agentburn

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<img src="assets/wordmark.svg" alt="agentburn — where does your AI agent burn money, while you sleep?" width="420">

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<a href="https://pypi.org/project/agentburn/"><img alt="PyPI" src="https://img.shields.io/pypi/v/agentburn?color=f7775a"></a>
<img alt="Python" src="https://img.shields.io/badge/python-3.9%2B-5ab0f7">
<img alt="zero deps" src="https://img.shields.io/badge/dependencies-0-7df0a8">
<img alt="offline checks" src="https://img.shields.io/badge/offline_checks-104-c89bf7">
<a href="LICENSE"><img alt="MIT" src="https://img.shields.io/badge/license-MIT-8a949e"></a>

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<img src="assets/demo.svg" alt="uvx agentburn — animated demo: TL;DR verdict, burn bars by source, the overnight bill, and what to change" width="760">

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**[Hermes Agent](#supported-agents) · [OpenClaw](#supported-agents) · [Claude Code](#supported-agents)** — one normalized core, local, read-only, zero dependencies

```
uvx agentburn
```

**[▶ &nbsp;Try it in your browser — no install](https://socialpranker.github.io/agentburn/)**

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---

## What is this, in plain words?

You run an AI assistant — OpenClaw, Hermes Agent, or Claude Code. It works around the clock: answers you in Telegram, runs scheduled jobs at night, spawns helper agents. Every word it reads and writes costs money — and the bill arrives as **one number with no explanation**.

agentburn is a free tool that reads your assistant's own diary (log files already sitting on your computer) and turns that number into answers:

| You ask | It answers | Command |
|---|---|---|
| Where does the money actually go? | scheduled jobs 79% · chats 7% · helpers 5% · you 9% | `agentburn` |
| What happened while I slept? | the overnight bill, isolated and named | `agentburn` |
| Why is it so expensive? | same file read 14×, a broken tool retried 6×, wake-ups that did nothing | `agentburn why` |
| What did it do in Telegram? | every function it called there, with counts and errors | `agentburn why --source telegram` |
| What exactly do I change? | ready config lines + expected saving in dollars | `agentburn fix` |
| Am I paying for a dying model? | your spend vs the world's 4-week trend, with a cheaper rising alternative | `agentburn drift` |
| Can I just ask the assistant? | yes — install the skill/MCP and ask *"where do you burn my money?"* | `agentburn mcp` |

No accounts. No cloud. Nothing leaves your computer. One command to try: `uvx agentburn`.

## Why this exists

Always-on agents bill you around the clock — and their built-in counters only show totals. Real threads that made this tool:

> *"73% of every API call is fixed overhead — ~13.9K tokens of tool definitions and system prompt, resent every time."* — [hermes-agent #4379](https://github.com/NousResearch/hermes-agent/issues/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](https://dev.to/chintanonweb/hermes-agent-gets-smarter-every-day-so-does-the-bill-4i8o)

> *"I've seen runs where step 3 costs 4× step 1 — **no alert, just a bill**."* — [comment, ibid.](https://dev.to/chintanonweb/hermes-agent-gets-smarter-every-day-so-does-the-bill-4i8o)

agentburn reads the agent's **own accounting data** (read-only) and answers the question the totals never do: **where**.

## What it answers

- **Where it burns** — by source: `cron` / `subagent` / `gateway:telegram|discord|whatsapp` / `cli`. Always-on ≠ free: scheduled jobs and gateways spend without you.
- **🌙 While you slept** — the overnight bill, isolated and named (configurable window: `--night 23-7`).
- **Fixed overhead** — average input tokens per API call per source. The "73% overhead" pattern is visible in one glance; with request dumps enabled, you get the sampled composition (system prompt vs tool definitions vs history).
- **Subagent rollups** — delegation cost chained back to the session that spawned it. Recursion compounds; here is the receipt.
- **Top tools** — which tool results weigh most in your context.
- **What to do** — up to 4 conservative, named recommendations with monthly estimates.

## How it compares

|  | **agentburn** | ccusage | codeburn | built-in `/usage` |
|---|---|---|---|---|
| Burn by *source* (cron · heartbeat · gateways · subagents) | ✅ | — | — | % only, 7 days, this machine |
| 🌙 the overnight bill, isolated | ✅ | — | — | — |
| Behavioral forensics (`why`: loops, retry storms, failed-run cost) | ✅ | — | — | — |
| Ready config patches (`fix`, source-verified keys) | ✅ | — | — | — |
| Accounting-gap detection (`doctor`, lower-bound honesty) | ✅ | — | — | — |
| MCP server (agent answers for its own bill) | ✅ | — | — | — |
| Totals / live blocks / many CLIs | basic | ✅ best-in-class | ✅ TUI, 25 providers | totals |

