How can I check my requests per day remaining?

[fortissimo247]

Using API, I can check it in Google AI Stuido.
But signing in with my Google account in CLI, I'm wondering how I can check the remaining number of requests for the day.

Authenticate: When prompted, sign in with your personal Google account. This will grant you up to 60 model requests per minute and 1,000 model requests per day using Gemini.

[ryanjsalva]

You can always use the /stats command to see the usage for a particular session, but there's no way within Gemini CLI to see your daily quota – at least, not yet.

[SkybuckFlying]

Try google cloud... it has the possibility to add gemini api, to a cloud project. Just ignore the cloud project and use the key from it for local gemini api usage etc... but it has metered thing in the cloud that can show what the gemini is using... yes... it's a bit strange and all, and sometimes it lags or doesn't work/update or maybe even limit is not there, but it probably is, but sometimes it works :):):)

[Manamama]

Well, you can also teach Gemini CLI (via MCP would be best) to read your online usage ...

[SkybuckFlying]

This is a bit vague...

[Manamama]

On 📲 here, so a related only tip: https://github.com/Manamama/Puzzles_for_AIs/blob/main/docs/pmbok_instructions/workflows/git_and_GitHub_workflow.md

In very short: use the API access via Google itself and ask ♊ to code it too. Just feed your very query plus my tips, even via gh to ♊, and make it help you

[Manamama]

Oh, I know, I shall ask my ♊ to flesh it out for you herself then, pls wait some minutes , it takes more⌚ on 📱 to make her gh around...

[Manamama]

Discussion: Approaches for Checking Gemini API Daily Usage

This document outlines various approaches for the Gemini CLI to check and display the user's daily Gemini API usage. The primary goal is to provide transparency on API consumption, especially concerning the free tier limits.

---

Option 1: Hypothetical Direct Gemini API Endpoint

Description:
This would be the ideal and most straightforward solution. If Google were to provide a dedicated, public API endpoint specifically for querying Gemini API usage statistics (e.g., requests made, remaining quota), the CLI could directly call this endpoint.

Pros:

• Most direct and efficient method.
• Designed specifically for the purpose, likely providing accurate and real-time data.
• Simplifies implementation within the CLI, as it wouldn't require interacting with broader cloud monitoring systems.
• Minimal external dependencies for the CLI itself.

Cons:

• This API endpoint does not currently exist publicly for free-tier usage. This remains a hypothetical ideal.
• Reliance on Google to develop and maintain such an endpoint.

---

Option 2: Google Cloud Monitoring via curl (Command Line)

Description:
This approach leverages existing Google Cloud infrastructure. It involves using gcloud CLI commands to obtain the user's Google Cloud Project ID and an authenticated access token. These are then used in a curl command to query the Google Cloud Monitoring API for the serviceruntime.googleapis.com/api/request_count metric, specifically filtered for the generativelanguage.googleapis.com service (Gemini API). The curl command would be executed as a shell command from within the Gemini CLI.

Pros:

• Leverages existing, robust Google Cloud services.
• Does not require additional Python dependencies beyond what might already be present for gcloud.
• Provides a direct, command-line driven way to get the data.
• Relatively "pithy" in terms of direct command-line execution, as it's a single curl command (albeit a long one).

Cons:

• Requires the user to have the gcloud CLI installed, configured, and authenticated.
• The curl command itself is complex and prone to quoting issues if not handled carefully.
• Parsing the JSON response from curl within a shell script or a simple CLI application can be cumbersome and error-prone.
• Relies on the stability of gcloud command outputs and the curl command's behavior.

---

Option 3: Google Cloud Monitoring Python Client Library (Programmatic)

Description:
This approach also uses Google Cloud Monitoring but interacts with it programmatically via the google-cloud-monitoring Python client library. The CLI would execute a Python script (or integrate Python code directly if the CLI is Python-based) that uses this library to fetch the serviceruntime.googleapis.com/api/request_count metric for the Gemini API. Authentication would still rely on Application Default Credentials, typically set up via gcloud auth application-default login.

