Evan Mattson
7464a59228
* Bump Python package versions for 1.11.0 release Bump the CHANGELOG-selected packages for the 1.11.0 release: core and the root package move to 1.11.0 for the new stable APIs, Foundry and OpenAI receive patch bumps, changed prerelease packages receive the 260709 stamp or next RC counter, and Monty joins the bump set for corrected published dependency metadata. No beta cohort bump was applied. Raise core floors conservatively on every package publishing this cycle and correct dependency floors exposed by lower-bound validation. Copilot-Session: ee33d338-c1fc-4182-9106-0345ccf26b8e * Fix Gemini streaming type suppression Move the targeted Pyright suppression to the SDK contents argument, where the google-genai invariant content-list alias produces the compatibility diagnostic, and remove the now-unnecessary member suppression. Copilot-Session: ee33d338-c1fc-4182-9106-0345ccf26b8e * Raise Monty core dependency floor Align Monty with the conservative release policy by requiring agent-framework-core 1.11.0 or later for the package version published in this cycle. Copilot-Session: ee33d338-c1fc-4182-9106-0345ccf26b8e
Get Started with Microsoft Agent Framework Gemini
Install the provider package:
pip install agent-framework-gemini --pre
Gemini Integration
The Gemini integration enables Microsoft Agent Framework applications to call Google Gemini models with familiar chat abstractions, including streaming, tool/function calling, and structured output.
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 either the package-prefixed or SDK-standard environment variable:
export GEMINI_API_KEY="your-api-key"
# or: export GOOGLE_API_KEY="your-api-key"
export GEMINI_MODEL="gemini-2.5-flash-lite"
# or: export GOOGLE_MODEL="gemini-2.5-flash-lite"
Vertex AI
Set the standard Vertex AI environment variables used by google-genai:
export GOOGLE_GENAI_USE_VERTEXAI=true
export GOOGLE_CLOUD_PROJECT="your-project-id"
export GOOGLE_CLOUD_LOCATION="global"
export GOOGLE_MODEL="gemini-2.5-flash-lite"
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