*As of June 2026; ccusage and codeburn are excellent at what they do — agentburn deliberately starts where they stop ([ccusage scoped per-tool analysis out](https://github.com/ryoppippi/ccusage/issues/688)).*

## Why trust these numbers

Most 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** (`~/.hermes/state.db`: per-session token counters and cost fields). No scraping, no proxies, no guessing.
- Provider-billed costs are shown as-is; Hermes estimates are marked with `~`. Mixed data is labeled mixed.
- Sessions with messages but **zero recorded tokens** (known Hermes accounting gaps, e.g. [#12023](https://github.com/NousResearch/hermes-agent/issues/12023)) are detected and reported: totals are then explicitly a **lower bound** — and fixing the accounting becomes recommendation #1.
- Input composition from request dumps is char-proportional and labeled *sampled estimate*, not truth.

## Privacy

Everything runs locally and reads your database **read-only**. No network calls. No telemetry. The report is yours.

## Usage

```bash
agentburn                        # every agent on this machine, last 30 days
agentburn --agent openclaw       # just one
agentburn --days 7
agentburn --agent hermes --db /path/to/state.db
agentburn why                    # behavioral forensics: loops, retry storms, idle heartbeats
agentburn why --source telegram  # decompose ONE source: functions called, errors, loops
agentburn --source cron          # cost report for one source only
agentburn explain --model llama3.1   # LLM reads the numbers back to you (local by default)
agentburn --night 23-7           # custom overnight window (local time)
agentburn --budget-month 50 --fail-over   # sentinel for cron/CI
agentburn --json                 # machine-readable, pipe it anywhere
agentburn --no-color
```

## Mechanics

**📤 Share your burn (`--share`).** An anonymized card — categories, models and totals only; session titles, paths and content are excluded *by construction*. Safe to paste into a post; `--svg card.svg` renders the same card as an image:

```text
🔥 my hermes agent · last 30d
~$45.50 → ~$430/mo pace · 1.75M tokens
where it burns: cron 79% · cli 9% · telegram 7% · subagent 5%
🌙 while I slept (00–08): ~$36.00 — 79% of everything
⚙️ telegram re-sends 20,000 tokens with EVERY call — 2.5× the community norm (≈8k)
— agentburn · local & private
```

`--svg card.svg` renders it as an image:

![sample burn card](assets/card-sample.svg)

**📏 Calibration against public benchmarks.** "Is 15k input tokens per call normal?" The report compares your fixed overhead with community-measured references embedded as dated constants (e.g. the [Phala always-on-agent benchmark](https://phala.com/posts/understanding-openclaws-token-usage), 2026-03: ≈8k/call baseline). No network — sources are cited inline.

**📐 Optimize → prove it (`--save-baseline` / `--compare`).** Snapshot your pace, change the config (cheaper cron model, trimmed toolsets), then `agentburn --compare` shows the delta in $/month — pace-normalized, so a 7-day baseline compares honestly with a 30-day window. Every recommendation becomes a testable promise.

**🔬 `agentburn why` — behavioral forensics.** `report` says *where* it burns; `why` says *why*, from the agent's own recorded actions and thoughts:

```text
🔬 agentburn why — openclaw · gateway:telegram

   WHAT IT ACTUALLY DID   browser 34× ≈210K in results · web_search 18× · shell 7× (2 errors)
   RE-READ LOOPS          5× browser(https://news.site/page) — every repeat re-paid in full
   RETRY STORMS           Bash: 3 errors / 6 calls — paying full price for every error
   IDLE HEARTBEATS        4 of 9 heartbeat runs did NOTHING — $2.40 of pure idle burn
   BURNED ON FAILURES     2 failed runs → ~$3.90 (timeout, killed)
   THINKS MORE THAN IT WORKS   62% thinking · 84K tokens · "rename files task"

   💡 WHAT TO CHANGE
   1. `/proj/big.md` was fetched 4× in one session ≈32K tokens re-paid — cache it…
```

Observations with numbers, not verdicts; only tool names, truncated argument keys and counters — message content never leaves the machine (and never enters the report).

**🧠 `agentburn explain` — LLM interpretation, local-first.** The numbers, read back to you in plain language with ranked actions:

```bash
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 # remote: explicit opt-in only
```

Privacy rules are hard-coded: the default endpoint is localhost (ollama / LM Studio); a remote endpoint requires `--yes-remote` and receives a **redacted** summary only — session titles become `session-N`, file paths shrink to basenames, message content is never in the payload to begin with. Works with any OpenAI-compatible API, zero new dependencies. (Yes — a cost profiler spending ~3K tokens to explain costs. The payload is compact and the answer capped; the irony is acknowledged.)

**🧭 `agentburn drift` — y