🦞 ClawMetry

See your agent think. Real-time observability for 30 AI agent runtimes: OpenClaw, NVIDIA NemoClaw, Claude Code, OpenAI Codex & 26 more. One dashboard for your whole agent fleet.

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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.

Works with 30 agent runtimes

Free in the open source app: 🦞 OpenClaw · 🟩 NVIDIA NemoClaw · 🪿 Goose

On a paid plan: ◆ Claude Code · Cursor · 🐙 GitHub Copilot · ⬡ OpenAI Codex · ♊ Gemini CLI · 💗 Lovable · ⠕ Replit Agent · 🖇 Cline · 🙌 OpenHands · 🧑‍💼 OpenWorker · opencode · Aider · 🔗 n8n · ◈ Qwen Code · 🅳 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 26 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?

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

Annual plans, Enterprise and the current numbers live at clawmetry.com/pricing. Self-hosted license keys work without the cloud (clawmetry license). The exact free/paid split is in docs/ENTITLEMENTS.md.

Your data stays on your machine

ClawMetry reads local session files and logs. No session data leaves your box unless you run clawmetry connect — no prompts, replies, tool arguments, file contents or log lines. When you do connect, the snapshot is end-to-end encrypted with a key that never leaves your machine, and decrypted in your browser. If a node has no key, the upload is skipped rather than sent in the clear, and no server response can turn that off.

Two things do run by default before you connect, both opt-out and neither carrying session data: an anonymous install ping and a version check against PyPI. A default install also looks up your public IP once for a startup banner line. Every destination, what it carries and how to switch it off is listed in docs/EGRESS.md; self-hosted, repointed and air-gapped installs make no discretionary outbound calls at all.

The decryption happens in your browser, in code we serve you. That used to be a promise; it is now something you can check. Every line that touches your key lives in one readable file, clawmetry/static/js/cm-e2e.js, which ships inside the wheel and is served verbatim, pinned with a Subresource Integrity hash. To confi