🧪 Constantly reduce the token usage of the Hermes system

Last updated: 7/20/2026, 1:43:33 AM | Total tasks: 20 | KPIs: 5
📊
test-agent tokens per session (state.db, latest session)
53858 tokens ↑
Source: state.db — session cron_562c14e4ebd4_20260720_014043 · Jul 20 08:43Z
Updated 7/20/2026, 1:43:33 AM
📈
test-agent tokens (3-session rolling avg)
38503 tokens ↑
Source: state.db — rolling avg
Updated 7/20/2026, 1:43:33 AM
📐
Loop output verbosity ratio (chars/tiktoken)
3.24 chars/tiktoken →
Source: 28-loop average from milestr/docs/K3-baseline.json (chars per tiktoken in loop output file)
Updated 7/19/2026, 1:22:00 AM
📐
Loop output verbosity (chars/tiktoken in .md file)
3.24 chars/tiktoken →
Source: 28-loop average from milestr/docs/K3-baseline.json
Updated 7/19/2026, 1:22:00 AM
⚠️
Heartbeat runs since last token-reduction action
1 runs ↑
Source: Counted from memory/loops/*.md — last concrete action was I5 state.db migration (Jul 19)
Updated 7/19/2026, 10:13:33 PM

Initiatives & Tasks

📋 I1 Instrument token measurement for agent invocations done 100% ROOT
📋 I2 Establish baseline token usage across agents done 100% ROOT
📋 I3 Identify and optimize high-token prompts and tool usage done 93% ROOT
📋 I4 Implement token-saving practices and monitor impact done 91% ROOT
📋 I5 Migrate K1 to state.db ground-truth measurement done 100% ROOT
📋 I6 Normalize K1: tokens-per-loop-output-byte ratio done 100% ROOT
🔧 I5.1 Build state-db-query.py for ground-truth K1 done 100% I5
🔌 I5.2 Wire state-db-query.py into loop report generator done 100% I5
✅ I5.3 5-loop K1 migration validation (old tiktoken vs new state.db) done 100% I5
📝 I6.1 Document K3 metric semantics + drift threshold done 100% I6
📊 I6.2 Build 30-loop K3 ratio baseline (chars/tiktoken) done 100% I6
🤔 I6.3 Decide: drop K1 tiktoken-of-md or keep as verbosity sub-metric? done 100% I6
🪝 A1 Deploy global post_api_request hook for per-agent token tracking done 100% ROOT
🔍 A2 Identify top-3 token-burning agents via agent_tokens.json not_started 0% ROOT
⏱️ A3 Reduce heartbeat frequency from 60m to 15m or disable done 100% ROOT
✂️ A4 Audit + trim system prompt files for token waste done 100% ROOT
🤖 A5 Implement script-only heartbeat (no LLM) for maintenance monitoring ongoing 75% ROOT
🛡️ A6 Fix stale KPI problem: K1 source session ID + timestamp required ongoing 75% ROOT
📊 A7 Find and fix the most recent token reduction with before/after evidence done 100% ROOT

not started (1)

🔍

Identify top-3 token-burning agents via agent_tokens.json

A2 | ROOT

analyzing (0)

ongoing (3)

🧪

Constantly reduce the token usage of the Hermes system

ROOT |
🤖

Implement script-only heartbeat (no LLM) for maintenance monitoring

A5 | ROOT
🛡️

Fix stale KPI problem: K1 source session ID + timestamp required

A6 | ROOT

done (16)

📋

Instrument token measurement for agent invocations

I1 | ROOT
📋

Establish baseline token usage across agents

I2 | ROOT
📋

Identify and optimize high-token prompts and tool usage

I3 | ROOT
📋

Implement token-saving practices and monitor impact

I4 | ROOT
📋

Migrate K1 to state.db ground-truth measurement

I5 | ROOT
📋

Normalize K1: tokens-per-loop-output-byte ratio

I6 | ROOT
🔧

Build state-db-query.py for ground-truth K1

I5.1 | I5
🔌

Wire state-db-query.py into loop report generator

I5.2 | I5
✅

5-loop K1 migration validation (old tiktoken vs new state.db)

I5.3 | I5
📝

Document K3 metric semantics + drift threshold

I6.1 | I6
📊

Build 30-loop K3 ratio baseline (chars/tiktoken)

I6.2 | I6
🤔

Decide: drop K1 tiktoken-of-md or keep as verbosity sub-metric?

I6.3 | I6
🪝

Deploy global post_api_request hook for per-agent token tracking

A1 | ROOT
⏱️

Reduce heartbeat frequency from 60m to 15m or disable

A3 | ROOT
✂️

Audit + trim system prompt files for token waste

A4 | ROOT
📊

Find and fix the most recent token reduction with before/after evidence

A7 | ROOT

blocked (0)

📋 I1 Instrument token measurement for agent invocations done 100% ROOT
📋 I2 Establish baseline token usage across agents done 100% ROOT
📋 I3 Identify and optimize high-token prompts and tool usage done 93% ROOT
📋 I4 Implement token-saving practices and monitor impact done 91% ROOT
📋 I5 Migrate K1 to state.db ground-truth measurement done 100% ROOT
📋 I6 Normalize K1: tokens-per-loop-output-byte ratio done 100% ROOT
🔧 I5.1 Build state-db-query.py for ground-truth K1 done 100% I5
🔌 I5.2 Wire state-db-query.py into loop report generator done 100% I5
✅ I5.3 5-loop K1 migration validation (old tiktoken vs new state.db) done 100% I5
📝 I6.1 Document K3 metric semantics + drift threshold done 100% I6
📊 I6.2 Build 30-loop K3 ratio baseline (chars/tiktoken) done 100% I6
🤔 I6.3 Decide: drop K1 tiktoken-of-md or keep as verbosity sub-metric? done 100% I6
🪝 A1 Deploy global post_api_request hook for per-agent token tracking done 100% ROOT
🔍 A2 Identify top-3 token-burning agents via agent_tokens.json not_started 0% ROOT
⏱️ A3 Reduce heartbeat frequency from 60m to 15m or disable done 100% ROOT
✂️ A4 Audit + trim system prompt files for token waste done 100% ROOT
🤖 A5 Implement script-only heartbeat (no LLM) for maintenance monitoring ongoing 75% ROOT
🛡️ A6 Fix stale KPI problem: K1 source session ID + timestamp required ongoing 75% ROOT
📊 A7 Find and fix the most recent token reduction with before/after evidence done 100% ROOT