Understand any codebase instantly. System intelligence for codebases, built for humans and AI.

Official Pulumi Agent Skills for writing, migrating, and operating infrastructure with AI coding agents

Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.

Let AI agents investigate and operate infrastructure through declared actions, bounded by policy and host-side checks. Approvals when required, with an audit trail. By Protectorate.

Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface

Open-source email infrastructure for AI agents.

Managed Claude Stack - Reproducible AI infrastructure for Claude Code

Agent-ready DevOps, security, infrastructure, and compliance knowledge base with 80+ skills across Kubernetes, Terraform, AWS/Azure/GCP, AI platform operations, container hardening, SOC2/ISO27001, and incident response—plus ready-to-run scripts, templates, and playbooks for SRE, platform, and security teams.

Open source software factory infrastructure for advanced AI coding workflows

A comprehensive framework for analyzing and defending against attacks targeting Software Development Life Cycle Infrastructure.

Persistent Intelligence Infrastructure for AI Agents

Claude Code skills and workflows, optimized for context-efficiency and skill quality. Skills ranging from cloud infrastructure to design to advanced maths.

Model Context Protocol (MCP) servers for managing homelab infrastructure through Claude Desktop. Monitor Docker/Podman containers, Ollama AI models, Pi-hole DNS, Unifi networks, and Ansible inventory. Includes security checks, templates, and automated pre-push validation. Production-ready for homelabs.

Email, SMS & phone-call infrastructure for AI agents — send and receive real email and text messages, and place agent-driven outbound voice calls, all programmatically

A lightweight service that enables AI assistants to execute AWS CLI commands (in safe containerized environment) through the Model Context Protocol (MCP). Bridges Claude, Cursor, and other MCP-aware AI tools with AWS CLI for enhanced cloud infrastructure management.

Ultimate Context Engineering Infrastructure, starting from MCPs and Integrations

Open-source infrastructure for AI agents — each gets an email address, folder, inbox, and API key. Move files through named inboxes instead of shared cloud credentials; signed receipts and MCP built in. Human email verification can unlock 5 GB transfers, durable storage, outbound email, and an HTTPS app subdomain. One Go binary, MIT.

Where AI agents hire AI agents — hiring and coordination infrastructure for the agent economy

Co-creation infrastructure for humans and code agents — visual environment, skills, continuous learning, and distribution.

Search & analytics data as infrastructure — MCP server for Google Search Console, Bing Webmaster Tools, Google Adsense and GA4, designed for AI agents and automation.

A Model Context Protocol server that connects AI assistants like Claude to AWS security services, allowing them to autonomously query, inspect, and analyze AWS infrastructure for security issues and misconfigurations.

Infrastructure that connects LLMs to ERPNext. Frappe Assistant Core works with the Model Context Protocol (MCP) to expose ERPNext functionality to any compatible Language Model

🧩 MCP Gateway - A lightweight gateway service that instantly transforms existing MCP Servers and APIs into MCP servers with zero code changes. Features Docker deployment and management UI, requiring no infrastructure modifications.

A comprehensive toolkit for deploying production-ready Generative AI infrastructure on Amazon EKS. Includes pre-configured components for: 🚀 AI Gateway (LiteLLM) 🤖 LLM Serving (vLLM, SGLang, Ollama) 📊 Vector Databases, 🔍 Embedding Models (TEI) 📈 Observability (Langfuse, Phoenix) etc. Fast-track your GenAI deployment with Kubernetes