Run and trace inspection
Follow messages and tool calls within agent runs to locate where behavior went wrong, with the original trace available as evidence.
Raindrop
Freemium
AI agent observability for tracing runs, detecting recurring failures, and testing changes before release.
O que é Raindrop?
Raindrop is an observability and issue-detection platform for teams operating AI agents. It captures agent runs, including messages and tool calls, then helps teams identify recurring failures that may complete without a conventional exception. Raindrop groups related traces into investigated issues, shows user impact and contributing context, and provides evidence for diagnosis. Its workflow extends into development: teams can simulate proposed changes against traces or real traffic, review results on pull requests, and compare behavior before merging.
O que Raindrop pode fazer?
Production issue detection
Monitor incoming traces and detect changes in tool errors, latency, and other signals when live behavior moves away from its expected pattern.
Related-failure grouping
Combine recurring failures across conversations into a single issue so teams can investigate the underlying pattern rather than isolated events.
Investigation context
Review affected users, example traces, concentration by tool or model version, and other issue details to support root-cause analysis.
Change simulation
Replay proposed agent changes against relevant traces and review simulation results in a pull request before merging.
Team investigation workflow
Publish investigated issues to Slack, ask follow-up questions through Slack, the web app, or MCP, and start a coding session with Fix.
Casos de uso
“Find silent production failures”
Use issue detection when an agent run appears successful but users experience repeated failures, wasted tool calls, or no forward progress.
“Diagnose recurring agent behavior”
Group related conversations and inspect their traces when an agent repeatedly applies the same incorrect action, configuration, or response pattern.
“Review user impact during triage”
Examine affected users, severity, example traces, and concentration by tool, model version, or customer cohort while prioritizing an issue.
“Validate an agent fix before merge”
Simulate a proposed change against failing traces or real traffic and review the comparison in the pull request to catch regressions.
“Coordinate investigation in team tools”
Send issue evidence to Slack, continue the investigation through supported interfaces, and hand a coding task to an agent-assisted fix workflow.
Perguntas frequentes
What does Raindrop monitor?
Raindrop monitors incoming agent traces and looks for changes in behavior such as tool errors, latency shifts, and other signals that move away from an expected pattern.
Can Raindrop detect failures without exceptions?
Yes. Its issue-detection workflow is designed to identify behavioral failures where a run can finish successfully but still fail the user.
How does Raindrop help investigate an issue?
It groups related failures, attaches the underlying traces, and presents context such as affected users, example conversations, concentration by tool or model version, and user impact.
Can teams test changes before deploying them?
Yes. Teams can simulate proposed changes against relevant traces or real traffic and review the results in a pull request before merging.
Where can teams continue an investigation?
Investigated issues can be published to Slack. Follow-up questions can be asked in Slack, the Raindrop web app, or through MCP.
Informações rápidas
- Category
- AI agent observability
- Primary users
- Teams building and operating AI agents
- Core workflow
- Trace, detect, investigate, simulate, verify
- Deployment context
- Production monitoring and CI review
- Source domain
- raindrop.ai
Análise de tráfego de Raindrop
Os dados de tráfego são apenas para referência.
- Classificação do domínio
- 67
Alternativas ao Raindrop
Galileo
www.galileo.ai
Galileo is an AI observability and evaluation platform for testing, debugging, and governing LLM and agent systems across development and production. It helps teams turn evaluation results into production guardrails and monitor AI behavior at scale.
Agnost AI
agnost.ai
Agnost AI monitors AI agents in production by analyzing conversations, traces, user corrections, and frustration. It helps teams identify recurring failures, review suggested prompt fixes, and apply approved changes with replay-based regression coverage.