AI agents fail differently than traditional software. A 500 error is obvious. An agent that confidently gives wrong information, forgets something the user said 3 messages ago, or takes a suboptimal path are failures invisible to traditional monitoring. Raindrop helps you:

• Visualize agent trajectories: See what your agent actually did, every tool call, error, and recovery, in seconds
• Detect issues automatically: default signals catch common failure modes like forgetting, user frustration, and task failures
• Search across millions of interactions: Use Deep Search to find specific issues in your production data with natural language
• Track custom signals: Define any signal you care about (e.g., syntax errors, aesthetic complaints, agent stuck in a loop) and track it at scale
• A/B test your agents: Run experiments to validate that your fixes actually worked

Integrate Raindrop

TypeScript \ \ Full-featured SDK with tracing support for Node.js and edge runtimes.

Python \ \ Native Python SDK for FastAPI, Django, and other Python frameworks.

HTTP API \ \ RESTful API for any language or platform.

Integrations \ \ Connect Raindrop with AI frameworks, cloud providers, and developer tools.

Core Features

Trajectories \ \ Visualize and search agent traces. See every tool call, spot errors instantly, and understand what actually happened.

Signals \ \ Ground truth indicators for agent performance. Track wins, failures, and custom behaviors across all your interactions.

Deep Search \ \ Find issues in your production data using natural language. Like deep research, but for your agent logs.

Experiments \ \ A/B test your agents to validate changes. Compare models, prompts, and configurations.

Alerts \ \ Get notified via Slack when issues spike. Daily summaries and custom alert thresholds.