---
title: "Gemini CLI & Code Assist"
canonical_url: "https://docs.getdx.com/connectors/gemini/"
md_url: "https://docs.getdx.com/connectors/gemini.md"
last_updated: "2026-05-08"
---

# Gemini CLI & Code Assist
This guide explains how to connect Google Gemini CLI & Gemini Code Assist to DX by automatically processing and exporting log data from your Google Cloud project to DX directly. The method detailed in this guide is a unified approach for handling both Gemini CLI (via OTel) and Gemini Code Assist (via logs) data ingestion into DX.

> **Note: This guide is still in beta.**

## Prerequisites

- This guide assumes a basic familiarity with Google Cloud.
- **Google Cloud project**: Access to the project where Gemini Code Assist/Gemini CLI is active.
- **Google Cloud billing enabled**: While costs are low, BigQuery and Cloud Run require a billing account.
- **DX Data Cloud API key**: The API token provided by DX for ingestion.
- This guide assumes that you have Gemini CLI and/or Gemini Code Assist enabled in your Google Cloud console.

## Setup instructions

### Step 1: Configure data sources

To begin, you will need to ensure that Gemini CLI and/or Gemini Code Assist are properly configured to send data back to Google Cloud.

#### Gemini Code Assist

By default, enterprise logging is disabled for privacy. You must explicitly enable it.

1. Navigate to **Google Cloud Console** -> **Admin for Gemini**.
2. Toggle **Logging for Code Assist metadata** to **ENABLED**.

#### Gemini CLI

Configure your developers' Gemini CLI environments to point to Google Cloud for telemetry. Follow the [Gemini CLI docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/telemetry.md#google-cloud-telemetry) for instructions.

### Step 2: Set up service account

Set up a dedicated service account to run this integration. Using a single service account simplifies permission management and follows Google Cloud best practices.

1. Navigate to **Google Cloud console** -> **IAM & Admin** -> **Service Accounts**.
2. Click **Create service account**.
3. Set the **Service account name** to `dx-gemini-export`.
4. Click **Create and continue**.
5. Add the following roles to the newly created service account:
   - **BigQuery Data Viewer** – to read data from the log sink dataset
   - **BigQuery User** – to run export queries
   - **Secret Manager Secret Accessor** – to read the DX API key
   - **Cloud Run Invoker** – to allow Cloud Scheduler to trigger the function

You will use this service account in Steps 7 and 9 when configuring Cloud Run and Cloud Scheduler.

### Step 3: Create BigQuery dataset

1. Navigate to **Google Cloud console** -> **BigQuery** -> **BigQuery Studio**.
2. In the **Explorer** panel (left side), click the three dots menu next to your project ID.
3. Select **Create dataset**.
4. Set **Dataset ID** to `dx_gemini_observability`.
5. Set **Location type** to **Multi-region (recommended)** or select a specific Region.
6. Leave everything else to default.
7. Click **Create dataset**.

### Step 4: Create Log Router sink

1. Navigate to **Google Cloud console** -> **Logging** -> **Log Router** -> **Create sink**.
2. Name the sink: `dx-gemini-sink`.
3. Set the **Destination** to the BigQuery dataset previously created (`dx_gemini_observability`).
4. Check **Use Partitioned Tables** (Required for cost efficiency).
5. Set the **Inclusion Filter** to the snippet below:
   ```
   logName:"cloudaicompanion" OR logName:"gemini_cli"
   ```

### Step 5: Grant write permissions

After creating the sink, you may need to manually grant the sink's Writer Identity permission to write to BigQuery.

1. On the **Log Router** page, find your sink (`dx-gemini-sink`).
2. Click the three dots menu next to the sink and select **View sink details**.
3. Copy the **Writer identity** service account email (e.g., `service-123...@gcp-sa-logging.iam.gserviceaccount.com`).
4. Navigate to **IAM & Admin** -> **IAM**.
5. Click **Grant Access** (at the top of the page).
6. In the **New principals** field, paste the service account email.
7. In the **Select a role** field, choose **BigQuery Data Editor**.
8. Click **Save**.

