Agno alternatives: 6 open-source agent frameworks compared
Updated By Agent Radar6 min read
Short answer
Agno alternatives compared: CrewAI, LangGraph, OpenAI Agents SDK, Pydantic AI, Google ADK and Microsoft Agent Framework, with licenses, stars and when to switch.
Top pick: LangGraph. MIT-licensed graph runtime for stateful agents, with checkpointing and human-in-the-loop built around explicit state.
Quick comparison
| Pick | License | Pricing | Source |
|---|---|---|---|
| 1.LangGraph | MIT | Free library; LangChain sells hosted deployment and tracing separately. | langchain-ai/langgraph on GitHub |
| 2.CrewAI | MIT | Free framework; CrewAI also sells a hosted enterprise platform. | crewAIInc/crewAI on GitHub |
| 3.Pydantic AI | MIT | Free framework; Pydantic Logfire for tracing is a separate product. | pydantic/pydantic-ai on GitHub |
| 4.OpenAI Agents SDK | MIT | Free SDK; you pay for model usage. | openai/openai-agents-python on GitHub |
| 5.Google ADK | Apache-2.0 | Free framework; Google Cloud hosting is billed separately. | google/adk-python on GitHub |
| 6.Microsoft Agent Framework | MIT | Free framework; Azure services are billed separately. | microsoft/agent-framework on GitHub |
Most people looking for an Agno alternative want one of two things: more explicit control over agent state than Agno's higher-level agents and teams give them, or a lighter library without the AgentOS runtime and its database. LangGraph covers the first case; Pydantic AI and the OpenAI Agents SDK cover the second.
Short answer
If you need long, branching runs that pause, resume and survive restarts, use LangGraph. If you want a multi-agent prototype running this afternoon, use CrewAI. If you want small, typed, plain-Python agents, use Pydantic AI. If you build mostly on OpenAI models, the OpenAI Agents SDK has the fewest moving parts. Teams committed to Google Cloud or Azure should look at Google ADK or Microsoft Agent Framework first.
Pick by what you are building
| You want | Start with | License | GitHub stars (Nov 13, 2026) |
|---|---|---|---|
| Explicit state, checkpoints, human review | LangGraph | MIT | about 44,900 |
| Role-based multi-agent crews | CrewAI | MIT | about 60,800 |
| Typed tools and validated outputs | Pydantic AI | MIT | about 21,200 |
| Few concepts, OpenAI models | OpenAI Agents SDK | MIT | about 30,600 |
| Gemini and Google Cloud | Google ADK | Apache-2.0 | about 22,200 |
| Python and .NET on Azure | Microsoft Agent Framework | MIT | about 14,700 |
For reference, Agno itself had about 43,200 stars on the same day and is Apache-2.0. The order above is editorial, not a benchmark.
What you give up by leaving Agno
Agno is more than an agent library. Version 3.2.0, released on November 12, 2026, ships AgentOS with homepage metrics for sessions, runs, tokens and cost, A2A on protocol v1.0, a Slack interface with approvals and a Stop button, and stored ClaudeAgent and CodexAgent components that wrap other vendors' coding agents. If you use those pieces, every framework below means building or buying them separately. If you only use Agno's Agent class with a few tools, switching costs much less. The details are in our Agno 3.2.0 launch notes.
The alternatives
LangGraph
LangGraph models an agent as a graph of nodes over shared, typed state. Checkpointers save that state after each step, which is what makes pause, resume, time travel and human approval straightforward. The price is more code for simple agents, and the open-source library has no control-plane UI like AgentOS; LangChain sells hosted deployment and tracing separately. Version 1.2.21 came out on November 12, 2026. Compare it directly in Agno vs LangGraph.
CrewAI
CrewAI is the most-starred project here. You describe agents by role, goal and backstory, give them tasks, and run them as a crew; Flows add event-driven control for production pipelines. It is quick to start, but the role-play layer can make it harder to see what each model call does. Version 1.15.41 shipped on November 12, 2026. See Agno vs CrewAI and LangGraph vs CrewAI.
Pydantic AI
Pydantic AI brings Pydantic's type checking to agents: typed dependencies, typed tools and validated structured outputs. It feels like ordinary Python and stays out of the way, which is why many people move to it from heavier frameworks. You give up a bundled runtime and admin UI. Version 2.68.0 shipped on November 12, 2026. See Agno vs Pydantic AI and Pydantic AI alternatives.
OpenAI Agents SDK
The OpenAI Agents SDK has a small vocabulary: agents, tools, handoffs, guardrails and sessions, with tracing on by default. It works with other providers, but it is smoothest on OpenAI's models. There is no serving layer or dashboard, so you write the API around it yourself. Version 0.24.0 is the current PyPI release. See OpenAI Agents alternatives.
