Supercharge AI Agents, Safely

n2-QLN: Intelligent Tool Router & Semantic Search Layer for MCP. Connect 1,000+ tools through 1 interface. Prevent AI model confusion and maximize context window efficiency

local-first MCP discovery runtime for agents — search before invoke, reduce prompt bloat, and route to local MCP tools

Semantic routing for MCP tools - .NET library that indexes MCP tool definitions and returns the most relevant tools via vector search

A retrieval-gated skill architecture for LLM agents that scales to hundreds of tools by exposing only the top-K relevant capabilities per request.

A governed skill system for long-horizon human-AI collaboration.

Orchestration MCP Server - Routes tool calls to MCP backends

Budget-aware context compilation and context firewall for tool-heavy AI agents.

Smart MCP router that routes tools by intent and reduces context rot for LLM hosts

Route semantic tool requests through one MCP router to simplify AI access to 1,000+ tools in n2-qln

Intelligent routing layer for AI agents: recommends the best MCP server and LLM for any task, scored on 132+ real benchmark executions.

An AI assistant with hot-pluggable MCP tool servers — one Router Agent, dynamic tool discovery, and LLM-powered routing. Built with FastAPI, Redis, Ollama, Streamlit, and Prometheus.

Reduce MCP tool overload with a single Go binary that indexes tools locally and serves search-before-invoke via SQLite

Hot-pluggable MCP multi-agent assistant — tool servers load on-demand, execute, and auto-detach without restart. LLM-powered routing via Ollama, Redis-backed tool registry + chat history, Prometheus/Grafana observability, and a Streamlit UI. Add new tools by dropping a Python file.

Dynamic MCP multiplexer — one gateway, many upstream MCP servers.