Add Cost, Runtime, and Session Observability to OpenClaw with a CLS Collector Skill
Deploy a collector from OpenClaw, send agent logs to Tencent Cloud CLS, and inspect cost, health, and conversation state from a ready-made dashboard.
An AI agent can look healthy from the outside while burning tokens, stalling in a queue, or failing silently in the background. OpenClaw users who connect the agent to QQ or Feishu often have the same operational questions: which conversations are expensive, whether the agent is stuck, why replies slow down, and where errors are accumulating.
The CLS-based OpenClaw observability Skill addresses that gap by deploying log collection and a ready-made dashboard without adding separate infrastructure or changing application code.
The failure mode: the agent is running, but invisible
OpenClaw becomes hard to operate when four signals are missing:
| Missing signal | Operational consequence |
|---|---|
| Token and cost breakdown | A monthly API bill shows the total, but not which conversations, models, or channels caused it |
| Runtime health | Queue backlog, stalled sessions, and error bursts may be invisible until users complain |
| Conversation trace | Tool calls, role flow, and context assembly are difficult to reconstruct from scattered logs |
| Exception notification | Token exhaustion, API error spikes, or risky shell actions may happen without an immediate alert |
The goal is not only to collect logs. The goal is to turn OpenClaw runtime data into views that are useful for cost governance, operations, and session review.
Deployment path 1: trigger collection from OpenClaw
After installing the OpenClaw observability collection Skill from the provided OpenClaw Skill page, trigger deployment by asking OpenClaw to deploy CLS collection.
The conversational deployment path asks for:
| Parameter | Required | Notes |
|---|---|---|
SecretId |
Yes | Tencent Cloud API key ID |
SecretKey |
Yes | Tencent Cloud API key |
Region |
Optional | Defaults to ap-guangzhou in the described flow |
This path is best for testing or fast evaluation because the agent performs the deployment steps after the credentials are provided.
Deployment path 2: run the terminal installer
For production usage, the terminal path is cleaner because credentials do not need to pass through the agent conversation.
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
The installer supports the deployment flow described for x86 Linux or macOS environments.
What the Skill deploys
Behind the prompt or terminal command, the deployment performs five concrete steps:
- Detects operating system and architecture compatibility.
- Downloads the collection deployment program from Tencent Cloud mirrors.
- Creates a dedicated CLS log topic for OpenClaw with a default 30-day retention period.
- Installs or reuses LogListener. If LogListener already exists and the version is at least 3.4.0, the deployment skips reinstallation and continues with configuration.
- Extracts the log topic ID from the output and returns a dashboard URL.
That final dashboard URL is the main handoff. Once opened in the CLS console, it gives the operator a prebuilt view of OpenClaw runtime behavior.
Dashboard view 1: cost governance
The cost view should group total cost, token volume, average session cost, daily trends, model/channel breakdowns, and high-cost sessions in one place.
The cost view is designed to answer questions such as:
- How much total token cost has OpenClaw generated?
- Which model or channel is contributing most to cost?
- Which individual sessions are the most expensive?
- Is spend rising because of input length, output length, model choice, or conversation volume?
For AI agent operations, this is often the first dashboard that matters. A runaway Skill, repeated tool loop, or unexpectedly long conversation can become visible before it turns into a surprise bill.
Dashboard view 2: runtime health
The operations view should track message volume, queue depth, execution duration, log levels, and module distribution.
The runtime view focuses on health and reliability:
| Metric family | What to look for |
|---|---|
| Message processing | Sudden drops, spikes, or uneven throughput |
| Queue depth | Backlog that suggests the agent is not keeping up |
| Execution duration | Slow tool calls or long-running sessions |
| Log level distribution | ERROR, WARN, and FATAL concentration |
| Module distribution | Which subsystem is generating the most operational events |
This is the view to open when OpenClaw is "running" but users experience slow replies or missing responses.
Dashboard view 3: session management
Session visibility matters when a response is wrong but the reason is unclear. Keep full conversation records available for filtering by channel and type, then present the dialogue chain with role coding. That gives operators a practical audit trail for what the agent received, how it behaved, and where a conversation became expensive or incorrect.
Data flow
The data path is intentionally simple:
| Layer | Role |
|---|---|
| OpenClaw AI Agent | Produces runtime, cost, and session logs |
| LogListener collector | Collects OpenClaw logs from the host |
| Tencent Cloud CLS | Stores, indexes, and searches the collected logs |
| CLS dashboard | Presents cost governance, operations health, and session management views |
Because the storage and query layer is managed by CLS, the setup avoids building and maintaining a separate ELK-style stack.
Why CLS is a practical base for this workflow
The described setup uses CLS for five reasons:
| Requirement | CLS fit |
|---|---|
| Low maintenance | Collection, storage, and search are managed services |
| Search depth | Operators can move from dashboard panels to CLS console search |
| Ready-made dashboards | Cost, operations, and session views are available after deployment |
| Elastic volume | Log volume can range from MB/day to TB/day |
| Alert integration | Future alert flows can connect to Tencent Cloud alerting capabilities |
The cost note in the deployment material is straightforward: CLS includes a free quota, and detailed billing should be checked in the CLS pricing documentation.
Roadmap items to watch
The next planned capabilities are:
- Smart alerts for token overrun, API error spikes, and stalled sessions, with notifications to IM channels such as Feishu or QQ.
- AI-assisted analysis that attaches root-cause clues and handling suggestions when an exception is triggered. Together, those features move the setup from passive visibility to active operations: the agent is no longer just observable after someone checks a dashboard, but can start surfacing cost and health risks when they happen.
Deployment checklist
Use this short checklist before treating the dashboard as production-ready:
- Use a Tencent Cloud key with the minimum required scope.
- Prefer terminal deployment for production credentials.
- Confirm the
Region, especially when the defaultap-guangzhouis not where logs should live. - Verify that LogListener is installed or reused correctly.
- Open the dashboard URL and confirm data appears under cost, operations, and session views.
- Add alerts once the alerting flow is available for the metrics that matter most to your agent.
FAQ
Does this require application code changes?
No code change is described for the OpenClaw application. The workflow deploys collection and dashboarding around the running agent.
When should the conversational deployment path be used?
Use it for testing or quick experience. For production, the terminal path is more appropriate because credentials stay out of the agent conversation.
What if LogListener is already installed?
If LogListener exists and its version is at least 3.4.0, the deployment skips installation and continues with configuration.
What are the first metrics to check after setup?
Start with total cost, token volume, high-cost sessions, queue depth, execution duration, and error-level logs. Together, they cover cost, responsiveness, and failure signals.
