AI Agent Frameworks Compared: LangChain, CrewAI, AutoGen and More

LangGraph gives the most control, CrewAI prototypes fastest, and AutoGen is in maintenance mode. Compare the leading AI agent frameworks on status.

By Galactic News Editorial · Published 2026-11-07T15:04:06+00:00 · Updated 2026-11-07T15:04:06+00:00

AI Agent Frameworks Compared: LangChain, CrewAI, AutoGen and More

An AI agent framework is the code that turns a language model into a system that can act. It adds tools, memory, planning, and control around the model. The best choice depends on how much control you need. LangGraph gives you the most control. CrewAI gets you a prototype fastest. AutoGen is in maintenance mode, so new projects should look elsewhere. This is a research-based assessment from official documentation, not hands-on testing.

AI agents frameworks compared: the short answer

Every framework here solves the same core problem. A model alone can only produce text. An agent must also call tools, remember state, and decide what to do next. The frameworks differ in how they model that work. LangGraph models it as a graph of steps. CrewAI models it as a team of roles. AutoGen models it as a conversation between agents. The table below gives the quick view. The sections after it explain each choice in detail.

LangGraph and LangChain: the control specialists

LangChain is the agent framework. Its own documentation describes it as the layer of abstractions and integrations for models, tools, and agent loops. LangGraph is the orchestration runtime underneath. The LangChain docs describe LangGraph as a low-level orchestration framework and runtime for long-running, stateful agents. You define workflows as graphs of shared state, nodes, and edges. You can mix deterministic steps with model-driven steps in the same graph. LangGraph focuses on durable execution, streaming, and human-in-the-loop steps. It does not prescribe prompts or agent architecture. That is the point. You get explicit control over every transition. You also get persistence and memory built in. The docs note that LangGraph does not require LangChain. You can use it on its own. For a higher-level agent loop, the docs point to Deep Agents or LangChain agents. LangGraph reached version 1.0 on October 22, 2025. It is open source under the MIT license. The learning curve is the steepest of the frameworks here. That cost buys production control. Use LangGraph when an agent must run for a long time, survive restarts, pause for human approval, or follow branching logic you can inspect. Use plain LangChain agents when you want a standard agent loop with broad model and tool integrations and less custom wiring.

CrewAI: the fastest route to a working multi-agent team

CrewAI models agents as roles inside a team. Its documentation calls it an open-source framework for orchestrating autonomous AI agents. Each agent has a role, a goal, and a backstory. Those three fields read like a job description. They shape how the model behaves on its task. Crews are teams of agents that collaborate on tasks. Flows are event-driven workflows that wrap the crew when you need deterministic control. The docs give a clear rule. Start with a Flow for any production-ready application. Use Crews inside it for the judgment calls. A crew runs tasks in sequence by default. A hierarchical process adds a manager agent that delegates and validates outcomes. That manager needs its own model, set as manager_llm. CrewAI is independent of LangChain. It works with many model providers. Its strength is speed. You can describe a small team of agents and have a running prototype with little code. Its weakness follows from the same design. The control flow is partly hidden inside the crew. Debugging a failed run can be hard when the model decided the order of steps. Use CrewAI when you need a multi-role prototype fast, or a research and content pipeline with clear roles. Wrap it in Flows before you ship it to real users.

AutoGen and Microsoft Agent Framework: do not start new work on AutoGen

AutoGen is Microsoft Research's open-source framework for multi-agent systems. Agents talk through a problem in structured conversation instead of calling tools in a fixed sequence. Version 0.4 was a ground-up rewrite to an asynchronous, event-driven architecture. Its packages are autogen-agentchat, autogen-core, and autogen-ext. That history matters less than the current status. Microsoft put AutoGen in maintenance mode on October 2, 2025. The repository banner says it will not receive new features or enhancements and is community managed going forward. Only bug fixes, security patches, and documentation improvements are planned. The last feature release, version 0.7.5, shipped on September 30, 2025. Microsoft directs new users to Microsoft Agent Framework. That framework merges AutoGen's orchestration ideas with Semantic Kernel's foundations. It reached version 1.0 on April 3, 2026, for Python and .NET. Microsoft publishes a migration guide from AutoGen to Agent Framework. A third codebase also carries the lineage. AG2 is a fork run by the original AutoGen authors. It is not a Microsoft product. The original autogen package name on PyPI now belongs to AG2's classic line. If you have a working AutoGen 0.4 app, it still runs. Nothing breaks today. But you give up new model features and any chance of Microsoft fixing a non-security bug. For new projects, start with Microsoft Agent Framework or one of the other frameworks here.

Other frameworks worth knowing

LangGraph, CrewAI, and AutoGen are not the whole field. Four more frameworks cover real needs. The OpenAI Agents SDK is the code-first option for teams building on OpenAI models. It keeps the agent loop close to the model provider. Semantic Kernel is Microsoft's older framework for plugins and planners. Microsoft now positions Agent Framework as its successor too. Mastra is the TypeScript-native option. It suits teams building long-running autonomous systems in JavaScript or TypeScript. Smolagents is a minimal Python framework from Hugging Face. It favors small, readable agent code over a large feature set. PydanticAI is a typed Python framework built on Pydantic. It suits developers who want validated, structured agent outputs. None of these is a wrong pick by itself. The wrong pick is the one that fights your stack. A TypeScript team should not adopt a Python-only framework. A team that needs typed outputs should weigh PydanticAI. A team that needs the lightest possible code should look at smolagents.

How to choose an AI agent framework

Choose in this order. First, match the language to your team. Second, decide how much control you need. Third, check the project's status. Control is the real fork in the road. If the agent must pause for approval, retry failed steps, or survive a restart, you need explicit state. That points to LangGraph. If the agent is a small team of roles doing research or content work, CrewAI gets you there faster. If the agent is a single loop with tools, plain LangChain or the OpenAI Agents SDK is enough. If you already run Microsoft infrastructure, look at Microsoft Agent Framework first. Status is the second filter. Do not build a new product on a framework in maintenance mode. AutoGen is the clear example. Check the repository before you commit. Look for recent releases, open issues, and a roadmap. A framework with weekly releases and a clear successor story is a safer bet than one with a frozen version number. Finally, prototype the failure case, not the happy path. Give the agent a bad tool result. Give it a contradictory instruction. See how it recovers. The framework that makes recovery visible and testable is the one you can operate in production.

Bottom line

Pick LangGraph when control and durability matter most. Pick CrewAI when you need a multi-agent prototype fast and can wrap it in Flows. Pick Microsoft Agent Framework if you are starting fresh in the Microsoft ecosystem. Do not start new work on AutoGen. It is in maintenance mode. Match the framework to your language and your control needs, and you will avoid the most expensive mistake: rebuilding the orchestration layer halfway through the project.