As a beginner learning to build ML models, I found it annoying to have to keep printing tensor shapes every other line, having to step through the debugger to check where did I mess up the shapes again.

So I built Trickle, it takes the data that flows through your code, caches the types and display them inline (as if you have type annotations).

The idea is: "Let types trickle from runtime into your IDE". You get types in Python without having the write them manually.

It works by rewriting your Python AST at import time — after every variable assignment, it inserts a lightweight call that records the type and value. No decorators, no code changes. Just run your script through trickle run python train.py and every variable gets its type visible.

One cool feature is Error snapshots, by toggling it in VSCode status bar, you can see the exact data that is in each variable when the error happened.

For AI agents, trickle-cli outputs the inline runtime types together with the source code so agent can better diagnose the issue.

For Jupyter notebooks: %load_ext trickle as your first cell, and every cell after is traced.

Quick try: pip install trickle-observe npm install -g trickle-cli code --install-extension yiheinchai.trickle-vscode

   trickle run python demo/demo.py
   trickle hints demo/demo.py

Limitations: - Expect 2-5x slowdown — designed for development, not production.

Also supports JavaScript/TypeScript (Express, Fastify, Koa, Hono) though the Python/ML side is where I've focused most effort.

In the future, I imagine there to be potential for using this as runtime observability in production via probabilistic sampling of types. Now, we know the code, we know the data, which is all the information we need to debug.

Happy to answer questions