Prune+Spill

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

## Technique: Prune+Spill
Pi's coding agent cuts context costs 88% by spilling tool output to disk

Drop-in applicable agent context management technique (AlphaSignal signal · 2026-08-25). Most coding agents handle large tool outputs by truncation — permanently deleting the middle. Pi's prune+spill fix: full output saves to disk, context holds only a compact file path reference (18 tokens), model greps back on demand. Results from 19 real sessions: 26–35% context reduction, up to 88% cost savings with zero information loss. Secondary finding: model+harness is one unit — Claude behaves differently outside Claude Code because it was trained on Claude Code's specific tool shapes; evaluate in production harness, not raw API. Directly applicable to any production coding agent, RAG orchestration chain, or multi-step tool-using system.

What the reference demo shows: Gradio demo with three sub-tabs — (1) Strategy Comparison: truncation vs prune+spill side-by-side on a 3,847-token pytest output with 2 FAILED tests buried in the middle, showing the permanent information loss; (2) Cost Calculator: adjustable session length, tool output size, model price → per-session cost chart comparing truncation vs prune+spill; (3) Production Guide: drop-in implementation pattern, when-to-apply table, intent log pattern for crash recovery.

## Reference implementation (Vector Ridge Labs, POC-024)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-024
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/pi_prune_spill
Original source: AlphaSignal signal · 2026-08-25

## 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 latency, throughput and cost per request at equal output quality, 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.