AI agent frameworks, ranked
Pick from here when you are building an agent rather than using one. Frameworks differ mostly in orchestration model (graphs, roles, conversations), language (Python or TypeScript) and observability; the score says whether the code and docs hold up to review, and your stack decides the rest.
| # | Agent | FARS | Trust | Stars | License | Language | Last commit |
|---|---|---|---|---|---|---|---|
| 1 | AgentScope 2.0 | 70 | 16/29 | 33.5k | Apache-2.0 | Python | 2026-10-19 |
| 2 | Pydantic AI | 65 | 13/29 | 20.7k | MIT | Python | 2026-10-19 |
| 3 | OpenAI Agents SDK ✓ OpenAI | 64 | 18/29 | 30.1k | MIT | Python | 2026-10-19 |
| 4 | Google ADK ✓ Google | 55 | 12/29 | 21.9k | Apache-2.0 | Python | 2026-10-19 |
| 5 | AutoGen Multi-Agent Framework ✓ Microsoft | 52 | 11/29 | 61.5k | CC-BY-4.0 | Python | 2026-10-16 |
| 6 | Mastra | 52 | 10/29 | 28.9k | NOASSERTION | TypeScript | 2026-10-19 |
| 7 | Haystack | 52 | 8/29 | 26.8k | Apache-2.0 | Python | 2026-10-19 |
| 8 | LlamaIndex | 48 | 7/29 | 52.6k | MIT | Python | 2026-10-14 |
| 9 | Letta | 47 | 10/29 | 25.2k | Apache-2.0 | Not stated | 2026-10-18 |
| 10 | LangGraph | 44 | 0/29 | 43.5k | MIT | Python | 2026-10-19 |
| 11 | smolagents | 42 | 7/29 | 29.9k | Apache-2.0 | Python | 2026-10-06 |
| 12 | LangChain | 41 | 0/29 | 148.1k | MIT | Python | 2026-10-19 |
| 13 | Agno Agent Platform | 41 | 7/29 | 42.8k | Apache-2.0 | Python | 2026-10-19 |
| 14 | CrewAI | 40 | 4/29 | 59.9k | MIT | Python | 2026-10-19 |
What qualifies: standalone, maintained tools of this kind that people search for by name. Plugins, skill packs and config layers built on top of them are left out and listed on the host tool's profile. The membership list is kept in our public repository, so every change is on record.
The order is the FARS total; stars only break ties, because popularity measures attention rather than whether something holds up. Two limits are worth knowing before you read the order. These are static reviews — source, documentation and repository metadata, with no runtime reproduction — which caps reliability, effectiveness and verifiability and puts the achievable maximum at 77 rather than 100. And a gap of a few points sits inside the noise of that method. Read the table as a shortlist worth investigating, not as a verdict; every score links to its full dimension breakdown and the evidence behind it. Review method →
1. AgentScope 2.0
A Python framework for building, operating, and deploying observable, controllable agents.
2. Pydantic AI
Build composable generative-AI applications in Python with type hints and Pydantic validation.
3. OpenAI Agents SDK
A Python framework for tool-using, observable multi-agent workflows.
Where it differs
For JavaScript or TypeScript projects, the repository points to a separate Agents SDK JS/TS project; this repository is the Python version.
4. Google ADK
A Python framework for building, orchestrating, evaluating, and locally running sophisticated AI agent applications.
Where it differs
The README only makes a direct version comparison: sessions generated by ADK 2.0 can be read by ADK 1.28+ with extra fields ignored, but not by older 1.x versions.
5. AutoGen Multi-Agent Framework
Build multi-agent applications that operate autonomously or collaborate with people.
Where it differs
Microsoft Agent Framework is presented as AutoGen's enterprise-ready successor. AutoGen is community-managed in maintenance mode and receives no new features, while Microsoft Agent Framework is described as production-ready with stable APIs, long-term support, multi-provider model support, and cross-runtime interoperability through A2A and MCP. The stated direction is to choose Microsoft Agent Framework for new projects and use the migration guide for existing AutoGen applications.
6. Mastra
A TypeScript framework for building, orchestrating, evaluating, and deploying AI applications and agents.
7. Haystack
A Python framework for composing controlled, production-ready LLM pipelines and agent workflows.
8. LlamaIndex
A Python framework for building retrieval, indexing, and document-intelligence applications over private data.
Where it differs
The README presents LlamaIndex as able to integrate with LangChain, but it provides no feature or performance comparison between them.
9. Letta
Build stateful agents with advanced memory that can learn and improve over time.
Where it differs
For new projects, the README recommends the Letta Agent SDK. Previous-generation V1 SDKs—@letta-ai/letta-client for TypeScript and letta-client for Python—remain available for direct use with the Letta API.
10. LangGraph
A low-level framework for resilient, long-running stateful agent and workflow orchestration.
Where it differs
Deep Agents is a higher-level package built on LangGraph for complex tasks involving planning, subagents, and file systems; LangGraph supplies the lower-level stateful orchestration layer. For JavaScript and TypeScript, the README identifies the separate LangGraph.js repository as the equivalent library.
11. smolagents
Hugging Face's barebones library for agents that think in code
Where it differs
Unlike multi-agent orchestration frameworks such as CrewAI or LangGraph that focus on process orchestration, smolagents concentrates on how a single agent executes actions — expressing tool calls as code rather than text/JSON, closer to a lightweight library than a heavyweight platform. If you need to orchestrate multiple collaborating agents, CrewAI or LangGraph fits better; if you need a simple, auditable, model-agnostic single-agent base, smolagents is more direct.
12. LangChain
A composable framework for building agent and LLM applications with interchangeable model integrations.
Where it differs
LangChain is positioned for composing general agent and LLM application components; LangGraph is positioned as the lower-level framework for controllable orchestration of complex tasks. For JavaScript and TypeScript, the README points to the equivalent LangChain.js library.
13. Agno Agent Platform
Build, self-host, and operate agent platforms with an SDK, AgentOS runtime, and control UI.
Where it differs
The README presents agentos-railway, agentos-docker, agentos-aws, agentos-gcp, agentos-azure, agentos-fly, agentos-render, agentos-modal, and agentos-helm as starter templates that differ in deployment scripts while otherwise remaining identical. They are deployment-entry alternatives, not competing products.
14. CrewAI
An open source Python framework for orchestrating multi-agent collaboration
Where it differs
Compared to graph-based orchestration frameworks like LangGraph, CrewAI's "role-playing" abstraction (each agent has a clear role and goal) is more intuitive for developers familiar with team-collaboration metaphors, with a gentler learning curve; if you need finer-grained state-graph control, LangGraph may fit better, and the two can also be combined (CrewAI's own docs mention interoperating with other frameworks).