- 1. OpenClaw — ~343k ⭐
- 2. n8n — ~182k ⭐
- 3. Dify — ~134k ⭐
- 4. LangChain — ~126k ⭐
- 5. LlamaIndex — ~48k ⭐
- 6. CrewAI — ~44.6k ⭐
- 7. MetaGPT — ~44k ⭐
- 8. SmolAgents — ~26k ⭐
- 9. LangGraph — ~24.6k ⭐
- 10. Mastra — ~22.3k ⭐
- 11. OpenAI Agents SDK — ~20.5k ⭐
- 12. Google ADK — ~18.6k ⭐
- 13. Agno — ~18.5k ⭐
- 14. Pydantic AI — ~16k ⭐
- 15. Claude Agent SDK — Growing Rapidly ⭐
- How to Choose
- References
🌏 中文版
In 2026, every major vendor has their own Agent framework, and the open-source community keeps producing new contenders. This post isn’t a “best of” ranking — GitHub Stars don’t equal quality, and download counts don’t mean it’s the right fit for you. But stars at least reflect community attention, making them a reasonable starting point.
Below are 15 frameworks in active development, sorted by GitHub Stars. Each comes with its positioning, core features, and ideal use cases.
1. OpenClaw — ~343k ⭐
Repo: openclaw/openclaw | Language: TypeScript | License: MIT
The biggest phenomenon project of 2026. Developed by PSPDFKit founder Peter Steinberger, it launched in November 2025 and surged from 9k to 343k stars in four months, surpassing React to become the highest-starred non-aggregation software project on GitHub. Nvidia CEO Jensen Huang called it “possibly the most important software release ever.”
Core Features:
- Messaging app as the interface (WhatsApp, Telegram, Discord, Slack)
- Local-first — memory stored as Markdown files on your machine
- Heartbeat daemon for autonomous task scheduling
- ClawHub skills marketplace with 13,000+ community skills
- Supports all major models (Claude, GPT, Gemini, Ollama)
Ideal Use Cases: Personal AI assistant, operating local tasks through a chat interface, quick automation.
Watch Out For: Security is a major concern — 9+ CVEs within 2 months, 42,665 exposed instances. Enterprise environments need additional security layers (e.g., Nvidia’s NemoClaw).
2. n8n — ~182k ⭐
Repo: n8n-io/n8n | Language: TypeScript | License: Fair-code (Sustainable Use License)
A visual workflow automation platform with 400+ integrations that fully embraced AI agent capabilities in 2026. It’s not a traditional agent framework per se, but it lets non-programmers build agent pipelines, and the self-hosted option gives you full data control.
Core Features:
- Drag-and-drop agent workflow design
- 400+ built-in integrations (Gmail, Slack, databases, APIs)
- Self-hostable — data never leaves your environment
- Custom code nodes mixed with visual flows
- 200k+ community members
Ideal Use Cases: AI automation for non-engineers, enterprise internal process automation, agents that quickly connect multiple services.
How It Differs from Dify: n8n leans toward general workflow automation (not just AI); Dify focuses specifically on LLM applications.
3. Dify — ~134k ⭐
Repo: langgenius/dify | Language: Python / TypeScript | License: Apache 2.0
A low-code LLM application development platform. In March 2026 it raised $30M in a Pre-A round, with 280 enterprise customers and 1.4M deployments. It includes built-in RAG pipeline management, workflow orchestration, multi-model support, and native MCP integration.
Core Features:
- Visual workflow builder
- Built-in RAG pipeline management
- Support for OpenAI, Anthropic, and other model providers
- Native MCP integration (can expose Dify agents as MCP servers)
- Self-hostable or use Dify Cloud
Ideal Use Cases: Teams rapidly building LLM application MVPs, domain experts building their own agents, avoiding building RAG from scratch.
