Provides trace logging, quality evaluation, and cost tracking for AI agents leveraging the Model Context Protocol.
Sponsored
Iris is an open-source Model Context Protocol (MCP) server designed to enhance the development and operation of AI agents. It offers comprehensive trace logging, enabling developers to capture detailed execution flows, tool calls, and performance metrics. Beyond simple logging, Iris facilitates quality evaluation of agent outputs through configurable rules and provides robust cost tracking. With a user-friendly web dashboard for visualization and built-in production-grade security features, Iris ensures that AI agents can be developed, monitored, and refined with confidence and transparency.
Key Features
01Comprehensive AI Agent Trace Logging
02Configurable Agent Output Quality Evaluation
03Automated AI Agent Cost Tracking
04Interactive Web Dashboard for Observability
05Production-Grade Security for HTTP Transport
062 GitHub stars
Use Cases
01Monitoring and debugging AI agent executions across various frameworks
02Evaluating the performance and output quality of large language model (LLM) agents
03Integrating observability directly into MCP-compatible agent environments like Claude Desktop
