# Analyze Cloud Run logs (gcloud)

> Use Warp to pull, organize, and analyze Cloud Run production logs by severity with natural language prompts and automated Python scripts.

- 网址：https://funcoding.ai/agents/warp/guides/devops/how-to-analyze-cloud-run-logs-gcloud/
- 来源：Warp 官方文档原文（英文），MIT 许可，同步于 2026-10-11
- 官方原文：https://docs.warp.dev/guides/devops/how-to-analyze-cloud-run-logs-gcloud/

---
Learn how to use Warp to retrieve, organize, and analyze production logs from your cloud servers — all with natural language prompts.

1. #### Setting the context

   Open Warp and enable **voice input** (optional) for hands-free prompting.

<div class="callout callout-note">

Voice input is optional — only enable it if you prefer hands-free prompting.

</div>

   **Prompt**

   ```
   Use the warp-server-staging gcloud project and pull logs
   for the last 10 minutes from the warp-server Cloud Run instance.
   Organize them by info, warning, and error levels.
   Create a histogram across message types,
   and highlight the most concerning errors to investigate.
   ```

2. #### Warp’s agent in action

   After you hit Enter:

   * Warp detects the command as an **Agent Mode** request.
   * It gathers project context (`warp-server-staging`).
   * Executes the necessary `gcloud` logging queries automatically.
   * Writes retrieved data to a temporary file for processing.

3. #### Automated analysis

   Warp’s agent generates a **Python script** on the fly to:

   * Parse logs
   * Count messages by severity
   * Output summary metrics

   Example output:

   ```
   1,000 log entries total
   980 info
   11 warning
   9 errors
   ```

   You can view or fast-forward execution, or stop the process at any point.

4. #### Reviewing results

   Warp outputs a readable histogram and highlights anomalies.\
   For example:

   > “Gemini AI error messages detected — worth reviewing.”

   You can expand each log group interactively or inspect the temporary Python code for debugging.