§ Comparison · Updated May 2026

LangGraph vs Agno.

LangGraph and Agno are frequently shortlisted together. Both compete in the ai agents space, so the right pick comes down to pricing model, ecosystem, and the specific features you'll lean on. This page lays out the spec sheet, an editor verdict, and answers to the questions people search before choosing.

§ Verdict

Highest rated

LangGraph

Editor score 4.5/5 — leads on overall quality across our evaluation.

Best value

LangGraph

open-source and self-hostable pricing — the lowest-friction option of the group.

Broadest feature set

LangGraph

5 headline features — the most all-in-one option.

OSS / self-host

LangGraph

Open-source — the only option in this group you can self-host or fork.

§ Spec sheet

Rating

LangGraph

4.5

Agno

4.1

Pricing

LangGraph

Open source

Agno

Open source

Category

LangGraph

AI Agents

Agno

AI Agents

Features

LangGraph

• Stateful agent graphs
• Human-in-the-loop control
• Multi-agent workflows
• Persistence and interrupts
• LangChain ecosystem integration

Agno

• Pure Python agent framework
• Built-in memory and knowledge
• Production monitoring tools
• Pre-built agent templates
• Multi-model support

Pros

LangGraph

+ Strong control model for real agent systems
+ Good fit for production-grade workflow orchestration

Agno

+ Clean Pythonic API
+ Production-focused features
+ Good starter templates

Cons

LangGraph

− Requires engineering skill
− Can be more complex than simple agent builders

Agno

− Smaller community than LangChain
− Less documentation
− Fewer integrations

Use Cases

LangGraph

Agent orchestrationWorkflow automationHuman-reviewed agentsMulti-step LLM apps

Agno

Building production AI agentsKnowledge-base powered assistantsMulti-tool AI workflowsEnterprise agent deployment

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LangGraph

Agno

	LangGraph Framework for building stateful, controllable AI agents and multi-step LLM workflows.	Agno Production-grade framework and runtime for building and operating multi-agent systems.
Rating	4.5	4.1
Pricing	Open source	Open source
Category	AI Agents	AI Agents
Features	• Stateful agent graphs • Human-in-the-loop control • Multi-agent workflows • Persistence and interrupts • LangChain ecosystem integration	• Pure Python agent framework • Built-in memory and knowledge • Production monitoring tools • Pre-built agent templates • Multi-model support
Pros	+ Strong control model for real agent systems + Good fit for production-grade workflow orchestration	+ Clean Pythonic API + Production-focused features + Good starter templates
Cons	− Requires engineering skill − Can be more complex than simple agent builders	− Smaller community than LangChain − Less documentation − Fewer integrations
Use Cases	Agent orchestrationWorkflow automationHuman-reviewed agentsMulti-step LLM apps	Building production AI agentsKnowledge-base powered assistantsMulti-tool AI workflowsEnterprise agent deployment
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§ Best for

Pick LangGraph for

AI engineers who need agent orchestration.

Read full review →

Pick Agno for

Python developers who need building production ai agents.

Read full review →

§ Common questions

LangGraph vs Agno — which is better?

It depends on what you're optimizing for. LangGraph edges Agno on our editor rating (4.5 vs 4.1), but ratings are a coarse signal. The verdict above breaks down which one wins for budget, feature breadth, and self-hosting.

Are these tools free?

Yes — every tool here has a free or freemium tier. The differences are in usage limits, advanced features, and how aggressive each free tier is.

When should I pick LangGraph over Agno?

Pick LangGraph when agent orchestration matters more than Agno's strengths in building production ai agents. The "best for" callouts above translate this into concrete personas.

Are there other tools to consider?

Yes — every tool in this comparison has its own alternatives page that ranks the closest competitors. Click any tool name to drill into its full review and alternatives list.

Editorial verdicts, not algorithmicDisagree? Tell us →