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In the Learn section of the documentation, you'll find a collection of tutorials, conceptual overviews, and additional resources to help you build powerful applications with LangChain and LangGraph.
Tutorials
Below are tutorials for common use cases, organized by framework.
Deep Agents
Deep Agents include built-in functionality for managing context, a virtual filesystem, and other common agent requirements.
Build a data analysis agent that sends reports to Slack.
Build a multi-step web research agent with subagent delegation and strategic reflection.
LangChain
LangChain agent implementations make it easy to get started for simple use cases.
Build a semantic search engine over a PDF with LangChain components.
Create a Retrieval Augmented Generation (RAG) agent.
Build a SQL agent to interact with databases with human-in-the-loop review.
Build an agent you can speak and listen to.
LangGraph
LangChain's agent implementations use LangGraph primitives. If deeper customization is required, agents can be implemented directly in LangGraph.
Build a RAG agent using LangGraph primitives for fine-grained control.
Implement a SQL agent directly in LangGraph for maximum flexibility.
Multi-agent
These tutorials demonstrate multi-agent patterns, blending LangChain agents with LangGraph workflows.
Build a personal assistant that delegates to sub-agents.
Build a customer support workflow where a single agent transitions between different states.
Build a multi-source knowledge base that routes queries to specialized agents.
Build an agent that loads specialized skills progressively using on-demand context loading.
Conceptual overviews
These guides explain the core concepts and APIs underlying LangChain and LangGraph.
Understand persistence of interactions within and across threads.
Learn methods for providing AI applications the right information and tools to accomplish a task.
Explore LangGraph’s declarative graph-building API.
Build agents as a single function.
Additional resources
Courses and exercises to level up your LangChain skills.
See how teams are using LangChain and LangGraph in production.