#langgraph

Marchward is an Apache-2.0 open-source runtime authority for AI agents. It routes an agent's tool calls through one control point: credential mediation (the agent holds a single Marchward key and never your real downstream credentials), an inference spend cap enforced before the call reaches the provider, human-in-the-loop approval gates on irreversible actions, and a tamper-evident hash-chained audit log. Public repo and packages are live (npm @marchward/*, PyPI marchward, official MCP registry ai.marchward/mcp-server), with a runnable governed-LangGraph example in the repo.

AgentDrive is a cross-agent artifact workspace with provenance and portable storage across Claude Code, Codex, Cursor, and LangGraph. 20 MCP tools: file CRUD, semantic recall over workspace artifacts, run logging, cross-agent attribution, comments, and shares. Connect to the hosted server at https://mcp.agentdrive.sh/mcp with an Authorization: Bearer key, or run locally via npx @adrive/mcp-server. Free tier (1 GB + 10k embeddings) at https://app.agentdrive.sh — no credit card.

Governance middleware for AI agents: deterministic policy enforcement, cryptographic audit trails with digital signatures, DLP/PII detection, Trust Score per agent (0-100), EU AI Act compliance (Art. 9, 11, 12, 14), Agent BOM generation, and Circuit Breaker. Native support for LangGraph, CrewAI, and AutoGen.

This project creates a LangGraph assistant using tool-calling supported by LLM, integrated with a local WhatsApp Mcp server for context-aware message automation.

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, CrewAI, RAG, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.

Evo AI is an open-source platform for creating and managing AI agents, enabling integration with different AI models and services.

LangGraph Agent that integrates with MCP servers

Multi Agents with MCP servers implementation using Langgraph

Tool-based LLM integration with Zenodo via the Model Context Protocol (MCP)

DataAnalysis Agent using LangGraph & MCP server and client

This is our final project , in this we have utlized langgraph coupled with various mcp server mounted of fastapi using fastmcp to create mutlifaceted application for healthcare vendor to make them understand more about their product performance and provide suggestions and strategies

This repo is for Praticing the Langgraph,Langchain,AI Automation and other stuff along with these MCP server,client implementation

Transforms MCP tools into collaborative, reasoning agents using modern agent frameworks.

This project demonstrates a decoupled real-time agent architecture that connects LangGraph agents to remote tools served by custom MCP (Modular Command Protocol) servers. The architecture enables a flexible and scalable multi-agent system where each tool can be hosted independently (via SSE or STDIO), offering modularity and cloud-deployable execut

About This repository features a collection of agentic AI projects across various domains, showcasing practical applications of AI agents with different frameworks, techniques, and tools.

LangGraph agent tutorial adapted to use MCP servers.

Transforms MCP tools into collaborative agents using the LangGraph framework.

Developed an MCP-based AI infrastructure enabling real-time tool execution, structured knowledge retrieval, and dynamic agentic interactions for AI clients like Claude and Cursor.

A documentation AI Agent built with LangGraph, MCP Docs, and Chainlit, designed to help users create different projects using natural language.

Simple MCP Server w/ Shell Exec. Connect to Local via Ngrok, or Host Ubuntu24 Container via Docker

A Mattermost integration that connects to Model Context Protocol (MCP) servers, leveraging a LangGraph-based Agent.

MCP server for understanding AWS spend