Get Started with Microsoft Agent Framework Gemini

Install the provider package:

pip install agent-framework-gemini --pre

Gemini Integration

The Gemini integration uses Google's current google-genai SDK for chat and text embeddings through the Gemini Developer API or Gemini Enterprise Agent Platform (formerly Vertex AI). Chat supports streaming, tool/function calling, and structured output.

Gemini Embeddings

GeminiEmbeddingClient defaults to the stable Gemini Embedding 2 model (gemini-embedding-2). It also accepts gemini-embedding-2-preview via GOOGLE_EMBEDDING_MODEL or model=; older embedding models are not supported. It produces one embedding per input: a text string, a Google SDK media Part, or a google.genai.types.Content aggregating text and media. Text strings require a task_type in each get_embeddings call; the client does not choose one by default.

from agent_framework.gemini import GeminiEmbeddingClient

client = GeminiEmbeddingClient()
try:
    document = await client.get_embeddings(
        ["Agent Framework helps build AI agents."],
        options={"task_type": "RETRIEVAL_DOCUMENT", "dimensions": 768, "title": "Agent Framework"},
    )
    query = await client.get_embeddings(
        ["How do I build an AI agent?"],
        options={"task_type": "RETRIEVAL_QUERY", "dimensions": 768},
    )
finally:
    await client.close()

The client maps Google's Embedding 2 task instructions. Use RETRIEVAL_DOCUMENT to index documents; pair it with RETRIEVAL_QUERY for search, or QUESTION_ANSWERING, FACT_VERIFICATION, or CODE_RETRIEVAL_QUERY for those specialized queries. For CLASSIFICATION, CLUSTERING, or SEMANTIC_SIMILARITY, use the same task on all inputs; SEMANTIC_SIMILARITY is not intended for retrieval. Use the same model and dimensions for indexing and searching. title applies to every text in a call, so embed documents with different titles separately. Generic vector-store embedding generators do not infer a task type: configure GeminiEmbeddingClient as the collection's embedding_generator, pass embeddings_options={"task_type": "RETRIEVAL_DOCUMENT"} to each text upsert, and set embeddings_options={"task_type": "RETRIEVAL_QUERY"} on create_vector_search_tool. Core supplies the selected vector field's dimensions, rejecting a conflicting value. See the agent search example.

For image, audio, video, PDF, or combined text-and-media input, pass a Google SDK Part or Content with at least one media part. The client preserves its parts without a task prefix, as Google recommends for multimodal aggregates:

from google.genai import types

with open("picture.png", "rb") as image:
    image_part = types.Part.from_bytes(data=image.read(), mime_type="image/png")
mixed = types.Content(parts=[types.Part.from_text(text="A landscape photo"), image_part])
client = GeminiEmbeddingClient()
try:
    result = await client.get_embeddings([mixed], options={"dimensions": 768})
finally:
    await client.close()

Text-only Part/Content values are rejected: use a string with task_type instead. If a call mixes text strings with media, its task_type applies only to the strings; media content is never prefixed. On Enterprise, Embedding 2 accepts one content per request, so the client sends multiple inputs as separate, ordered requests. See the image search Agent sample for cross-modal text-to-image retrieval. Images are embedded without a text task and upserted with generate_vectors=False; the search tool uses RETRIEVAL_QUERY through its embeddings_options.

For chat and embeddings on Enterprise, use the current SDK setting GOOGLE_GENAI_USE_ENTERPRISE=true (or pass enterprise=True) together with GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_LOCATION. The older GOOGLE_GENAI_USE_VERTEXAI=true / vertexai=True setting remains supported. An injected google.genai.Client can also provide either authentication mode.

Structured Output

Gemini structured output can be configured with either a Pydantic model in response_format, a JSON schema mapping in response_format, or a Gemini-specific response_schema. Declarative agents that define outputSchema pass that schema through response_format.

Authentication

The connector supports both google-genai authentication modes.

Gemini Developer API

Obtain an API key from Google AI Studio and set the connector's environment variables:

export GOOGLE_API_KEY="your-api-key"
export GOOGLE_MODEL="gemini-2.5-flash-lite"

The connector no longer reads GEMINI_API_KEY, GEMINI_MODEL, or GEMINI_EMBEDDING_MODEL. Rename those variables to their GOOGLE_* equivalents, or pass the API key and model explicitly. An injected google.genai.Client retains the Google SDK's own authentication behavior.

Gemini Enterprise Agent Platform (Vertex AI)

Set the standard Enterprise environment variables used by google-genai:

export GOOGLE_GENAI_USE_ENTERPRISE=true
export GOOGLE_CLOUD_PROJECT="your-project-id"
export GOOGLE_CLOUD_LOCATION="global"
export GOOGLE_MODEL="gemini-2.5-flash-lite"

The older GOOGLE_GENAI_USE_VERTEXAI=true setting remains supported.

Examples

See the Google Gemini samples for runnable end-to-end scripts covering:

• Basic agent with tool calling and streaming
• Extended thinking with ThinkingConfig
• Google Search grounding
• Google Maps grounding
• Built-in code execution
• Text embeddings for document indexing and query retrieval