agentburn

Claude Code · OpenClaw · Hermes Agent — one normalized core, local, read-only, zero dependencies

uvx agentburn

▶  Try it in your browser — no install

You didn't run out on your average day

You ran out inside one window. On this machine that window was 5.4× the median one — same person, same week, same subscription.

Your assistant's own logs already know which window it was and what filled it. Nothing else on your machine does: the built-in counter shows a total, your invoice shows a total, and neither says which five hours took you out.

⏳ agentburn limits — claude-code · rolling 5-hour windows

   PEAK WINDOW        Aug 04 12:45–17:45 · 555M weighted
                      opus 91% · sonnet 9%   ·   cli 93% · subagent 7%
   TYPICAL WINDOW     104M    median of 83 active 5h slots
   PEAK / TYPICAL     5.4×    a wall is hit by the peak, not by the median

   WHAT FILLS THE WINDOW
   cache reads     64%   ·   cache writes 25%   ·   output 11%

One command, no account, nothing leaves your computer:

uvx agentburn            # where it burns, and what to change
uvx agentburn limits     # how fast you fill a usage window

Related MCP server: AgentCost

Two ways agents cost you, two questions

Both read the same local logs. Neither invents a number the data doesn't contain.

agentburn limits — the subscription view

Optimizing a subscription doesn't change your bill. It changes how far you get before you're cut off. That is a window problem, and windows need intra-session resolution — a single session routinely spans several of them.

• Peak vs typical. Your worst rolling 5-hour window against the median of your own active ones. The ratio is the finding: a wall is hit by the peak.

• What filled it — by model, by source (you / subagents / scheduled work), and by kind (cache reads vs cache writes vs output).

• Measured against your own wall. Anthropic doesn't publish the formula behind those allowances, so agentburn refuses to invent a threshold. Tell it when you were actually cut off and the arithmetic becomes yours:

  agentburn limits --hit "2026-08-20 14:30"
  #   ceiling         38.4M weighted tokens   ← measured from your own cut-off
  #   peak window       107% of your ceiling
  #   last 5h            12% of your ceiling

Weighted tokens = tokens × published price ratios (cache read 0.1×, cache write 1.25×, output per model), normalized to one input token of the reference model. Every ratio is public; none of them is a guess about how the provider counts.

agentburn — the money view

• Where it burns — by source: cron / subagent / gateway:telegram|discord|whatsapp / cli. Always-on ≠ free.

• 🌙 While you slept — the overnight bill, isolated and named (--night 23-7).

• Fixed overhead — uncached input tokens per API call, per source, calibrated against a public benchmark.

• Subagent rollups — delegation cost chained back to the session that spawned it.

• agentburn why — behavioral forensics: re-read loops, retry storms, idle heartbeats, per-cron receipts, context thrash.

• agentburn fix — ready-to-paste config patches, dry-run by design.

agentburn fix — findings become config, not advice

Not "consider a cheaper model" but the exact file and the exact lines. Patch generators exist only for levers verified against the agent's own source or documented configuration:

🔧 agentburn fix — claude-code · DRY-RUN (nothing was changed)

   1. Drop 2 MCP server(s) you never called
      why    : registered but not called once in the last 30d: blender-mcp, pixellab.
               Every registered server ships its tool definitions with the context
               of every session that loads it.
      proposed:
        claude mcp remove blender-mcp

   2. Trim the always-loaded memory files (2,254 tokens)
      why    : loaded into every session's context and re-sent whenever the prompt
               cache expires or the context is compacted — at least 3,565× this window.

There is no --apply on purpose: it's your agent's config. Paste it yourself, then prove the saving with --save-baseline → --compare.

Why trust these numbers

Token trackers quietly disagree with each other (2–91× in public issue threads). agentburn takes the opposite stance:

• Numbers come from the agent's own accounting, read-only. No scraping, no proxies, no guessing.

• Provider-billed costs are shown as-is; estimates are marked ~; mixed data is labeled mixed.

• Where a price doesn't exist, none is invented. Claude Code records no costs and subscription usage has no honest per-token price — so that adapter reports tokens and windows, never dollars.

• Sessions with messages but zero recorded tokens (known accounting gaps, e.g. hermes-agent #12023) are detected: totals become an explicit lower bound, and fixing the accounting becomes recommendation #1.

