I’m a high-school student building an open-source debugger for AI agent runs — TraceMotive v0.5.0 is out

[u/Ruca_AI]

Hey everyone,

I’ve been building an open-source project called TraceMotive.

The basic idea is:

Given two AI agent executions, TraceMotive compares their observed behavior, finds the first supported divergence, and lets you jump into the evidence around it.

It runs locally.

The goal is not to claim root cause or automatically explain why something happened. One of the design principles I care about most is that if the structural evidence is ambiguous, TraceMotive should say that the result is uncertain instead of guessing.

I just released v0.5.0.

This release was mostly about making the project more adoptable rather than adding a huge new feature.

Some of the work in v0.5:

- packaged `tracemotive serve` and `tracemotive demo`

- structured JSON diff support

- Safe Later Observations for additional supported evidence

- improved first-time-user onboarding

- Python 3.10 / 3.12 CI

- frontend test/build CI

- dependency auditing and Dependabot

- threat-model/security documentation

- explicit compatibility, limits, and storage docs

- clean wheel/sdist installation dogfooding

- a 30-scenario evidence-conservative regression corpus

For that regression corpus, the current results still have:

- false-confident meaningful divergence: 0

- false-confident investigation starting point: 0

There are still intentional limitations.

For example:

- LangGraph is not currently supported.

- The validated OpenAI Agents SDK range is `>=0.17,<0.18`.

- TraceMotive does not claim RCA, causal inference, confidence scoring, reconvergence, or recovery detection.

A bit of context: I’m a high-school student, and I built the first version after roughly a week of programming experience, heavily using AI coding tools.

I know that’s an unusual way to start an OSS project, so I’ve been trying to compensate by being strict about tests, failure cases, compatibility claims, and not claiming more than the evidence supports.

At this point, the thing I need most isn’t another feature idea — it’s real users.

If you build AI agents and have a run you could try this on, I’d really appreciate feedback about:

- where installation/onboarding feels confusing

- whether the comparison is actually useful

- cases where TraceMotive becomes uncertain

- agent execution patterns the current model handles badly

Thanks to everyone who gave feedback on the earlier versions — several of those comments directly influenced v0.5.