Raindrop

Raindrop is an observability platform built specifically for AI agents—think Sentry for LLM apps, but with a focus on the messy realities of production: silent failures like hallucinations, loops, and broken tools. Every agent run—messages, tool calls, retries, errors—is captured in unified traces that you can inspect in a purpose-built viewer called Trajectories. Unlike generic logging, Raindrop reads the trace, identifies what actually went wrong, and notifies your team in Slack, where you can @Raindrop to query, triage, or create custom Signals on the spot. The latest release, Raindrop 2.0, pushes beyond passive monitoring into self-healing: when a failure is detected, a coding agent automatically applies a fix, and the incident becomes a permanent eval so it doesn't recur. The platform also includes Triage, an agent that investigates other agents directly in Slack or the web app, and Agent Self Diagnostics, where agents proactively report their own failures, loops, and capability gaps. For deep local debugging, Raindrop Workshop is an open-source, MCP-native debugger you can install with one command. Engineers get alerted with real-time messages and daily summaries, can run A/B Experiments against live traffic to validate fixes, and use Deep Search—natural-language search across all traces—to find patterns like expensive, slow, or error-prone tool calls. The platform supports SDKs for TypeScript, Python, Go, Rust, and Java, plus a REST API, and works with frameworks like LangChain, LangGraph, CrewAI, and the Vercel AI SDK. It's SOC 2 Type II certified and trusted by engineering teams at Fortune 100 companies, per vendor claims. Where Raindrop differentiates itself is its shift from deterministic, test-time evaluation to proactive, in-production discovery. Competitors like LangSmith, Arize, and Braintrust focus on call/response traces and eval suites; Raindrop targets the next era of multi-agent systems, where issues emerge at runtime and need immediate