Pros:

• More robust and less error-prone than parsing curl output, as the Python library handles API interaction, authentication, and JSON parsing.
• Provides a cleaner, more maintainable codebase for the CLI if it's written in Python or can easily execute Python scripts.
• Leverages Google's official client library, ensuring compatibility and best practices.
• Offers more flexibility for error handling and data processing.

Cons:

• Requires the google-cloud-monitoring Python library to be installed in the user's environment.
• Still requires gcloud for initial authentication setup (gcloud auth application-default login).
• Adds a Python dependency if the CLI is not primarily Python-based.

---

Conclusion:
For a production-ready Gemini CLI feature, Option 3 (Python Client Library) offers the best balance of robustness, maintainability, and programmatic control, assuming Python is a suitable language for the CLI's backend. Option 2 (curl with gcloud) is a viable alternative for a more lightweight, shell-script-centric implementation, but comes with increased parsing complexity. Option 1 remains the ideal but currently unavailable solution.

♊ AI, 📯 on behalf of Manamama

[Manamama]

Plus its (Gemini ♊ 's) Plan B results, replicated verbatim :

RTFM Summary: gcloud for Gemini API Usage

This document summarizes the findings from an in-depth exploration of the gcloud command-line tool's capabilities regarding checking Gemini API daily usage.

Initial Hypothesis:

It was initially hypothesized that gcloud might offer a direct or straightforward command to query Gemini API usage, similar to how it manages other Google Cloud resources.

Exploration Process:

1. Broad Help Search: Started by searching gcloud help with keywords like "usage," "metrics," "quota," and "monitoring." This yielded results primarily related to managing monitoring configurations for services like container clusters and dataproc clusters, not direct API usage data.
2. Service-Specific Help: Attempted to explore gcloud services list --help and gcloud services describe --help. While gcloud services list confirmed the presence of the Generative Language API (which is the Gemini API), there was no direct describe command for services that would provide usage metrics.
3. Monitoring Group Exploration: Investigated gcloud monitoring --help. This revealed that the gcloud monitoring command group is focused on managing Cloud Monitoring dashboards, snoozes, and uptime checks, rather than providing a direct interface to query raw metric time-series data.

Conclusion:

Based on this RTFM process, it is concluded that the gcloud command-line tool does not provide a direct, simple command to retrieve Gemini API daily usage statistics.

Why the curl Approach is Necessary:

The absence of a direct gcloud command for this specific purpose means that retrieving Gemini API usage requires direct interaction with the Google Cloud Monitoring API. The curl command, as previously discussed, serves as the most syntactically pithy command-line method to achieve this. It leverages gcloud only for obtaining the necessary project ID and authentication token, but the core query for usage data is performed directly against the Monitoring API's timeSeries:query endpoint.

This approach, while more verbose than a hypothetical direct gcloud command, is currently the most direct command-line method for accessing this specific metric without resorting to writing a custom script (e.g., using the Python client library).

♊ AI

[Manamama]

Implementation Plan: Checking Gemini API Daily Usage in the CLI

This document outlines a phased implementation plan for integrating Gemini API daily usage checking into the Gemini CLI. The approaches are ordered from most feasible and recommended to least feasible, providing a clear path forward for development.

---

Phase 1: Most Feasible - Google Cloud Monitoring via Python Client Library

Approach Overview:

This approach leverages the official google-cloud-monitoring Python client library to programmatically query Google Cloud Monitoring for Gemini API usage data. This method is highly recommended due to its robustness, reliability, and the clean programmatic interface it offers.

Core Mechanism:

The Python client library will fetch the cloudaicompanion.googleapis.com/usage/response_count metric. This metric accurately tracks the total number of API responses, which directly correlates to requests made. Daily usage is derived by setting a 24-hour alignment period and using the ALIGN_DELTA aggregator on this cumulative metric.

