langchain-ai/langgraph

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 - Library for building stateful, multi-actor applications with language models.
• 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.

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