ClawMetry
An agent can make a hundred tool calls without making progress. ClawMetry reads the session files your coding agents already write, and puts the timeline, the tool calls and whatever token and cost data the runtime exposes into one view — so you can tell a long run that is working from one that is stuck.
Works with 33 AI agent runtimes — Claude Code, OpenAI Codex, Hermes, OpenClaw & 29 more. One dashboard for your whole agent fleet. (the full list, generated from the catalogue.)
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One command. Zero config. Auto-detects everything.
pip install clawmetry && clawmetry
Opens at http://localhost:8900. Zero config: it finds the agent runtimes you already have, reads them read-only, and changes nothing about how they run.
Before you install
| What it does | Reads the session files and logs your agents already write. No SDK, no code change, no instrumentation in your app. |
| What you see | Session timeline, tool-by-tool replay, token and cost breakdown, and trajectory signals (looping, repeated failures) — per runtime. |
| What's free | pip install clawmetry reads OpenClaw, NVIDIA NemoClaw, Goose and Qwen Code with no account, no key and no network call. The other 28 — Claude Code, Codex, Cursor and the rest — are read by the closed-source clawmetry-pro companion, which arrives with the 7-day trial or a plan — see docs/ENTITLEMENTS.md for the exact split. |
| How to start | pip install clawmetry && clawmetry, then open localhost:8900. No agents on this machine yet? clawmetry --sample opens on three labelled synthetic sessions. |
| What leaves your machine | No session data, unless you run clawmetry connect. Two things do run by default, both opt-out and neither carrying session content: an anonymous install ping and a PyPI version check. Every destination is inventoried in docs/EGRESS.md, rebuilt from a wire capture rather than from reading comments. |
Two limits worth knowing before you judge the output: runtimes expose very different data (some publish no cost at all — the matrix says which, per runtime), and observing an action is not the same as being able to block it (which controls are real, per runtime).
Works with 33 agent runtimes
Free in the open source app: 🦞 OpenClaw · 🟩 NVIDIA NemoClaw · 🪿 Goose · ◈ Qwen Code
On a paid plan: ◆ Claude Code · Cursor · 🐙 GitHub Copilot · ⬡ OpenAI Codex · ♊ Gemini CLI · 💗 Lovable · ⠕ Replit Agent · 🖇 Cline · 🙌 OpenHands · 🧑💼 OpenWorker · 🎭 Muse Code · 🏛️ OpenExecutive · ⠿ OpenDots · opencode · Aider · 🔗 n8n · 🅳 Devin · 🪐 Antigravity · Grok Build · 🤖 Grok Bot · ⚡ Hermes · Pi · Deep Agents · 🌙 Kimi CLI · 🐋 DeepSeek Harness · 🦾 Exo · NanoClaw · PicoClaw · QM
Every runtime gets the same dashboard. Run several at once and the header switcher re-scopes every tab to one of them.
Built your own agent on an SDK instead? The interceptor tracks its LLM calls too. See docs/SDK_TRACKING.md.
What you get
- Sessions & transcripts: what each agent did, turn by turn, with replay
- Cost & tokens: per runtime, model, session and day, with anomaly flags
- Flow: live diagram of messages moving through channels, models and tools
- Brain: the reasoning and tool-call event stream as it happens
- Context blowout: window utilization sized per provider, compaction vs forced overflow, plus a per-runtime map of what we can't see (how)
- Memory & skills: the files and skills each runtime actually loaded
- Health & logs: disk, memory, error rates, rate limits, live log stream
- Alerts: budget caps, error spikes, agent-offline, routed to Slack, Discord, PagerDuty, Telegram, Email
- Approvals: pause risky tool calls before they run and approve from your phone (how)
Context blowout, and what watching costs
Two questions worth answering before you trust any agent-comparison tool.
How does it handle context-window blowout across runtimes?
A utilization percentage is only as honest as what it divides by. ClawMetry sizes the window per provider from a table you can read and PR, covering Anthropic, OpenAI, Google, xAI, DeepSeek, Kimi, Qwen, Mistral, Llama and GLM. It does not measure all 33 runtimes with one vendor's ruler. That matters: a 300K GPT-5 turn scored against Anthropic's 200K reads ">100%, blown" when it is really at 75% of GPT-5's 400K. The same ruler hides a genuinely overflowed 130K DeepSeek turn as a comfortable 65%.
Every window ships with its provenance: model_table, explicit_marker,
observed_floor, or an honest default when we don't know the model. A
gauge built on a guess never renders with the same authority as one built on
a lookup.
ClawMetry can only see compaction events on some runtimes. So
GET /api/context-coverage reports, per runtime, whether a zero means
"ran clean" or "we're blind". A 0 that actually means blind says so.
Full detail
What does the instrumentation cost?
| Path | Added to your agent | Default? |
|---|---|---|
| Session-file tailing (all 33 runtimes) | 0. Separate process, no ClawMetry code in your agent | on |
HTTP interceptor (CLAWMETRY_INTERCEPT=1) |
+0.44 ms per LLM call, or 0.009% of a 5s call | off |
| Pre-tool hook gate (warm cache) | +44 ms per gated tool call, over a 36 ms interpreter floor | off |
| Enforcement proxy | +9.7 ms per LLM call | off |
Daemon host cost: 2,762 events/sec ingest, 710 bytes/event on disk (67.7 MB per 100k events), and ~12% of one core sustained on a busy install. That last number is over our own stated 5-10% budget, so it is published as a bug to chase rather than left off the page.
Measured on an Apple M2 Pro with benchmarks/overhead.py. The harness runs
each condition in a separate process, alternates their order, and refuses
to print a number when the rounds disagree on its sign. Run it on your own
machine in a minute:
pip install clawmetry && python -m benchmarks.overhead
Every path is measured, including the hook gates and the enforcement proxy, and the harness runs on Linux, macOS and Windows in CI. Two results worth knowing: the proxy costs about seven times more on Windows than on Linux, and the daemon currently sustains about 12% of one core, over our own 5-10% budget. The raw JSON, the method, and what is still unmeasured are in docs/OVERHEAD.md.
Pricing
| Plan | What it covers | Price |
|---|---|---|
| Free | OpenClaw + NVIDIA NemoClaw + Goose + Qwen Code, full dashboard, local only | $0 |
| Starter | Every other runtime above, fleet view, cloud sync | $9 per node / month |
| Pro | Starter + control and evaluation: approvals, tool-risk policies, evals, anomal |