AI Agent Frameworks Compared: LangGraph, CrewAI and More

There is no single best AI agent framework. Pick a provider SDK (OpenAI Agents SDK, Claude Agent SDK, Google ADK) if you are committed to one model company, or an independent framework (LangGraph, CrewAI, Pydantic AI, Microsoft Agent Framework) if you want to swap models. Then choose the simplest option that handles your workflow.

If the word agent itself is still fuzzy, start with our plain-English guide to what an AI agent is and our breakdown of how agents differ from chatbots, then come back here for the tooling. Every framework below was checked against its official documentation and release history in October 2026.

What Is an AI Agent Framework?

A framework is the scaffolding you build an agent on. Left to yourself, you would have to write all the plumbing by hand: how the agent calls tools, how it remembers what it has done, how multiple agents hand work to each other, and how you watch what it is doing when something breaks. A framework gives you those pieces so you can focus on the actual task. Some frameworks are thin and get out of your way. Others are opinionated and do a lot for you, at the cost of learning their way of thinking.

Provider SDKs vs Independent Frameworks: What Is the Difference?

Almost every option falls into one of two camps, and knowing which camp you are looking at tells you most of what you need to know.

• Provider SDKs are built by the model labs themselves, around their own platforms. The OpenAI Agents SDK, Anthropic's Claude Agent SDK, and Google's Agent Development Kit are the main examples. They offer the tightest integration with that provider's models, hosted tools, and features. The trade-off is that they are oriented around one ecosystem.
• Independent frameworks are model-agnostic orchestration layers. LangGraph, CrewAI, Pydantic AI, and Microsoft Agent Framework sit here. They aim for portability and control, letting you swap the underlying model, at the cost of owning more of the decisions yourself.

Neither camp is better in the abstract. If you are committed to one model provider and want the smoothest path, a provider SDK is hard to beat. If you want the freedom to change models later or mix them, an independent framework earns its keep.

Which AI Agent Frameworks Are Worth Knowing?

Here is a plain description of the frameworks that come up most often. These are capabilities, not rankings.

• LangGraph. An open-source framework from LangChain for building long-running, stateful agents, available for Python and JavaScript. It models an agent's steps as a graph, with durable execution, persistence that can resume after an interruption, memory, and human-in-the-loop checkpoints. It reached version 1.0 in October 2025. It gives you a lot of control for complex workflows, and the trade-off is a steeper learning curve.
• CrewAI. An open-source Python framework built around two ideas: Crews, which are teams of role-based agents that divide up a task, and Flows, which are event-driven workflows that manage state and control the order of work. It takes little code to stand up a team of agents with clear roles, which makes it a common pick for prototyping multi-agent setups.
• OpenAI Agents SDK. OpenAI's lightweight SDK for Python and TypeScript. Its core pieces are agents, handoffs between agents, guardrails, sessions, and tracing, and it plugs into OpenAI's hosted tools such as web search, file search, and code interpreter.
• Claude Agent SDK. Anthropic's SDK for Python and TypeScript, formerly called the Claude Code SDK. It gives you the same tools, agent loop, and context management that power Claude Code, plus hooks, subagents, MCP connections, and permissions. A strong fit if you are building on Claude and want an agent that works like a capable computer user.
• Google Agent Development Kit (ADK). Google's open-source framework, with official libraries for Python, TypeScript, Go, Java, and Kotlin. It is built to deploy on Google Cloud options such as Cloud Run and GKE, and it supports the A2A protocol for agents that talk to other agents.
• Pydantic AI. An independent Python framework from the team behind Pydantic, typed end to end, with structured outputs and the ability to switch models by changing one setting. It appeals to teams that want predictable, well-typed results.
• Microsoft Agent Framework. Microsoft's open-source framework for Python and .NET, which reached version 1.0 in April 2026. Microsoft describes it as the direct successor to both Semantic Kernel and AutoGen. It adds graph-based workflows with checkpointing and human-in-the-loop, and supports MCP and A2A.

You will also see LlamaIndex, which describes itself as a Python toolkit for building agents over your data. It is less about orchestration and more about connecting an agent to your documents, the retrieval layer many agents need. Our explainer on RAG covers how that retrieval step works.

What Happened to AutoGen and Semantic Kernel?

Both are Microsoft projects, and both now point new users to Microsoft Agent Framework. AutoGen's repository says it is in maintenance mode: it will not get new features and is community managed. Semantic Kernel still ships updates, but its repository calls Microsoft Agent Framework its enterprise-ready successor and links a migration guide.

