OpenClaw Integration

⚠️ 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.

Setup

Get your LangWatch API Key

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

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.

Restart the gateway

openclaw gateway restart

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.

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

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.

Configuration Reference

Option	Description	Default
`diagnostics.otel.enabled`	Turn OTEL export on/off	`false`
`diagnostics.otel.endpoint`	OTLP endpoint URL	,
`diagnostics.otel.traces`	Export traces	`true`
`diagnostics.otel.metrics`	Export metrics	`true`
`diagnostics.otel.logs`	Export logs	`false`
`diagnostics.otel.headers`	Auth headers (include your API key)	`{}`
`diagnostics.otel.serviceName`	Service name in traces	`"openclaw"`
`diagnostics.otel.sampleRate`	Sampling rate (0.0–1.0)	`1.0`
`diagnostics.otel.captureContent`	Include message content in traces	`false`

For high-volume deployments, consider reducing sampleRate to control costs. A rate of 0.1 samples 10% of traces.

GenAI Semantic Conventions

The plugin emits traces compliant with the OTEL GenAI semantic conventions:

Attribute	Description
`gen_ai.operation.name`	`"chat"` for LLM inference spans
`gen_ai.system`	Provider identifier (e.g. `"anthropic"`, `"openai"`)
`gen_ai.request.model`	Model requested
`gen_ai.response.model`	Model actually used
`gen_ai.usage.input_tokens`	Total input tokens (including cached)
`gen_ai.usage.output_tokens`	Completion tokens
`gen_ai.usage.cache_read_input_tokens`	Tokens served from cache
`gen_ai.usage.cache_creation_input_tokens`	Tokens 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.

Overview

SDKs

Frameworks

Model Providers

Tools

No-Code Platforms

Direct Integrations

OpenClaw Integration

Setup

What You Get

Content Capture

Configuration Reference

GenAI Semantic Conventions

​Setup

​What You Get

​Content Capture

​Configuration Reference

​GenAI Semantic Conventions

Setup

What You Get

Content Capture

Configuration Reference

GenAI Semantic Conventions