# Apply Technique: Top AI Agent Frameworks Ranked by GitHub Stars (2026)
## What This Is
This entry benchmarks the most popular AI agent frameworks in 2026 by GitHub community adoption, comparing LangChain (106k+ stars), Langflow (54.9k+), AutoGen (43.1k+), and CrewAI (44.3k+) alongside newer entrants like OpenAI Agents SDK and Google ADK. It highlights key architectural differences—stateful graphs, role-based agents, RAG pipelines, and multi-agent orchestration—so you can match a framework to your actual use case. Microsoft is consolidating AutoGen and Semantic Kernel into a unified Agent Framework targeting GA in Q1 2026, signaling major enterprise adoption momentum.
Source: https://brightdata.com/blog/ai/best-ai-agent-frameworks
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://brightdata.com/blog/ai/best-ai-agent-frameworks) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Technique** — a pattern or methodology for working with AI models.
- Explain how this technique applies to my current project and what benefit it provides
- Implement it in a way that fits my existing codebase — suggest concrete files to modify or create
- If it requires specific model capabilities (structured output, function calling, etc.), verify my current provider supports them
- Show me a working example I can test immediately
## Additional Context
- Open the GitHub pages for LangChain (github.com/langchain-ai/langchain), CrewAI (github.com/crewAIInc/crewAI), and OpenAI Agents SDK today and read each README to identify which fits your existing tech stack (Python vs. .NET, hosted vs. self-managed).
- Install CrewAI locally in under 10 minutes using 'pip install crewai' and run the built-in example to deploy a two-agent research-and-summarize pipeline before end of day.
- Evaluate LangGraph if your use case requires stateful, multi-step agent loops by checking its 34.5M monthly downloads as a proxy for production reliability, then clone the quickstart notebook from its GitHub repo and execute it locally.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it