Mirrors
Rebuilds the systems your agents call so you can replay real sessions and catch regressions pre-deploy
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runmirrors.comAbout Mirrors
Mirrors is a staging environment for AI agents. The problem it targets is specific: an agent that calls internal tools, databases and third-party APIs has no safe place to be tested, because nobody will hand out a test instance of the billing system or the reservation backend, and replaying production traffic against the real thing means real refunds and real emails. Mirrors takes what you already have — a trace export, the agent's code, the tool definitions, or docs — and mines a runnable copy of those systems from it: schema, seed data and tool behaviour, ready in minutes rather than after a quarter of environment work. Once the environment exists, past sessions replay against it on every pull request, so a change that makes the agent issue a second refund on the same order fails a CI gate instead of reaching a customer. A collector can stream live sessions from production afterwards to keep the mirror in step as the real systems drift. It drops into the frameworks teams already use — LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Pydantic AI, smolagents, Google ADK, the Vercel AI SDK, Mastra — and speaks MCP, so Claude Code, Codex, Cursor and Windsurf sessions can be replayed too. There is a public /v1 API and a CI gate, and the company is a Y Combinator company.
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Key Features
Mirrors Pros & Cons
✅ Pros
- +Solves the 'nobody will give me a test instance of the internal tool' problem directly
- +Per-minute billing means idle environments cost nothing
- +Framework-agnostic rather than tied to one agent SDK
⚠️ Cons
- −Environment fidelity depends on the quality of the traces you feed it
- −Replay-minute billing is hard to forecast before you know your CI volume
- −Early-stage — the published plan ladder is Free and Enterprise only
Tags
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