langgraph and langchain-course
LangGraph is the core framework for building agent graphs, while the course is an educational resource that teaches how to implement various agent patterns using that framework—they are complements meant to be used together.
About langgraph
langchain-ai/langgraph
Build resilient language agents as graphs.
Supports durable execution with automatic state persistence across failures, human-in-the-loop interrupts for agent inspection/modification, and comprehensive memory management combining short-term working state with long-term persistence. Built as a low-level orchestration framework inspired by Pregel and Apache Beam, it integrates seamlessly with LangChain ecosystem tools including LangSmith for observability and LangSmith Deployments for production scaling of stateful workflows.
About langchain-course
emarco177/langchain-course
A project-based course repository for developing AI agents using LangChain v1+ and LangGraph: search agents, RAG systems, reflection agents, and code interpreters.
Structures learning through commit-by-commit progression across 7 projects that integrate Tavily for web search, Pinecone/FAISS for vector storage, and Streamlit for deployment. Emphasizes tool calling, agentic workflows with self-correction loops (reflection/reflexion patterns), and vector database implementation for RAG systems. Targets intermediate developers with production-ready code patterns using LangChain v0.3+ and LangGraph's state machine abstractions.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work