The request "debug-model" names an action rather than a product, so the answer lists products whose sources show model debugging. Modular's MAX stack has a dedicated debug-model action for models whose output disagrees with a reference implementation 111, while MLflow 212, Tensorleap 9 and Phoenix via GitHub 515 cover model and trace debugging. Note that Tensorleap and InterpretML 3 were not read at action level.
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Modular MAXmodular.comverified and signed
Its debug-model action debugs a MAX model that loads, compiles, serves and generates tokens but whose output disagrees with a reference implementation 11.
for agents: debug-model · profile-model · eval-model
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MLflowmlflow.orgverified and signed
Its analyzing-mlflow-trace action analyzes a single trace to debug and investigate a model or agent run from a trace ID 12.
+6 community tools built for it
for agents: analyzing-mlflow-trace · analyzing-mlflow-session · mlflow-agent
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Sources
- 1Modular: Inference from Kernel to Cloudmodular.com · read 2026-10-30
- 2MLflow - Open Source AI Platform for Agents, LLMs & Modelsmlflow.org · read 2026-10-29
- 3InterpretMLinterpret.ml · read 2026-10-30
- 5GitHub · Change is constant. GitHub keeps you ahead. · GitHub · GitHubgithub.com · read 2026-10-29
- 9Debugging & Explainability for Physical AI | Tensorleaptensorleap.ai · read 2026-10-30
- 11debug-model: declares "debug-model" (the record we read and signed)unfragile.ai · read 2026-10-24
- 12analyzing-mlflow-trace: declares "analyzing-mlflow-trace" (the record we read and signed)unfragile.ai · read 2026-10-24
- 15model-recommendation: declares "model-recommendation" (the record we read and signed)unfragile.ai · read 2026-10-24
For agents: the 10 actions behind this answer
debug-model · debug-model verified and signed
Debug a MAX model that loads, compiles, serves, and generates tokens but whose output disagrees with a reference implementation. Use whenever parity debugging stalls on scalar taps, the model returns gibberish or wrong greedy tokens, logit cosine is high but argmax differs, or generation is coherent
analyzing-mlflow-trace · analyzing-mlflow-trace verified and signed
Analyzes a single MLflow trace to answer a user query about it. Use when the user provides a trace ID and asks to debug, investigate, find issues, root-cause errors, understand behavior, or analyze quality. Triggers on "analyze this trace", "what went wrong with this trace", "debug trace", "investig
· Interpret website read, tools not yet checked
An open source toolkit for analyzing models and explaining behavior
debugging-streamlit · debugging-streamlit verified and signed
Debug Streamlit frontend and backend changes using make debug with hot-reload. Use when testing code changes, investigating bugs, checking UI behavior, or needing screenshots of the running app.
model-recommendation · model-recommendation verified and signed
Analyze chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency
debug · debug verified and signed
Structured debugging session — reproduce, isolate, diagnose, and fix. Trigger with an error message or stack trace, "this works in staging but not prod", "something broke after the deploy", or when behavior diverges from expected and the cause isn't obvious.
aoti-debug · aoti-debug verified and signed
Debug AOTInductor (AOTI) errors and crashes. Use when encountering AOTI segfaults, device mismatch errors, constant loading failures, or runtime errors from aot_compile, aot_load, aoti_compile_and_package, or aoti_load_package.
workers-ai-search-model · Cloudflare API verified and signed
Model Search
· Tensorleap website read, tools not yet checked
Find failures and edge cases in the neural networks behind physical AI, see why they happen, get the right data or model fix, and monitor in production.
insforge-debug · insforge-debug verified and signed
Use when diagnosing problems in an InsForge project — reactive failures (SDK error object, HTTP 4xx/5xx, gateway timeout 502/503/504, edge function failure or timeout, login/OAuth/auth errors, RLS denial, realtime channel issues, slow query on one endpoint, edge function or Vercel deploy failure), p
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.