LangGraph is a library for building stateful, multi-actor applications with LLMs. It extends LangChain with a flexible graph system to coordinate agent workflows.
Why this server?
Referenced as part of research workflow implementation, though listed as requiring additional validation and re-integration
Why this server?
Provides search capabilities for LangGraph documentation for building complex LLM applications
Why this server?
Utilizes LangGraph for workflow management and state tracking during the multi-step research process
Why this server?
Referenced as a comparison point for building agent workflows, with CrewAI offering performance advantages over LangGraph for certain tasks.
Why this server?
Provides compatibility with LangGraph for building agent workflows that can access and manipulate database data using the tools defined in the MCP server.
Why this server?
Mentioned as an example project that can be accessed through the GitMCP service, specifically through the GitHub Pages integration.
Why this server?
Utilizes LangGraph's React agent framework to orchestrate the reasoning process when determining which database operations to perform.
Why this server?
Provides access to LangGraph documentation through its llms.txt file, enabling contextual information retrieval for development tasks.
Why this server?
Utilizes LangGraph for creating complex AI workflows and model pipelines.
Why this server?
Includes a complete LangGraph integration that allows using MCP file operations as tools within LangGraph workflows, with support for all file operation capabilities.
Why this server?
Supports creation of ReAct agents using LangGraph to orchestrate tool use with the MCP server's capabilities
Why this server?
Utilizes LangGraph for orchestrating the agentic RAG workflow, including retrieval, self-evaluation, and result compression
Why this server?
Supports building complex, non-linear AI agent workflows with branching and loops through LangGraph integration.
Why this server?
Leverages LangGraph for building the workflow that transforms user queries into optimized prompts
Why this server?
Provides the foundation for building the MCP client with a well-designed agent flow architecture that enables comprehensive configuration and management of Higress.
Why this server?
Provides access to LangGraph documentation through llms.txt, allowing tools to retrieve information about LangGraph features and capabilities.
Why this server?
Supports building and implementing retrieval-based agent systems using the LangGraph framework.
Why this server?
Uses LangGraph to implement a ReAct (Reasoning and Acting) agent that can process user queries about stocks and determine which tools to use.
Why this server?
Connects with LangGraph to create an intelligent ReAct agent that can access real-time financial data and maintain persistent conversation memory.
Why this server?
Leverages LangGraph alongside LangChain to build the intelligent agent in the client application that interacts with the MCP servers
Why this server?
Leverages LangGraph ReAct agents to create a multi-agent system for analyzing logs and suggesting fixes
Why this server?
Powers the ReAct agent that processes user queries and executes appropriate actions using the real estate data tools.
Why this server?
Provides integration with LangGraph through a dedicated example showing how to combine crewAI capabilities with LangGraph's workflow management.
Why this server?
Implements the REACT-style reasoning agent framework for processing user queries and determining appropriate actions
Why this server?
Used to create state-based AI workflows for the multi-agent system.
Why this server?
Supports integration with LangGraph's react agent pattern, enabling the creation of AI agents that can interact with Firebase services through the MCP protocol.
Why this server?
Integrates with the system to create conversational AI agents that can interact with multiple MCP tools
Why this server?
Uses LangGraph for graph-based tool routing, enabling multi-turn interactions and sophisticated conversation flows
Why this server?
Incorporates LangGraph for structuring the conversational flow and processing between the natural language queries and MLflow operations
Why this server?
Powers the workflow for processing and responding to weather queries
Why this server?
Provides access to LangGraph documentation, allowing retrieval of specific documentation files and fetching additional content from URLs within those files.
Why this server?
Provides LangGraph agent integration for accessing GoHighLevel sub-account tools through the MCP protocol
Why this server?
Used to create a ReAct agent that can interact with the math tools exposed by the server
Why this server?
Used for orchestrating conversation flow and tool calls in the calculator server, managing the sequence of mathematical operations.
Why this server?
Uses LangGraph as part of the agent orchestration framework to manage workflow between different tools.
Why this server?
Integrates with LangGraph to provide the AI interface for the client component of the architecture
Why this server?
Provides access to LangGraph documentation through dedicated corpus integration, enabling agents to efficiently retrieve information about this framework