What are the best AI agent frameworks in 2026?

The seven worth shortlisting are LangGraph, Microsoft Agent Framework, CrewAI, the OpenAI Agents SDK, Google's Agent Development Kit, LlamaIndex Workflows, and Pydantic AI. There is no single best one. LangGraph leads on explicit control, CrewAI on quick multi-agent prototypes, Pydantic AI on type safety, and the rest on their own strengths. Pick by the job, your language, and who will maintain the code.

What is the best agentic AI framework for multi-agent systems?

For multi-agent work, LangGraph gives the most control over how agents pass work between each other, CrewAI is the fastest way to stand up a role-based crew, and Microsoft Agent Framework is the strongest choice in a .NET or Azure shop. All three handle multiple agents. The right one depends on your language and how much control over execution you need.

Is AutoGen still worth using in 2026?

Not for new projects. Microsoft moved AutoGen into maintenance mode in late 2025 and folded it, along with Semantic Kernel, into the Microsoft Agent Framework, which reached 1.0 in April 2026. AutoGen still runs and still gets security fixes, but new features land in Agent Framework. If you are starting fresh, start there.

What is the difference between LangGraph and LangChain?

LangChain is the broader library for building LLM applications, including chains and integrations. LangGraph is the graph-based runtime for agents, where you define nodes and edges to control execution order and keep durable state. LangChain's own agents now run on LangGraph underneath. For stateful, multi-step agents you want direct control over, LangGraph is the piece that matters.

Do you need a framework to build an AI agent?

No. A framework is right when engineers will own the agent's logic in code. If a customer support or CX team needs to build, run, and improve an agent without maintaining infrastructure, an agent platform such as Voiceflow is a better fit. Frameworks give control and expect engineering time; platforms trade some low-level control for far less operational work.

AI Agent Frameworks Compared: The 7 That Matter in 2026

Everyone is building AI agents, and the list of agentic AI frameworks to build them with keeps growing. The good news for 2026 is that the field finally sorted itself out. Some tools reached a stable 1.0. Others merged or dropped into maintenance mode. Clear winners emerged for specific jobs.

This is a comparison, not an explainer. If you want the groundwork first, what a framework is, how the agent loop runs, and the parts every framework shares, read our guide to what an AI agent framework is. Here we line up the seven AI agent frameworks that matter, score them on the things that decide a real project, and mark where a code framework stops being the right tool at all.

Short answer. The pick comes down to the job. Choose LangGraph for tight control over how an agent moves step to step. Choose CrewAI for a working multi-agent prototype this week. Choose Pydantic AI for type safety and clean production Python. Choose Microsoft Agent Framework in a .NET or Azure shop, Google ADK in a Google Cloud one, LlamaIndex Workflows when retrieval is the heart of the app, and the OpenAI Agents SDK when you want the leanest possible starting point. If no engineer will own the code, skip the frameworks and use a platform. More on each below.

The Six Criteria That Actually Decide It

Feature lists all look alike. These six differences are what you feel three months in.

Control over execution. Does the framework let you define the exact path an agent takes, or does it hide that behind its own logic? Graph-based tools give you the most say. Role-based tools trade some control for speed.
Language and ecosystem. Python still leads, but not every team is Python. Some frameworks now ship for .NET, Java, Go, and TypeScript, which decides a lot in an enterprise stack.
Model support. Model-agnostic tools let you pick and switch the LLM. Tightly coupled ones tie you to a provider. Model choice protects your cost and your roadmap.
State and durability. Long-running agents need memory that survives a restart and support for a human stepping in mid-task. This is where hobby tools and production tools separate.
Observability and evaluation. When an agent runs on its own, you have to see what it did and test changes safely. Built-in tracing and evals keep you from flying blind.
Maturity and backing. Who stands behind it, how stable is the release, and will it still be maintained in three years? Framework risk is real, and it is easy to underweight early.

The table scores the seven on the criteria that fit in a grid. The write-ups after it cover the rest.

Framework	Best for	Execution model	Languages	Model support	Maturity and backing
LangGraph	Explicit control over multi-step agents	Graph of nodes and edges, durable state	Python, JS/TS	Model-agnostic	1.0 GA, by LangChain; runs at Uber, LinkedIn, Klarna
Microsoft Agent Framework	Enterprise .NET and Azure teams	Graph-based workflows plus agent threads	.NET, Python	Model-agnostic	1.0 GA April 2026; merges AutoGen and Semantic Kernel
CrewAI	Fast role-based multi-agent prototypes	Role-and-task crews, optional flows	Python	Model-agnostic	Standalone 1.x, very high adoption; smaller backer
OpenAI Agents SDK	The leanest starting point	Minimal loop with handoffs and guardrails	Python, TS/JS	OpenAI-first, other providers supported	Successor to Swarm; by OpenAI; deliberately small
Google ADK	Google Cloud and Gemini stacks	Code-first agents and workflow agents	Python, Java, Go, TS	Gemini-first, other models supported	1.0 across languages; by Google; ties to Vertex AI
LlamaIndex Workflows	Retrieval-heavy, data-first agents	Event-driven steps; AgentWorkflow on top	Python, TS	Model-agnostic	Workflows 1.0; strong RAG heritage
Pydantic AI	Type-safe production Python	Typed agents and tools, end to end	Python	Model-agnostic	Stable since 2025; by the Pydantic team

LangGraph

LangGraph is the pick when you want to decide exactly how your agent moves. You define the agent as a graph: nodes are steps, edges are the paths between them, and state persists across the whole run. If a server restarts mid-task, the agent picks up where it left off. That control is why teams like Uber, LinkedIn, and Klarna run it in production.

