Mirrors compared

Five platforms come up whenever a team asks how to test an AI agent: LangSmith, Braintrust, Langfuse, Confident AI, and Arize. All five record what your agent did and score it. None of them stands in for the database, the internal API, or the tool the agent called. Mirrors rebuilds those, which is why most teams end up running one of them alongside it.

Last updated 2 August 2026. Every price on these pages was checked against the vendor's own site, none of them earlier than 2 August 2026.

Mirrors vs Confident AI: The broadest metric library in the category, and the one competitor that ships a real simulation product. It simulates the user.
Mirrors vs LangSmith: The observability and eval platform with the most gravity. It simulates the user, not the backend.
Mirrors vs Braintrust: The sharpest eval loop in the category, and the one vendor that argues in writing against rebuilding the environment.
Mirrors vs Langfuse: The most generous open source offer in the category. Its tool mocking is a typed-in response, not a running backend.
Mirrors vs Arize: The only platform here that covers classic ML and LLM agents in one place. Nothing in it stands in for your backend.

What this page compares, and what it does not

These five are the observability and evaluation platforms a team already has open when it asks how to test an agent, which is why they are the pages we wrote. They are not the whole field. Tools that do stand something in for an agent's counterpart, LangWatch's Scenario and Laminar's agent rollouts among them, are outside this comparison, and nothing here is a claim about them.

Standing in for a live dependency is also not a new idea. Record-and-replay HTTP mocking, VCR-style cassettes, predates LLM agents by about fifteen years and is free. What Mirrors adds is the construction and the state: the environment is built from your traces, code, or docs instead of hand-written, and it is a seeded, stateful backend you can reset between runs instead of a fixed transcript of responses.