Agent tracing
Collect traces to inspect how an agent behaved across a run, which helps teams understand failures that do not show up in ordinary application logs.
Chirpz AI is an applied AI lab focused on agent engineering. Its public-facing product is PandaProbe, an open-source agent engineering platform for traces, evals, and monitoring.
The site frames the product around a specific gap: AI agents need tooling for tracing, evaluation, and monitoring because they fail in ways that differ from traditional software and standalone LLMs. Chirpz AI says PandaProbe is built to help teams debug agents, improve them, and ship them with more confidence.
Collect traces to inspect how an agent behaved across a run, which helps teams understand failures that do not show up in ordinary application logs.
Evaluate agent behavior so teams can compare outputs and judge changes instead of relying on ad hoc manual review.
Monitor agents in production to watch for failures and quality regressions after deployment.
Treat observability as a first-class concern so debugging and improvement happen around the agent lifecycle rather than as an afterthought.
Use an open-source platform, which the company says it chose because transparency is foundational to the product.
Inspect agent traces to understand why a run failed, where behavior diverged, and what happened before an unexpected outcome.
Compare agent outputs with evals when changing prompts, models, or workflows so you can assess whether the change improved behavior.
Track agent behavior in production to spot regressions and monitor reliability over time.
Adopt an open-source agent engineering platform when transparency and inspectability matter to the team.
Chirpz AI presents PandaProbe as its product, an open-source agent engineering platform for traces, evals, and monitoring. The site describes it as a tool for debugging, evaluating, and improving AI agents in production.
The site says the team builds products and publishes research in agent engineering. Their stated focus is the gap between prototype and production for AI agents, especially observability, tracing, evaluation, and monitoring.
The source text does not describe a public signup flow, pricing tiers, or paid plans. The pricing page currently returns a 404, so pricing details are not available from the provided evidence.
PandaProbe is described as open source from day one, and the site links to GitHub and the pandaProbe.com domain. Beyond that, the provided sources do not list specific integrations.
Orca 是面向编码代理的 Agent 开发环境,支持在隔离的 git worktree 中并行运行多个 CLI agent,并提供桌面端与移动端协作流程。
AI Magicx 是一体化 AI 工作区,集聊天、图片、视频、语音、音乐、邮件和开发任务于一处,帮助创作者、团队和开发者集中使用多种模型,无需在多个工具和订阅间切换。
Paper 是一款设计工具,连接画布、代码和 AI agent,让团队在一个工作流中完成创建、协作与交付。支持桌面应用、基于 MCP 的 agent 访问,以及真实内容和设计 token 工作流。
blop 是一款 QA agent,可在仓库中将浏览器测试以代码形式编写并运行于 CI,聚合重复失败,还可发起 PR 修复失效测试,适合需要可审查、版本控制浏览器 QA 的团队。
RLAMA 是一款本地 AI 平台,适用于在 macOS、Linux 和 Windows 上构建 RAG 系统与智能体。支持本地处理、交互式终端工作流和用于文档问答及多智能体自动化的 HTTP API。
Kastra 为 AI 系统提供授权基础设施,在执行前检查提示词、工具调用、Shell 命令、API 请求和浏览器操作,帮助团队执行策略并管理本地与企业 AI 工作流。