Which AI agent framework should you actually use?

An AI agent framework is a library that handles the plumbing of building agents: calling a model in a loop, giving it tools to use, keeping track of state and memory, and coordinating more than one agent. Instead of writing that orchestration yourself, you describe the agents and tasks and let the framework run the loop. LangGraph, CrewAI, AutoGen, and LlamaIndex are popular examples in 2026.

There is no single best framework; the right pick depends on your language and goal. For complex production agents that need state and human approval, LangGraph leads. For quickly standing up a team of role-based agents, CrewAI is fastest. For data-heavy retrieval agents, LlamaIndex. For TypeScript teams, Mastra. For OpenAI-only stacks, the OpenAI Agents SDK is the lowest-friction start. Use the picker above to match your situation.

Use CrewAI when you want to ship a working multi-agent crew fast and think in terms of roles and tasks. Use LangGraph when you need explicit control over state, checkpoints, rollbacks, and human-in-the-loop approval, which matters for production systems with audit requirements. CrewAI is gentler to learn; LangGraph gives more control at the cost of a steeper curve and more verbose code.

Yes. Every framework compared here is open source. LangGraph, CrewAI, LlamaIndex, AutoGen, OpenAI Agents SDK, Pydantic AI, and Semantic Kernel use the MIT license; Google ADK, Mastra, and Smolagents use Apache-2.0. The frameworks are free to use, though you still pay your model provider for API calls, and some vendors sell hosted platforms and observability on top.

Match the framework to your existing stack. Python has the widest choice: LangGraph, CrewAI, LlamaIndex, AutoGen, Pydantic AI, Google ADK, Semantic Kernel, and Smolagents all support it. For TypeScript and JavaScript teams, Mastra is native and LangGraph, LlamaIndex, and the OpenAI Agents SDK have TypeScript versions. For C# or Java, Semantic Kernel is the strongest option, with Google ADK also offering Java.

Most real tasks start with a single agent that has a few tools. Reach for multi-agent only when the work genuinely splits into roles that benefit from separate prompts and context, like a researcher feeding a writer feeding a reviewer. Multi-agent setups add token cost and coordination complexity, so start single and split later. CrewAI, LangGraph, AutoGen, and Semantic Kernel all handle multi-agent well when you need it.

Human-in-the-loop means the agent can pause and wait for a person to approve, edit, or reject a step before continuing. It is essential when an agent can take consequential actions like sending an email, spending money, or changing a database. LangGraph, CrewAI, AutoGen, Google ADK, Semantic Kernel, and Mastra support it natively; lighter libraries like Smolagents leave it to you to add.

State persistence means the framework can save where an agent run is and resume it later, even after a crash or across separate requests. It is what lets a long-running agent survive a restart and what powers rollback to an earlier checkpoint. LangGraph makes this a core feature with its checkpointer. If your agent runs for seconds and never needs to resume, you may not need it; if it runs for minutes or coordinates many steps, it matters a lot.

Observability means seeing every model call, tool call, and decision an agent made, which is how you debug and improve it. Several frameworks ship it: LangGraph integrates LangSmith, Pydantic AI uses Logfire, AutoGen and Semantic Kernel expose OpenTelemetry, and CrewAI and Mastra include built-in dashboards. If a framework has no built-in tracing, you can usually wire in OpenTelemetry or a third-party tool like Langfuse.

Mostly yes. LangGraph, CrewAI, LlamaIndex, AutoGen, Pydantic AI, Semantic Kernel, Mastra, and Smolagents are provider-agnostic and let you swap between OpenAI, Anthropic, Google, and open models. The two provider-native SDKs, OpenAI Agents SDK and Google ADK, work best with their own models but can reach others through a compatibility layer like LiteLLM. If avoiding lock-in matters, pick a provider-agnostic framework.

CrewAI, the OpenAI Agents SDK, Pydantic AI, Mastra, and Smolagents all have gentle learning curves and small APIs. CrewAI and Mastra are good if you want multi-agent results quickly; Pydantic AI is great if you already know Python and want type-safe outputs; the OpenAI Agents SDK is the simplest if you are committed to OpenAI. LangGraph is the most powerful but the steepest to learn, so it is usually not the first framework to try.

No, but they are related. LangChain is the broader library of model wrappers, tools, and integrations. LangGraph is a separate library from the same team focused specifically on orchestrating stateful agents as a graph. You can use LangGraph with LangChain's integrations, but LangGraph is the part you reach for when you need agent control flow, persistence, and human-in-the-loop.

The star counts are approximate figures rounded to the nearest thousand as of May 2026, based on publicly reported numbers. Stars move daily and are a rough proxy for popularity, not quality or fit. Always open the linked repository for the current count, and weigh release activity, issue response time, and documentation quality alongside stars when you choose.

No. AI Agent Framework Comparison is an independent tool built by Choppy Toast. We are not affiliated with LangChain, CrewAI, Microsoft, OpenAI, Google, Hugging Face, Pydantic, or any framework maker. The comparison reflects public documentation and our reading of each project as of May 2026. Always confirm details in the official docs linked on each framework page before you commit.