CLS Observability for OpenClaw Agents: Logs, Cost, and Runtime Health

An OpenClaw agent can be connected to QQ or Feishu and still be hard to operate. Token cost may grow without a clear session breakdown, queues may get stuck, API errors may spike, and slow responses may appear before anyone knows where to look. The OpenClaw observability Skill uses Tencent Cloud Log Service (CLS), LogListener, and a ready dashboard workflow to collect OpenClaw runtime logs and make cost, health, and session behavior searchable without changing application code.

OpenClaw runtime signals that need CLS observability

Use this pattern when OpenClaw is already running but the operator cannot answer basic runtime questions.

Operational question	Why it matters
Which conversations consumed the most tokens?	A broken Skill can loop and burn API quota overnight
Is the agent queue healthy?	Queue backlog and stuck sessions may happen before users complain
Which module is producing errors?	Error floods are hard to diagnose from scattered files
What happened in this conversation chain?	Debugging tool calls and context construction manually is slow

This workflow is designed for OpenClaw deployments on lightweight cloud servers or CVMs where operators need a managed log service instead of another self-hosted observability stack.

OpenClaw observability Skill deployment path

The deployment creates a collection path from OpenClaw runtime logs into CLS.

Step	Operation	Result
Environment check	Checks operating system and architecture	Confirms compatibility with x86 Linux or macOS
Deployment tool download	Fetches openclaw-cls-collector from Tencent Cloud mirrors	Provides the installer used by the workflow
CLS log topic creation	Uses the supplied Tencent Cloud credentials and region	Creates an OpenClaw-specific log topic with 30-day default retention
LogListener setup	Installs LogListener or reuses an existing version 3.4.0 or later	Starts collecting OpenClaw runtime logs into CLS
Dashboard URL generation	Extracts the log topic ID from deployment output	Returns a CLS dashboard link for the operator

Installing and triggering the Skill

Install the OpenClaw observability collection Skill:

https://clawhub.ai/trumphuang/openclaw-lens

After installation, ask OpenClaw to deploy collection:

Deploy CLS collection

or:

One-click deploy collector

The deployment can then proceed through conversational setup or terminal setup.

Conversational deployment for testing

Conversational setup is convenient for quick testing. The agent asks for:

Input	Required	Notes
SecretId	Yes	Tencent Cloud API key ID
SecretKey	Yes	Tencent Cloud API key secret
Region	No	Defaults to ap-guangzhou

Credential and region references:

https://console.cloud.tencent.com/cam/capi
https://cloud.tencent.com/document/product/614/18940

Use this mode for testing or quick evaluation. For production, prefer terminal deployment so credential handling stays closer to normal operations practice.

Terminal deployment for production

Run the collector command and substitute your Tencent Cloud credentials:

curl -fsSL -o openclaw-cls-collector https://mirrors.tencent.com/install/cls/openclaw/openclaw-cls-collector && chmod +x openclaw-cls-collector && ./openclaw-cls-collector --secret-id <SecretId> --secret-key <SecretKey> --region ap-guangzhou && rm ./openclaw-cls-collector

This command downloads the collector, makes it executable, runs deployment, passes the region, and removes the local installer afterward.

OpenClaw metrics for the CLS dashboard

The dashboard is organized around cost governance, runtime operations, and session management.

Area	Metrics or records	Operational use
Cost governance	Total cost, total tokens, average session cost, daily trends, model breakdown, channel breakdown, high-cost session ranking	Find expensive conversations, models, or channels
Runtime operations	Message volume, queue depth, execution duration, ERROR / WARN / FATAL distribution, module distribution	Detect stuck queues, slow execution, and abnormal modules
Session management	Full session list, channel and type filters, conversation-chain records, role coding	Reconstruct what happened during a problematic interaction

These views help move debugging from manual log searching to structured operational inspection.

Tencent Cloud CLS as the backend

CLS fits this workflow because it provides managed collection, storage, search, and dashboarding for operational logs.

CLS capability	OpenClaw value
Managed log ingestion and storage	Avoids building a separate ELK-style stack
Full-text and key-value search	Supports both broad troubleshooting and structured field lookup
Ready dashboard workflow	Gives the operator cost, runtime, and session views after deployment
Elastic capacity	Handles workloads from small daily logs to much larger volumes
Alerting ecosystem	Provides a later path for token, API-error, and stuck-session notifications

Pricing reference:

https://cloud.tencent.com/document/product/614/45802

Future alerting scope

The planned direction is to use CLS alerting for events such as:

• Token limit pressure.
• API error rate spikes.
• Stuck sessions.
• Notifications to IM channels such as Feishu or QQ.
• AI-assisted root-cause and handling suggestions when an exception is triggered.

Those additions matter because the current operational pain is not only missing data. It is also late discovery.

Common pitfalls when observing OpenClaw with CLS

• Entering long-lived production credentials through a chat flow instead of a controlled terminal workflow.
• Deploying into the wrong region and then wondering why data is missing.
• Forgetting to check whether an existing LogListener version is at least 3.4.0.
• Looking only at total token cost instead of breaking it down by session, model, and channel.
• Treating runtime logs as useful only after failure instead of using them for ongoing cost and health monitoring.

FAQ

How can I see which OpenClaw conversations cost the most?

Use the cost governance view to inspect total cost, token total, average session cost, and high-cost session rankings. The useful breakdowns are by model, channel, and session.

Does the OpenClaw CLS setup require application code changes?

The workflow is designed to deploy collection without changing OpenClaw application code. It uses the Skill, the collector, LogListener, and a CLS log topic.

Which operating systems are supported by the deployment flow?

The source workflow checks for x86 Linux or macOS compatibility before deployment.

What should I check if the dashboard has no OpenClaw data?

Check the region, credentials, log topic creation, LogListener installation, LogListener version, and whether the collector successfully started sending OpenClaw runtime logs.

Final checklist

• Install the OpenClaw Lens Skill.
• Trigger deployment with Deploy CLS collection or One-click deploy collector.
• Prefer terminal deployment for production credentials.
• Confirm the target region, default retention, and created CLS log topic.
• Verify LogListener is installed or reused at version 3.4.0 or later.
• Open the returned CLS dashboard and check cost, runtime, and session panels.