LangGraph provides low-level supporting infrastructure for building long-running, stateful agents and workflows that persist through failures. It enables durable execution, human-in-the-loop interactions, and stateful memory, though coding is required to implement and manage these agents.
Project overview
The framework offers durable execution for stateful workflows and agents that can resume after failures, inspired by systems like Google's Pregel and Apache Beam.
Project type
Workflow
Use cases
Automation
Deployment
Library integration
License
MIT
Best for
Developers, enterprise teams, and AI engineers who need programmatic infrastructure to build and manage stateful, long-running agents.
Teams requiring human-in-the-loop interactions and durable execution for complex workflows.
Key capabilities
Create stateful agents with short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
Limitations and risks
Coding is required to utilize the framework; it is a library intended for developers.
External services such as LangSmith are available for observability and deployment, which may affect cost and data boundaries.
GPU, minimum hardware, and telemetry specifications are not documented.
Getting started
The framework is easy to install quickly via pip using the command 'pip install -U langgraph'.
README: LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
README: While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
README: Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
README: Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.