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docs: update main site layout (#2191)
* Remove land hand sidebar on most pages * Cleans up some headings * Adds error reference information to index (it was already on the sidebar for the how-to page) -- should probably be its own tab? * Adds an index page for the reference (so it's easier to link to a main reference page), alternatively we can set up a redirect from index to graph
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Eugene Yurtsev
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title: Concepts
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description: Conceptual Guide for LangGraph
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---
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# Conceptual Guide
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This guide provides explanations of the key concepts behind the LangGraph framework and AI applications more broadly.
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We recommend that you go through at least the [Quick Start](../tutorials/introduction.ipynb) before diving into the conceptual guide. This will provide practical context that will make it easier to understand the concepts discussed here.
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The conceptual guide does not cover step-by-step instructions or specific implementation examples — those are found in the [Tutorials](../tutorials/index.md) and [How-to guides](../how-tos/index.md).
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For detailed reference material, please see the [API reference](../reference/index.md).
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## Concepts
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- [Why LangGraph?](high_level.md): A high-level overview of LangGraph and its goals.
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- [LangGraph Glossary](low_level.md): LangGraph workflows are designed as graphs, with nodes representing different components and edges representing the flow of information between them. This guide provides an overview of the key concepts associated with LangGraph graph primitives.
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- [Common Agentic Patterns](agentic_concepts.md): An agent are LLMs that can pick its own control flow to solve more complex problems! Agents are a key building block in many LLM applications. This guide explains the different types of agent architectures and how they can be used to control the flow of an application.
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- [Multi-Agent Systems](multi_agent.md): Complex LLM applications can often be broken down into multiple agents, each responsible for a different part of the application. This guide explains common patterns for building multi-agent systems.
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- [Human-in-the-Loop](human_in_the_loop.md): Explains different ways of integrating human feedback into a LangGraph application.
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- [Persistence](persistence.md): LangGraph has a built-in persistence layer, implemented through checkpointers. This persistence layer helps to support powerful capabilities like human-in-the-loop, memory, time travel, and fault-tolerance.
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- [Memory](memory.md): Memory in AI applications refers to the ability to process, store, and effectively recall information from past interactions. With memory, your agents can learn from feedback and adapt to users' preferences.
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- [Streaming](streaming.md): Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
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- [FAQ](faq.md): Frequently asked questions about LangGraph.
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title: How-to Guides
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description: How to accomplish common tasks in LangGraph
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---
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# How-to guides
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# How-to Guides
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Welcome to the LangGraph how-to guides! These guides provide practical, step-by-step instructions for accomplishing key tasks in LangGraph.
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## Controllability
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LangGraph is known for being a highly controllable agent framework.
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LangGraph offers a high level of control over the execution of your graph.
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These how-to guides show how to achieve that controllability.
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- [How to create branches for parallel execution](branching.ipynb)
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## Persistence
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LangGraph makes it easy to persist state across graph runs (thread-level persistence) and across threads (cross-thread persistence). These how-to guides show how to add persistence to your graph.
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[LangGraph Persistence](../concepts/persistence.md) makes it easy to persist state across graph runs (thread-level persistence) and across threads (cross-thread persistence). These how-to guides show how to add persistence to your graph.
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- [How to add thread-level persistence to your graph](persistence.ipynb)
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- [How to add thread-level persistence to subgraphs](subgraph-persistence.ipynb)
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## Human in the Loop
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One of LangGraph's main benefits is that it makes human-in-the-loop workflows easy.
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These guides cover common examples of that.
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[Human-in-the-loop](../concepts/human_in_the_loop.md) functionality allows
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you to involve humans in the decision-making process of your graph. These how-to guides show how to implement human-in-the-loop workflows in your graph.
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- [How to add breakpoints](human_in_the_loop/breakpoints.ipynb)
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- [How to add dynamic breakpoints](human_in_the_loop/dynamic_breakpoints.ipynb)
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## Streaming
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LangGraph is built to be streaming first.
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These guides show how to use different streaming modes.
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[Streaming](../concepts/streaming.md) is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
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- [How to stream full state of your graph](stream-values.ipynb)
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- [How to stream state updates of your graph](stream-updates.ipynb)
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## Tool calling
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[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of chat model API that accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
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These how-to guides show common patterns for tool calling with LangGraph:
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- [How to call tools using ToolNode](tool-calling.ipynb)
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- [How to handle tool calling errors](tool-calling-errors.ipynb)
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- [How to pass runtime values to tools](pass-run-time-values-to-tools.ipynb)
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## Subgraphs
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[Subgraphs](../concepts/low_level.md#subgraphs) allow you to reuse an existing graph from another graph. These how-to guides show how to use subgraphs:
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- [How to add and use subgraphs](subgraph.ipynb)
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- [How to view and update state in subgraphs](subgraphs-manage-state.ipynb)
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- [How to transform inputs and outputs of a subgraph](subgraph-transform-state.ipynb)
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## Prebuilt ReAct Agent
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These guides show how to use the prebuilt ReAct agent.