4. LangChain — ~126k ⭐
Repo: langchain-ai/langchain | Language: Python / JavaScript | License: MIT
The Swiss Army knife of LLM application development. The most established framework with the most complete ecosystem, supporting chains, agents, memory, retrieval, and tool use. In 2026, it’s positioned more as an “infrastructure layer” — many other frameworks (including CrewAI, LlamaIndex) use LangChain components under the hood.
Core Features:
- Modular components: chains, agents, memory, retrievers
- Massive integration ecosystem (LLMs, vector databases, tools)
- LangSmith for tracing and evaluation
- Most abundant documentation and community resources
- Python + JS dual-language support
Ideal Use Cases: LLM applications requiring maximum flexibility, RAG systems, serving as the foundation layer for other frameworks.
Relationship with LangGraph: LangChain is the component library; LangGraph is the orchestration engine. Use LangGraph for complex workflows; LangChain alone is sufficient for simple agents.
5. LlamaIndex — ~48k ⭐
Repo: run-llama/llama_index | Language: Python / TypeScript | License: MIT
Originally a RAG framework, it pivoted to a full agent platform in 2026. In their own words: “2026 is the year agents go from workflow to employee.” The core abstraction is AgentWorkflow, supporting single-agent to multi-agent teams.
Core Features:
- 300+ integration packages (LLMs, embeddings, vector databases)
- AgentWorkflow multi-agent orchestration
- Built-in Agent Client Protocol (ACP) integration
- LlamaAgents one-click deployment for document processing agents
- AgentFS secure filesystem access
Ideal Use Cases: Agents that interact heavily with documents and knowledge bases, document processing automation (invoices, contracts, regulations).
6. CrewAI — ~44.6k ⭐
Repo: crewAIInc/crewAI | Language: Python | License: MIT | PyPI Monthly Downloads: 12M+
A multi-role collaboration framework. The core concepts are Agent (role), Task, and Crew (team) — you can go from concept to prototype in 2-4 hours. 60% of Fortune 500 companies have tried it. Natively supports MCP and A2A protocols.
Core Features:
- Role-playing style multi-agent collaboration
- Intuitive API: Agent → Task → Crew
- Built-in memory system (short-term, long-term, entity memory)
- CrewAI Flows for flexible orchestration
- 100,000+ developers certified through community courses
Ideal Use Cases: Multi-role business automation, content generation pipelines, research and analysis.
How It Differs from LangGraph: CrewAI has a lower learning curve and suits scenarios with clear role divisions; LangGraph is better for complex workflows requiring fine-grained state control.
7. MetaGPT — ~44k ⭐
Repo: FoundationAgents/MetaGPT | Language: Python | License: MIT
Organizes multi-agent systems using a “software company” metaphor. Each agent plays a role — product manager, architect, engineer — simulating a real software development process. It’s more research and demo-oriented, but the concepts are inspiring.
Core Features:
- Software company simulation: PM → Architect → Engineer workflow
- Automatically generates PRDs, design documents, and code
- SOP (Standard Operating Procedure)-driven agent collaboration
- Supports human-in-the-loop review
Ideal Use Cases: Experimenting with automated software development processes, research purposes, proof of concept.
The Reality: Few people use MetaGPT directly in production, but its multi-role collaboration pattern has influenced many subsequent frameworks (including CrewAI).
8. SmolAgents — ~26k ⭐
Repo: huggingface/smolagents | Language: Python | License: Apache 2.0
A minimalist agent framework from Hugging Face. The core codebase is only ~1,000 lines, but it’s surprisingly capable. Its standout feature is Code Agent — the agent writes Python code directly to execute tasks, requiring 30% fewer steps and LLM calls than JSON tool calling.
Core Features:
- Minimal: ~1,000 lines of core code
- Code Agent mode (agent writes and executes Python)
- Model-agnostic (supports OpenAI, Anthropic, Ollama, HF Hub)
- Sandboxed execution (E2B, Docker, Pyodide)
- Integrates with MCP servers, LangChain tools, HF Hub Spaces
Ideal Use Cases: Lightweight agent prototypes, research experiments, scenarios requiring minimal dependencies.
