From e06249569fdf1472c4e888922df222490c32d640 Mon Sep 17 00:00:00 2001
From: Leonard <leonard@hermes.local>
Date: Thu, 18 Jun 2026 05:28:43 +0000
Subject: [PATCH] Added Headroom evaluation summary + densification
 implementation notes. Cherry-picked the useful lossless densification for
 search_files.

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+# Headroom Evaluation & Densification Implementation for Hermes
+
+**Source**: X post by @teknium (status 2067292705710031117) + detailed Hermes Agent self-evaluation.
+**Date**: 2026-06 (approx from context)
+**Status**: Partial integration - densification cherry-picked; CCR rejected.
+
+## TL;DR
+Headroom (github.com/chopratejas/headroom) is a token compression proxy claiming 60-95% savings. 
+For Hermes agent workloads (search_files JSON, multi-turn tool use, persistent context):
+- CCR (remove + <<marker>> + retrieve) is **net-negative** (duplication + cache disruption).
+- Lossless densification is **useful** (~60% on search_files per original eval).
+
+**Action taken**: Implemented internal lossless densifier for search_files outputs (no external dep).
+
+## Key Implementation
+File: `~/.hermes/profiles/leonard/scripts/densify_search_results.py`
+
+```python
+def densify_search_results(result: dict) -> str:
+    # Converts 
+    # {"total_count": N, "matches": [{"path":.., "line":.., "content":..}, ...]}
+    # to
+    # total:N
+    # path|line|content
+    # ./file.py|10|code here
+```
+
+- Lossless (roundtrip parser included).
+- Escapes | and newlines.
+- Handles truncated, target fields.
+
+## Measured Benefits (tests)
+- Small sample (6 matches): ~20% char savings.
+- Larger realistic (36 matches, long snippets): ~18% savings.
+- Expected higher on pure location searches or many short results (removes repeated JSON keys like "path","line","content" 100+ times).
+
+Rough token impact: 15-30%+ reduction on search_files heavy paths (depends on content length vs structure).
+
+## Why not full Headroom
+- CCR causes agents to re-retrieve → pay twice.
+- Breaks KV/prompt caching.
+- Conflicts with Hermes existing compression/memory.
+- Adds bloat/latency for our loop-heavy use.
+
+## Replicable Ideas
+- Apply similar densification to other JSON tools (mcp_gitea responses, terminal structured output).
+- For search_files specifically: group by file or use minimal schema when context=0.
+
+## Files
+- Densifier: `/home/vpsadmin/.hermes/profiles/leonard/scripts/densify_search_results.py`
+- Usage: After any `search_files(...)` call, `densify_search_results(result)` before injecting to context.
+
+## Next
+- Test in live multi-turn sessions.
+- Add to agent tool response post-processing (optional flag).
+- Extend to other high-volume tools.
+- Monitor real token usage in daily briefs / opportunity scans.
+
+See full evaluation in conversation history for details.