langchain-ai/langgraph
Langgraph
LangGraph is a framework for building stateful, multi-step agentic workflows by modeling application logic as a directed graph. It provides a runtime environment where complex tasks are orchestrated through interconnected nodes and edges, allowing developers to manage state transitions, persistent memory, and control flow across long-running automated processes.
The platform distinguishes itself through its native support for human-in-the-loop automation, enabling developers to define breakpoints that pause execution for manual review, modification, or approval. It also features checkpoint-based persistence, which serializes the entire graph state to external storage to facilitate fault tolerance, process recovery, and the ability to inspect or replay historical execution states for debugging.
Beyond its core orchestration capabilities, the project functions as a comprehensive agent deployment platform. It includes administrative tools for scaling and monitoring agent instances, enforcing metadata-driven access control, and managing resource consumption through rate and usage limits. The system also provides real-time visibility into internal processes by streaming execution updates from individual nodes as they progress.
Features
- Agent Deployment Management - Scales and monitors autonomous agent instances while managing secure access and resource limits.
- Agent Orchestration Frameworks - Builds complex, multi-step AI workflows that maintain persistent memory and handle state transitions across long-running tasks.
- Fault-Tolerant Workflow Execution - Creates resilient automated processes that support state recovery and replaying past execution steps.
- Graph Application Runtimes - Constructs complex applications using high-level abstractions for reliable task processing.
- Graph Construction Frameworks - Builds directed graphs to orchestrate complex tasks and manage control flow.
- Human-in-the-Loop Workflows - Pauses automated workflows to allow for manual review and approval of agent actions.
- Workflow Orchestrators - Executes complex logic by traversing a defined network of nodes and edges.
- Agent Lifecycle Management - Initializes new graph-based assistants to serve as functional agents capable of executing workflows.
- Stateful Agent Runtimes - Provides a runtime environment for building complex, multi-step agentic workflows that maintain persistent state.
- Agent Memory Models - Creates a shared data structure that serves as a central repository for task execution.
- State Management - Tracks and updates shared data across graph nodes to maintain consistent context.
- State Persistence - Saves the current state of a graph to enable fault tolerance and recovery.
- Agent Orchestration - Establishes programmatic links to agent servers to enable remote management and configuration.
- Human-in-the-Loop Runtimes - Pauses automated execution flows for manual review, intervention, or approval before resuming task processing.
- Human-in-the-Loop Systems - Designs intelligent systems that pause for manual review or intervention to ensure accuracy in critical decision-making.
- API Authentication - Validates incoming requests using personal access tokens or service keys with configurable expiration.
- Execution Time Travel - Replays or inspects previous states of a graph execution to debug logic.
- Graph Wiring - Connects nodes into a graph structure to enable state persistence and recovery.
- Persistence Layers - Serializes graph state to external storage to enable fault tolerance and process recovery.
- Workflow Nodes - Runs discrete logic units that transform state and handle errors within automated processes.
- Agent Frameworks - Builds resilient language agents as graphs.
- Agent Orchestration - Build resilient, stateful agents using graph-based structures.
- AI Agent Frameworks - Orchestration framework for stateful multi-agent systems.
- AI Agents and Automation - Framework for building stateful, multi-actor applications with LLMs, with cycles and control flow.
- AI and Agents - Low-level orchestration framework for building stateful, long-running LLM agents.
- Example Projects - Example code for building applications with LangGraph.
- Agentic AI - Listed in the “Agentic AI” section of the The Incredible Pytorch awesome list.
- Agent Deployment Platforms - Provides a managed infrastructure layer for hosting, scaling, and securing autonomous agent instances.
- Session Management - Initiates secure login flows and retrieves authorization tokens for protected management interfaces.
- Execution Breakpoints - Pauses graph processing at predefined breakpoints to allow for manual intervention.
- Execution Interrupts - Pauses graph execution at specific points to allow for manual review.
- Workflow Routing - Organizes complex processes into functional nodes with explicit routing logic.
- Rate Limiting - Monitors and restricts API throughput to maintain service stability and prevent unexpected usage overages.
- Access Policies - Specifies granular permissions using structured rules for resource access.
- Attribute-Based Access Control - Enforces granular security policies by evaluating resource tags and user attributes.
- Execution Streaming - Emits real-time updates from graph nodes to provide visibility into progress.
- Graph Actors - Creates autonomous nodes that subscribe to data channels to execute specific logic.
- Workflow Debugging - Defines breakpoints to pause graph execution for debugging and manual verification of node logic.
- Workflow Engines - Models automated processes as interconnected nodes and edges to manage complex control flow logic.
- Usage Quotas - Establishes monthly thresholds for data processing to control costs and manage resource consumption.
- Access Control Policies - Provides fine-grained security policies that restrict resource access based on metadata tags.
- Data Channels - Coordinates information flow between graph nodes using specialized channels.
- Execution Observability - Emits real-time progress notifications to provide visibility into internal task state.
- Logic Modeling Frameworks - Structures complex application logic as a directed graph of nodes and edges to visualize and manage control flow.
- State Reducers - Coordinates information flow between nodes using specialized reducers that aggregate shared state.
