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Raindrop

Detect agent issues. Prevent them from happening.

Raindrop monitors AI agents in production, finds silent failures and tests fixes before they ship. It traces every message, tool call and decision, groups recurring failures into issues, and runs a Triage Agent that investigates them in Slack, on the web or through MCP. It also runs simulations of agent changes in CI and offers Workshop, an open-source local tool for replaying and debugging agents.

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People

Co-founder & CTO

Evals & benchmarks · Open harnesses

Featured

The de-facto standard toolkit for building LLM applications and agents.

LangChain makes the LangChain framework and LangGraph, the low-level orchestration standard for stateful, controllable agents, plus the LangSmith platform for tracing, evaluating and monitoring them in production.

~$75M raised

4 pros · 4 cons

Enterprise-grade evals, data and AI gateway.

Braintrust turns prompts and agent traces into testable datasets and CI, plus an inference gateway — helping teams measure whether harness changes actually improve agent quality.

4 pros · 4 cons

Observability and evaluation for AI — makers of OSS Phoenix.

Arize traces and evaluates LLM and agent systems in production; their open-source Phoenix tracer is a common instrumentation choice for agent harnesses that need to be debuggable.

4 pros · 5 cons

Connections

Relations from the Harness Hub knowledge graph, as charted in the State of Harness reports.

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Sources

  1. Raindrop website