Google ADK
Google ADK is Apache-2.0, like Agno, and ships a local dev UI and evaluation tools with the framework, plus A2A support. It runs with other models, but its defaults and deployment docs lean towards Gemini and Google Cloud. Version 2.14.1 shipped on November 12, 2026. See ADK vs Agno and Google ADK alternatives.
Microsoft Agent Framework
Microsoft Agent Framework is Microsoft's successor to Semantic Kernel and AutoGen, with Python and .NET SDKs and graph-based workflows. It is the obvious pick for .NET shops and teams already on Azure, and the least obvious for anyone else. Python 1.26.0 was released on November 13, 2026. See Microsoft Agent Framework vs Semantic Kernel.
How to switch without losing a week
Pick one Agno agent that does real work and write down what it depends on: tools, memory, knowledge, storage, and any AgentOS features such as approvals or metrics. Rebuild only that agent in the alternative with the same model and tools, then run both on the same set of inputs and compare answers, token use and failure handling. Port the rest only once the AgentOS features you rely on have a plan.
Sources checked November 13, 2026: release notes, official repositories, PyPI and GitHub license and star counts. We did not benchmark these frameworks. For neighbouring guides, see LangChain alternatives and the frameworks category.
The picks
MIT-licensed graph runtime for stateful agents, with checkpointing and human-in-the-loop built around explicit state.
Pros
- Explicit graph and state make long, branching runs easy to reason about.
- Checkpointers let a run pause, resume and be inspected step by step.
Cons
- More code than Agno for a simple agent with tools.
- No bundled control-plane UI like AgentOS in the open-source library.
Pricing Free library; LangChain sells hosted deployment and tracing separately.
MIT-licensed framework built around crews of role-based agents and event-driven Flows.
Pros
- Fast to get a multi-agent prototype running with roles and tasks.
- Largest community of the group by GitHub stars.
Cons
- Role-play abstractions can hide what each model call does.
- Less control over low-level state than LangGraph.
Pricing Free framework; CrewAI also sells a hosted enterprise platform.
MIT-licensed, type-safe agent framework from the Pydantic team, with typed dependencies and validated outputs.
Pros
- Typed tools, dependencies and outputs catch mistakes before runtime.
- Small surface area that feels like ordinary Python.
Cons
- No bundled runtime or admin UI comparable to AgentOS.
- Multi-agent patterns are left more to your own code.
Pricing Free framework; Pydantic Logfire for tracing is a separate product.
MIT-licensed SDK with agents, tools, handoffs, guardrails and tracing, in Python and JavaScript.
Pros
- Very few concepts to learn; handoffs cover most multi-agent needs.
- Built-in tracing and realtime voice agents.
Cons
- Smoothest on OpenAI models and the Responses API.
- You build your own serving layer and dashboard.
Pricing Free SDK; you pay for model usage.
Apache-2.0 Agent Development Kit from Google, with a local dev UI, evaluation tools and A2A support.
Pros
- Dev UI and evaluation tooling ship with the framework.
- Clear path to deploy on Google Cloud.
Cons
- Defaults and docs lean towards Gemini and Google Cloud.
- Fewer third-party integrations than Agno's toolkit list.
Pricing Free framework; Google Cloud hosting is billed separately.
MIT-licensed successor to Semantic Kernel and AutoGen for Python and .NET, with graph-based workflows.
Pros
- Python and .NET from one project.
- Natural fit for teams already on Azure and Semantic Kernel.
Cons
- Youngest community of the six by GitHub stars.
- Azure-first examples and integrations.
Pricing Free framework; Azure services are billed separately.
Questions
What is the best open-source Agno alternative?
For long, stateful runs with pause and resume, LangGraph. For quick multi-agent prototypes, CrewAI. For typed, plain-Python agents, Pydantic AI. All three are MIT-licensed. Our [Agno vs LangGraph](/blog/agno-vs-langgraph) and [Agno vs CrewAI](/blog/agno-vs-crewai) comparisons go deeper.
Agno vs CrewAI: which should I pick?
Pick Agno if you want a framework plus a runtime and admin UI (AgentOS) for serving agents, approvals and metrics. Pick CrewAI if your problem maps cleanly to a crew of role-based agents and you want the fastest prototype. See [Agno vs CrewAI](/blog/agno-vs-crewai).
Is there an Agno alternative in TypeScript?
The OpenAI Agents SDK has a JavaScript version, and Mastra is a TypeScript-first agent framework. Check Mastra's LICENSE file before building a product on it, since the GitHub API does not report a single license for the repository.