The cost is a steeper start. You think in graphs, and a simple agent feels heavier than it would in a lighter tool. LangGraph reached a stable 1.0 in late 2025, and LangChain's own agents now run on it underneath. If you have outgrown a quick prototype and need real control, this is the default.

Microsoft Agent Framework

If your stack is .NET or Azure, this is your framework. Microsoft Agent Framework reached 1.0 in April 2026 and folded the company's two earlier efforts, AutoGen and Semantic Kernel, into one production SDK for .NET and Python. It pairs AutoGen's simple multi-agent patterns with Semantic Kernel's enterprise features: state management, telemetry, and type safety.

The practical point is about your notes, not just your code. If a shortlist you wrote last year still says AutoGen, replace it. AutoGen moved to maintenance mode and gets security fixes only. New work goes to Agent Framework, which is where Microsoft is putting its weight.

CrewAI

CrewAI is the fastest way to get a team of agents working together. You define agents by role, give them tasks, and let the crew collaborate. A useful multi-agent prototype can come together in an afternoon, which is why CrewAI is one of the most-adopted agent frameworks in Python.

It is now fully standalone, with the old LangChain dependency gone, so it runs lean. The honest caution is backing. CrewAI is a smaller company than Google, Microsoft, or OpenAI, so weigh framework risk if you are committing for three to five years. For prototypes and plenty of production crews, the speed is worth it.

OpenAI Agents SDK

The OpenAI Agents SDK is the smallest starting point on this list, on purpose. It gives you four primitives, agents, handoffs, guardrails, and built-in tracing, then gets out of the way. It replaced OpenAI's earlier Swarm experiment, which is now retired, and it ships for both Python and TypeScript.

It leans toward OpenAI's own models but supports other providers. The trade is scope. You get a clean core rather than a deep toolbox, so complex orchestration and durable state are more do-it-yourself than in LangGraph or Agent Framework. For a focused agent, or a team already living in OpenAI's stack, that simplicity is the appeal.

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Google ADK

Google's Agent Development Kit is the code-first way to build agents on Google Cloud. It reached 1.0 and, unusually, ships across Python, Java, Go, and TypeScript, so it fits more than just Python teams. It is built to compose single agents and workflow agents, with native tracing through OpenTelemetry.

It works best when you are already on Gemini and Vertex AI, which is where the integration pays off. You can connect other models, but the gravity pulls toward Google's stack. If your infrastructure lives there, ADK is the natural choice. If it does not, that pull is a real consideration.

LlamaIndex Workflows

If retrieval is the center of your app, start with LlamaIndex. Its Workflows engine reached 1.0 as an event-driven way to compose multi-step applications, and AgentWorkflow sits on top to coordinate one or several agents. The whole thing comes from a team that has focused on connecting models to data since before agents were the story.

That heritage is both the reason to choose it and the shape of its limit. For a data-heavy or RAG-first agent, nothing here is more at home. For orchestration-heavy work that is not really about retrieval, a graph-first tool gives you more direct control.

Pydantic AI

Pydantic AI brings something the others often lack: real type safety, end to end. It comes from the team behind Pydantic, the validation library that half the Python world already uses through FastAPI. Agents, tools, and outputs are typed, so errors surface at the edges instead of deep in a run.

It has been stable since 2025 and stays model-agnostic. For a Python team that values typed, testable code and clean production habits, it is the most natural fit on this list. It is younger than LangChain's world, so the ecosystem around it is smaller, but the core is solid.

Where a Code Framework Stops Being the Right Tool

Every framework above assumes the same thing: an engineering team that will write and own the agent's logic in code. That is the right setup for a lot of products. It is the wrong setup for a lot of others, and it is worth being honest about which one you are.

If your goal is a customer support agent that a CX team can build, change, and watch, a code framework puts a wall between the people who understand the customer and the system that serves them. Every tweak becomes an engineering ticket. That is where an agent platform fits better than any framework on this list.

Voiceflow is that kind of platform, and it is honest to say it is not a LangChain-style code framework. It is the place a team builds and runs the agent. The parts that matter for support:

Model-agnostic. Use OpenAI, Anthropic, or Google, or bring your own model. The same model choice the frameworks give you, without the code.
Native human handoff. When the agent hits its limit, it hands off to a live agent with the full conversation, and a Call Forward step does the same on voice.
Built for support scale. SOC 2 Type 2 and PII masking for regulated data, plus observability to see what every agent did and why.
Usage-based pricing. You pay for what the agent handles, not a flat seat count, so cost tracks value.

The choice is not framework versus platform in the abstract. It is who builds and owns the agent. Engineers writing custom logic want a framework. A support team shipping and running an agent wants a platform. Name that honestly and the shortlist gets short fast.

How to Choose

Three questions settle most decisions.

Who owns the agent? If the answer is not an engineering team, stop evaluating frameworks and look at a platform.
What is your language and cloud? Python and an open stack point to LangGraph, CrewAI, or Pydantic AI. .NET or Azure points to Microsoft Agent Framework. Google Cloud points to ADK.
How much control do you need over execution? High control favors graph-based tools like LangGraph and Agent Framework. Speed over control favors CrewAI or the OpenAI Agents SDK.

Start there, match to the write-ups above, and you land on one or two candidates instead of seven. For a wider buyer's view of building and running agents in production, our guide to choosing an AI agent builder goes deeper on the platform side.