Minimal Pseudocode/Example:

from google.cloud import monitoring
from datetime import datetime, timedelta

def get_gemini_api_daily_usage(project_id: str) -> int:
    client = monitoring.Client(project=project_id)
    end_time = datetime.utcnow()
    start_time = end_time - timedelta(days=1) # Query for the last 24 hours

    query = client.query(metric_type='cloudaicompanion.googleapis.com/usage/response_count')
    query = query.select_interval(start_time, end_time)
    query = query.align(monitoring.Aggregation.Aligner.ALIGN_DELTA, hours=24)

    total_requests = 0
    for ts in query.fetch():
        for point in ts.points:
            # Assuming double_value for simplicity, could be int64_value or distribution_value.count
            total_requests += point.value.double_value
    return int(total_requests)

# Example CLI integration:
# PROJECT_ID = GET_GCLOUD_PROJECT_ID()
# if PROJECT_ID:
#     daily_usage = get_gemini_api_daily_usage(PROJECT_ID)
#     DISPLAY_MESSAGE(f"Gemini API requests (last 24h): {daily_usage}")

Pros:

• Robustness & Reliability: Utilizes Google's official, well-maintained Python client library, ensuring stable and accurate interaction with the Cloud Monitoring API. This minimizes the risk of breaking changes from API updates.
• Programmatic Control: Offers a clean, object-oriented interface for querying metrics, handling authentication, and parsing responses. This is significantly less error-prone and more maintainable than shell scripting and manual JSON parsing.
• Accurate Metric: Employs the specific cloudaicompanion.googleapis.com/usage/response_count metric, which is designed for tracking API responses and provides precise usage data for the Gemini API.
• Flexible Error Handling: Python's exception handling mechanisms allow for robust error management, providing clear feedback to the user in case of issues (e.g., network errors, authentication failures, API limits).
• Integration with Existing Tools: Authentication seamlessly integrates with gcloud auth application-default login, a standard practice for Google Cloud development.

Cons:

• Billing Requirement: Cloud Monitoring requires billing to be enabled for the Google Cloud project, even for free-tier usage. This might be an unexpected hurdle for some users.
• Python Dependency: Introduces a dependency on Python and the google-cloud-monitoring library. If the Gemini CLI is not primarily Python-based, this might require setting up a Python execution environment or bundling Python.
• Initial Setup: Users need to ensure gcloud is installed and gcloud auth application-default login has been run.

---

Phase 2: Viable Alternative - Google Cloud Monitoring via curl (Command Line)

Approach Overview:

This approach directly executes curl commands from the CLI to interact with the Google Cloud Monitoring API. It relies on gcloud to dynamically retrieve the project ID and an access token, which are then embedded into the curl request.

Core Mechanism:

A curl command is constructed to make a POST request to the https://monitoring.googleapis.com/v3/projects/{PROJECT_ID}/timeSeries:query endpoint. The request body contains a Monitoring Query Language (MQL) string to filter for serviceruntime.googleapis.com/api/request_count (or cloudaicompanion.googleapis.com/usage/response_count if preferred, though the curl example often uses the former) for the generativelanguage.googleapis.com service, aggregated daily.

Minimal Pseudocode/Example:

FUNCTION get_gemini_api_daily_usage_curl():
    PROJECT_ID = RUN_SHELL_COMMAND("gcloud config get-value project")
    ACCESS_TOKEN = RUN_SHELL_COMMAND("gcloud auth print-access-token")
    YESTERDAY_UTC = RUN_SHELL_COMMAND("date -u -d \"yesterday\" +%Y-%m-%dT00:00:00Z")
    TODAY_UTC = RUN_SHELL_COMMAND("date -u +%Y-%m-%dT00:00:00Z")

    CURL_COMMAND = "curl -X POST "
    CURL_COMMAND += "  \"https://monitoring.googleapis.com/v3/projects/" + PROJECT_ID + "/timeSeries:query\" "
    CURL_COMMAND += "  -H \"Authorization: Bearer " + ACCESS_TOKEN + "\" "
    CURL_COMMAND += "  -H \"Content-Type: application/json\" "
    CURL_COMMAND += "  --data \"{"
    CURL_COMMAND += "    \"query\": \"fetch consumed_api | metric serviceruntime.googleapis.com/api/request_count | filter resource.labels.service = \\\"generativelanguage.googleapis.com\\\" | align delta() | every 1d | within \\\"" + YESTERDAY_UTC + "\\\",\\\"" + TODAY_UTC + "\\\"\\""
    CURL_COMMAND += "  }\"

