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⚠️ Experimental — OpenClaw’s diagnostics-otel plugin is under active development. The instrumentation spec is stabilizing but may change. Follow the discussion at PR #11100 for the latest.
OpenClaw is an open-source AI agent framework that runs as a personal assistant or production copilot — handling tasks like code reviews, monitoring, incident response, and more. Its built-in diagnostics-otel plugin emits OpenTelemetry GenAI Semantic Conventions-compliant traces that LangWatch can ingest natively.

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Setup

1

Get your LangWatch API Key

Go to app.langwatch.ai/authorize to create your account and project, then grab your API key.
2

Configure OpenClaw

Add the following to your ~/.openclaw/openclaw.json:
{
  "diagnostics": {
    "enabled": true,
    "otel": {
      "enabled": true,
      "endpoint": "https://app.langwatch.ai/api/otel",
      "traces": true,
      "metrics": true,
      "headers": {
        "X-Auth-Token": "sk-lw-YOUR_API_KEY_HERE"
      },
      "serviceName": "my-clawdbot",
      "sampleRate": 1,
      "captureContent": true
    }
  },
  "plugins": {
    "allow": ["diagnostics-otel"],
    "entries": {
      "diagnostics-otel": {
        "enabled": true
      }
    }
  }
}
If you already have an openclaw.json, just merge the diagnostics and plugins sections into your existing config.
3

Restart the gateway

openclaw gateway restart
4

Test the integration

Send a message to your agent and check the LangWatch dashboard. Each agent turn produces a trace with the full execution tree.

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What You Get

Each agent turn produces a span tree like this: 
run (root span)
├── chat claude-opus-4-6        (LLM inference)
│   ├── tool.Read               (tool execution)
│   └── tool.Edit               (tool execution)
├── chat claude-opus-4-6        (follow-up LLM call)
│   └── tool.exec               (tool execution)
└── chat claude-opus-4-6        (final LLM call)
LLM spans are children of the run. Tool spans are children of the LLM call that invoked them. Each span includes:
  • Model info — which model was requested and which was used
  • Token usage — prompt tokens, completion tokens, cache read/write breakdown
  • Latency — duration of each LLM call and tool execution
  • Cost — calculated from token usage
  • Content — full input/output messages when captureContent is enabled

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Content Capture

The captureContent flag controls whether message content is included in traces. When enabled, traces include:
  • gen_ai.input.messages — the full prompt sent to the model
  • gen_ai.output.messages — the model’s response
  • gen_ai.system_instructions — the system prompt
  • gen_ai.request.tools — tool definitions available to the model
  • Tool input/output on execution spans
When disabled, you still get the full trace structure, token counts, latency, and model metadata — just no message content. Useful if you want observability without logging sensitive conversations.

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Configuration Reference

OptionDescriptionDefault
diagnostics.otel.enabledTurn OTEL export on/offfalse
diagnostics.otel.endpointOTLP endpoint URL—
diagnostics.otel.tracesExport tracestrue
diagnostics.otel.metricsExport metricstrue
diagnostics.otel.logsExport logsfalse
diagnostics.otel.headersAuth headers (include your API key){}
diagnostics.otel.serviceNameService name in traces"openclaw"
diagnostics.otel.sampleRateSampling rate (0.0–1.0)1.0
diagnostics.otel.captureContentInclude message content in tracesfalse
For high-volume deployments, consider reducing sampleRate to control costs. A rate of 0.1 samples 10% of traces.

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GenAI Semantic Conventions

The plugin emits traces compliant with the OTEL GenAI semantic conventions:
AttributeDescription
gen_ai.operation.name"chat" for LLM inference spans
gen_ai.systemProvider identifier (e.g. "anthropic", "openai")
gen_ai.request.modelModel requested
gen_ai.response.modelModel actually used
gen_ai.usage.input_tokensTotal input tokens (including cached)
gen_ai.usage.output_tokensCompletion tokens
gen_ai.usage.cache_read_input_tokensTokens served from cache
gen_ai.usage.cache_creation_input_tokensTokens written to cache
LLM spans use SPAN_KIND_CLIENT per the GenAI spec (outbound RPCs to model providers).
For more information, check out the OpenClaw documentation and the diagnostics-otel discussion.