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name: token-usage description: > Nested swiss-knife reference for token usage, cost, cache, and tool-call/API-call reports. Use for model cost reports, cache rates, budget/burn analysis, and tools-per-API-call trends across LingTai logs. version: 2.1.0 tags: [python, cost, tokens, litellm, budget, tools, agents] last_changed_at: "2026-07-18T00:00:00Z" maintenance: "If you find stale or incorrect information here, use the lingtai-issue-report skill to assemble evidence and obtain per-issue human consent before filing an issue. Never include secrets, credentials, tokens, or private paths."

Token Usage, Cost, and Agentic-Intensity Reports v2

Network-wide token cost analysis powered by litellm's model pricing database (2700+ models), plus runtime-event trend analysis for tool calls per API call.

Quick Usage

Run the bundled cost script:

~/.lingtai-tui/runtime/venv/bin/python3 ~/.lingtai-tui/utilities/swiss-knife/reference/token-usage/scripts/cost_report.py /path/to/.lingtai

Cost-report optional flags:

• --json — output as JSON instead of table
• --by-model — group by model instead of by agent
• --since YYYY-MM-DD — only count entries after this date
• --top N — show only top N agents (default: all)
• --custom-pricing FILE — load custom pricing overrides from JSON

Tool-call/API-call trend

Use this when the human asks for “tool calls per API call”, “tools per API call”, “agentic intensity”, or how tool-heavy the local LingTai network has been over time. The script reads LingTai logs/events.jsonl files, not token_ledger.jsonl, because it needs runtime event IDs.

~/.lingtai-tui/runtime/venv/bin/python3 ~/.lingtai-tui/utilities/swiss-knife/reference/token-usage/scripts/tool_calls_per_api_call_trend.py \
  /path/to/project/.lingtai \
  --days 5 \
  --timezone America/Los_Angeles \
  --model gpt-5.5

Optional flags:

• --out-prefix PREFIX — write PREFIX.md, PREFIX.json, and PREFIX.csv
• --model MODEL — also emit a model-specific table for an exact model name; repeatable
• --include-daemons — include daemon event logs instead of excluding them

Metric definitions:

• API calls: unique successful LLM response events (type == "llm_response"), deduplicated by (events log path, api_call_id).
• Tool calls: unique model-requested top-level tool calls (type == "tool_call_received"), deduplicated by (events log path, api_call_id, tool_call_id) and assigned to the local-day bin of the producing API response.
• Tool calls per API call: tool_calls / api_calls.
• Tool-using API-call rate: fraction of API calls that produced at least one tool call.

Treat the metric as behavioral intensity, not token volume: a higher value means each model API call is asking the runtime to do more tool work on average.

How It Works

cost_report.py scans every logs/token_ledger.jsonl under .lingtai/, prices each entry via litellm model_cost → OpenRouter → custom-pricing fallback (see below), and reports per-agent/grand-total cost plus cache-hit and burn rates. tool_calls_per_api_call_trend.py instead scans logs/events.jsonl, counts llm_response / tool_call_received events, and writes Markdown/JSON/CSV daily trend artifacts.

Pricing Sources

Primary: litellm's model_cost dictionary — covers OpenAI, Anthropic, Google, Meta, Mistral, Xiaomi, and 100+ other providers.

Secondary: OpenRouter API — real-time pricing for 368 models, more up-to-date than litellm for some providers.

Fallback: Custom pricing in scripts/custom_pricing.json — for models not yet in litellm or OpenRouter (e.g., MiMo v2.5 Pro direct API).

Override: Pass --custom-pricing FILE to add project-specific pricing.

Custom Pricing Format

{
  "mimo-v2.5-pro": {
    "input_cost_per_token": 0.000001,
    "output_cost_per_token": 0.000003,
    "cache_read_input_token_cost": 0.0000002
  }
}

Ledger Format

Each agent's logs/token_ledger.jsonl has one JSON object per LLM call:

{
  "source": "main",
  "ts": "2026-05-06T19:31:40Z",
  "input": 53295,
  "output": 277,
  "thinking": 172,
  "cached": 12288,
  "model": "mimo-v2.5-pro",
  "endpoint": "https://api.xiaomimimo.com/v1"
}

Dependencies

• cost_report.py requires litellm. Check with python3 -c "import litellm". If missing, ask the human before installing:

  token-usage needs litellm (~5MB) for model pricing. Install it? (pip install litellm)

  Install only after they say yes.

• tool_calls_per_api_call_trend.py uses only the Python standard library.

• Python 3.9+

Tips

• Check after spawning multiple avatars — they each burn their own system prompt
• Cache hit rate is the key efficiency metric — aim for >90%
• Tool calls per API call is a useful agentic-intensity metric: values above 1 mean the average model response asks for more than one tool call
• Daemon tokens are tracked in daemons/<run_id>/logs/token_ledger.jsonl
• For per-session breakdown, use --since to filter by date

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