The landscape

An agent framework is the glue between a language model and the work it has to do. It owns the things you would otherwise hand-roll: the loop that calls the model, the schema for tool calls, the memory of what happened on previous turns, the orchestration when more than one agent is involved, and the plumbing for tracing, evaluation, and deployment.

Six frameworks come up in every serious conversation in 2026: LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, and Microsoft Agent Framework. Some are layered on top of each other. Two of them are explicitly being folded into a third. The rest of this article walks each one in turn, then lines them up side by side.

LangChain

LangChain is the framework most teams meet first. It started in 2022 as a Python toolkit for stitching together model calls, prompt templates, and external integrations - the original chain in the name was a literal sequence of steps the LLM walked through.

Today LangChain is a broad ecosystem. The core library is the integration layer: hundreds of connectors for model providers, vector stores, document loaders, and tools, all behind a consistent interface. On top of that sit higher-level abstractions for agents, retrieval, and structured output. Around it sit LangSmith (tracing, evals, prompt management) and LangServe (deployment), the commercial layer the LangChain company sells.

LangChain is broadest where breadth helps: prototyping, one-off automations, glue code between a model and a stack of tools. When the agent logic gets complex enough that the linear-chain model creaks, teams reach for LangGraph next.

LangGraph

LangGraph is LangChain's answer to the limits of linear chains. Built by the same team, it models an agent as a state graph: nodes are units of work, edges are transitions between them, and a shared state object flows along the edges and gets updated at each node.

That shape unlocks the things linear chains struggle with - cycles, branching, conditional routing, multi-agent handoffs, and human-in-the-loop pauses where the graph waits for input before continuing. State is durable, so a long-running graph can be paused, persisted, and resumed days later without losing its place.

LangGraph is the substrate most LangChain-shaped production agents end up running on. The LangChain abstractions still apply for the parts of the agent that are linear; LangGraph takes over the moment the workflow needs structure.

CrewAI

CrewAI takes a different shape. Where LangGraph models an agent as a graph of nodes, CrewAI models a system as a crew of agents, each with a role, a goal, a backstory, and a set of tools. You assemble the crew, give it a task, and the framework orchestrates how the agents collaborate to deliver the result.

The role-based metaphor is the point. Writing a CrewAI app feels less like wiring a graph and more like staffing a team - a researcher, an editor, a fact-checker - and letting the framework handle the back-and-forth. CrewAI ships with a process model for sequential and hierarchical task delegation, built-in memory, and integrations with the same model providers and tool ecosystem the rest of the field uses.

The trade-off is opinion. CrewAI is more prescriptive than LangChain or LangGraph; the framework decides what the collaboration loop looks like. For teams that want the role-based abstraction baked in, that is exactly the appeal.

AutoGen

AutoGen came out of Microsoft Research as one of the first frameworks to take multi-agent conversations seriously. The core idea was simple and influential: model an agent as a conversational participant, then let multiple agents talk to each other - and to humans - to solve a task.

AutoGen's contribution was less about a specific orchestration pattern and more about the abstractions. Conversable agents, group chats, code-executing agents, human proxies - the vocabulary AutoGen introduced is the vocabulary most multi-agent frameworks still use. It became the research-grade reference point for what an agent system could look like.

AutoGen is still maintained, but its long-term future is convergence: the same Microsoft teams behind it are building the Agent Framework as its production-grade successor. New work tends to start there.

Semantic Kernel

Semantic Kernel is the framework Microsoft built for the other half of the problem - shipping AI inside real enterprise applications. It is a model-orchestration SDK with first-class support for C#, Python, and Java, designed to drop into existing .NET and Java codebases without bending them around the AI layer.

Where AutoGen optimized for multi-agent research patterns, Semantic Kernel optimized for the things production teams care about: type safety, structured plugins, dependency injection, telemetry, filters and middleware, an opinionated story for prompt management, and broad model and embedding support. It is the framework you find inside Microsoft Copilot and the agents shipped by enterprise customers on top of Azure OpenAI.

Like AutoGen, Semantic Kernel is now converging into the Agent Framework - the goal being one SDK that covers both halves of what those two frameworks were doing in parallel.

Microsoft Agent Framework

Microsoft Agent Framework is the direct successor to both Semantic Kernel and AutoGen, built by the same teams. The intent is to collapse the two lines into a single SDK rather than continue evolving them in parallel.

From AutoGen it inherits the conversable-agent and multi-agent orchestration abstractions. From Semantic Kernel it inherits the enterprise scaffolding: thread-based state management, type safety, filters, telemetry, and the broad model and embedding support that production teams already depend on. On top of those two genomes, Agent Framework adds workflows - explicit, graph-shaped control over multi-agent execution paths - and a state-management system designed for long-running and human-in-the-loop scenarios.

Practically, Agent Framework is positioned as the next generation of both. Existing Semantic Kernel and AutoGen codebases have documented migration paths, and new Microsoft-stack agent work is expected to start there.

How they compare

The frameworks differ on three axes worth holding in your head:

  • Shape of the abstraction. LangChain is chains and tools. LangGraph is a state graph. CrewAI is a crew of role-based agents. AutoGen is a conversation. Semantic Kernel is a kernel of plugins. Agent Framework is conversational agents plus workflows on top.
  • Center of gravity. LangChain, LangGraph, and CrewAI are Python-first ecosystems. Semantic Kernel and Agent Framework are equally at home in C# and Python and meet enterprise teams where the existing code lives.
  • Lifecycle stage. LangChain, LangGraph, CrewAI, and Agent Framework are where new work is happening. AutoGen and Semantic Kernel are still maintained, but the active investment from Microsoft has moved to Agent Framework as their joint successor.

None of these are mutually exclusive in practice. Teams routinely use LangChain integrations from inside a LangGraph workflow, call Semantic Kernel plugins from a CrewAI tool, or run an Agent Framework workflow that delegates to a CrewAI crew. The framework is the orchestration layer; the model, the tools, and the data underneath are shared.

How to pick one

A few honest defaults:

  • Prototyping or a single-agent toolchain in Python - start with LangChain. The integration surface is the largest and the path to a working demo is the shortest.
  • Complex, stateful, branching agent logic - reach for LangGraph. The graph model fits the problem and the durable state is what keeps long-running agents sane.
  • Role-based multi-agent systems where the metaphor matches the work - CrewAI. Best fit when "a team of specialists" is a useful way to describe what the agent is actually doing.
  • Enterprise application inside a .NET, Java, or Python stack on Azure - Microsoft Agent Framework. Semantic Kernel and AutoGen still exist, but new work belongs in the successor.
  • Reference reading on multi-agent patterns - AutoGen is still the canonical text, even as the production line moves on.

The frameworks change quickly; the underlying decisions do not. Pick the one whose abstraction matches the shape of your problem and whose language and runtime match the codebase your agent has to live inside. Everything else - the model, the tools, the protocols on the wire - you can swap without rewriting the framework choice underneath.

References