failproof ai vs raindrop

What Each Product Is

Raindrop monitors agents in production from their event stream. You send it events through its SDKs, an HTTP API or OpenTelemetry; one event is one turn, meaning the user input, the tools the agent ran and the assistant output. Its own classification model, rd-signal-2, runs Signals over those events. Failures are grouped into Stumbles and Issues, a Triage Agent looks for the root cause and hands it to your coding agent over MCP, and alerts go to Slack.

Failproof AI covers both halves of the job: it finds silent failures, and it stops them from happening again. It connects natively to twelve agent harnesses, including Claude Code, Codex, Cursor, GitHub Copilot CLI and Goose, and its Python SDK traces custom agents, with adapters for LangChain and LangGraph, CrewAI, LlamaIndex and Pydantic AI. Every trace and session is evaluated with code checks, LLM judges or the eval suite you already use. Audits group failures into findings with a recommended fix, alerts go to email, Slack, a webhook or the dashboard, and a policy turns a finding into a rule that blocks the action before it runs.

Both start from the same problem: an agent can finish a run with every call returning 200 and still have done the wrong thing. Raindrop's job ends once the failure is reported and handed off. Failproof AI's ends when the next one is blocked.

Features Compared

Raindrop's column comes from its public site and docs as of September 2026. Every page used is linked under Sources.

Most of the table is overlap: both find silent failures, group them and point you at a fix. The row that separates them is "Stops the next failure". A Raindrop alert arrives after the agent has acted. A Failproof AI policy decides before it acts.

Where Raindrop Stands Out

Raindrop's clearest difference is its own model. Signals run on rd-signal-2, a classifier Raindrop builds and runs itself and markets as "frontier accuracy, production scale", across what it says are billions of traces a month. You describe a behavior, Raindrop drafts the classifier, and you refine it by marking sample events as Match or Not Match. A new signal backfills the last 90 days, sampled.

• Experiments. Compare a baseline cohort of logged events with an experiment cohort, split by model, prompt version, date range or feature flag, and read the change in signal rates. Creating experiments needs Pro.
• Workshop. An MIT-licensed local debugger that builds an eval from a real failure and re-runs your agent against it until it passes.
• Customers. Speak, Vercel, Clay, Framer and AngelList are among the logos on its homepage.

Where Failproof AI Goes Further

• Prevention. Policies run at PreToolUse, before the tool executes, and return allow, instruct or deny; a Stop policy can refuse to end a turn. Raindrop has no enforcement: Signals label events and never block or change a response.
• Bring your own evals. Code checks, LLM judges and the eval suite you already run (DeepEval, Ragas, promptfoo or your own) all score each trace and session. Raindrop scores with its own Signals.
• A fix you can test. From an issue, generate policy drafts a policy. A backtest replays it against calls your agents already made and counts the good calls it would have blocked, before you deploy it in observe mode and then enforce it.
• Alerts where your team works. Email, Slack, webhook or the dashboard, on metric thresholds, custom SQL, evaluation scores or single events. Raindrop alerts in Slack.
• Evaluations per customer. Each customer is its own organization, with its own evaluations, audits, issues and keys.
• Price. Team is $99 a month with 50,000 runs included. Raindrop Pro is $299 a month, and every event is metered on top.

A custom policy for a failure your evaluations keep flagging takes a few lines of JavaScript:

// deploy-policies.js: a failure the evaluations kept flagging, blocked before it runs
import { customPolicies, allow, deny } from "failproofai";

customPolicies.add({
  name: "block-unreviewed-deploy",
  description: "Deny production deploy commands from the agent",
  match: { events: ["PreToolUse"] },
  fn: async ({ toolName, toolInput }) => {
    if (toolName !== "Bash") return allow();
    const command = String(toolInput?.command ?? "");
    if (/\bdeploy\b/i.test(command) && /\bprod(uction)?\b/i.test(command)) {
      return deny("Production deploys go through the release pipeline, not the agent. Open a PR instead.");
    }
    return allow();
  },
});

Install it locally with failproofai policies --install --custom ./deploy-policies.js, or publish it from the policy editor, backtest it, and deploy it to machines in observe mode before you enforce it. The coding agent pack, FailproofAI/policies, adds 38 ready-made policies, 10 of them on by default, including block-push-master, block-env-files and block-rm-rf.

One Silent Failure, Both Tools

Take an engineering agent in Claude Code that closes tickets. The silent failure: it reports "done, tests pass" and ends the session while CI is red. There is no exception and no error, and the transcript looks green. Raindrop lists a Claude Code integration, in beta per its docs, so both tools can see this session.

1. Raindrop: classify, rank, alert, hand off

   You create a Signal ("the agent says the task is complete but the tests failed or never ran"), label a sample of matches, and let it run on every new event. The rate crosses your threshold and a Slack alert fires. The Triage Agent finds that the prompt never asks for a CI check, and your coding agent picks the issue up over MCP and changes the prompt.

2. Failproof AI: evaluate, audit, fix, block

   An evaluation scores task completion on each finished session. An audit whose agent contract says "done when CI is green" groups the violations into one finding with a recommended fix. From the issue, generate policy drafts a Stop policy that refuses to end the turn while CI is red; you backtest it against past sessions, deploy it in observe mode, then enforce it.

3. The next premature "done"

   With Raindrop, the session ends red, and the Signal counts it so you can see whether the prompt fix held. With Failproof AI, the Stop policy refuses to end the turn, the agent goes back to the failing test, and the task-completion evaluation shows the failure rate falling.

Both tools catch the failure. Only Failproof AI stops the next premature "done" before it reaches your team.

Pricing

Raindrop's Pro fee includes no events; every event is metered. At 100,000 events a month that is $299 + (100,000 x $0.003) = $599. Issue Detection and Experiments need Pro; on Hobby they are preview-only. The meters are different units (a Raindrop event is one turn), so price your own traffic in both. The full arithmetic is on how much Raindrop costs.

Both let you start without a sales call: Raindrop offers "Start building" next to "Book a demo", and Failproof AI starts with npm install -g failproofai.

Moving from Raindrop, or Running Both

If Raindrop is already instrumented, you do not have to remove it. Add Failproof AI at the hook layer for the actions its Signals keep flagging, such as the deploy, the force push or the write to a file full of secrets, and let the policies block them. When you want one tool for both halves, Failproof AI's evaluations and audits cover the detection, and its policies cover the prevention.