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# Tutorials
Welcome to the LangGraph Tutorials! These notebooks provide end-to-end walkthroughs for building various types of language agents and applications using LangGraph.
Welcome to the LangGraph Tutorials! These notebooks introduce LangGraph through building various language agents and applications.
## Agent Executors
## AgentExecutor
- **Chat Agent (Function Calling)**
- [Base](chat_agent_executor_with_function_calling/base.ipynb): Implementing a chat agent executor with function calling
- [High-Level](chat_agent_executor_with_function_calling/high-level.ipynb): Using the high-level chat agent executor API
- [High-Level Tools](chat_agent_executor_with_function_calling/high-level-tools.ipynb): Integrating tools into the high-level chat agent executor
- **Modifications**
- [Human-in-the-Loop](chat_agent_executor_with_function_calling/human-in-the-loop.ipynb)
- [Force Tool First](chat_agent_executor_with_function_calling/force-calling-a-tool-first.ipynb)
- [Respond in Format](chat_agent_executor_with_function_calling/respond-in-format.ipynb)
- [Dynamic Direct Return](chat_agent_executor_with_function_calling/dynamically-returning-directly.ipynb)
- [Manage Agent Steps](chat_agent_executor_with_function_calling/managing-agent-steps.ipynb)
Learn to build a simple agent in LangGraph.
- **LangChain Agent**
- [Base](agent_executor/base.ipynb): Implementing an agent executor with Langchain agents
- [High-Level](agent_executor/high-level.ipynb): Using the high-level Langchain agent executor API
- **Modifications**
- [Human-in-the-Loop](agent_executor/human-in-the-loop.ipynb)
- [Force Tool First](agent_executor/force-calling-a-tool-first.ipynb)
- [Manage Agent Steps](agent_executor/managing-agent-steps.ipynb)
- [Base](agent_executor/base.ipynb): Learn to build a LangGraph agent "from scratch"
- [High-Level](agent_executor/high-level.ipynb): Learn to use the `create_agent_executor`
## Planning Agents
## Chat Agent Executor
- [Plan-and-Execute](plan-and-execute/plan-and-execute.ipynb): Implementing a basic planning and execution agent
- [Reasoning without Observation](rewoo/rewoo.ipynb): Reducing re-planning by saving observations as variables
- [LLMCompiler](llm-compiler/LLMCompiler.ipynb): Streaming and eagerly executing a DAG of tasks from a planner
Learn to build a simple chat agent executor, which is a basic graph with an agentic loop that also supports dialog with a user.
## Reflection & Critique
- [Base](chat_agent_executor_with_function_calling/base.ipynb): Build a chat agent executor with function calling
- [High-Level](chat_agent_executor_with_function_calling/high-level.ipynb): Using the high-level chat agent executor API
- [High-Level Tools](chat_agent_executor_with_function_calling/high-level-tools.ipynb): Integrating tools into the high-level chat agent executor
- [Basic Reflection](reflection/reflection.ipynb): Prompting the agent to reflect on and revise its outputs
- [Reflexion](reflexion/reflexion.ipynb): Critiquing missing and superfluous details to guide next steps
- [Language Agent Tree Search](lats/lats.ipynb): Using reflection and rewards to drive a tree search over agents
## Use cases
## Multi-Agent Systems
Learn from example implementations of graphs designed for specific scenarios and that implement common design patterns.
#### Chatbots
- [Customer Support](chatbots/customer-support.ipynb): Building a customer support chatbot
- [Info Gathering](chatbots/information-gather-prompting.ipynb): Building an information gathering chatbot
- [Code Assistant](code_assistant/langgraph_code_assistant.ipynb): Building a code analysis and generation assistant
- [Web Navigation](web-navigation/web_voyager.ipynb): Building an agent that can navigate and interact with websites
#### Multi-Agent Systems
- [Collaboration](multi_agent/multi-agent-collaboration.ipynb): Enabling two agents to collaborate on a task
- [Supervision](multi_agent/agent_supervisor.ipynb): Using an LLM to orchestrate and delegate to individual agents
- [Hierarchical Teams](multi_agent/hierarchical_agent_teams.ipynb): Orchestrating nested teams of agents to solve problems
## Research & QA
#### RAG
- [Adaptive RAG](rag/langgraph_adaptive_rag.ipynb)
- [Adaptive RAG using Cohere](rag/langgraph_adaptive_rag_cohere.ipynb)
- [Adaptive RAG using local models](rag/langgraph_adaptive_rag_local.ipynb)
- [Agentic RAG.ipynb](rag/langgraph_agentic_rag.ipynb)
- [Corrective RAG](rag/langgraph_crag.ipynb)
- [Corrective RAG with local models](rag/langgraph_crag_local.ipynb)
- [Self-RAG](rag/langgraph_self_rag.ipynb)
- [Self-RAG with local models](rag/langgraph_self_rag_local.ipynb)
- **Retrieval-Augmented Generation**
- [langgraph_adaptive_rag.ipynb](rag/langgraph_adaptive_rag.ipynb)
- [langgraph_adaptive_rag_cohere.ipynb](rag/langgraph_adaptive_rag_cohere.ipynb)
- [langgraph_adaptive_rag_local.ipynb](rag/langgraph_adaptive_rag_local.ipynb)
- [langgraph_agentic_rag.ipynb](rag/langgraph_agentic_rag.ipynb)
- [langgraph_crag.ipynb](rag/langgraph_crag.ipynb)
- [langgraph_crag_local.ipynb](rag/langgraph_crag_local.ipynb)
- [langgraph_self_rag.ipynb](rag/langgraph_self_rag.ipynb)
- [langgraph_self_rag_local.ipynb](rag/langgraph_self_rag_local.ipynb)
- [Web Research (STORM)](storm/storm.ipynb): Generating Wikipedia-like articles via research and multi-perspective QA
## Applications
- **Chatbots**
- [Customer Support](chatbots/customer-support.ipynb): Building a customer support chatbot
- [Info Gathering](chatbots/information-gather-prompting.ipynb): Building an information gathering chatbot
- [Code Assistant](code_assistant/langgraph_code_assistant.ipynb): Building a code analysis and generation assistant
- [Web Navigation](web-navigation/web_voyager.ipynb): Building an agent that can navigate and interact with websites
#### Planning Agents
## Evaluation & Analysis
- [Plan-and-Execute](plan-and-execute/plan-and-execute.ipynb): Implementing a basic planning and execution agent
- [Reasoning without Observation](rewoo/rewoo.ipynb): Reducing re-planning by saving observations as variables
- [LLMCompiler](llm-compiler/LLMCompiler.ipynb): Streaming and eagerly executing a DAG of tasks from a planner
- **Chatbot Evaluation via Simulation**
- [Agent-based](chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): Evaluating chatbots via simulated user interactions
- [Dataset-based](chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb): Evaluating chatbots over a dialog dataset
#### Reflection & Critique
- [Basic Reflection](reflection/reflection.ipynb): Prompting the agent to reflect on and revise its outputs
- [Reflexion](reflexion/reflexion.ipynb): Critiquing missing and superfluous details to guide next steps
- [Language Agent Tree Search](lats/lats.ipynb): Using reflection and rewards to drive a tree search over agents
- [Self-Discovering Agent](self-discover/self-discover.ipynb): Analyzing an agent that learns about its own capabilities
#### Evaluation
- [Agent-based](chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): Evaluating chatbots via simulated user interactions
- [Dataset-based](chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb): Evaluating chatbots in LangSmith over a dialog dataset