agentburn

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

uvx agentburn

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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, and how long until the wall
uvx agentburn context    # what long contexts cost — and what a /clear at 150k would have saved

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 — automatically. Anthropic doesn't publish the formula behind those allowances, so agentburn refuses to invent a threshold. But Claude Code writes the cut-off into the transcript itself ("You've hit your session limit · resets 8:30pm"), and every one of those moments is a measured ceiling. With several, the ceiling is their median:

  YOUR MEASURED CEILING
  median of 35 cut-offs Claude Code recorded itself
  ceiling                146M   weighted tokens
  peak window            137%   of your ceiling
  last 5h                 16%   of your ceiling
  TIME TO WALL          2.7 h   at the pace of the last 30 min
  

  No cut-off in your logs yet? --hit "2026-08-20 14:30" names one by hand. A measured ceiling is remembered in ~/.agentburn/ceiling.json, so the status line below knows it too.

• Codex: the provider's own reading. Codex CLI writes rate_limits.used_percent next to every request. agentburn pairs each reading with your weighted usage of the same window and takes the median — a ceiling from the provider's arithmetic, not from a cut-off. Treat it as an estimate: that percentage counts every device and app on the account, while your local rollouts are only part of it — and when Codex stops reporting a window (plan or client change), a later peak is flagged as measured on earlier windows, not sold as an overrun.

• Time to wall. Ceiling minus the current window, divided by the pace of the last half hour. The number you actually want while working.

• The week, too. The heaviest rolling 7-day span, how much of it this week already is, and a weekly ceiling when Claude Code recorded a weekly cut-off.

• By project. Sessions record their working directory; the peak window is split by it.

agentburn statusline — the wall, live, inside Claude Code

One line, no colour, built for Claude Code's statusLine:

⏳ 5h 63% · wall in 47 min · week 71%

{ "statusLine": { "type": "command", "command": "uvx agentburn statusline" } }

Reads only the last three days of logs (the ceiling comes from the state file), so it stays cheap enough to run on every turn.

agentburn context — what a long context costs

Every call re-reads its whole context, and on a subscription that re-reading is the window: a turn at 300k costs what three turns at 100k cost. Claude Code records the exact context size of every call, so this is measured, not modelled:

📏 agentburn context — claude-code · what a long context costs

   CALLS                        156,226   median context 143K · p90 316K · max 704K

   WHERE THE WINDOW GOES, BY CONTEXT SIZE
   100–200k     ██████············   35%    59,780 calls
   200–400k     ████████··········   43%    42,420 calls
   >400k        ██················   11%     7,257 calls

   IF YOU HAD RESTARTED AT…
   /clear at 100K     →   41% of the window not spent   (108,573 calls were past it)
   /clear at 150K     →   26% of the window not spent   (73,600 calls were past it)

   WHAT A SKILL COSTS
   handoff                                 7.96K per load ×  226 =     1.8M
   claude-api                              33.6K per load ×   14 =     470K

• The /clear arithmetic — the part of every call's context above a threshold, at the cache-read rate: the honest saving of a restart habit, assuming the same work in shorter sessions.
• Skill costs, measured — the context growth right after a lone Skill call, median of recent loads. Bundled skills never touch the disk; the transcript sees all of them.
• By effort level — how much of the window each effort setting took.
• Findings with a lever land in agentburn fix: the restart threshold, and the heavy skills.

agentburn commits — what a commit cost you

Sessions record their working directory and branch; your repositories record when each commit landed. The usage between two consecutive commits is what the second one cost — read-only git log, nothing written:

   COSTLIEST COMMITS
       124M   33_Thoforge        1f7a31a1  Aug 30  fix(ui): правки UX-аудита — раскладка, навигация
      81.2M   33_Thoforge        ad19bff7  Aug 28  feat(ui): цель над деревом и развилка в карточке

   BY REPOSITORY
   33_Thoforge                 1.95M median ·  287 commits ·    1.52B total

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.
• One reply is counted once. Claude Code writes one transcript line per content block, each carrying the same usage; summing lines inflates calls and tokens ~1.8×. agentburn deduplicates by requestId (found and fixed in 0.14.0 — earlier absolute totals from this tool were inflated by that factor; ratios were not).
• 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 — PRs for the next one welcome.

Everything else

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

🔥 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

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

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

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}