# LangGraph

open-mcp-client is an open-source project aimed at providing client support for Multi-Cloud Platform (MCP). It integrates LangGraph agent and a CopilotKit-based frontend application, supporting interaction with the MCP server and tool invocation. The project is developed using TypeScript, CSS, Python, and JavaScript, emphasizing development efficiency and user experience. It is suitable for developers and enterprises for managing and interacting with multi-cloud resources. Open-source and free, it's ideal for users who want to rapidly develop and deploy in multi-cloud environments.

LangGraph Multi-Agent Supervisor is a Python library built on the LangGraph framework for creating hierarchical multi-agent systems. It allows developers to coordinate multiple specialized agents through a centralized supervisor agent, enabling dynamic task allocation and communication management. The significance of this technology lies in its ability to efficiently organize complex multi-agent tasks, enhancing system flexibility and scalability. It is suitable for scenarios requiring multi-agent collaboration, such as automated task processing and complex problem-solving. This product is positioned for advanced developers and enterprise-level applications. While pricing is not explicitly public, its open-source nature allows users to customize and extend it according to their needs.

This product is a full-stack application that leverages LLM (Large Language Models) and LangChain technology, combined with LangGraph, to retrieve and analyze stock data and news. It uses ChromaDB as a vector database to support semantic search and data visualization, providing users with deep insights into the stock market. It is primarily aimed at investors, financial analysts, and data scientists, helping them quickly acquire and analyze stock-related information to support decision-making. The product is currently open-source and free, making it suitable for users who need to efficiently process financial data and news.

Coding-agent is an open-source programming assistant designed to simplify developers' tasks by integrating advanced programming assistance technologies. It leverages LangGraph proxy technology to generate high-quality code responses. Built on Next.js and CopilotKit, this tool combines efficiency and user-friendliness. It allows developers to quickly solve issues, generate code snippets, and optimize their development processes during everyday coding. As an open-source project, it provides developers with a flexible and customizable programming environment while continuously improving and expanding features through community contributions.

PostBot 3000 is an open-source project showcasing how to build a powerful AI agent that streams responses and generates artifacts. The project leverages LangGraph Python for AI workflows and utilizes FastAPI to create a robust API. It incorporates multiple tech stacks, including LangGraph, Vercel AI SDK, gpt-4o-mini, FastAPI, Next.js, and TailwindCSS. The open-source nature of PostBot 3000 makes it easier for anyone looking to implement similar solutions for development and deployment.

Curiosity is a chatbot project based on the ReAct framework, aimed at exploring and building user interaction experiences similar to Perplexity through the LangGraph and FastHTML technology stack. At its core, the project features a simple ReAct agent that utilizes Tavily search to enhance text generation. It supports three different large language models (LLMs), including OpenAI's gpt-4o-mini, Groq's llama3-groq-8b-8192-tool-use-preview, and Ollama's llama3.1. The front end is built with FastHTML, and while there may be challenges during debugging, it generally provides a swift user experience.

LangGraph Engineer is an alpha version tool designed to expedite the setup of LangGraph applications. It focuses on creating the correct nodes and edges, leaving the logic to populate them up to the user.

story-writing is an application built on LangGraph Cloud, designed to showcase the features of LangGraph Cloud in an engaging and interactive manner. The app enables users to write stories with the help of the LangGraph agent, allowing them to edit existing chapters or continue writing the next one. Users can create multiple versions of the same chapter and choose their preferred version to continue the story. At the start, users provide a story summary, desired writing style, and any other relevant details. From that point, they only need to provide editing and continuation commands to guide the agent in the desired direction. Note: This application is a prototype and is not yet ready for deployment; it may contain bugs or yield suboptimal results from the agent.

This is an advanced application course focused on LangGraph, providing implementations of Reflective RAG, Self-RAG, and Adaptive RAG, designed to help developers and technical personnel in production environments leverage LangGraph.