### Step 6: Add DX Data Cloud API key to Secret Manager

1. Navigate to **Google Cloud console** -> **Secret Manager**.
2. Create a new secret named `dx-api-key` and paste the value. API keys can be managed from the [**Data Cloud API**](https://app.getdx.com/admin/datacloud/api-keys) settings.

### Step 7: Create Cloud Run function

1. Navigate to **Google Cloud console** -> **Cloud Run Functions** and create a new **Python** function.
2. Select **Use an inline editor to create a function**.
3. Set **Service name** to `dx-gemini-export`.
4. Set **Runtime** to **Python 3.14**.
5. Select **Require authentication**.
6. Expand **Containers, Networking, Security**.
7. Under **Security**, select the `dx-gemini-export` service account created in Step 2.
8. Under **Variables & Secrets**, add the following:
   - `DX_API_URL`: Your DX API URL found in the [Data Cloud API settings](https://app.getdx.com/admin/datacloud/api-keys).
   - `GCP_PROJECT`: Your Google Cloud Project ID.
   - `DATASET_ID`: `dx_gemini_observability`.
9. Under **Variables & Secrets**, click **Reference a secret** and mount the `dx-api-key` secret as an environment variable named `DX_API_KEY`.
10. Click **Next** to proceed to the code editor.

### Step 8: Deploy function code

1. Set **Function entry point** to `export_gemini_metrics`.
2. In the file browser, select `requirements.txt` and replace its contents with:
   ```
   functions-framework==3.*
   google-cloud-bigquery>=3.10.0
   requests>=2.31.0
   ```
3. Select `main.py` and replace its contents with the [provided example script](https://drive.google.com/file/d/16nruBDgUGB2Wzjr_3_4id_Eap4gyO7is/view?usp=sharingg).
4. Click **Save and redeploy** and wait for the function to deploy successfully.
5. Once deployed, copy the **Function URL** from the function details page—you will need this in the next step.

### Step 9: Configure Cloud Scheduler

1. Navigate to **Google Cloud console** -> **Cloud Scheduler** and click **Create Job**.
2. Name the job: `gemini-export-job`.
3. Set **Frequency** to `0 * * * *` (Hourly) or `0 0 * * *` (Daily).
4. Set **Target** type to **HTTP**.
5. Set the **URL** to the Cloud Run Function URL (copied from Step 8).
6. Add an **Auth Header** by selecting **Add OIDC Token**.
7. Select the `dx-gemini-export` service account created in Step 2.

### Step 10: Test Cloud Run function

1. Navigate to **Google Cloud console** -> **Cloud Scheduler**.
2. Find the row for `gemini-export-job`.
3. Click the three dots menu and select **Force run**.
4. Navigate to **Google Cloud console** -> **Cloud Run Functions**.
5. Click on the name of your function.
6. Click the **Logs** tab.
7. Look for a log entry with Severity **INFO** that says: `Success: X records exported`.

### Step 11: Verify data in DX

Assuming you are not getting errors in Step 10, you can check whether your import was successful by querying for the AI tool metrics data:

1. Go to **DX** -> **Data Studio** and use the following query to look for AI tool metrics records where the **tool** is either **Gemini CLI** or **Gemini Code Assist**:

```sql
SELECT
    batdm.tool AS tool_name,
    batdm.email,
    COUNT(batdm.id) AS metric_count,
    MIN(batdm.date) AS earliest_date,
    MAX(batdm.date) AS latest_date,
    COUNT(CASE WHEN batdm.is_active = true THEN 1 END) AS active_days
FROM bespoke_ai_tool_daily_metrics batdm
WHERE (batdm.tool ILIKE '%gemini%'
    OR batdm.tool ILIKE '%gemini cli%'
    OR batdm.tool ILIKE '%gemini code assist%')
GROUP BY batdm.tool, batdm.email
HAVING COUNT(batdm.id) > 0
ORDER BY metric_count DESC, tool_name, email
```

2. If successful, you should get results showing you active users for Gemini CLI and/or Gemini Code Assist. Note, it will take up to an hour for Gemini CLI and Gemini Code Assist metrics to start showing up in reports outside of Data Studio.

![Successful metrics query](https://docs.getdx.com/assets/images/connectors/gemini/data-studio.png)
---

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