• Result weights on agents that don't record them are labeled estimates, and only ever used to rank findings against each other.

Speed

Transcripts are append-only, so they are parsed once. Each file's parse is cached under its size and mtime in ~/.agentburn/cache, and a run reuses every file that hasn't changed:

A file that grew is re-parsed and re-cached; nothing else is touched. --no-cache (or AGENTBURN_NO_CACHE=1) forces a full re-parse, --clear-cache deletes it. The cache is derived data — deleting it costs time, nothing else.

Privacy

Everything runs locally and reads your logs read-only. No network calls, no telemetry, no accounts. The report is yours. The only commands that touch the network say so: drift GETs a public trends file, --submit opens a prefilled issue you review and send.

The parse cache in ~/.agentburn/cache (mode 0700) holds the same tool names and truncated argument keys the reports show, derived from logs already on this machine — never message content. --clear-cache removes it.

Why this exists

Always-on agents bill you around the clock — and their built-in counters only show totals:

"73% of every API call is fixed overhead — ~13.9K tokens of tool definitions and system prompt, resent every time." — hermes-agent #4379

"One entrant wrote about waking up to a $47 surprise bill from an overnight run — that's not an exotic failure, it's the default behavior of an unsupervised loop." — dev.to

How it compares

ccusage and codeburn are excellent at what they do — agentburn deliberately starts where they stop (ccusage scoped per-tool analysis out).

Supported agents

One normalized model, one adapter per agent. Run agentburn and every agent found on the machine gets its own report.

Adapters are ~150 lines over a shared model. Codex CLI / opencode are natural next targets — PRs welcome.

Everything else

A zero-dependency MCP stdio server exposing burn_report / burn_limits / burn_why / burn_card. Register it and ask "where do you burn my money?" — it profiles its own database and explains.

claude mcp add agentburn -- agentburn mcp
# Hermes / OpenClaw: add an stdio MCP server with command `agentburn mcp`

Prefer skills? There's a ready SKILL.md for ~/.claude/skills/agentburn/ (or the Hermes/OpenClaw equivalents).

Categories, models and totals only; session titles, paths and content are excluded by construction. --svg card.svg renders the same card as an image.

🔥 my claude-code agent · last 30d
3.01B tokens · 19,255 API calls
where it burns: cli 77% · subagent 23%
⏳ my peak 5h window: 555M weighted tokens — 5.4× my own median window
🌙 while I slept (00–08): 75.3M tokens — 3% of everything
— agentburn · local & private

Snapshot your pace, change the config, then agentburn --compare shows the delta — pace-normalized, so a 7-day baseline compares honestly with a 30-day window. Every recommendation becomes a testable promise.

Are you paying for a model the world is leaving? Your side is computed locally; the world side is one read-only GET of token-history's public trend JSON (archived daily from OpenRouter's rankings). Nothing about you is sent anywhere; --trends FILE works fully offline.

agentburn explain --model llama3.1          # local ollama — nothing leaves the machine
agentburn explain --llm https://openrouter.ai/api/v1 \
  --model deepseek/deepseek-chat --yes-remote --lang ru

The default endpoint is localhost; a remote one requires --yes-remote and receives a redacted summary (titles → session-N, paths → basenames, content never present to begin with).

doctor names the broken combinations (provider × model × source) behind zero-usage and unpriced sessions, and generates a ready-to-paste upstream bug report — counters only.

Sentinel mode is a budget guard for server agents:

agentburn --agent openclaw --budget-night 5 --fail-over --no-color \
  || notify-send "🚨 agent is burning money at night"

Anonymous percentiles of efficiency — the benchmark volume-leaderboards can't be: nothing here rewards burning more. Joining is consent-by-click: agentburn --submit prints the exact anonymized payload (ratios and a coarse spend band — never raw volumes, titles or paths), then a prefilled GitHub-issue link that you open and submit. Percentiles need 5+ setups per metric before they mean anything.

Related

token-history — the macro view: daily archive of which agents the world uses. agentburn is the micro view: where yours burns.

License

MIT

mcp-name: io.github.Socialpranker/agentburn

the token-* family · token-history — which agents the world runs · agentburn — where yours burns

if this saved you a window's worth of work, a ⭐ helps the next person find it