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Please note that here will we use a **prebuilt agent**. One of the big benefits of LangGraph is that you can easily create your own agent architectures. So while it's fine to start here to build an agent quickly, we would strongly recommend learning how to build your own agent so that you can take full advantage of LangGraph.
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The LangGraph [prebuilt ReAct agent](../reference/prebuilt.md#langgraph.prebuilt.chat_agent_executor.create_react_agent) is pre-built implementation of a [tool calling agent](../concepts/agentic_concepts.md#tool-calling-agent).
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One of the big benefits of LangGraph is that you can easily create your own agent architectures. So while it's fine to start here to build an agent quickly, we would strongly recommend learning how to build your own agent so that you can take full advantage of LangGraph.
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These guides show how to use the prebuilt ReAct agent:
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- [How to create a ReAct agent](create-react-agent.ipynb)
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- [How to add memory to a ReAct agent](create-react-agent-memory.ipynb)
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## Troubleshooting
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### Errors
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The [Error Reference](../troubleshooting/errors/index.md) page contains guides around resolving common errors you may find while building with LangChain. Errors referenced below will have an `lc_error_code` property corresponding to one of the below codes when they are thrown in code.
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- [GRAPH_RECURSION_LIMIT](../troubleshooting/errors/GRAPH_RECURSION_LIMIT.md)
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- [INVALID_CONCURRENT_GRAPH_UPDATE](../troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE.md)
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- [INVALID_GRAPH_NODE_RETURN_VALUE](../troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md)
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- [MULTIPLE_SUBGRAPHS](../troubleshooting/errors/MULTIPLE_SUBGRAPHS.md)
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- [Error reference](../troubleshooting/errors/index.md)
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title: Home
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{!README.md!}
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title: Reference
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description: API reference for LangGraph
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---
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# Reference
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Welcome to the LangGraph API reference! This reference provides detailed information about the LangGraph API, including classes, methods, and other components.
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If you are new to LangGraph, we recommend starting with the [Quick Start](../tutorials/introduction.ipynb) in the Tutorials section.
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title: Tutorials
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# Tutorials
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Learn from example implementations of graphs designed for specific scenarios and that implement common design patterns.
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#### Chatbots
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### Chatbots
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- [Customer Support](customer-support/customer-support.ipynb): Build a customer support chatbot to manage flights, hotel reservations, car rentals, and other tasks
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- [Prompt Generation from User Requirements](chatbots/information-gather-prompting.ipynb): Build an information gathering chatbot
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- [Code Assistant](code_assistant/langgraph_code_assistant.ipynb): Build a code analysis and generation assistant
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#### Multi-Agent Systems
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- [Network](multi_agent/multi-agent-collaboration.ipynb): Enable two or more agents to collaborate on a task
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- [Supervisor](multi_agent/agent_supervisor.ipynb): Use an LLM to orchestrate and delegate to individual agents
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- [Hierarchical Teams](multi_agent/hierarchical_agent_teams.ipynb): Orchestrate nested teams of agents to solve problems
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#### RAG
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### RAG
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- [Agentic RAG](rag/langgraph_agentic_rag.ipynb): Use an agent to figure out how to retrieve the most relevant information before using the retrieved information to answer the user's question.
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- [Adaptive RAG](rag/langgraph_adaptive_rag.ipynb): Adaptive RAG is a strategy for RAG that unites (1) query analysis with (2) active / self-corrective RAG. Implementation of: https://arxiv.org/abs/2403.14403
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- For a version that uses a local LLM: [Self-RAG using local LLMs](rag/langgraph_self_rag_local.ipynb)
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- [SQL Agent](sql-agent.ipynb): Build a SQL agent that can answer questions about a SQL database.
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### Agent Architectures
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#### Multi-Agent Systems
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- [Network](multi_agent/multi-agent-collaboration.ipynb): Enable two or more agents to collaborate on a task
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- [Supervisor](multi_agent/agent_supervisor.ipynb): Use an LLM to orchestrate and delegate to individual agents
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- [Hierarchical Teams](multi_agent/hierarchical_agent_teams.ipynb): Orchestrate nested teams of agents to solve problems
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#### Planning Agents
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- [Plan-and-Execute](plan-and-execute/plan-and-execute.ipynb): Implement a basic planning and execution agent
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- [Language Agent Tree Search](lats/lats.ipynb): Use reflection and rewards to drive a tree search over agents
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- [Self-Discover Agent](self-discover/self-discover.ipynb): Analyze an agent that learns about its own capabilities
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#### Evaluation
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### Evaluation
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- [Agent-based](chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): Evaluate chatbots via simulated user interactions
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- [In LangSmith](chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb): Evaluate chatbots in LangSmith over a dialog dataset
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#### Experimental
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### Experimental
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- [Web Research (STORM)](storm/storm.ipynb): Generate Wikipedia-like articles via research and multi-perspective QA
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- [TNT-LLM](tnt-llm/tnt-llm.ipynb): Build rich, interpretable taxonomies of user intentand using the classification system developed by Microsoft for their Bing Copilot application.
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