based tool use.”
⚙️🦀 Build modular and scalable LLM Applications in Rust
calls mcp-agent “Choose when you want to build agents around Model Context Protocol with simple workflow patterns.”
AG2 (formerly AutoGen): The Open-Source AgentOS.Join us at: https://discord.gg/sNGSwQME3x
calls mcp-agent “Choose it when MCP-native tooling is the priority and you want a Python framework built around MCP and simple workflow patterns.”
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.
calls mcp-agent “Use when MCP and simple workflow patterns are all you need and you don't need seed's broader provider/tool/memory stack.”
Build an agent harness and control it end-to-end. Open-source SDK for production AI agents in Python & TypeScript - any model, any cloud.
calls mcp-agent “Choose for simpler MCP-centric agent workflows when Java distributed enterprise features are unnecessary.”
Build distributed, production-grade, long-running agents.
calls mcp-agent “MCP-centric agent builder; choose if your stack is entirely MCP and you accept slower maintenance.”
The Harness of Harnesses: a trusted, persistent, self-evolving multi-agent ecosystem for all-domain collaboration.