207 contributors, growing from 3k stars in early 2025 to 26k — a very active community.
9. LangGraph — ~24.6k ⭐
Repo: langchain-ai/langgraph | Language: Python / JavaScript | License: MIT | PyPI Monthly Downloads: 38M+
The agent orchestration engine from the LangChain team. It reached v1.0 in late 2025 and became the default runtime for all LangChain agents. The core concept models agent workflows as directed graphs — nodes are actions, edges are transitions, with support for conditional branching and loops.
Core Features:
- State graph workflow modeling
- Durable execution — failure recovery, automatic retries
- Human-in-the-loop
- Checkpointing and time-travel debugging
- LangSmith integration (tracing, monitoring)
Ideal Use Cases: Complex multi-step agent workflows, scenarios requiring fine-grained flow control, production-grade deployments.
Only 24k stars but 38M monthly downloads — the highest-downloaded agent framework. The lower star count is because many people use it indirectly through LangChain.
10. Mastra — ~22.3k ⭐
Repo: mastra-ai/mastra | Language: TypeScript | License: MIT | npm Weekly Downloads: 300k+
A TypeScript-native agent framework built by the Gatsby team. It graduated from Y Combinator W25 in January 2026 with $13M in funding. If your tech stack is TypeScript, this is currently the most mature option.
Core Features:
- Designed from the ground up for TypeScript (not a Python port)
- Connects to 40+ model providers
- Built-in workflows, memory, RAG, evals, tracing
- Interactive playground for testing agents
- Deployable to Vercel, Cloudflare, Netlify
Ideal Use Cases: Agent development in TypeScript/Node.js stacks, integrating AI into Next.js apps, full-stack JS projects.
11. OpenAI Agents SDK — ~20.5k ⭐
Repo: openai/openai-agents-python | Language: Python / JavaScript | License: MIT | PyPI Monthly Downloads: 14.7M+
OpenAI’s official agent framework, released March 2025 as the formal successor to the Swarm SDK. Extremely streamlined design: four core primitives — Agent, Tool, Handoff, and Guardrail. If you’re already using OpenAI, this is the path of least resistance.
Core Features:
- Minimalist API design
- Agent-to-agent Handoff mechanism
- Built-in Guardrails for safety
- Built-in Tracing (free)
- Supports 100+ LLMs (via Chat Completions-compatible endpoints)
Ideal Use Cases: Rapid development in the OpenAI ecosystem, customer service agents, multi-agent task delegation.
While it technically supports other models, the best experience is still with OpenAI.
12. Google ADK — ~18.6k ⭐
Repo: google/adk-python | Language: Python / TypeScript / Go / Java | License: Apache 2.0
Google’s official Agent Development Kit, announced at the 2025 Cloud Next conference. Its biggest highlight is the A2A (Agent-to-Agent) protocol — enabling your agents to communicate with agents built on other frameworks, with 50+ partners (Salesforce, ServiceNow, etc.).
Core Features:
- Four language support (Python, TypeScript, Go, Java)
- A2A protocol for cross-framework agent communication
- Native Gemini multimodal support (text, images, audio, video)
- Workflow agents (Sequential, Parallel, Loop)
- Deployable to Vertex AI Agent Engine
Ideal Use Cases: Google Cloud ecosystem, multimodal agents, cross-framework agent interoperability.
13. Agno — ~18.5k ⭐
Repo: agno-agi/agno | Language: Python | License: Apache 2.0
Formerly known as Phidata, renamed to Agno (Greek for “pure”) in January 2025. The design philosophy is “no graphs, chains, or convoluted patterns — just pure Python.” Agent instantiation takes <5μs, with 50x lower memory usage than LangGraph.
Core Features:
- Blazing fast: agent instantiation <5μs
- Extremely low memory usage
- Multimodal support (text, images, audio, video)
- 100+ integrations, built-in Agent UI
- Team mode for multi-agent collaboration
Ideal Use Cases: Agents requiring extreme performance, rapid prototyping, multimodal applications.
