openclaw-usage-tracker

name	openclaw-usage-tracker
description	Track and report OpenClaw model usage and costs. Generate daily/weekly cost reports with per-model token breakdowns, interactive vs cron classification, provider summary, and trend analysis.
metadata	{"version":"1.2.0"}

OpenClaw Usage Tracker

Scan OpenClaw session transcripts (*.jsonl) to report token usage and estimated costs.

How It Works

Each assistant message in ~/.openclaw/agents/<agent>/sessions/*.jsonl carries a usage object (input, output, cache read, cache write). Cost comes from the provider API response (usage.cost.total) or is estimated via per-model pricing in openclaw.json.

Usage

# Yesterday (default)
python3 scripts/daily-cost-report.py

# Specific date
python3 scripts/daily-cost-report.py 2026-03-14

# Date range (逐日明细 + 汇总)
python3 scripts/daily-cost-report.py 2026-03-10 2026-03-15

# All history
python3 scripts/daily-cost-report.py --all

# Include top N most expensive sessions
python3 scripts/daily-cost-report.py 2026-03-14 --top-sessions 10

# Combine: range + top sessions
python3 scripts/daily-cost-report.py --all --top-sessions 5

Output Schema

JSON output. Structure adapts to mode:

• Single day → {date, total, categories, providers, models, topSessions?}
• Range/all → {range: {from, to}, total, daily: [{date, ...}], stats, categories, providers, models, topSessions?}

total / each daily entry / each provider / each model / each topSession includes:

• cost, entries, tokens/tokens_fmt
• input/input_fmt, output/output_fmt, cacheRead/cacheRead_fmt, cacheWrite/cacheWrite_fmt
• pct_cost, pct_tokens (share within the report scope)

models[] uses a display-safe name that includes provider + model:

• Example: astralor/Opus-4.6, gptclub/GPT-5.4, minimax/M2.5

Daily Report Template (Discord)

Goal: clear view (token + money), not maximum density.

💰 {date} 费用日报

总计
  费用  ${total.cost}
  调用  {total.entries} 次
  Token {total.tokens_fmt}
    In {total.input_fmt} · Out {total.output_fmt}
    Cache Read {total.cacheRead_fmt} · Write {total.cacheWrite_fmt}

分类
  💬 对话   ${interactive.cost} ({interactive.pct_cost}%) · {interactive.tokens_fmt} ({interactive.pct_tokens}%)
  ⏰ Cron   ${cron.cost} ({cron.pct_cost}%) · {cron.tokens_fmt} ({cron.pct_tokens}%)
  💓 心跳   ${heartbeat.cost} ({heartbeat.pct_cost}%) · {heartbeat.tokens_fmt} ({heartbeat.pct_tokens}%)

供应商（按费用）
  {provider.name}  ${provider.cost} ({provider.pct_cost}%) · {provider.tokens_fmt} ({provider.pct_tokens}%)

模型（按费用）
  {model.name}  ${model.cost} ({model.pct_cost}%) · {model.tokens_fmt} ({model.pct_tokens}%) · {model.entries} 次
    In {model.input_fmt} · Out {model.output_fmt} · CR {model.cacheRead_fmt} · CW {model.cacheWrite_fmt}

趋势（近 7 天）
  日均 ${avg_cost} · {avg_tokens}
  本日 ${today_cost}（较日均 {delta_vs_avg}，较昨日 {delta_vs_prev}）
  峰值 {max_day} ${max_cost} · 低谷 {min_day} ${min_cost}

Skip categories with 0 entries. Skip models/providers with $0.00 and 0 entries.

Daily Cron Setup

Recommended: isolated cron, delivery=none (agent sends via message tool).

Daily job should generate a report for yesterday plus a short 7-day trend summary:

• Run once for yesterday (no args) → build the main report
• Run once for last 7 days (yesterday-6 .. yesterday) → compute avg/max/min + deltas

Session Classification

1. Session key — cron: → cron, heartbeat → heartbeat, else → interactive
2. Content fallback — orphaned sessions checked for [cron:...] prefix in the first user message

Cost Calculation

1. Provider-returned usage.cost.total (priority)
2. Estimated: (input × cost.input + output × cost.output + cacheRead × cost.cacheRead + cacheWrite × cost.cacheWrite) / 1M

Pricing: models.providers.<provider>.models[].cost in openclaw.json ($/M tokens).

Notes

• Scans all agent directories (~/.openclaw/agents/*/sessions/)
• Filters out delivery-mirror and acp-runtime in display output
• Cache Read typically dominates total tokens due to prompt caching