I built an open source Python framework called VectorWave. It connects your code execution flow directly to a Vector Database (Weaviate).

Instead of dumping inputs/outputs into unstructured text logs, VectorWave embeds them as vectors. This allows you to: 1. Search execution history semantically (e.g., find "bad input" errors without exact keyword matching). 2. Implement semantic caching (skip expensive calls if similar inputs were processed). 3. Enable "Self-Healing" by comparing error traces with past successful runs.

It is designed for AI engineers needing long-term memory for agents, or backend devs debugging complex workflows.

Repo: https://github.com/cozymori/vectorwave Docs: https://cozymori.github.io/vectorwave-docs/

I'd love to hear your feedback on the concept of "Execution RAG".