LangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful AI agents. It is designed for complex, multi-step workflows and supports both single-agent and multi-agent architectures.

How is LangGraph different from LangChain?

LangGraph is focused on agent orchestration and workflow management, providing fine-grained control over agent behavior, state management, and execution flow. In contrast, LangChain provides higher-level abstractions for interacting with language models, data retrieval, and simple chains, making LangGraph more suitable for complex, stateful, and multi-agent applications.

Do I need to use LangChain to use LangGraph?

No. While LangGraph is built on LangChain and integrates well with it, you can use LangGraph independently for agent orchestration. LangGraph is more low-level and controllable than LangChain agents.

What types of workflows can LangGraph support?

LangGraph supports single-agent, multi-agent, hierarchical and sequential workflows, as well as cyclic data flows for improved agent performance.

How does LangGraph handle state and memory?

LangGraph provides built-in state management for persistent, long-running agents, short-term working memory for ongoing reasoning, long-term memory across sessions, and memory summarization to control size and improve efficiency.

Can LangGraph integrate with different LLM providers?

Yes. LangGraph supports easy switching between LLM providers (e.g., OpenAI, Groq) with a consistent interface and fallback options for reliability.

How does LangGraph support human-in-the-loop workflows?

LangGraph allows for human oversight and approval of agent actions, inspection and modification of agent state, and "time-travel" debugging to roll back and correct agent actions.

What are the key features of LangGraph?

The key features of LangGraph include flexible control flows (single, multi-agent, hierarchical), robust moderation and quality controls, native token-by-token streaming for better user experience, durable execution (where agents persist through failures), comprehensive debugging with LangSmith, and production-ready deployment infrastructure.

How do I get started with LangGraph?

To get started with LangGraph, you should install it using pip install -U langgraph, then create a simple workflow using StateGraph by defining nodes and edges, and finally use the LangGraph documentation and quickstart guides for step-by-step instructions.

Can LangGraph handle multi-agent competition or handoffs?

Currently, LangGraph assumes a single handoff at a time. For scenarios where multiple agents compete to handle a question, you can implement custom handoff tools or improve handoff descriptions.

Is LangGraph suitable for production use?

Yes. LangGraph is designed for production-ready deployment with scalable infrastructure to handle stateful, long-running workflows.

Where can I find more resources and tutorials?

More resources and tutorials can be found in the Official LangGraph documentation at https://docs.langchain.com/oss/python/langgraph/, the LangChain Academy Introduction to LangGraph at https://academy.langchain.com/courses/intro-to-langgraph, and various community guides and blog posts from sources like Towards AI and ToTheNew.

LangGraph

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LangGraph helps you build reliable and complex AI agents for any task. It gives you deep control over agent workflows, built-in memory for long-term interactions, and tools for human oversight, making sure your AI apps are robust and user-friendly.

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Overview Features Use Cases FAQ Integrations Pricing

About LangGraph

Who It's For

LangGraph is for developers building smart, reliable AI agents. It suits apps where agents perform complex tasks, remember conversations, or work with humans. If you need precise control and scalable AI solutions, this tool is for you.

What You Get

You get a flexible system to design custom AI agent workflows. It includes built-in memory for long-term interactions, moderation tools, and human collaboration. Plus, real-time streaming improves user experience, and robust infrastructure ensures scalable deployments.

How It Works

LangGraph provides core building blocks to define how AI agents operate. You can set up various workflows, allowing agents to work together or with human approvals. It manages agent memory, supports "time-travel" debugging, and helps deploy and monitor your agents at scale.

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Features & Capabilities

⚙️ Core Agent Orchestration

Diverse Control Flows

Supports single, multi-agent, hierarchical, and sequential control flows for robustly handling complex scenarios.

Customizable Agent Design

Provides low-level primitives for creating fully customizable and expressive agent workflows.

Moderation & Quality Loops

Enables easy addition of moderation and quality checks to prevent agents from veering off course.

Built-in Memory

Persists conversation histories and maintains context for rich, personalized interactions across sessions.

🤝 Human-Agent Collaboration

Human-in-the-Loop Control

Integrates human review and approval steps to guide, moderate, and steer agent actions.

Agent Action Inspection

Allows seamless inspection of agent actions and 'time-travel' to roll back and correct course.

Real-time Streaming

Offers native token-by-token and intermediate step streaming for transparent agent reasoning and actions, improving UX.

🚀 Deployment & Scalability

Scalable Infrastructure

Provides infrastructure built for agents to quickly deploy and scale applications, handling large workloads gracefully.

Fault-Tolerant Design

Enhances resilience with horizontally scaling servers, task queues, persistence, intelligent caching, and automated retries.

Flexible Deployment Options

Supports various deployment models including cloud, hybrid, and fully self-hosted solutions for agents.

Dynamic APIs for UX

Offers APIs with long-term memory to craft personalized user experiences and manage application state across sessions.

🛠️ Developer Tools & Productivity

LangGraph Studio

Simplifies prototyping, debugging, and sharing of agents within a visual development environment.

One-Click Deployment

Facilitates easy deployment of applications via SaaS offerings or within a private VPC.

LangSmith Integration

Enables comprehensive monitoring of application performance and activity through integration with LangSmith.

Templatized Architectures

Allows templatizing cognitive architectures with Assistants for easily configurable tools, prompts, and models.

Use Cases

Ensuring Reliable AI Agent Performance with Human Oversight

Developers face challenges in building AI agents that reliably execute complex, multi-step tasks without veering off course. LangGraph addresses this by providing a flexible architecture for multi-agent workflows, combined with moderation loops and human-in-the-loop capabilities, allowing humans to review, approve, and "time-travel" to correct agent actions, thereby building trust and ensuring accuracy.

Enterprise SoftwareFor: AI Developers

Deploying Scalable and Fault-Tolerant AI Agent Applications

Bringing AI agents from development to a production environment at scale requires robust infrastructure to handle large workloads, ensure resilience, and maintain long-term user context. LangGraph Platform offers fault-tolerant scalability with horizontal scaling, intelligent caching, automated retries, and dynamic APIs with persistent memory, enabling developers to confidently deploy and manage performant, stateful AI applications.

Enterprise ITFor: AI Engineers

Delivering Engaging and Context-Aware User Experiences with AI

Users expect real-time feedback and personalized interactions from AI agents, but achieving this with complex agents can be difficult. LangGraph bridges this gap through native token-by-token streaming, which visually communicates agent reasoning and actions in real-time, coupled with built-in long-term memory to recall past conversations and personalize interactions across sessions, creating a more intuitive and responsive user experience.

B2C ApplicationsFor: Product Managers

Automating Multi-Stage Business Workflows with Intelligent Agents

Organizations often grapple with automating intricate business processes that involve multiple steps, conditional logic, and the need for human input or review at various stages. LangGraph provides the low-level primitives to design highly customizable, multi-agent workflows with durable execution and integrated human-in-the-loop controls, enabling the automation of tasks from complex research to dynamic approval processes, improving operational efficiency.

Enterprise OperationsFor: Business Process Engineers

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