Langgraph

What Is LangGraph?

LangGraph is an open-source framework for building stateful, multi-agent applications using large language models. Created by the team behind LangChain, LangGraph introduces a new way to design AI systems that can reason, plan, and coordinate across complex workflows.

Unlike traditional chatbots, LangGraph lets developers define graphs of nodes—each representing a tool, model, or function—that work together in loops and branches. This graph-based design makes it possible to build AI agents with memory, control flow, and persistence—all while maintaining transparency and flexibility.

LangGraph is the foundation behind the next generation of autonomous AI agents, capable of managing projects, executing business logic, or integrating with enterprise tools.

How To Use LangGraph

LangGraph can be installed via pip (pip install langgraph) and extended from existing LangChain projects. Developers define nodes (tools, models, or logic functions) and connect them through a graph configuration that determines how information flows.

Using LangGraph’s state management system, you can give agents long-term memory, decision-making abilities, and multi-step reasoning across sessions. The framework also includes integrations for OpenAI, Anthropic, Google Gemini, and other APIs, allowing developers to orchestrate complex agent behaviors with ease.

To get started:

1. Install LangGraph and LangChain.
2. Define your nodes (LLM calls, conditionals, tools, etc.).
3. Create an agent graph using LangGraph’s StateGraph or MessageGraph API.
4. Run and monitor your agents in a loop or deploy them with persistence.

LangGraph is compatible with major AI ecosystems such as LangSmith (for observability), LangServe (for deployment), and LangChain Hub (for templates).

LangGraph Key Features

• Graph-Based Architecture: Build AI workflows as modular, connected graphs instead of linear chains.
• Stateful Memory: Agents persist information across steps and sessions for long-term reasoning.
• Multi-Agent Coordination: Create multiple agents that communicate and collaborate within a shared graph.
• Control Flow Logic: Add conditionals, loops, retries, and branching for complex decision paths.
• Tool Integration: Connect agents to APIs, databases, CRMs, or custom functions via LangChain tools.
• Streaming and Persistence: Keep conversations and states alive over time, enabling continuous agents.
• Observability and Debugging: Integrated with LangSmith for real-time tracing, analytics, and debugging.
• Open Source and Extensible: Fully open-source under the LangChain ecosystem, with community support and frequent updates.

LangGraph Use Cases

• Autonomous AI Agents: Build agents that plan tasks, execute actions, and report results automatically.
• Workflow Automation: Replace static business processes with adaptive, intelligent automation flows.
• Customer Support Systems: Create proactive agents that handle multi-step resolutions and remember context.
• AI Research Assistants: Manage multi-turn reasoning, citations, and dynamic retrieval using tool graphs.
• Data Analysis Agents: Connect LLMs to data warehouses and analysis tools for real-time insights.
• DevOps and API Orchestration: Automate technical workflows across APIs, databases, and cloud systems.

LangGraph FAQ

• Is LangGraph free or paid?
  LangGraph is fully open-source and free to use under the LangChain ecosystem license.
• Does LangGraph have an enterprise version?
  LangGraph can be integrated with LangSmith and LangServe for enterprise-grade monitoring, deployment, and scalability.
• Does LangGraph require LangChain?
  LangGraph extends LangChain, but can also work independently with any Python-based tool or model API.
• Can I deploy LangGraph in production?
  Yes. LangGraph applications can be deployed via LangServe, FastAPI, or your preferred orchestration framework.
• Does LangGraph store user data?
  No. LangGraph itself does not store data. State and persistence are developer-controlled through external storage or memory backends.
• What programming language is LangGraph built with?
  LangGraph is written in Python and integrates seamlessly with the LangChain ecosystem.

Why We Featured LangGraph on Add AI Agents

LangGraph marks a turning point in how developers build AI agents. Rather than chaining prompts, it enables true agentic systems—agents that reason, remember, and act autonomously.

At Add AI Agents, we highlight frameworks that empower creators to go beyond chatbots. LangGraph stands out for its graph-based design, persistent state, and multi-agent coordination, all of which make it one of the most powerful open-source foundations for next-generation AI applications.