🧪 Constantly reduce the token usage of the Hermes system

Last updated: 7/19/2026, 8:00:50 AM | Total tasks: 13 | KPIs: 3
📊
test-agent tokens per heartbeat loop (state.db ground truth)
1298 tokens ↓
Source: state.db — cron_3c4b5e1c2b99_20260719_073443 · Jul 19 07:34
Updated 7/19/2026, 8:00:50 AM
📈
test-agent tokens (3-loop rolling avg, state.db)
41741 tokens ↓
Source: state.db 3-session rolling avg (updated to state.db query)
Updated 7/19/2026, 7:30:00 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

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 ongoing 93% ROOT
📋 I4 Implement token-saving practices and monitor impact ongoing 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

not started (0)

analyzing (0)

ongoing (3)

🧪

Constantly reduce the token usage of the Hermes system

ROOT |
📋

Identify and optimize high-token prompts and tool usage

I3 | ROOT
📋

Implement token-saving practices and monitor impact

I4 | ROOT

done (10)

📋

Instrument token measurement for agent invocations

I1 | ROOT
📋

Establish baseline token usage across agents

I2 | 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

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 ongoing 93% ROOT
📋 I4 Implement token-saving practices and monitor impact ongoing 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