Jev Memory Compaction

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: Jev Memory Compaction
TypesafeAI's Jev speeds up memory compaction by scoring and dropping irrelevant tool calls

Jev (TypesafeAI) introduces a scoring-based memory compaction layer for agent context windows: each tool call in the agent's history is assigned a relevance score (recency × importance × task_alignment), and low-scoring calls are dropped to compact context before the next LLM call. The open-source Browser Use + Jev demo finds flights in 7s at $0.0039 per query by reading pages as a numbered structured element table instead of screenshots — one AI call selects action AND target simultaneously, reducing browser calls from 1,092 to 101 (90% reduction, 25% speed improvement). Directly applicable to any agent pipeline where tool-call history bloats the context window.

What the reference demo shows: 4-sub-tab Gradio demo — (1) Problem Overview: context window bloat from tool calls in long agent sessions; (2) Scoring Mechanism: relevance score = recency × importance × task_alignment with interactive sliders; (3) Live Compaction Demo: input a list of tool calls, see scored and compacted output; (4) Implementation Guide: 4 drop-in patterns from passive logging to active compaction.

## Reference implementation (Vector Ridge Labs, POC-038)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-038
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/jev_memory
Original source: AlphaSignal Top Repo + Signal · 2026-09-18 · 4,044 likes (Signal) + 7,129 likes (Top Repo)

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures task success rate, number of steps and token cost per task, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.