LangGraph

LangGraph is an open-source orchestration framework built by LangChain for designing and running stateful AI agents. It structures your agent’s logic as a directed graph, a network of nodes (processing steps) connected by edges (transitions), so you can define exactly how an agent thinks, branches, loops, and hands off work. For AI engineers building production systems, it solves the core problem of keeping agents controllable without sacrificing their ability to handle complex, multi-step tasks.

How LangGraph works

LangGraph models your application as a state graph. State means a shared object (usually a Python dictionary) that every node in the graph can read from and write to. Each node updates a slice of that state, and edges decide which node runs next.

Three core concepts power everything:

• Nodes: Each node is a function that does one thing: call an LLM, run a tool, or make a routing decision. Nodes receive the current state, do their work, and return an updated version of it.
• Edges: Edges connect nodes and control flow. A standard edge always moves to the same next node. A conditional edge uses a routing function to evaluate the current state and pick the next node at runtime. This is how branching and loops work.
• State: A user-defined schema (typically a TypedDict) that persists across every step in the graph. Because state is explicit, you can pause execution, inspect exactly what the agent knows, and resume from any point.

A directed acyclic graph (DAG), the kind used by simple pipeline frameworks, can only move forward in a fixed sequence. LangGraph supports cycles, meaning an agent can loop back to an earlier node based on its output. That loop is what makes agentic behaviour possible: the agent can re-evaluate, retry, or request more information before moving on.

LangGraph also ships with built-in support for human-in-the-loop checkpoints, where execution pauses and waits for a human to approve or correct the agent’s next action before it continues.

What you can build with LangGraph

LangGraph is suited to developers building systems where agent behaviour needs to be both flexible and reliable. Here are concrete projects you can ship with it:

• Customer support agent: An agent that reads an incoming message, classifies its urgency and topic, searches a knowledge base, drafts a response, and escalates to a human if confidence is low. LangGraph’s conditional edges handle the routing; its state management keeps the full conversation context intact across steps.
• Deep research agent: An agent that takes a query, searches the web, evaluates whether the results are sufficient, and loops back to run more searches if not. Once it has enough information, it compiles a cited report. Google open-sourced a reference implementation of this pattern using LangGraph and Gemini.
• Multi-agent SQL assistant: A system where one agent interprets a natural language question and routes it to a second agent that writes and executes a SQL query, then returns the result in plain English. LinkedIn built an internal version of this pattern using LangGraph to give non-technical employees direct access to data.
• Code generation and self-debugging agent: An agent that writes code, runs it immediately, catches errors, and loops back to fix them automatically before returning a working result to the user. Replit uses LangGraph to power real-time code generation in their product.
• Property management copilot: An agent that handles intake forms, enriches records from external APIs, and routes complex requests to human reviewers. AppFolio built this on LangGraph and reported saving over ten hours per week for their property managers.
• Automated test generation pipeline: A multi-step workflow that reads source code, generates unit tests, runs them, and iterates based on failures. Uber used LangGraph to automate this process and reduce development time across their engineering teams.