LangGraph Examples and Use Cases

Enterprise Production Examples

LinkedIn: AI Recruiting Agent

LinkedIn built an AI-powered recruiter on LangGraph that automates candidate sourcing, profile matching, and outreach messaging. The system uses a hierarchical multi-agent architecture where a supervisor agent coordinates specialized agents for different stages of the recruiting pipeline. One agent searches LinkedIn's talent pool against job requirements, another evaluates candidate fit by analyzing profiles and experience, and a third generates personalized outreach messages.

The LangGraph implementation enables human recruiters to review and approve agent-selected candidates before outreach begins, using interrupt gates for oversight. The checkpointing system maintains the full context of each recruiting workflow, so recruiters can resume reviewing candidates across sessions without losing state. LinkedIn reports that the system has freed up human recruiters to focus on high-level relationship building and strategy.

Uber: Code Migration Agent

Uber integrated LangGraph into their developer platform to automate large-scale code migrations. When a core library changes its API, Uber's agent system identifies all affected code across their massive codebase, generates migration patches, runs tests, and submits pull requests. The network of specialized agents is structured so that each step of the unit test generation and migration process is handled by a focused agent with specific expertise.

LangGraph's checkpointing is critical for this use case because code migrations can span thousands of files and take hours to complete. If any step fails, the system resumes from the last successful checkpoint rather than restarting the entire migration.

Replit: Coding Copilot

Replit's AI coding copilot uses LangGraph to orchestrate a multi-agent system that helps users build software from scratch. The copilot analyzes user requirements, generates code, runs it in a sandboxed environment, evaluates the results, and iterates until the output meets the user's expectations. LangGraph's human-in-the-loop capabilities let users guide the copilot's decisions at key points rather than accepting fully autonomous output.

Banking: IT Operations Triage

A global bank deployed a LangGraph multi-agent system for IT operations incident management. A triage agent ingests alerts from monitoring systems including Splunk, Datadog, and PagerDuty. It classifies each alert by severity and category, routes it to the appropriate response team, and triggers automated remediation for known issues. The system achieved 94% routing accuracy and reduced critical incident acknowledgment time from 18 minutes to under 3 minutes.

Elastic: Security Threat Detection

Elastic uses LangGraph to orchestrate AI agents for real-time security threat detection. The agent network monitors event streams, correlates suspicious patterns across data sources, and generates threat assessments with supporting evidence. LangGraph's graph architecture allows the detection pipeline to branch into specialized analysis paths based on the type of threat detected, with human security analysts reviewing high-severity alerts before response actions are taken.

AppFolio: Property Management

AppFolio created a property management copilot on LangGraph that assists property managers with tenant communications, maintenance scheduling, lease processing, and financial reporting. The system saves property managers over 10 hours per week and doubled the accuracy of operational decisions compared to manual processes. LangGraph's state management ensures that complex multi-step processes like lease renewals maintain full context across interactions.

Common Application Patterns

Retrieval-Augmented Generation (RAG) Agent

A RAG agent retrieves relevant documents from a knowledge base, uses them as context for LLM responses, and evaluates whether the retrieved information is sufficient to answer the user's question. If the initial retrieval is insufficient, the agent reformulates the search query and tries again. LangGraph's conditional edges handle this retry logic cleanly, and checkpointing preserves the retrieval history so the agent does not repeat failed searches.

Customer Support Agent

A multi-step support agent gathers information from the user, searches internal documentation and past tickets for solutions, drafts a response, and routes complex issues to human support staff. LangGraph's interrupt gates enable the handoff to human agents at the right moment, with full conversation context preserved in the checkpointed state.

Data Analysis Pipeline

An analytical agent receives a data question, determines which data sources to query, writes and executes analysis code, interprets the results, and generates a report. The pipeline uses conditional edges to route between different analysis approaches based on the data type, and the graph's cyclic capabilities allow the agent to iterate on its analysis until the results meet quality thresholds.

Research Assistant

A research agent uses scatter-gather parallelism to search multiple sources simultaneously, consolidates findings, identifies gaps, and iterates until the research question is thoroughly answered. LangGraph's parallel execution support and state management handle the coordination of multiple concurrent searches and the progressive accumulation of research findings.

Content Generation with Review

A content agent generates initial drafts, submits them for automated quality checks (readability, factual accuracy, brand voice compliance), revises based on feedback, and presents the final version for human review. The graph structure naturally models this edit-review-revise cycle, with conditional edges determining whether further revision is needed based on quality scores.

Getting Started with Examples

LangChain maintains an official repository of LangGraph example projects covering common patterns. These examples include fully functional code with state definitions, graph structures, and node implementations that you can use as starting points for your own applications. The community also maintains contributed examples for specialized use cases including healthcare, legal, and financial applications.