langgraph/docs/docs/tutorials/index.md

Tutorials

Welcome to the LangGraph Tutorials! These notebooks introduce LangGraph through building various language agents and applications.

Introduction to LangGraph

Learn the basics of LangGraph through the onboarding tutorials.

• Introduction to LangGraph

Use cases

Learn from example implementations of graphs designed for specific scenarios and that implement common design patterns.

Chatbots

• Customer Support: Build a customer support chatbot to manage flights, hotel reservations, car rentals, and other tasks
• Info Gathering: Build an information gathering chatbot
• Code Assistant: Building a code analysis and generation assistant

Multi-Agent Systems

• Collaboration: Enabling two agents to collaborate on a task
• Supervision: Using an LLM to orchestrate and delegate to individual agents
• Hierarchical Teams: Orchestrating nested teams of agents to solve problems

RAG

• Adaptive RAG
  • Adaptive RAG using local models
• Agentic RAG.ipynb
• Corrective RAG
  • Corrective RAG with local models
• Self-RAG
  • Self-RAG with local models
• Web Research (STORM): Generating Wikipedia-like articles via research and multi-perspective QA

Planning Agents

• Plan-and-Execute: Implementing a basic planning and execution agent
• Reasoning without Observation: Reducing re-planning by saving observations as variables
• LLMCompiler: Streaming and eagerly executing a DAG of tasks from a planner

Reflection & Critique

• Basic Reflection: Prompting the agent to reflect on and revise its outputs
• Reflexion: Critiquing missing and superfluous details to guide next steps
• Language Agent Tree Search: Using reflection and rewards to drive a tree search over agents
• Self-Discovering Agent: Analyzing an agent that learns about its own capabilities

Evaluation

• Agent-based: Evaluating chatbots via simulated user interactions
• Within LangSmith: Evaluating chatbots in LangSmith over a dialog dataset

Text Mining

• TNT-LLM: learn to build rich, interpretable taxonomies of user intentand using the classification system developed by Microsoft for their Bing Copilot application.

Competitive Programming

• Can Language Models Solve Olympiad Programming?: Build an agent with few-shot "episodic memory" and human-in-the-loop collaboration to solve problems from the USA Computing Olympiad; adapted from the paper of the same name by Shi, Tang, Narasimhan, and Yao.

Other Experimental Architectures

• Web Navigation: Building an agent that can navigate and interact with websites