The framework question comes up in week one of every agent project, and it is usually the wrong question. Most teams do not need a framework until they need tracing, retries and durable state, which is month three.

That said, when you do need one, the choice matters. Here is how the leading options differ in 2026.

1. LangGraph

The de facto standard for production agent systems. It replaces implicit chains with explicit graphs, which means you can see and control the state machine rather than hoping the abstraction behaves. Reported enterprise adoption leads the field at around 34.5 million monthly downloads.

Best for: complex stateful workflows that must be debuggable. Trade-off: more concepts to learn before your first useful agent.

2. CrewAI

The fastest path from idea to working multi-agent system. You define roles, give each a goal, and let them collaborate. The mental model is a team, which makes it unusually easy to explain to stakeholders.

Best for: quick multi-agent prototypes and role-shaped problems. Trade-off: less control when you need precise orchestration.

3. Microsoft Agent Framework

The consolidation of Microsoft agent tooling, aimed squarely at teams already in the Azure ecosystem. Documented orchestration patterns including sequential, concurrent, group chat, handoff and magentic give you a shared vocabulary.

Best for: Microsoft-centric enterprises. Trade-off: most natural inside that ecosystem.

4. OpenAI Agents SDK

Deliberately minimal. If your workflow is one capable model plus tools plus a handoff or two, this gets you there with very little ceremony.

Best for: straightforward agents where the framework should stay out of the way. Trade-off: less suited to elaborate state machines.

5. LlamaIndex Workflows

Strongest when retrieval is the centre of gravity rather than an afterthought. If your agent is fundamentally a knowledge system that occasionally acts, this is the natural home.

Best for: document-heavy and RAG-first applications. Trade-off: retrieval-shaped rather than orchestration-shaped.

6. Google ADK

Google agent development kit, with tight integration into its own model and cloud tooling. A sensible default if your infrastructure already lives there.

Best for: Google Cloud teams. Trade-off: ecosystem gravity, same as the Microsoft option.

7. Dify

The most-starred project in the category, at around 144,000 GitHub stars, and a different shape from the rest. It is a platform with a visual builder rather than a code-first library, which puts agent building within reach of people who do not write Python.

Best for: teams wanting a self-hostable platform with a UI. Trade-off: less flexible than code when requirements get unusual.

How to Choose Without a Two-Week Bake-Off

Conclusion

Build your first agent with no framework at all, so you learn what you actually need. When the pain becomes state management and debugging, take LangGraph. When it becomes speed of assembly, take CrewAI. Whatever you pick, keep your tool definitions and prompts outside the framework so switching later costs a day rather than a quarter.

Frequently Asked Questions

Can I switch frameworks later?

Easier than people fear, if you kept prompts, tool definitions and evaluation sets separate from framework code. The framework should own orchestration and nothing else.

Do frameworks support MCP?

The major ones do, which is one of the strongest arguments for using MCP for tools. Your integrations survive a framework change.

Is a managed platform better than an open framework?

Managed platforms trade control for speed and are excellent for standard shapes. The moment your requirements go sideways, that trade reverses.