Compare LangGraph, CrewAI, AutoGen, AutoGPT, PydanticAI, smolagents, and more. Filter by language, paradigm, and production readiness.
11 frameworks found
| Framework | Language | Paradigm | Stars | Multi-agent | Memory | Human loop | Production | Learning |
|---|---|---|---|---|---|---|---|---|
LangGraph LangChain | Python / JS | graph | 30k+ | Yes | Yes | Yes | Yes | high |
CrewAI CrewAI | Python | crew | 28k+ | Yes | Yes | No | Yes | low |
AutoGen Microsoft | Python / .NET | conversation | 42k+ | Yes | Yes | Yes | Yes | medium |
AutoGPT AutoGPT Team | Python | autonomous | 170k+ | Yes | Yes | No | No | medium |
PydanticAI Pydantic | Python | workflow | 12k+ | Yes | No | Yes | Yes | low |
smolagents Hugging Face | Python | autonomous | 20k+ | Yes | No | No | No | low |
Letta Letta | Python | autonomous | 15k+ | No | Yes | Yes | Yes | medium |
OpenAI Swarm OpenAI | Python | conversation | 19k+ | Yes | No | No | No | low |
Prefect Prefect | Python | workflow | 18k+ | No | No | Yes | Yes | medium |
DSPy Stanford NLP | Python | workflow | 22k+ | No | No | No | Yes | high |
Semantic Kernel Microsoft | C# / Python / Java | workflow | 24k+ | Yes | Yes | Yes | Yes | medium |
LangChain
A graph-based orchestration framework for building stateful, multi-agent workflows. LangGraph gives fine-grained control over agent loops, checkpoints, and edges.
Best for
CrewAI
A role-based framework where agents are organized into crews with tasks, tools, and hierarchical processes. Popular for business automation and content workflows.
Best for
Microsoft
A conversational multi-agent framework where agents chat with each other to solve tasks. Strong for coding, research, and nested team simulations.
Best for
AutoGPT Team
One of the first autonomous agent projects. AutoGPT aims for fully autonomous goal completion with agents that plan, execute, and reflect without human input.
Best for
Pydantic
A Python agent framework built on Pydantic for type-safe agent development. Emphasizes structured outputs, dependency injection, and testability.
Best for
Hugging Face
A minimal agent framework from Hugging Face focused on code agents. It prioritizes simplicity and lets models write Python code to solve tasks.
Best for
Letta
An open-source framework for building agents with persistent memory. Letta focuses on long-term context, reflection, and agent identity.
Best for
OpenAI
An educational framework for multi-agent orchestration using handoffs. It demonstrates agent routing patterns but is explicitly not production-ready.
Best for
Prefect
A workflow orchestration platform that increasingly supports AI agent workflows. Good for teams that need reliable scheduling, retries, and observability.
Best for
Stanford NLP
A framework for programming language models through declarative modules and optimizers. DSPy is strong for prompt optimization and retrieval-augmented generation.
Best for
Microsoft
Microsoft SDK for building AI agents with planners, plugins, and memories. Strong integration with Azure OpenAI and enterprise .NET stacks.
Best for
Start with CrewAI for role-based crews or PydanticAI if you want type-safe Python. Both have gentle learning curves and good documentation.
Choose LangGraph for complex stateful workflows, AutoGen for conversational coding agents, or Semantic Kernel for enterprise .NET/Azure stacks.
Letta is purpose-built for persistent memory and long-running conversational agents. LangGraph also supports memory via checkpoints.
AutoGen and smolagents are excellent for code-generation agents. smolagents is simpler; AutoGen is more mature.