====== Agent Framework Comparison ======

A practical side-by-side comparison of all major AI agent frameworks as of Q1 2026. Use this to pick the right framework for your project.

===== Framework Comparison Table =====

^ Framework ^ Language ^ Stars ^ Multi-Agent ^ Memory ^ Tool Use ^ MCP Support ^ Best For ^
| **LangChain** | Python, JS | 106k | Yes (via LangGraph) | Yes | Yes (extensive) | Yes (community) | General-purpose chains, largest ecosystem |
| **LangGraph** | Python, JS | 25k | Yes (graph-based) | Yes (stateful) | Yes | Yes (community) | Complex stateful workflows, conditional branching |
| **CrewAI** | Python | 45k | Yes (role-based crews) | Yes | Yes | Yes | Role-based multi-agent teams, rapid prototyping |
| **AutoGen (AG2)** | Python, .NET | 43k | Yes (group chat) | Yes | Yes | Yes | Conversational multi-agent, enterprise, human-in-loop |
| **DSPy** | Python | 22k | No | Yes (traces) | Limited | No | Prompt optimization, structured LLM programming |
| **LlamaIndex** | Python, TS | 41k | Limited (workflows) | Yes (indexing) | Yes | Yes (community) | RAG-first agents, data indexing, knowledge retrieval |
| **OpenAI Agents SDK** | Python | 19k | Yes | Yes | Yes | Yes (native) | OpenAI-native multi-agent with guardrails |
| **Claude Agent SDK** | Python, TS | N/A | Yes | Yes | Yes | Yes (native) | Anthropic-native agents, Claude Code foundation |
| **Google ADK** | Python, Java, TS, Go | 18k | Yes (hierarchical, A2A) | Yes | Yes (multimodal) | No | GCP-native, multimodal, deterministic pipelines |
| **PydanticAI** | Python | 8.4k | No | Yes (structured) | Yes | No | Type-safe agents, structured outputs, validation |
| **Haystack** | Python | 20k | No | Yes (pipeline) | Yes | No | Production RAG pipelines, tech-agnostic, evaluation |
| **Agno** | Python | 29k | Yes | Yes (sessions) | Yes | Yes | Full-stack multi-agent, high performance, observability |
| **Smolagents** | Python | 15k | Yes | Yes | Yes (code agents) | No | Lightweight HuggingFace agents, code execution |
| **OpenHands** | Python | 46k | Yes | Yes | Yes | Yes | Autonomous software development, SWE-bench leader |
| **Semantic Kernel** | C#, Python, Java | 24k | Yes | Yes | Yes | Yes | Microsoft/.NET enterprise, Azure integration |

===== Decision Tree: Picking Your Framework =====

<mermaid>
graph TD
    A["What kind of agent<br>are you building?"] --> B["Single agent<br>with tools"]
    A --> C["Multi-agent<br>collaboration"]
    A --> D["RAG / Data<br>retrieval agent"]
    A --> E["Coding / Software<br>development agent"]

    B --> B1{"Need type safety<br>& validation?"}
    B1 -->|Yes| B2["PydanticAI"]
    B1 -->|No| B3{"Which LLM<br>provider?"}
    B3 -->|OpenAI| B4["OpenAI Agents SDK"]
    B3 -->|Anthropic| B5["Claude Agent SDK"]
    B3 -->|Any| B6["LangChain"]

    C --> C1{"Architecture<br>preference?"}
    C1 -->|"Role-based teams"| C2["CrewAI"]
    C1 -->|"Graph workflows"| C3["LangGraph"]
    C1 -->|"Chat-based"| C4["AutoGen"]
    C1 -->|"Enterprise .NET"| C5["Semantic Kernel"]
    C1 -->|"Full-stack"| C6["Agno"]

    D --> D1{"Primary need?"}
    D1 -->|"Data indexing"| D2["LlamaIndex"]
    D1 -->|"Pipeline flexibility"| D3["Haystack"]
    D1 -->|"Prompt optimization"| D4["DSPy"]

    E --> E1["OpenHands"]

    style A fill:#4a90d9,color:#fff
    style B2 fill:#2ecc71,color:#fff
    style B4 fill:#2ecc71,color:#fff
    style B5 fill:#2ecc71,color:#fff
    style B6 fill:#2ecc71,color:#fff
    style C2 fill:#e67e22,color:#fff
    style C3 fill:#e67e22,color:#fff
    style C4 fill:#e67e22,color:#fff
    style C5 fill:#e67e22,color:#fff
    style C6 fill:#e67e22,color:#fff
    style D2 fill:#9b59b6,color:#fff
    style D3 fill:#9b59b6,color:#fff
    style D4 fill:#9b59b6,color:#fff
    style E1 fill:#e74c3c,color:#fff
</mermaid>

===== Key Decision Criteria =====

^ Criteria ^ Top Pick ^ Runner-Up ^ Notes ^
| Fastest prototyping | CrewAI (~35 lines of code) | LangChain | CrewAI's role abstraction is unmatched for speed |
| Production graph workflows | LangGraph | Semantic Kernel | LangGraph offers checkpointing + LangSmith observability |
| Enterprise / Microsoft stack | Semantic Kernel | AutoGen | Native Azure AD, C#/Java/Python support |
| RAG-first architecture | LlamaIndex | Haystack | LlamaIndex has the deepest indexing pipeline |
| OpenAI ecosystem | OpenAI Agents SDK | LangChain | Native MCP, built-in guardrails |
| Anthropic ecosystem | Claude Agent SDK | Agno | Powers Claude Code itself |
| Google Cloud / multimodal | Google ADK | LangChain | A2A protocol, multi-language support |
| Autonomous coding | OpenHands | Smolagents | 53%+ SWE-bench, fully autonomous |
| Lightweight / minimal | Smolagents | PydanticAI | HuggingFace ecosystem, code-execution agents |

===== MCP (Model Context Protocol) Support =====

MCP is becoming the standard for tool integration. Frameworks with native MCP support allow agents to connect to any MCP server for tools, data sources, and prompts.

**Native MCP:** OpenAI Agents SDK, Claude Agent SDK, Semantic Kernel, Agno, OpenHands

**Community/Plugin MCP:** LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex

**No MCP yet:** DSPy, PydanticAI, Haystack, Google ADK

//Last updated: March 2026//