How does Ormcpy integrate with AI agents and LLMs?

Ormcpy functions as an MCP server, allowing various AI agent frameworks (like Vercel AI SDK, LangChain, or CrewAI) to call its powerful OR tools programmatically. It also features natural language support to help formulate problems from descriptions.

What types of optimization problems can Ormcpy solve?

Ormcpy provides comprehensive solvers for Linear, Mixed-Integer, and Nonlinear Programming. It also includes specialized micro-servers for problems in Graphs, Scheduling, and Simulation.

How is Ormcpy designed for scalability and deployment?

Ormcpy is built for scalable deployment using Docker and Kubernetes, employing stateless HTTP transport for its specialized micro-servers. It supports running individual servers or a multi-agent web interface.

Ormcpy

Name: Ormcpy
Author: a-marzoug

Provides a high-performance Model Context Protocol (MCP) server equipping AI agents with deterministic solvers for a wide range of operations research problems.

About

Ormcpy is a monorepo that delivers specialized micro-servers for various operations research domains, all accessible via a high-performance Model Context Protocol (MCP) server. It enables AI agents and other applications to tap into powerful deterministic solvers for tasks like Linear Programming, Mixed-Integer Programming, Scheduling, Vehicle Routing, Network Optimization, and Supply Chain Optimization. The architecture supports scalable deployment via stateless HTTP transport, making it suitable for Docker and Kubernetes environments, and integrates with leading AI frameworks like Chainlit, Claude Desktop, Vercel AI SDK, LangChain, and CrewAI.

Key Features

0 GitHub stars
Specialized Micro-Servers for Graphs, Scheduling, and Simulation
Natural Language Support for problem formulation from descriptions
Comprehensive Optimization Solvers: Linear, Mixed-Integer, and Nonlinear Programming
Domain Templates for common problems like production, portfolio, and facility location
Scalable Deployment via Docker/Kubernetes with stateless HTTP transport

Use Cases

Integrating advanced optimization capabilities into AI agent workflows for decision intelligence
Analyzing business scenarios for optimal production planning, investment portfolio management, and workforce scheduling
Solving complex logistical challenges such as vehicle routing, resource allocation, and supply chain design

About

Key Features

0 GitHub stars
Specialized Micro-Servers for Graphs, Scheduling, and Simulation
Natural Language Support for problem formulation from descriptions
Comprehensive Optimization Solvers: Linear, Mixed-Integer, and Nonlinear Programming
Domain Templates for common problems like production, portfolio, and facility location
Scalable Deployment via Docker/Kubernetes with stateless HTTP transport

Use Cases

Integrating advanced optimization capabilities into AI agent workflows for decision intelligence
Analyzing business scenarios for optimal production planning, investment portfolio management, and workforce scheduling
Solving complex logistical challenges such as vehicle routing, resource allocation, and supply chain design