Local-First Forensics
Built for Developers
Who Treat AI like Software
AI agents fail in highly complex, non-deterministic ways. I built Orchid to make AI behavior as debuggable and testable as normal code.
The Story
The Pain: Digging through logs
In late 2025, I was building multi-agent systems. Whenever an output went off the rails, I spent hours digging through JSON logs. Existing tools required intrusive tracking code that littered my clean application logic.
The Idea: A local capture layer
I realized the solution was simple: route traffic through a local capture layer. I could inspect inputs, outputs, and token counts seamlessly without writing a single line of extra tracking code.
The Prototype: Orchid is born
Orchid (short for Orchestration interactive debugger) was built to separate payload recording from execution. It stored traces securely in your own environment for offline playback and agentic exploration.
The Evolution: Cost & Governance
Solving the debugging problem revealed something bigger. Because Orchid sees every token of every request, it naturally evolved into a control plane for engineering teams who need strict budget enforcement and local audit trails.
No tracking code required. No sending your data to a third party.
First Principles
No Tracking Code
Orchid hooks into your app natively. You don't have to adopt proprietary AI wrappers or write messy tracking code to see what your agents are doing.
Local-First Data Storage
All data persists securely in your own environment. Your proprietary prompts and user data remain completely in your control, never touching a third-party cloud.
Zero Cost Testing
Replay recorded runs in your test suite offline with zero API cost, ensuring your agents behave predictably every time without burning your budget.
AI-Assisted Debugging
Inspect runs visually in your browser, or let your AI coding assistant (like Claude or Cursor) query your traces directly to write fixes for you.