Debugging Google’s Gemini API Migration: Resolving 404 NOT_FOUND and limit:0 Resource Exhausted Errors

Resuming a complex, production-grade AI pipeline after an extended hiatus often presents unforeseen technical shifts. This was the exact challenge encountered during the reactivation of Project Taniv-Hawk—an autonomous, GitHub-Actions-orchestrated Python agent that acts as a career headhunter, searching job markets, parsing listings, generating tailored cover letters with LLMs, and routing applications through the Gmail API.

While the database (Supabase) and Gmail integrations re-established easily, the natural language generation layer ran directly into a series of errors that highlight the fast-moving deprecation schedules of major LLM providers.

The Initial Roadblock: Gemini 1.5 and the v1beta 404 Error

Our agent was configured to use the industry-standard gemini-1.5-flash model. However, during the workflow run in GitHub Actions, the API client returned a generic but fatal error:

This message pointed out that the endpoint was no longer exposing gemini-1.5-flash to the legacy v1beta API. When an SDK is updated (which happened as we ran pip install --upgrade langchain-google-genai to clear a different dependency mismatch), it changes how endpoints are queried. Google regularly modifies the availability of older model versions on specific API endpoints, leading to unexpected failures on older configurations.

The Second Hurdle: The limit:0 RESOURCE_EXHAUSTED Quota

To bypass the 404 error, we migrated our agent’s backend config to target the newer model version: gemini-2.0-flash. This successfully bypassed the 404, but the next execution immediately hit a rate-limiting wall:

The crucial detail here is limit: 0. Typically, a rate limit suggests you are sending requests too quickly. But a limit of zero indicates that Google has actively restricted your API key’s capability to access that specific model on the free tier.

This often happens to accounts or projects that have been dormant. When an API key remains inactive, or when Google shifts active development toward newer architecture tiers, older free-tier models (such as Gemini 1.5 or 2.0) are systematically locked down with a daily quota limit of zero on older keys. This encourages developers to migrate to the most current production-ready models.

The Diagnostic Solution: Building a Programmatic Model Finder

Instead of guessing which model identifiers were still active and allowed on our API key, we constructed a lightweight, automated diagnostic tool directly in our CI/CD pipeline. We added a new manual GitHub workflow, find_gemini_models.yml, to query Google’s model metadata service using the active GEMINI_API_KEY.

Here is the clean YAML workflow configuration:

name: Find My Gemini Models

on:
  workflow_dispatch: # Allows manual trigger

jobs:
  find-models:
    runs-on: ubuntu-latest
    steps:
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.x'
      - name: Install Google GenAI
        run: pip install google-generativeai
      - name: List Available Models
        env:
          GOOGLE_API_KEY: ${{ secrets.GEMINI_API_KEY }}
        run: |
          python -c "
          import os
          import google.generativeai as genai
          genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
          print('--- My Available Models ---')
          for model in genai.list_models():
              if 'generateContent' in model.supported_generation_methods:
                  print(model.name)
          print('-------------------------')
          "

Executing this workflow gave us a clear, real-time index of every model available on our specific key. The output logs revealed the path forward:

The diagnostic log confirmed that Google had rolled out the Gemini 3.x architecture (including gemini-3.5-flash and gemini-3.1-flash-lite), migrating the default free-tier allowances over to these newer, more advanced models, while setting the limits on older generations to zero.

The Resolution: Upgrading to Gemini 3.5

With this information, we updated our Python agent’s agent.py core generation logic to transition from the failing gemini-2.0-flash to the fully active gemini-3.5-flash model. We configured the LLM using the modern langchain-google-genai SDK parameters:

llm = ChatGoogleGenerativeAI(
    model="gemini-3.5-flash", # Transitioning to active 3.5 architecture
    google_api_key=api_key,
    temperature=0.7,
    convert_system_message_to_human=True
)

On the subsequent run, the agent executed flawlessly: searching the target markets, retrieving 12 unique listings, and successfully passing the descriptions to the newly active model:

Key Takeaways for AI Developers

• Quota Limits of Zero: If you receive a RESOURCE_EXHAUSTED error with a limit of 0, do not assume you’ve used up your free quota. It is often Google’s way of indicating that the targeted model version has been locked or deprecated for your specific API key.
• Programmatic Discovery: When dealing with fast-changing APIs, do not guess at supported model strings. Writing simple discovery scripts to call list_models() saves hours of manual debugging.
• Keep SDK Instantiations Clean: Ensure your LangChain wrappers are kept clean and use standard model names as exposed by the model directory of your active keys.