AgentCat
AgentCat is an analytics and observability platform built for AI agent traffic, helping teams building MCP servers, Claude Connectors, and ChatGPT Apps understand how agents interact with their products. It provides end-to-end session replay, automatic error grouping, and LLM goal classification to reveal agent intent and identify where agents get stuck. The platform offers SDKs for TypeScript and Python, with a free tier and enterprise-grade security features.
- Data & Analytics
- Developer Tools
- Artificial Intelligence
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Traditional analytics and observability tools are designed for human users, tracking clicks, form inputs, and page views. As AI agents increasingly become the primary users of software products, teams lack visibility into what agents are doing, what they're trying to achieve, and where they get stuck, making it difficult to build products optimized for agent interaction.
Solution
AgentCat is an analytics and observability platform purpose-built for AI agent traffic. It provides end-to-end session replay that lets teams step through every tool call, request, response, and error to see exactly what an agent experienced. The platform automatically groups sessions by agent intent and activity, classifying what agents are trying to accomplish, and surfaces errors, hallucinations, and crashes grouped by impact with per-tool performance tracking. AgentCat integrates via SDK packages for TypeScript and Python, making it easy for teams building MCP servers and agent-first products to embed comprehensive agent analytics with minimal setup. The platform ships with enterprise-grade security controls and an open-source friendly free tier.
Target Audience
Primary customers are development teams building AI agent products, MCP servers, Claude Connectors, and ChatGPT Apps who need to understand and optimize how agents interact with their software.
Features
- Session replay showing every tool call, request, response, and error with shareable links for team collaboration
- Automatic error grouping and per-tool performance tracking to identify issues by impact
- LLM Goal classifications that cluster sessions into use cases and classify each session based on agent intent and activity
- zero-touch setup with no additional configuration required
- SDK packages for TypeScript and Python, with the legacy `mcpcat` package remaining fully functional and a clear migration path to the new `agentcat` package
- Enterprise features including SSO/SAML, audit logs, S3/Snowflake/BigQuery exports, SLA guarantees, and up to 2 years of data retention
- Open-source friendly with free plans available for open source projects