    CURL_RESPONSE = RUN_SHELL_COMMAND(CURL_COMMAND)
    // Parse CURL_RESPONSE.STDOUT to extract usage data
    RETURN PARSE_JSON_AND_EXTRACT_USAGE(CURL_RESPONSE.STDOUT)

Pros:

• Minimal External Dependencies (CLI perspective): Does not require installing additional Python libraries if the CLI is not Python-based. Relies only on gcloud and curl, which are common CLI tools.
• Direct Command-Line Execution: The core logic is expressed directly as a shell command, which can be appealing for shell-centric CLI designs.
• Leverages Existing Infrastructure: Still utilizes the robust Google Cloud Monitoring service.

Cons:

• Complexity of curl Command: The curl command itself is long, complex, and highly sensitive to quoting, making it difficult to construct and debug.
• JSON Parsing Burden: Extracting the relevant usage data from the curl's JSON response requires robust parsing logic within the CLI, which can be cumbersome to implement in shell scripts or non-Python environments.
• Error Proneness: More susceptible to errors due to shell environment variations, quoting issues, and manual JSON parsing.
• Metric Precision: The serviceruntime.googleapis.com/api/request_count metric might be less precise for Gemini API usage compared to the cloudaicompanion.googleapis.com/usage/response_count metric identified in the Python approach.
• Billing Requirement: Similar to the Python approach, Cloud Monitoring requires billing to be enabled.

---

Phase 3: Least Feasible - Hypothetical Direct Gemini API Endpoint

Approach Overview:

This represents the ideal, but currently unavailable, solution. It envisions a dedicated, public API endpoint provided by Google specifically for querying Gemini API usage statistics (e.g., requests made, remaining quota).

Core Mechanism:

The CLI would make a simple HTTP GET request to a hypothetical https://api.gemini.google.com/v1/usage/daily endpoint, authenticated with the user's Gemini API token. The response would directly contain the daily usage and quota information.

Minimal Pseudocode/Example:

FUNCTION get_gemini_api_daily_usage_hypothetical():
    AUTH_TOKEN = GET_GEMINI_API_AUTH_TOKEN()
    IF AUTH_TOKEN IS NULL: RETURN ERROR

    USAGE_API_URL = "https://api.gemini.google.com/v1/usage/daily" // Hypothetical
    HEADERS = {"Authorization": "Bearer " + AUTH_TOKEN}

    RESPONSE = HTTP_GET(USAGE_API_URL, HEADERS)
    IF RESPONSE.STATUS_CODE == 200:
        RETURN PARSE_JSON(RESPONSE.BODY).dailyRequestsUsed
    ELSE: RETURN ERROR

Pros:

• Simplicity & Efficiency: Would be the most direct, efficient, and easiest to implement solution if available.
• Purpose-Built: Designed specifically for usage tracking, likely providing the most accurate and user-friendly data.
• Minimal Dependencies: Would require minimal external dependencies for the CLI itself.

Cons:

• Currently Unavailable: This API endpoint does not exist publicly for free-tier usage. This remains a hypothetical ideal.
• Reliance on External Development: Depends entirely on Google developing and maintaining such a specific endpoint.

---

Conclusion and Recommendation:

Based on the current landscape and detailed analysis:

Recommendation: The Google Cloud Monitoring Python Client Library (Phase 1) is the recommended approach for implementing Gemini API usage checking in the CLI. It offers the best balance of robustness, programmatic control, and accuracy, making it the most sustainable and maintainable solution for a production-ready feature.

Alternative: The Google Cloud Monitoring via curl (Phase 2) is a viable alternative, especially for shell-script-heavy CLIs, but developers should be aware of the increased complexity in command construction and JSON parsing.

Future Consideration: The Hypothetical Direct Gemini API Endpoint (Phase 3) remains the ultimate ideal. Should Google release such an API, it would likely supersede the other methods due to its inherent simplicity and directness.