Mirrors vs Raindrop

What Raindrop is good at

• Failure detection is the thing they are actually good at, and it is not a dashboard you have to remember to open. Raindrop watches the distribution of what your agent does and publishes an issue when it shifts, with a severity and an affected count, rather than waiting for you to write the eval that would have caught it. For the class of bug that has no exception and no 500, that is the difference between finding out and not.
• The instrumentation surface is the broadest here. First-party SDKs for TypeScript, Python, Go, Rust and Java, a plain HTTP API, browser JavaScript, and an OpenTelemetry path, plus framework integrations for the Vercel AI SDK, the Claude and OpenAI Agents SDKs, LangChain, Mastra, Pydantic AI, CrewAI, Google ADK and Temporal, among others.
• Workshop, their local agent debugger, is open source under MIT with around 970 stars, runs entirely on your machine, and is designed so a coding agent can read a trace and write an eval against it. It is a genuine free tool, not a funnel with a time limit.
• The triage agent, the MCP server and the query API mean the answer to "what went wrong last night" can be asked from your editor instead of assembled by hand out of a trace viewer.
• The free tier asks for no credit card, and the paid tier is one price with a published per-event rate rather than a quote. SOC 2 Type II, with subprocessors listed publicly.
• They have raised $15M led by Lightspeed with the founders of Vercel, Replit, Framer and Notion participating, and they ship quickly. This is a well-funded team that will keep shipping.

Side by side

Prices, limits and the event definition checked against raindrop.ai on 13 August 2026. Star count is approximate and moves.

The actual difference

Both products are about an agent doing the wrong thing. They sit on opposite sides of the moment it does it. Raindrop is the smoke alarm: the agent is live, a user is in the conversation, and the job is to notice that something went wrong and tell you fast enough to matter. Mirrors is the fire drill: the agent is not live, the user is fabricated, and the job is to find out what a change does before anyone is in the building.

The feature where this is easiest to confuse is Experiments, so it is worth quoting rather than characterising. Raindrop Experiments compare two cohorts of production data across signals, tools and models. Their own documentation is explicit about the limit: "Experiments compare cohorts of data you've already logged; they don't re-run or replay your agent." So you can compare last week against this week, or GPT against Claude, on traffic that already went out. What you cannot do is take last week's conversation, change the prompt, and see what the agent would have done, because there is nothing on the other end of the tool calls to answer them a second time.

That is the piece Mirrors builds. Each tool the agent calls becomes something running: a schema, fabricated seed records, and state that changes when the agent acts and resets between runs, constructed from your traces, code, or docs. A conversation can then be replayed against it as many times as you like, on a pull request, with a refund actually issued and an email actually sent to nothing real.

The practical consequence is where the check sits in your week. Raindrop tells you a regression is live and gives you the evidence to fix it; the users who hit it already hit it. A replay against a rebuilt environment runs before the merge, which means it catches a narrower class of problem (the ones your recorded conversations cover) but catches them before anyone is affected. Neither ordering is wrong, and most teams end up wanting both.

When to use Raindrop instead

Your agent is already in production and you do not know what it is doing wrong. That is Raindrop's question, it is the harder half of the problem, and no amount of pre-merge testing answers it: a suite only checks the cases you thought of, and production is where the ones you did not think of live.

You want failure detection without first writing evals. Raindrop publishes issues off your live traffic; a replay suite needs someone to decide what to assert.

You want a free local debugger for the inner loop of building an agent. Workshop is MIT, runs on your machine, and costs nothing.

You need broad instrumentation coverage across several languages and agent frameworks, or an OpenTelemetry path, and you want one place that already speaks all of them.

Your agent is read-only, or its actions are cheap to undo. If nothing it does is expensive to get wrong, the argument for a rebuilt environment is much weaker and watching production may be all you need.

Most teams run both

The seam is the trace, and it points one way cleanly. Raindrop is the better instrument for what production actually did, including the failures nobody predicted. Those traces are exactly what Mirrors reads to build an environment, so an issue Raindrop surfaces becomes a case you can replay on every pull request instead of a fix you ship and hope about.

Run that way, the loop closes: Raindrop finds a class of failure once, and the environment built from it keeps that failure from coming back the next time somebody edits the prompt.

What this page compares, and what it does not

These are the observability, evaluation, and agent monitoring 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. Neither is mirrord by MetalBear, which shares most of a name with us and solves an unrelated problem.

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.