14. Pydantic AI — ~16k ⭐
Repo: pydantic/pydantic-ai | Language: Python | License: MIT
Built by the Pydantic team, aiming to bring the FastAPI developer experience to agent development. Its biggest selling point is type safety — shifting an entire class of errors from runtime to write-time. Model-agnostic, supporting virtually all major LLMs.
Core Features:
- Type-safe agent development (IDE autocomplete, type checking)
- Model-agnostic (OpenAI, Anthropic, Gemini, DeepSeek, Ollama, etc.)
- Capabilities module: composable, reusable agent behavior units
- Pydantic Logfire integration (OpenTelemetry observability)
- AgentSpec support for loading agents from YAML/JSON
Ideal Use Cases: Production applications prioritizing type safety, projects already using Pydantic/FastAPI, scenarios requiring structured output.
15. Claude Agent SDK — Growing Rapidly ⭐
Repo: anthropics/claude-agent-sdk-python (Python) / anthropics/claude-agent-sdk (TypeScript) | License: MIT
Anthropic’s official SDK, powered by the same agent harness that drives Claude Code. Renamed from Claude Code SDK to Claude Agent SDK in January 2026, reflecting a broader vision. While not the highest in star count, it powers one of the most widely used AI coding tools of 2026.
Core Features:
- Same agent engine as Claude Code
- Native MCP (Model Context Protocol) support
- Custom tools and hooks (Python in-process MCP servers)
- Extended thinking integration
- V2 Session API supporting multi-turn conversations and session persistence
Ideal Use Cases: Agent applications based on Claude, security-critical scenarios, code automation.
Locked to the Claude model, but if you’re already using Claude, this is the most native choice.
How to Choose
There is no “best framework” — only the one best suited to your scenario:
| Your Situation | Recommendation |
|---|---|
| TypeScript stack | Mastra |
| Already using OpenAI | OpenAI Agents SDK |
| Already using Claude | Claude Agent SDK |
| Google Cloud ecosystem | Google ADK |
| Need complex stateful workflows | LangGraph |
| Multi-role team collaboration | CrewAI |
| Don’t want to write code | Dify or n8n |
| Want minimal dependencies | SmolAgents |
| Prioritize type safety | Pydantic AI |
| Want extreme performance | Agno |
| Heavy document interaction | LlamaIndex |
| Want maximum flexibility | LangChain |
On Model Lock-in
OpenAI Agents SDK is tied to OpenAI, Claude Agent SDK is tied to Claude, and Google ADK is optimized for Gemini. LangGraph, CrewAI, Pydantic AI, and Mastra are model-agnostic. If you anticipate switching models, choose the latter group.
On MCP
In 2026, virtually all major frameworks support MCP (Model Context Protocol), a universal protocol for connecting agents to any tool. When choosing a framework, MCP support is no longer a differentiating factor.
A Pragmatic Suggestion
The cost of choosing a framework isn’t the learning curve — every framework can get a first demo running within a day. The real cost is migration cost: your agent logic, tool definitions, memory architecture, and deployment patterns all become coupled to the framework. Think through your scenario first, then choose your framework.
References
- LangGraph GitHub Repository — Agent orchestration framework with 38M+ monthly downloads, the standard-bearer for stateful workflows and durable execution
- CrewAI GitHub Repository — Multi-role collaboration framework, 47k stars, widely trialed by Fortune 500 companies
- Hugging Face smolagents GitHub Repository — Minimalist agent framework from Hugging Face featuring Code Agent mode
- Model Context Protocol Introduction — Official MCP documentation, the universal tool protocol for virtually all frameworks in 2026
- Building Effective Agents — Anthropic’s agent design philosophy: “start with the simplest solution” as a framework selection principle
- Claude Code Overview — Official documentation for Claude Agent SDK, the harness engine powering Claude Code
- A Survey on Large Language Model based Autonomous Agents — arXiv paper providing a comprehensive academic survey of LLM agent systems, theoretical background for framework selection
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