Chirpz records 0 monthly visits with 0% month-over-month growth and Freemium, Contact for Pricing pricing. Raindrop records 42,807 monthly visits with -18.4% growth and Free Trial, Paid, Contact for Pricing pricing.
An applied AI lab making autonomous agents observable, reliable and production-ready
Find and fix problems in your AI agents before users do
| Dimension / Metric | | |
|---|---|---|
| Official Domain | chirpz.ai | raindrop.ai |
| Pricing Model | Freemium, Contact for Pricing | Free Trial, Paid, Contact for Pricing |
| Monthly Visits | 0 (0) | 42.81K (42,807) |
| MoM Traffic Delta | 0 (0%) | -9.63K (-18.4%) |
| Primary Market & Share | 🌐 Global (—) | 🇺🇸 United States (77.1%) |
| Direct Visits / mo | 0 | 23.55K |
| User Rating | 4.1 | 4.4 |
| Supported Platforms | Website | Website |
An applied AI lab making autonomous agents observable, reliable and production-ready
The company's core focus is the engineering layer around agents: instrumentation, traces and signals that let teams see what an autonomous workflow actually did and why.
PandaProbe is Chirpz's flagship product, aimed at making agent behavior easier to inspect, validate and keep stable as prompts, tools and models change.
Rather than a thin chat wrapper, Chirpz works on the underlying infrastructure — evaluation, guardrails and runtime plumbing — that teams need before agents can ship.
Chirpz positions itself as a lab solving agent engineering as a discipline, publishing open-source work on GitHub alongside its commercial product.
Find and fix problems in your AI agents before users do
See every message, tool call and decision in a run, with inputs, outputs and durations, so you can pinpoint where a run broke down.
Recurring failures are grouped into issues with severity, affected users, last-seen time and a confidence score, instead of scattered alerts.
An agent investigates issues and automates follow-up across Slack, the web app and MCP, so debugging happens where your team already works.
Run scenarios against a change on a pull request and compare results with real traffic to catch regressions before merge.
SpecialistUnified observability, security and incident response for cloud-scale engineering teams
SpecialistUnified observability, security and incident response for cloud-scale engineering teams
Explore the academic literature as a visual graph of related papers
Explore the academic literature as a visual graph of related papers
AI-native workspace for literature reviews and auditable systematic reviews
AI-native workspace for literature reviews and auditable systematic reviews
Free / Open-sourceAgent observability, evaluation, and continuous improvement for production AI
Free / Open-sourceAgent observability, evaluation, and continuous improvement for production AI
Build, automate and publish AI agents without starting from scratch
Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents
Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production
SpecialistThe collaboration layer for AI engineering teams
SpecialistThe collaboration layer for AI engineering teams
Standardize LLM evaluation, observability, and red teaming across your whole org
Standardize LLM evaluation, observability, and red teaming across your whole org
Automated QA, simulation and observability for voice and chat AI agents
One AI agent for finding, reading and writing with real research
One AI agent for finding, reading and writing with real research
Free / Open-sourceOpen-source observability that finds your AI agent's failures — and fixes them
Free / Open-sourceOpen-source observability that finds your AI agent's failures — and fixes them
