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While. vs Raindrop.
Raindrop finds problems in a running agent. While. learns from production traces and continuously improves the model with mid-training and post-training SFT and RL.
Why you need While.
Monitoring tells you the agent failed. It does not make the agent stop failing.
The fix is a change to the model. That takes three things: training data from the failures, a training run, and a held-out evaluation with an interval on the difference.
While. does all three. The other tools on this page stop at the red check.
What Raindrop is
Raindrop is monitoring for AI agents in production. It ingests traces, finds issues, groups them, and has an agent that triages them for you. On September 17, 2026 it announced a Series A, $50 million raised in total from CRV and Lightspeed, and a new product called Simulations that replays production traffic and existing tests against a proposed change, then runs anomaly detection on the results.
Its site names Vercel, Clay, Framer, AngelList and Browserbase among its customers and says it processes billions of traces a month. Workshop, a local debugger for agents, is open source under MIT. The platform and Simulations are closed. Simulations is in early access behind a waitlist.
What While. is
While. is a mid-training and post-training platform for language models, built for the team that owns a company's models. The open-source SDK builds and optimizes the dataset: it learns from production traces, writes situations from the agent's tools, plays the agent against them, and scores every run with a judge you write as a program. A hosted trainer runs mid-training and post-training on an open model with the latest SFT and RL algorithms, the model is served on your own endpoint, and a held-out set with a confidence interval says whether the change is real. Curb a behavior, build character, make tool use more efficient, harden the model against adversarial attacks.
The SDK is open source under Apache 2.0 and runs offline with no key. The platform is hosted. We do not publish customer names yet. Public results: a telecom support agent, trained to hand customers off at the right time, went from 5% to 30% on the held-out split of τ²-bench, and a refund agent, trained to refund only within policy, went from 18% to 85% on a held-out set.
Side by side
| Raindrop | While. | |
|---|---|---|
| What it does | Watches an agent in production and tells you what broke. Simulations, once it is generally available, tests a change before merge. | Tests the agent, turns the failures into training data, trains a model, serves it, and measures the change on a held-out set with a confidence interval. |
| Open source | Workshop, the local debugger, under MIT. The platform and Simulations are closed. | The whileai SDK under Apache 2.0. The platform is closed. |
| Can you use it today | Monitoring, yes. Simulations is early access with a waitlist. | Yes. pip install whileai. It runs offline with no key. |
| Where the test comes from | Your production traffic and existing tests, replayed against the change. | Your production traffic, or situations the SDK writes from your agent's tools when you have no traffic yet. |
| What the agent runs against | Copies of the services it calls, built from your schemas. | Fake tool backends built from your tool descriptions, or your real tools inside one function. |
| The number you get | Anomalies found across the replayed runs. | A first-try pass rate with a 95% confidence interval, a rate per named behavior, and a before and after with an interval on the difference. |
| On every pull request | Built in. It can block the merge. | A Python step in your CI that exits 1 when a protected behavior got worse. |
| After the test | You fix the agent by hand and replay. | The failed runs become training data. Train with SFT, DPO or GRPO and host the result. |
| Customers | Vercel, Clay, Framer, AngelList, Browserbase and others named on its site. | Design partners in finance and customer support. Names not published yet. |
| Funding | $50 million total. Series A from CRV and Lightspeed, September 2026. | Not disclosed. |
Pick Raindrop if
- You want monitoring for an agent with a lot of traffic, today.
- You need copies of services like Stripe or Slack built from your own schemas.
- You want a merge gate you switch on rather than a script you keep.
Pick While. if
- You want the agent to get better, not only to know when it got worse.
- You have little or no traffic yet and still need a test.
- You want the test to run offline, with no key, in seconds.
- You want your own model on your own endpoint, with the weights.
Raindrop alternative
If you came here for a Raindrop alternative: While. covers the part of Raindrop that tests a change before it ships, from your traffic or from simulated situations. It is open source, runs offline with no key, and has no waitlist. It does not replace Raindrop's production monitoring at high volume. What it adds is the fix. The runs that failed become training data, and the trained model is served on your own endpoint.
Questions
- Is Raindrop open source?
- Partly. Workshop, its local debugger, is open source under MIT. The Raindrop platform and Simulations are closed and hosted.
- Does Raindrop train models?
- No. Its pages describe monitoring, issue detection, triage and Simulations. There is no training, fine-tuning or dataset export.
- Can I use Raindrop and While. together?
- Yes. While. reads OpenTelemetry traces and JSONL, so you can keep Raindrop for monitoring and cut training data from the same runs.
Sources
Everything here about Raindrop comes from these pages, read on September 17, 2026. If something changed, tell us and we will fix it.