Mirrors vs Confident AI

What Confident AI is good at

• Metric breadth, and it is not close. More than 50 metrics including G-Eval and DAG, a deterministic decision-tree judge you can actually reason about, plus 11 agentic metrics and academic benchmarks in the box.
• RAG evaluation depth: faithfulness, relevancy, precision and recall on retrieval, worked out properly rather than bolted on.
• DeepTeam, red teaming as an entire second product: 40+ vulnerability types with mappings to the OWASP LLM Top 10, NIST AI RMF, and MITRE ATLAS. If someone is asking you for a compliance artifact, this is the only one here that produces something shaped like the answer.
• Apache-2.0 with 17,327 GitHub stars, and it runs fully local with no account, which is the whole ballgame when the data cannot leave the building.
• pytest ergonomics. Assertions look like tests, so an eval suite lands in CI the same way the rest of your tests already did, plus synthetic dataset generation and OpenTelemetry tracing.

Side by side

Prices and limits checked against confident-ai.com on 2 August 2026. Starter is priced per organization with unlimited seats, so an older per-seat figure quoted anywhere else is out of date.

The actual difference

This is the closest comparison of the five, and it deserves precision rather than a dismissal.

ConversationSimulator is real, shipped, and sold from the paid tier. It drives a multi-turn conversation with your agent through a simulation_graph state machine that can branch, so you can evaluate a whole exchange instead of a single reply. It is a serious feature and it is solving a serious problem: writing realistic multi-turn test cases by hand does not scale.

What it simulates is the user. Its loop calls model_callback(...), and model_callback is your application. Whatever that application does when it runs, it does: the order is created, the ticket is opened, the card is charged. Searching the deepeval package for mock, sandbox, or stub returns nothing, and that is not an oversight. Standing in for a dependency is not what the library is for.

One line: DeepEval simulates who is talking to your agent. It never simulates what your agent is talking to.

The consequence is concrete rather than philosophical. You cannot replay a January session against a February prompt and get a comparable diff if every replay charges a real card. You cannot gate a pull request on a suite that leaves rows behind in a system another team owns. And the destructive paths, the refund, the cancellation, the delete, are exactly the ones you most need covered and least able to run.

That is the constraint Mirrors removes. The environment is fabricated, stateful while a run is executing, and reset between runs, so the same session can be replayed a hundred times and the hundredth is as clean as the first. Point DeepEval metrics at those runs and you get the breadth of their judges on top of runs that were safe to produce.

When to use Confident AI instead

If what you need is metrics, use DeepEval. The library is broader than ours will be for a long time, it is free, it runs locally, and if your agent mostly reads (a RAG assistant, a summarizer, a classifier) then nothing it touches needs standing in for and the environment problem does not exist for you.

Use Confident AI too if red teaming and compliance mappings are on your roadmap: DeepTeam is a whole second product and there is no equivalent here. And if your data cannot leave your infrastructure at all, DeepEval running fully local with no account is a shorter conversation with your security team than any hosted product, including ours.

Most teams run both

This pairing is the most natural of the five, because the two halves are literally different sides of the same conversation. DeepEval simulates the user turn; Mirrors answers the tool calls that turn provokes; DeepEval metrics then score the transcript that comes out.

A team running both keeps its metric definitions in DeepEval, unchanged, and only swaps what the agent was talking to while the conversation happened.

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.