Nexus: An Open-Source AI Router for Governance, Control and Observability

Summary

Today, we're excited to introduce Nexus - a powerful AI router designed to optimize how AI agents interact with multiple MCP tools and Large Language Models. Nexus serves as a central hub that aggregates Model Context Protocol (MCP) servers while providing intelligent LLM routing, security and governance capabilities. Nexus is an AI router that solves two critical challenges in the AI ecosystem: MCP Server Aggregation: Instead of managing connections to multiple MCP servers individually, Nexus consolidates them into a single, unified interface Intelligent LLM Routing: Nexus intelligently routes requests to the most appropriate language model based on the task, cost considerations, and performance requirements As AI applications become more sophisticated, they increasingly need to interact with multiple external services, APIs, and different language models. This creates several pain points: Context: Helps the LLM select from a potentially large number of MCP tools without overwhelming it Cost: Lack of strategic model selection can lead to unnecessary expenses Observability: Provides insights into the performance and behavior of the AI system, enabling better decision-making and troubleshooting Security: Ensures that requests are routed securely and in compliance with governance policies Nexus acts as a proxy layer that connects to multiple MCP servers simultaneously. When your AI agent needs to access external tools or data sources, it makes a single request to Nexus, which then: Helps the LLM to identify the appropriate MCP server(s) for the request Handles authentication and connection management Aggregates responses from multiple sources when needed Provides a consistent API interface regardless of the underlying MCP implementations Nexus considers multiple factors when selecting the optimal language model: Task Type: Different models excel at different tasks (reasoning, coding, creative writing) Latency Requirements: Route to faster models when speed is prioriti...