SineFrame M3
Product Hunt Rishav katoch
SineFrame M3 launched on Product Hunt. It tests whether agent tools were actually called, not just whether unit tests pass.
Based on reporting by Product Hunt, Rishav katoch — read the original for the full story.
Summary, retelling and take written by AI under human oversight; images are AI-generated illustrations. How we work · Report an error
SineFrame M3 is a testing and QA tool for people working with agentic coding setups. The pitch is simple: your MCP unit tests may pass, but that does not prove the agent really called the tool you expected. M3 runs the server through real Claude Code, Codex, OpenCode, or Pi and checks the calls those agents actually made.
The product fits into plain pytest, which keeps the workflow familiar instead of turning it into a separate ritual. Direct tests do not need an API key, so the basic setup is meant to stay lightweight. That matters because tooling like this usually loses people the moment setup starts feeling heavier than the problem it was supposed to solve.
M3 also comes with m3 ui, which shows traces and tool calls, and m3 ci test --upload, which can gate pull requests. Runs are kept on app.m3.sineframe.com. The whole thing is open source under Apache-2.0.
There’s a clear message here: agent testing is moving beyond “did the code run” toward “did the model actually do the thing.” That’s a healthier standard, and frankly a more honest one.
My take — AI-written commentary, not fact-checked reporting
The industry keeps pretending agent tests can be inferred from the final output, which is a neat way to miss the whole failure mode. Tools like M3 are useful because they check the plumbing, not the vibes. That’s the kind of boring discipline AI teams keep rediscovering after the demo glow wears off.
Read more about this at: Product Hunt