AG2 is a separate, community-run project that describes itself as formerly AutoGen. It is actively developed and released version 1.0 in July 2026, with the classic AutoGen-style code moved to its own repository. If you have an existing AutoGen project, the practical choice is between migrating to Microsoft Agent Framework or moving to AG2.

What Changed in the Agent Framework Landscape Recently?

The field has moved from early experiments to stable releases. The milestones below come from each project's official releases.

• October 2025: LangGraph and CrewAI both released version 1.0.
• December 2025: Anthropic contributed MCP to the Linux Foundation's new Agentic AI Foundation.
• April 2026: Microsoft Agent Framework reached 1.0 for Python and .NET.
• May 2026: Google's ADK for Python reached version 2.0.
• June 2026: Pydantic AI reached version 2.0.
• July 2026: AG2 released version 1.0.

Release numbers move fast, so check each project's own release page before you commit.

Which Protocols Matter: MCP and A2A

Two open standards keep coming up, and they matter because they reduce lock-in. The Model Context Protocol (MCP) is a common way for agents to connect to tools and data sources, so a tool you build once can work across different frameworks. Anthropic created it and contributed it to the Linux Foundation's Agentic AI Foundation in December 2025. The Agent2Agent protocol (A2A), which Google created and contributed to the Linux Foundation in June 2025, lets agents from different systems discover and talk to each other. You do not need to master either one to start, but favoring frameworks that support them keeps your options open.

How Do You Choose an AI Agent Framework?

Skip the feature checklists and answer a few practical questions.

• Are you tied to one model provider? If yes, and you want the smoothest integration, start with that provider's SDK. If you want to stay portable, pick an independent framework.
• How complex is the workflow? For a simple, mostly linear task, a lightweight SDK like the OpenAI Agents SDK or Pydantic AI keeps things clean. For a branching, stateful process with retries and human check-ins, a graph-based framework like LangGraph or Microsoft Agent Framework fits better.
• Do you need several agents working together? If distinct roles dividing a task is the point, CrewAI or Microsoft Agent Framework give you coordination out of the box.
• What language does your team use? Python is covered by every option here. TypeScript teams can look at LangGraph, the OpenAI Agents SDK, the Claude Agent SDK, and ADK. .NET teams have Microsoft Agent Framework.
• What does your team already know? A framework your developers can read and debug beats a theoretically superior one they fight with. Match the tool to the skills in the room.

A Practical Starting Point

If you are not sure, resist the urge to pick the most powerful framework on day one. Choose the simplest tool that covers your use case, build one narrow agent with it, and see how it holds up. You will learn more from a small working agent than from weeks of comparing feature tables. When you are ready to go from choosing to building, our guide to building an agent walks through the first steps, and our system prompt templates for AI agents give you a starting set of instructions.

The market will keep shifting, frameworks will merge, and new ones will appear. What will not change is the method: define the job, pick the plainest tool that does it, keep a human in the loop while it proves itself, and stay flexible enough to switch when something better arrives.

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Frequently Asked Questions

What is the best AI agent framework?

There is no single best framework. The right choice depends on your project, your team's skills and language, and the model you want to use. The practical answer is to pick the simplest tool that covers your use case rather than the most powerful one.

What is the difference between a provider SDK and an independent framework?

Provider SDKs like the OpenAI Agents SDK, Claude Agent SDK, and Google's ADK are built by model labs around their own platforms. Independent frameworks like LangGraph, CrewAI, Pydantic AI, and Microsoft Agent Framework are model-agnostic and trade tight integration for portability.

Is AutoGen still maintained?

AutoGen's repository says it is in maintenance mode: no new features, community managed, and new users should start with Microsoft Agent Framework, its official successor. AG2, a separate community project formerly called AutoGen, is still actively developed and released version 1.0 in July 2026.

What is Microsoft Agent Framework?

It is Microsoft's open-source agent framework for Python and .NET, which reached version 1.0 in April 2026. Microsoft calls it the direct successor to Semantic Kernel and AutoGen, with graph-based workflows, checkpointing, human-in-the-loop support, and MCP and A2A support.

Which framework is best for multi-agent systems?

If distinct roles dividing a task is the point, CrewAI stands up role-based agent crews with little code, and Microsoft Agent Framework provides coordination out of the box. For branching, stateful workflows with retries and human check-ins, LangGraph gives you more control.

What are MCP and A2A?

The Model Context Protocol (MCP) lets agents connect to tools and data sources, so a tool built once works across frameworks; Anthropic contributed it to the Linux Foundation in December 2025. Google's Agent2Agent protocol (A2A) lets agents from different systems talk to each other.

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