# Apply Technique: Top Open-Source AI Agent Frameworks in 2025–2026: LangGraph, OpenAI Agents SDK, and Google ADK Compared
## What This Is
A curated comparison of the leading open-source AI agent frameworks as of 2026, ranked by GitHub stars and recency. LangGraph, OpenAI Agents SDK, and Google ADK are the top newcomers for building autonomous, multi-step AI workflows without vendor lock-in. Business owners and developers can use this landscape overview to pick the right framework before writing a single line of code.
Source: https://www.firecrawl.dev/blog/best-open-source-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://www.firecrawl.dev/blog/best-open-source-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 OpenAI Agents SDK GitHub repo (github.com/openai/openai-agents-python) and run the quickstart example locally to build a two-agent workflow in under 20 minutes using pip install openai-agents.
- Visit the LangGraph documentation (langchain-ai.github.io/langgraph) and clone the prebuilt customer support agent template to see stateful, controllable agent behavior with built-in LangSmith tracing.
- Compare Google ADK's sample multi-agent app (github.com/google/adk-python) against your current stack by mapping its Gemini and Vertex AI integrations to your existing Google Cloud services.
## 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