Langgraph
LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. It provides durable execution, human-in-the-loop capabilities, comprehensive memory, and production-ready deployment. LangGraph is designed to be used standalone or integrated with other LangChain products, such as Deep Agents, LangChain, and LangSmith. The framework is inspired by Pregel and Apache Beam, and its public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc and is available under the MIT license. The project has a comprehensive documentation, including conceptual overviews, guides, and API reference docs. Additionally, there are resources such as guides, LangChain Academy, case studies, and a contributing guide to help developers get started and contribute to the project. LangGraph has a wide range of applications, including building AI agents, LLM applications, and complex tasks. It is trusted by companies such as Klarna, Replit, Elastic, and more. The project has a strong community, with a forum for discussions, a chat for questions, and a code of conduct for participation. Overall, LangGraph is a powerful tool for building and deploying stateful agents, and its open-source nature makes it accessible to developers and organizations alike. LangGraph can be used in a variety of scenarios, such as building agents that can plan, use subagents, and leverage file systems for complex tasks. It can also be used for developing, debugging, and deploying AI agents and LLM applications. Furthermore, LangGraph provides a range of features, including durable execution, human-in-the-loop capabilities, and comprehensive memory, making it a versatile and reliable choice for building and deploying stateful agents.
- Category
- AI Agents
- Pricing
- Open Source
Problem solved
LangGraph solves the problem of building and deploying long-running, stateful agents, which is a challenging task in the field of AI and machine learning. By providing a low-level orchestration framework, LangGraph enables developers to build agents that can persist through failures, incorporate human oversight, and create truly stateful agents with both short-term working memory and long-term persistent memory. This makes it an ideal solution for companies and organizations that need to deploy AI applications at scale, such as Klarna, Replit, Elastic, and more. The problem of building and deploying stateful agents is complex and requires a robust and reliable framework, which LangGraph provides.
Use cases
- Building AI agents
- Developing LLM applications
- Deploying stateful agents
- Creating complex tasks
- Building agents that can plan and use subagents
Audience
- Developers
- ML Engineers
- Data Scientists
Pros
- Low-level orchestration framework
- Durable execution
- Human-in-the-loop capabilities
- Comprehensive memory
- Production-ready deployment
Cons
- Steep learning curve
- Requires expertise in AI and machine learning
- Limited support for non-Python languages
Official website: https://github.com/langchain-ai/langgraph
Canonical LambdaBase page: https://www.lambdabase.com/tools/langgraph