lmux-google

Google (Gemini) provider for lmux. Wraps the google-genai SDK, which serves Google's models through either backend:

  • Vertex AI (default) — authenticated with Google Cloud credentials
  • Gemini Developer API (AI Studio) — authenticated with an API key (vertexai=False)

Supports chat completions, streaming, and embeddings for Google-published models: Gemini and the Gemini/text embedding models.

Part of the lmux ecosystem: standardized interface, cost tracking on every response, and registry-based routing across providers.

Auth

Three authentication methods:

Application Default Credentials (default)

Uses google.auth.default(), which works with GOOGLE_APPLICATION_CREDENTIALS, gcloud CLI, or instance metadata.

from lmux_google import GoogleProvider

provider = GoogleProvider(project="my-project", location="us-central1")

Service Account

from lmux_google import GoogleServiceAccountAuthProvider

provider = GoogleProvider(
    project="my-project",
    location="us-central1",
    auth=GoogleServiceAccountAuthProvider(service_account_file="/path/to/key.json"),
)

API Key

Set GOOGLE_API_KEY in your environment:

from lmux_google import GoogleAPIKeyAuthProvider

provider = GoogleProvider(auth=GoogleAPIKeyAuthProvider(), vertexai=False)

Usage

Chat

from lmux import UserMessage

response = provider.chat("gemini-2.5-pro", [UserMessage(content="Hello")])
print(response.content)
print(response.cost)

Streaming

for chunk in provider.chat_stream("gemini-2.5-pro", [UserMessage(content="Hello")]):
    if chunk.delta:
        print(chunk.delta, end="")

Embeddings

response = provider.embed("text-embedding-005", "Hello")
print(response.embeddings)

Async

All methods have async variants: achat, achat_stream, aembed.

Registry

Use with the lmux registry to route across multiple providers:

from lmux import Registry

registry = Registry()
registry.register("google", provider)
response = registry.chat("google/gemini-2.5-pro", messages)

Provider Params

from lmux_google import GoogleParams

response = provider.chat(
    "gemini-2.5-pro",
    messages,
    provider_params=GoogleParams(thinking_config={"thinking_budget": 1024}),
)
Parameter Type Description
safety_settings list[SafetySetting] Content safety thresholds
presence_penalty float Presence penalty
frequency_penalty float Frequency penalty
seed int Deterministic sampling seed
labels dict[str, str] Request labels
thinking_config dict Thinking/reasoning configuration

Constructor Options

GoogleProvider(
    auth=...,       # AuthProvider, default: GoogleADCAuthProvider()
    project=...,    # GCP project ID
    location=...,   # GCP region
    vertexai=...,   # Use Vertex AI (default: True) vs. AI Studio
)

Project links

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Key dates

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  • Released:
Newer release available (0.11.0)

1 maintainer

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Avatar for cluebbehusen from gravatar.com cluebbehusen

Credits

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Author: Connor Luebbehusen

GitHub Statistics

Data verified by PyPI on Jun 30, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.13

Tags

llm ai google gemini vertex language-model

Classifiers

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