An AI agent can look healthy from the outside while still hiding expensive token usage, stuck background tasks, silent errors, and slow responses. The source article frames this as the "invisible OpenClaw" problem: the agent is running, but the operator cannot see cost, runtime health, or session context clearly.
The proposed solution is an OpenClaw observability Skill based on Tencent Cloud Log Service (CLS). After installation and deployment, it collects OpenClaw runtime logs and returns a ready-to-use CLS dashboard.
What can go wrong when OpenClaw is not observable?
The source article lists four operator pain points:
| Pain point | Operational impact |
|---|---|
| Cost is unclear | You may know the monthly API bill, but not which sessions, models, channels, inputs, or outputs consumed the tokens. A broken Skill may loop and burn quota overnight. |
| Agent health is hidden | Message queues may pile up, sessions may become stuck, or error logs may flood the system before the operator notices. |
| Conversation debugging is manual | Conversation records, Skill calls, tool calls, and context construction are scattered, leaving operators to manually grep logs. |
| Exceptions are discovered by luck | Token limits, API error spikes, and risky shell commands may have no notification path until the agent stops responding. |
In short: OpenClaw may be running, but without observability you have little visibility into how it is running.
The two-step deployment flow
The source article describes a two-step process.
Step 1: Install the Skill and trigger deployment
Install the OpenClaw observability collection Skill. The source article points to this installation link:
https://clawhub.ai/trumphuang/openclaw-lens
After installation, ask OpenClaw to deploy the collector with a phrase such as:
Deploy CLS collection
or:
One-click deploy collector
The article gives two deployment modes.
Mode A: Deploy through conversation
This mode is positioned as suitable for testing or a quick experience.
The agent asks for:
| Field | Requirement |
|---|---|
SecretId |
Required Tencent Cloud API key ID. |
SecretKey |
Required Tencent Cloud API key. |
Region |
Optional CLS region. The default in the source is ap-guangzhou. |
The source links the Tencent Cloud API key page and CLS region documentation:
https://console.cloud.tencent.com/cam/capi
https://cloud.tencent.com/document/product/614/18940
After credentials are provided, the Skill runs the deployment on the machine.
Mode B: Deploy through the terminal
This mode is recommended in the source article for production use.
Replace the placeholders with your 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
What the Skill does behind the scenes
The article breaks down the deployment into five actions:
- It detects the operating system and system architecture. The source states that x86 Linux or macOS is required.
- It downloads and runs the deployment program from the Tencent Cloud mirror site.
- It creates a CLS log topic dedicated to OpenClaw in the selected region, with 30-day default retention.
- It deploys the LogListener collector. If LogListener is not installed, it installs the latest version. If an existing version is
3.4.0or newer, it skips installation and configures the existing collector. - It extracts the log topic ID from the output and returns a complete CLS dashboard URL.
Step 2: Open the dashboard
After deployment, the agent returns a dashboard link. Opening that link takes you to the CLS console dashboard generated for OpenClaw.
What the dashboard shows
The dashboard is organized around cost governance, operations observability, and session management.
Cost governance
The source article says this view includes:
| Cost view | Purpose |
|---|---|
| Total cost, total tokens, average session cost | Track the high-level cost baseline. |
| Daily trend | See cost and token movement over time. |
| Model and channel breakdown | Find the source of token spending. |
| High-cost session ranking | Identify the most expensive conversations. |
Operations observability
This view focuses on runtime health:
| Operations view | Purpose |
|---|---|
| Message processing volume | Understand workload. |
| Queue depth | Detect backlog. |
| Execution latency | See whether the agent is slowing down. |
| Log level distribution | Surface ERROR, WARN, and FATAL events. |
| Log module distribution | Understand which modules are producing messages. |
Session management
The session view provides a full session list and supports filtering by channel and type. The source article also says it can restore the conversation chain with readable role encoding, which helps debug agent behavior.
Architecture overview
The source diagram can be summarized as this data flow:
| Component | Role |
|---|---|
| OpenClaw AI Agent | Generates runtime, cost, and session logs. |
| LogListener | Collects the OpenClaw logs. |
| CLS log topic | Stores the collected logs. |
| CLS dashboard | Provides cost, operations, and session views. |
Why use CLS as the observability backend?
The article gives five reasons:
| Reason | Source-backed explanation |
|---|---|
| No extra operations burden | Log collection, storage, and search are fully managed. |
| Search capability | CLS supports full-text search and key-value search for troubleshooting in the console. |
| Ready-to-use dashboard | The preset dashboard covers cost, operations, and sessions. |
| Elastic scaling | The article says the log volume can range from MB per day to TB per day and be handled smoothly. |
| Cloud ecosystem integration | The article says it can connect to the Tencent Cloud alerting system later. |
The source also mentions that CLS provides a free quota and points to its pricing documentation:
https://cloud.tencent.com/document/product/614/45802
Planned next steps from the source
The source article lists two planned capabilities:
| Planned capability | What it would add |
|---|---|
| Smart alerts | Push notifications to Feishu, QQ, or other IM channels when token limits, API error spikes, or stuck sessions occur. |
| AI analysis | Attach root-cause analysis and handling suggestions when an exception is triggered. |
Summary
Deploying OpenClaw is only the first step. The source article's main argument is that operators also need to observe, understand, and optimize the agent after it starts running.
With the CLS-based OpenClaw observability Skill, the operator gets three practical views:
- A cost view that shows where token spending goes.
- An operations view that shows whether the system is healthy.
- A session audit view that restores conversation context for troubleshooting.
FAQ
What does the OpenClaw observability Skill deploy?
It deploys a collector, creates an OpenClaw-specific CLS log topic, configures LogListener, and returns a CLS dashboard URL.
Which deployment mode should be used in production?
The source article recommends terminal deployment for production environments.
What LogListener version is enough to skip installation?
If LogListener already exists and its version is 3.4.0 or newer, the deployment skips installation and configures the existing collector.
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