If you've followed my journey building autonomous AI agents, you know my motto: "If I can do it, anyone can." I don't have a deep coding background, but I'm driven by the power of automation. But recently, I hit a wall. A bug so stubborn, so illogical, it felt like it shouldn't exist. It nearly broke my project.
This post is the story of that bug, our epic troubleshooting journey, and the final, definitive solution. If you're building with Google's Gemini API and GitHub Actions, this might just save you days of frustration.
Imagine building a brand-new car. You install the latest engine, the newest software, everything is state-of-the-art. But when you turn the key, the dashboard flashes an error from a 10-year-old model. That's exactly what this bug felt like.
My AI agent, running on GitHub Actions, would fail every single time with this cryptic message:
google.api_core.exceptions.NotFound: 404 models/gemini-pro is not found for API version v1beta...
The error was maddening for a simple reason: the v1beta API is old and deprecated. My code was using the latest libraries, so it should have been calling the modern v1 API. It was like having the right address, but the GPS kept taking me to an old, demolished building.
We tried everything:
Nothing worked. The traceback log always showed the same thing: the code was somehow being forced to use the old v1beta endpoint.
After days of debugging, we realized the problem wasn't just one thing. It was a conspiracy of three separate issues that created the perfect storm. The solution wasn't a single silver bullet, but a precise, three-part playbook that attacks the problem at its roots.
The first clue was that the problem was the environment itself. The GitHub Actions runner, by default, uses the latest operating system (`ubuntu-latest`), which can sometimes have subtle bugs. We also discovered that its caching mechanism could be causing it to reuse old, broken library versions even when we asked for new ones.
The Fix: Edit your .github/workflows/your-workflow-file.yml to be more explicit.
runs-on: ubuntu-latest to the more stable, time-tested runs-on: ubuntu-22.04.--no-cache-dir --force-reinstall to your `pip install` command to guarantee a fresh, clean installation every single time.
jobs:
build:
# Using a stable, time-tested OS is crucial.
runs-on: ubuntu-22.04
steps:
# ... your other steps ...
- name: Install Python dependencies (Force Clean Install)
run: |
python -m pip install --upgrade pip
# This command guarantees a fresh install, defeating any caching issues.
pip install --no-cache-dir --force-reinstall requests "google-generativeai>=0.5.4"
The second problem was that the `langchain` library, while powerful, was acting as a "black box." It was getting confused by the faulty environment. We needed to take direct control.
The Fix: We rewrote the AI call in the agent.py script to use Google's official library directly and explicitly told it which API endpoint to use. This is the kill shot for the v1beta bug.
# scripts/agent.py
import google.generativeai as genai
from google.api_core.client_options import ClientOptions
# ... inside your function that calls the AI ...
try:
# This is the magic line. It forces the code to use the modern API endpoint.
client_options = ClientOptions(api_endpoint="generativelanguage.googleapis.com")
genai.configure(
api_key=YOUR_GEMINI_API_KEY,
client_options=client_options
)
# ... now you can create and use your model ...
except Exception:
# This detailed error log is crucial for future debugging!
print(f"!!! ERROR: The AI call failed. See full traceback below. !!!")
traceback.print_exc(file=sys.stdout)
This was the final, critical lesson. We assumed we knew the right model name (`gemini-pro`), but the `404 Not Found` error was literally true—for my specific account, that model wasn't available.
What Not to Do: Never guess a model name or copy it from a tutorial without checking. Model availability can differ between accounts.
The Fix: Go to the source of truth—your Google AI Studio.
In my case, the correct ID was gemini-flash-latest. I replaced my old model name with this verified one, and the agent roared to life.
After applying these three fixes, the agent ran perfectly. The `v1beta` error was gone, and my automated publisher was finally live.
The journey was frustrating, but the lesson is powerful: sometimes the bug isn't in your code's logic, but in the invisible environment around it. By taking control of the environment, bypassing unnecessary layers, and verifying your assumptions at the source, you can defeat even the most "impossible" bugs.
Now, go build your agent!