Raindrop provides a real‑time monitoring platform for AI agents that captures every token, tool call, and decision, visualizing interaction trajectories and automatically detecting silent failure modes such as forgetting, loops, or user frustration. It offers live dashboards, natural‑language search, custom signal tracking, Slack alerts, A/B testing, and a privacy‑preserving PII Guard, enabling AI product teams to quickly debug and improve agents in production.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents can fail silently, producing incorrect answers, forgetting context, or getting stuck in loops without generating obvious error codes, making it difficult for developers to detect and resolve issues in production.
Solution
Raindrop offers a real‑time monitoring platform that observes every interaction of AI agents, capturing each token, tool call, and decision. It visualizes agent trajectories, automatically detects common failure modes such as forgetting, user frustration, or task errors, and surfaces alerts through integrations like Slack. Users can search millions of interactions with natural‑language queries, define custom signals to track specific problems, and run A/B experiments to validate fixes. The platform also includes a PII Guard that redacts sensitive data at ingestion, ensuring privacy while retaining observability. By providing live‑streamed traces and a local, open‑source debugging environment, Raindrop enables developers to quickly understand, iterate on, and improve their agents.
Target Audience
Primary customers are AI product teams, developers, and engineers building LLM‑powered agents and chatbots who need production‑level observability and debugging tools.
Features
- Live streaming of every token, tool call, and decision into a visual dashboard with no polling required
- Automatic detection of silent failures using built‑in signals for forgetting, loops, and user frustration
- Real‑time Slack alerts and customizable notification channels for immediate issue awareness
- Deep search across all interactions using natural‑language queries to locate specific problems
- Custom signal definition and scalable tracking for any metric (e.g., syntax errors, cost, latency)
- A/B testing framework to measure the impact of agent updates and fixes
- PII Guard with server‑side intelligent redaction and optional client‑side SDK redaction to protect sensitive data
- Open‑source local debugging tool (Workshop) compatible with major SDKs and frameworks (TypeScript, Python, Rust, Go, LangChain, CrewAI, etc.)