🦞 ClawMetry

See your agent think. Real-time observability for 12 AI agent runtimes: OpenClaw, NVIDIA NemoClaw, Claude Code, OpenAI Codex & 8 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 and you're done.

Works with 12 agent runtimes

ClawMetry started as observability for OpenClaw, and now meters your whole agent fleet in one dashboard, auto-detecting each runtime on your machine:

🦞 OpenClaw · 🟩 NVIDIA NemoClaw · ◆ Claude Code · ⬡ OpenAI Codex · Cursor · 🪿 Goose · ⚡ Hermes · opencode · ◈ Qwen Code · Aider · NanoClaw · PicoClaw

OpenClaw and NemoClaw are free in the open-source app; the other runtimes light up with ClawMetry Cloud or a self-hosted Pro license. Switch runtimes from the header and every tab — cost, tokens, tools, traces — re-scopes to that runtime.

What You Get

• Flow — Live animated diagram showing messages flowing through channels, brain, tools, and back
• Overview — Health checks, activity heatmap, session counts, model info
• Usage — Token and cost tracking with daily/weekly/monthly breakdowns
• Sessions — Active agent sessions with model, tokens, last activity
• Crons — Scheduled jobs with status, next run, duration
• Logs — Color-coded real-time log streaming
• Memory — Browse SOUL.md, MEMORY.md, AGENTS.md, daily notes
• Transcripts — Chat-bubble UI for reading session histories
• Alerts — Budget caps, error-rate triggers, agent-offline detection; routes to Slack, Discord, PagerDuty, Telegram, Email
• Approvals — Gate destructive deletes, force pushes, DB mutations, sudo, package installs, network calls behind one-click sign-off

Screenshots

🧠 Brain — Live agent event stream

📊 Overview — Token usage & session summary

⚡ Flow — Real-time tool call feed

💰 Tokens — Cost breakdown by model & session

🧬 Memory — Workspace file browser

🔐 Security — Posture & audit log

🚨 Alerts — Budget caps, error-rate triggers, webhooks to Slack / Discord / PagerDuty / Email

✋ Approvals — Gate risky tool calls behind manual sign-off; policy-backed protection rules

Install

One-liner (recommended):

curl -sSL https://raw.githubusercontent.com/vivekchand/clawmetry/main/install.sh | bash

pip:

pip install clawmetry
clawmetry

From source:

git clone https://github.com/vivekchand/clawmetry.git
cd clawmetry && pip install flask && python3 dashboard.py

v2 Frontend Development

The v2 React app lives in frontend/ and is served at /v2 when the Flask server is started with v2 enabled.

Use two terminals while developing:

# Terminal 1: Flask API/server on :8900
CLAWMETRY_V2=1 python3 dashboard.py

# Terminal 2: Vite dev server on :5173
cd frontend
nvm use
npm ci
npm run dev

Open http://localhost:5173/v2/. Vite proxies /api requests to http://localhost:8900, so the React app can talk to the local Flask server without extra CORS setup.

To build the bundle that ships with the Python package:

cd frontend
npm run build

The production bundle is written to clawmetry/static/v2/dist/.

Runtime / Agent Compatibility

ClawMetry observes many AI-agent runtimes, not just OpenClaw. Each non-OpenClaw runtime ships a dedicated reader adapter that translates its native session format into ClawMetry's unified shapes; the daemon ingests them into the same DuckDB store + cloud snapshot, tagged with the runtime, and the Session replay tab shows a runtime switcher when more than one is present. See docs/compatibility.md for the full matrix + a guide to adding runtimes, and docs/RUNTIME_FAMILY.md for the OpenClaw-family primer.

"Beta adapter" means ClawMetry ships a reader for that runtime's real on-disk format, each built + verified against a real install on a real machine (see tests/fixtures/runtimes/<rt>/). Adapters are read-only; each is honest about what its runtime actually stores (e.g. PicoClaw/NanoClaw/Cursor don't write token cost to disk). When several runtimes run on one node, the runtime switcher scopes the sessions view to one for a clean deep-dive.

Track any SDK agent — out-loop cost attribution

The runtimes above all write sessions to disk. Your own production agent — the one you built on the OpenAI Agents SDK, LangChain, the Vercel AI SDK, LlamaIndex, E2B, or a plain httpx loop — doesn't. ClawMetry's zero-config interceptor still captures its LLM calls (cost, tokens, latency, errors) by monkey-patching httpx/requests:

import clawmetry.track            # activate the interceptor
clawmetry.track.set_source("support-agent")   # name this product

# ...your agent runs as normal; every LLM call is now tracked + attributed.

set_source() (or the CLAWMETRY_SOURCE=support-agent env var) tags each call with a named source, so every product you run shows up as its own first-class, cost-attributable line in the dashboard's 🔌 Out-loop sources card on Overview — calls, providers, latency, error rate per agent. No source set? The calls are still tracked; the card just stays hidden.

CLAWMETRY_SOURCE=billing-agent python my_agent.py

This is the same data layer the runtime adapters feed (DuckDB → cloud snapshot), so out-loop sources sync to the cloud dashboard the same as everything else, E2E-encrypted.

OpenTelemetry — vendor-neutral, send your traces anywhere

ClawMetry speaks OpenTelemetry in both directions, using the GenAI semantic conventions, so your agent traces are never locked into one tool.

