Tutorials (original) (raw)

New to LangGraph or LLM app development? Read this material to get up and running building your first applications.

Get Started 🚀¶

• LangGraph Quickstart: Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works.
• Common Workflows: Overview of the most common workflows using LLMs implemented with LangGraph.
• LangGraph Server Quickstart: Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
• LangGraph Template Quickstart: Start building with LangGraph Platform using a template application.
• Deploy with LangGraph Cloud Quickstart: Deploy a LangGraph app using LangGraph Cloud.

Use cases 🛠️¶

Explore practical implementations tailored for specific scenarios:

Chatbots¶

• Customer Support: Build a multi-functional support bot for flights, hotels, and car rentals.
• Prompt Generation from User Requirements: Build an information gathering chatbot.
• Code Assistant: Build a code analysis and generation assistant.

RAG¶

• Agentic RAG: Use an agent to figure out how to retrieve the most relevant information before using the retrieved information to answer the user's question.
• SQL Agent: Build a SQL agent that can answer questions about a SQL database.

Agent Architectures¶

Multi-Agent Systems¶

• Network: Enable two or more agents to collaborate on a task
• Supervisor: Use an LLM to orchestrate and delegate to individual agents

Planning Agents¶

• Plan-and-Execute: Implement a basic planning and execution agent
• Reasoning without Observation: Reduce re-planning by saving observations as variables
• LLMCompiler: Stream and eagerly execute a DAG of tasks from a planner

Reflection & Critique¶

• Basic Reflection: Prompt the agent to reflect on and revise its outputs
• Reflexion: Critique missing and superfluous details to guide next steps
• Tree of Thoughts: Search over candidate solutions to a problem using a scored tree
• Language Agent Tree Search: Use reflection and rewards to drive a monte-carlo tree search over agents
• Self-Discover Agent: Analyze an agent that learns about its own capabilities

Evaluation¶

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

Experimental¶

• Web Research (STORM): Generate Wikipedia-like articles via research and multi-perspective QA
• TNT-LLM: Build rich, interpretable taxonomies of user intentand using the classification system developed by Microsoft for their Bing Copilot application.
• Web Navigation: Build an agent that can navigate and interact with websites
• Competitive 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 "Can Language Models Solve Olympiad Programming?" paper by Shi, Tang, Narasimhan, and Yao.
• Complex data extraction: Build an agent that can use function calling to do complex extraction tasks

LangGraph Platform 🧱¶

Authentication & Access Control¶

Add custom authentication and authorization to an existing LangGraph Platform deployment in the following three-part guide:

1. Setting Up Custom Authentication: Implement OAuth2 authentication to authorize users on your deployment
2. Resource Authorization: Let users have private conversations
3. Connecting an Authentication Provider: Add real user accounts and validate using OAuth2