panthrocorp-token-usage（panthrocorp 代币使用）

Token Usage

Analyse token usage and estimated costs across all registered OpenClaw agents. Dynamically discovers agents, groups sessions by channel category, flags anomalies, and presents a per-agent breakdown with combined totals.

Step 1: Discover agents

List the contents of ~/.openclaw/agents/. Each subdirectory is an agent.

For each agent directory, attempt to read sessions/sessions.json. If the file does not exist, is empty, or is not valid JSON, skip that agent and note it as "(no session data)" in the output.

Step 2: Parse session entries

Each sessions.json is a JSON object. The keys are session key strings with the format:

agent:<agentId>:<channel>:<subtype>[:<identifier>]

For each session entry, extract these fields (all may be absent):

The category is the third segment of the session key (index 2 when splitting on :).

Examples:

• agent:alice:discord:channel:123456789 -> category = discord
• agent:alice:telegram:direct:987654321 -> category = telegram
• agent:bob:cron:xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx -> category = cron
• agent:alice:main -> category = main

Step 3: Apply time window filter

The user specifies a time window. If not specified, default to "this week".

If updatedAt is absent or 0, include the session only when the window is "all time". For all other windows, exclude it.

Convert updatedAt from epoch milliseconds to a date for comparison.

Step 4: Aggregate per agent, per category

For each agent, group the filtered sessions by category. Within each group, compute:

• Total tokens: sum of totalTokens (skip entries where the field is absent)
• Input tokens: sum of inputTokens (skip entries where the field is absent)
• Output tokens: sum of outputTokens (skip entries where the field is absent)
• Estimated cost: sum of estimatedCostUsd (skip entries where the field is absent)
• Session count: number of sessions in the group
• Models: the set of distinct model values (skip entries where the field is absent)

Step 5: Flag anomalies

Apply two flags to each category group:

SINKHOLE: totalTokens > 10,000 AND (estimatedCostUsd is absent across all entries in the group, or the summed cost is less than $0.01). This indicates high token volume on free or untracked models and may signal wasted computation.

EXPENSIVE: summed estimatedCostUsd > $5.00. This indicates a high-spend category. Review whether the spend is justified.

A single category can have both flags (high tokens on a mix of free and paid models).

Step 6: Present results

Per-agent sections

For each agent that has session data, print a heading with the agent name and total session count, then a table with one row per category:

## alice (120 sessions)

| Category | Sessions | Tokens | Input | Output | Est. Cost | Models | Flags |
|----------|----------|--------|-------|--------|-----------|--------|-------|
| discord  | 80       | 1.2M   | 800K  | 400K   | $42.50    | model-a | EXPENSIVE |
| telegram | 25       | 120K   | 80K   | 40K    | $3.20     | model-a | |
| cron     | 10       | 95K    | 60K   | 35K    | $0.00     | model-b | SINKHOLE |
| main     | 5        | 20K    | 15K   | 5K     | $0.32     | model-a | |

Sort rows by estimated cost descending within each agent.

For agents skipped in Step 1, print: ## <agent> (no session data)

Combined summary

After all agent sections, print:

## Combined Totals

- **Total sessions**: <count>
- **Total tokens**: <sum> (input: <sum>, output: <sum>)
- **Total estimated cost**: $<sum>

### Top 3 by cost
1. <agent>/<category>: $<cost> (<tokens> tokens)
2. ...
3. ...

### Top 3 by token volume
1. <agent>/<category>: <tokens> tokens ($<cost>)
2. ...
3. ...

Formatting rules

• Token counts: use human-readable format (e.g. 1.2M, 45K, 320)
• Costs: round to 2 decimal places, prefix with $
• If a summed value is zero because all entries were absent, display as - not 0

Step 7: Persist structured history

Append exactly one JSON line to:

~/.openclaw/workspace/memory/token-usage-history.ndjson

Create the file if it does not exist. Never modify existing lines.

This step always runs. It does not require explicit user request.

Record structure

The line is a compact JSON object (no newlines within the record):

{"date":"YYYY-MM-DD","window":"<time window>","runAt":<epoch ms>,"agents":[...],"combined":{...}}

Each agent entry:

{
  "id": "<agentId>",
  "categories": [
    {
      "category": "<category>",
      "sessions": 80,
      "totalTokens": 1200000,
      "inputTokens": 800000,
      "outputTokens": 400000,
      "estimatedCostUsd": 42.50,
      "models": ["model-a"],
      "flags": ["EXPENSIVE"]
    }
  ],
  "totals": {
    "sessions": 120,
    "totalTokens": 1435000,
    "inputTokens": 955000,
    "outputTokens": 480000,
    "estimatedCostUsd": 46.02
  }
}

The combined object mirrors the totals shape without an id field.

Use null for any numeric field where all entries in the group had the field absent. Use [] for flags when no flags apply. Agents with no session data are omitted from the record entirely.

Step 8: Optional log write

Only if the user explicitly requests logging, append a timestamped summary to:

~/.openclaw/workspace/memory/token-diet-log.md

Create the file if it does not exist.

Append in this format:

## YYYY-MM-DD -- Token Usage Report (window: <time window>)

- Total: <tokens> tokens, $<cost> estimated
- <agent>: <tokens> tokens, $<cost> (<flags if any>)
- ...
- Anomalies: <comma-separated list of flagged agent/category pairs with flag name>

Do not write to this file unless the user explicitly asks.

Permissions

No environment variables. No network access. No credentials.