Export every session — LLM calls, tools, sub-agents, tokens, cost — as OTLP/HTTP GenAI spans to any collector (Datadog, Grafana, Honeycomb, or your own OTel Collector):

clawmetry --otel-export http://localhost:4318/v1/traces
# equivalently:
CLAWMETRY_OTEL_EXPORT_ENDPOINT=http://localhost:4318/v1/traces clawmetry

Auth headers and poll interval are optional env vars:

CLAWMETRY_OTEL_EXPORT_HEADERS='{"X-API-Key":"…"}'   # extra HTTP headers
CLAWMETRY_OTEL_EXPORT_INTERVAL=60                    # seconds (default 60)

Ingest — the built-in OTLP receiver accepts traces and metrics from anything else at /v1/traces and /v1/metrics (pip install clawmetry[otel] for protobuf ingest).

You get the zero-config, local-first ClawMetry dashboard and your data in whatever backend your team already runs — no lock-in, no second agent to install.

Configuration

Most people don't need any config. ClawMetry auto-detects your workspace, logs, sessions, and crons.

If you do need to customize:

clawmetry --port 9000              # Custom port (default: 8900)
clawmetry --host 127.0.0.1         # Bind to localhost only
clawmetry --workspace ~/mybot      # Custom workspace path
clawmetry --name "Alice"           # Your name in Flow visualization

All options: clawmetry --help

Supported Channels

ClawMetry shows live activity for every OpenClaw channel you have configured. Only channels that are actually set up in your openclaw.json appear in the Flow diagram — unconfigured ones are automatically hidden.

Click any channel node in the Flow to see a live chat bubble view with incoming/outgoing message counts.

Auto-detection: ClawMetry reads your ~/.openclaw/openclaw.json and only renders the channels you've actually configured. No manual setup required.

Docker Deployment

Want to run ClawMetry in a container? No problem! 🐳

Quick start with Docker:

# Build the image
docker build -t clawmetry .

# Run with default settings
docker run -p 8900:8900 clawmetry

# Or mount your agent's data dir (shown: OpenClaw's ~/.openclaw)
docker run -p 8900:8900 \
  -v ~/.openclaw:/root/.openclaw \
  -v /tmp/moltbot:/tmp/moltbot \
  clawmetry

Docker Compose example:

version: '3.8'
services:
  clawmetry:
    build: .
    ports:
      - "8900:8900"
    volumes:
      - ~/.openclaw:/root/.openclaw:ro
      - /tmp/moltbot:/tmp/moltbot:ro
    restart: unless-stopped

Note: When running in Docker, mount your agent's data + log directories (e.g. ~/.openclaw, ~/.claude, ~/.codex) so ClawMetry can auto-detect your setup.

Requirements

• Python 3.8+
• Flask (installed automatically via pip)
• An AI agent runtime on the same machine: OpenClaw, NVIDIA NemoClaw, Claude Code, Codex, Cursor, Goose, Hermes, opencode, Qwen Code, Aider, NanoClaw, or PicoClaw (or mounted volumes for Docker)
• Linux or macOS

NemoClaw / OpenShell Support

ClawMetry automatically detects NemoClaw — NVIDIA's enterprise security wrapper for OpenClaw that runs agents inside sandboxed OpenShell containers.

No extra configuration is needed in most cases. The sync daemon auto-discovers session files whether they live in ~/.openclaw/ on the host or inside an OpenShell container.

How it works

ClawMetry detects NemoClaw in two ways:

1. Binary detection — checks for the nemoclaw CLI and runs nemoclaw status to get sandbox info
2. Container detection — scans running Docker containers for openshell, nemoclaw, or ghcr.io/nvidia/ images, then reads sessions via volume mounts or docker cp

Session files synced from NemoClaw containers are tagged with runtime=nemoclaw and container_id metadata in the cloud dashboard, so you can tell them apart from standard OpenClaw sessions at a glance.

Recommended setup: sync daemon on the HOST

For the best experience, run ClawMetry's sync daemon on the host machine (not inside the sandbox). This avoids NemoClaw network policy restrictions.

# On the host (outside the sandbox)
pip install clawmetry
clawmetry connect
clawmetry sync

The sync daemon will automatically find sessions inside any running OpenShell containers.

Optional: explicit sandbox name

If auto-detection doesn't work, point ClawMetry at the right sandbox:

export NEMOCLAW_SANDBOX=my-sandbox-name
clawmetry sync

Running inside the sandbox (advanced)

If you must run the sync daemon inside the OpenShell sandbox, add this egress rule to your NemoClaw network policy so it can reach the ClawMetry ingest API:

# nemoclaw-policy.yaml
network:
  egress:
    - host: ingest.clawmetry.com
      port: 443
      protocol: https

Apply with:

nemoclaw policy apply --file nemoclaw-policy.yaml

Ports and endpoints

The sync daemon only makes outbound HTTPS calls to ingest.clawmetry.com. No inbound ports are required.

Cloud Deployment

See the Cloud Testing Guide for SSH tunnels, reverse proxy, and Docker.

Testing

This project is tested with BrowserStack.

Telemetry

ClawMetry sends a single anonymous "first run" ping to https://app.clawmetry.com/api/install the first time you run the clawmetry CLI on a new machine. We use this to count installs (the only marketing metric we have for an OSS project) and to learn which agent frameworks our users have installed.

Exactly one POST per install, containing:

What we do NOT send: IP (cloud derives the country code server-side from the request, then discards the IP), hostname, username, workspace path, file contents, your api_key, your email, anything PII or workspace-specific. The wire payload is auditable in clawmetry/telemetry.py.

Opt out (any one of these disables it permanently):

export CLAWMETRY_NO_TELEMETRY=1                # per-shell
export DO_NOT_TRACK=1                          # W3C cross-tool standard
touch ~/.clawmetry/notelemetry                 # persistent file marker

A network failure here never blocks clawmetry from running — the ping is fire-and-forget on a daemon thread with a 3 s timeout.

Star History

License

MIT

🦞 See your agent think
_{Built by @vivekchand · clawmetry.com · Part of the OpenClaw ecosystem}