Gemini API and SDK

Connect Gemini through Passion8 using either the native Gemini protocol or the OpenAI compatible protocol. Both use a Passion8 key and an enabled console model; no Google account or Google API key is required.

#Choose a protocol

Google's own OpenAI compatible endpoint has its own path. Passion8's compatible endpoint is /v1; do not append Google's example path to it.

#Prepare environment variables

macOS / Linux:

export PASSION8_API_KEY="replace-with-your-Passion8-key"
export PASSION8_GEMINI_MODEL="replace-with-an-enabled-Gemini-model-ID"

Windows PowerShell:

$env:PASSION8_API_KEY="replace-with-your-Passion8-key"
$env:PASSION8_GEMINI_MODEL="replace-with-an-enabled-Gemini-model-ID"

Copy the full model ID from the console. These variable names belong to this SDK example and do not update Gemini CLI. For CLI variables, see installation and configuration.

#Native Gemini: a minimal request

On macOS / Linux:

curl --fail-with-body \
  "https://passion8.cc/v1beta/models/${PASSION8_GEMINI_MODEL}:generateContent" \
  -H "x-goog-api-key: $PASSION8_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"contents":[{"role":"user","parts":[{"text":"Reply only OK."}]}]}'

Text appears in candidates[].content.parts[].text. If it is absent, inspect the error body or finish reason; an empty candidate is not evidence of a successful connection.

#OpenAI compatible: Python SDK

Install the SDK in your project's virtual environment:

python -m pip install --upgrade openai

Save this as gemini_smoke.py, then run python gemini_smoke.py:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["PASSION8_API_KEY"],
    base_url="https://passion8.cc/v1",
)

response = client.chat.completions.create(
    model=os.environ["PASSION8_GEMINI_MODEL"],
    messages=[{"role": "user", "content": "Reply only OK."}],
)
print(response.choices[0].message.content)

This uses the OpenAI compatible protocol and does not load Gemini CLI configuration. Verify a regular request before testing streaming, images or tools; compatibility does not guarantee every native Gemini field.

#Stream output

Replace the request section with:

stream = client.chat.completions.create(
    model=os.environ["PASSION8_GEMINI_MODEL"],
    messages=[{"role": "user", "content": "Explain this project in three sentences."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices:
        print(chunk.choices[0].delta.content or "", end="", flush=True)
print()

Production code should handle authentication, rate limits and timeouts using the SDK's actual error types. Check the key for 401; inspect error details and retry guidance for 429. Do not repeatedly retry an invalid model ID.

#Verify results and capability boundaries

Check request time, model and billing in the Passion8 console. Images, structured outputs, function calls, search and caching are separate features to verify individually. Google's hosted Grounding or search documentation does not establish availability on every Passion8 channel.

For editor setup, see Gemini in VS Code. For project tasks, see coding workflow.

#API Setup Questions

#Can native Gemini and OpenAI-compatible requests use the same key?

Both protocols use a Passion8 key, without a Google API key. Access to the selected model still depends on token permissions. Native requests use x-goog-api-key and https://passion8.cc/v1beta/models/{model}:generateContent; the OpenAI SDK uses Bearer authentication and https://passion8.cc/v1 as its base URL. Keep the URL and request body consistent with the protocol.

#Can I copy every Google example directly into the OpenAI SDK?

You cannot copy native contents and parts request bodies directly into Chat Completions. OpenAI-compatible requests use messages; native requests use contents. Verify images, tools, search and caching separately for the protocol and Passion8 route you choose.

#Verified sources

Checked: 2026-10-11.

• Gemini OpenAI compatibility
• Gemini generateContent API
• Gemini CLI configuration