AI Agent Framework Comparison

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AutoGPT

PythonBeta
v0.6.47 · MIT
183.3k

The viral autonomous agent project. Evolved from a script into a full platform with a visual builder and marketplace.

Multi-Agent
Limited
Tools
Yes
Stream
No
RAG
Yes
MCP
No
Medium difficulty

LangChain / LangGraph

PythonProduction
v0.3 / v0.3 · MIT
133.2k

The most popular LLM framework. LangGraph adds stateful, graph-based orchestration for complex multi-step agents.

Multi-Agent
Yes
Tools
Yes
Stream
Yes
RAG
Yes
MCP
Via Adapter
High difficulty

AutoGen (Microsoft)

PythonStable
v0.4 · MIT
57.0k

Microsoft's multi-agent conversation framework. v0.4 is a complete redesign. Merging with Semantic Kernel into unified Microsoft Agent Framework.

Multi-Agent
Yes
Tools
Yes
Stream
Yes
RAG
Yes
MCP
Planned
Medium difficulty

CrewAI

PythonProduction
v1.9.3 · MIT
48.6k

Lean, lightning-fast Python framework for orchestrating role-playing autonomous AI agents. Built entirely from scratch, independent of LangChain.

Multi-Agent
Yes
Tools
Yes
Stream
Yes
RAG
Yes
MCP
Yes
Low difficulty

LlamaIndex

PythonProduction
v0.12 · MIT
48.5k

The leading framework for building LLM-powered agents over your data. Specializes in connecting, indexing, and querying data from 160+ sources.

Multi-Agent
Limited
Tools
Yes
Stream
Yes
RAG
Yes
MCP
Via Adapter
Medium difficulty

DSPy (Stanford)

PythonStable
v3.1.3 · MIT
33.6k

The framework for programming -- not prompting -- language models. Uses compilers to optimize prompts and weights automatically.

Multi-Agent
No
Tools
Yes
Stream
No
RAG
Yes
MCP
No
High difficulty

Semantic Kernel (Microsoft)

.NETProduction
v1.30 · MIT
27.7k

Microsoft's enterprise AI SDK for .NET/Python/Java. Merging with AutoGen into a unified Microsoft Agent Framework.

Multi-Agent
Yes
Tools
Yes
Stream
Yes
RAG
Yes
MCP
Yes
Medium difficulty

Smolagents (HuggingFace)

PythonStable
v1.9 · Apache-2.0
26.6k

A barebones library for agents that think in code. Minimal abstractions, maximum transparency.

Multi-Agent
Yes
Tools
Yes
Stream
Yes
RAG
Yes
MCP
No
Low difficulty

Haystack (deepset)

PythonProduction
v2.9 · Apache-2.0
24.8k

AI orchestration framework for building customizable, production-ready LLM applications. Strong focus on RAG and NLP pipelines.

Multi-Agent
Limited
Tools
Yes
Stream
Yes
RAG
Yes
MCP
No
Medium difficulty

Vercel AI SDK

TypeScriptProduction
v6.0 · Apache-2.0
23.4k

The AI toolkit for TypeScript from the creators of Next.js. Over 20 million monthly downloads. Unified API for any LLM provider.

Multi-Agent
Limited
Tools
Yes
Stream
Yes
RAG
Yes
MCP
Yes
Low difficulty

Mastra

TypeScriptStable
v1.0 · Elastic-2.0
22.9k

TypeScript-first AI agent framework from the Gatsby team. Provides workflows, RAG, and first-class MCP support out of the box.

Multi-Agent
Yes
Tools
Yes
Stream
Yes
RAG
Yes
MCP
Yes
Medium difficulty

BabyAGI

PythonAlpha
v2.0 · MIT
22.2k

An experimental framework for a self-building autonomous agent. Minimal by design, used primarily for research and learning.

Multi-Agent
No
Tools
Yes
Stream
No
RAG
No
MCP
No
Low difficulty

OpenAI Agents SDK

PythonBeta
v0.1 · MIT
20.7k

Official OpenAI agent framework. Lightweight and powerful with multi-agent handoffs, guardrails, and tracing out of the box.

Multi-Agent
Yes
Tools
Yes
Stream
Yes
RAG
No
MCP
Yes
Low difficulty

Pydantic AI

PythonStable
v1.57 · MIT
16.3k

GenAI agent framework the Pydantic way. Type-safe, validated, and production-ready with V1 API stability commitment.

Multi-Agent
Limited
Tools
Yes
Stream
Yes
RAG
No
MCP
Yes
Low difficulty

Anthropic Agent SDK

PythonAlpha
v0.1.33 · MIT
6.3k

Official Python SDK for building agents with Claude. Alpha status but rapidly evolving with native MCP support.

Multi-Agent
Limited
Tools
Yes
Stream
Yes
RAG
No
MCP
Yes
Low difficulty
Showing 15 of 15 frameworksData as of April 12, 2026

What This Tool Does

AI Agent Framework Comparison is built for deterministic developer and agent workflows.

Compare AI agent frameworks: LangChain, CrewAI, AutoGen, Mastra, and more side by side.

Use How to Use for execution steps and FAQ for constraints, policies, and edge cases.

Last updated:

This tool is provided as-is for convenience. Output should be verified before use in any production or critical context.

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How to Use AI Agent Framework Comparison

  1. 1

    Select frameworks to compare

    Choose from LangChain, CrewAI, AutoGen, Mastra, and others. Compare up to 5 frameworks side-by-side to see their strengths and weaknesses.

  2. 2

    Compare core features

    Check features like multi-agent orchestration, tool/function use, memory management, streaming support, and RAG integration across frameworks.

  3. 3

    Review learning curve and community

    See estimated learning time (easy/medium/hard), community size, GitHub stars, and available documentation/tutorials for each framework.

  4. 4

    Check language and runtime support

    Filter by supported languages (Python, JavaScript, Go, etc.) and deployment targets (serverless, Docker, on-premise) to match your stack.

  5. 5

    Find example projects and starter templates

    Access links to GitHub examples and starter projects for each framework. Clone and experiment with real-world implementations before committing.

Frequently Asked Questions

What is AI Agent Framework Comparison?
AI Agent Framework Comparison lets you compare popular AI agent frameworks like LangChain, CrewAI, AutoGen, and Mastra side by side. It's built for developers choosing an agent framework for their project.
How do I use AI Agent Framework Comparison?
Select two or more frameworks to compare their features, supported models, architecture patterns, and community metrics in a side-by-side table. Filter by language, license, or specific capabilities.
Is AI Agent Framework Comparison free?
Yes. This tool is free to use with immediate access—no account required.
Does AI Agent Framework Comparison store or send my data?
No. All processing happens entirely in your browser. Your data never leaves your device — nothing is sent to any server.
Which agent frameworks are included?
The comparison includes major frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Mastra, Semantic Kernel, and more. Data is updated regularly to reflect new releases, features, and ecosystem changes.