Giles Odigwe
e39a8a2e79
* Bump Python package versions for 1.13.0 release Bump all 37 Python package projects because the CHANGELOG-driven release includes cross-package feature-usage telemetry, with core and root advancing to 1.13.0, OpenAI to 1.12.0, patch bumps for other stable packages, and 260730 stamps for alpha and beta packages. No optional beta cohort bump was applied; every prerelease package changed. Raise core floors conservatively across co-released packages. Copilot-Session: e234a28b-c2fd-4ff4-a51d-3d8917936541 * Align co-released Python package dependencies Update the four hosting adapter pins to the co-released agent-framework-hosting alpha and raise the Azure Functions Durable Task floor to the co-released beta. Copilot-Session: e234a28b-c2fd-4ff4-a51d-3d8917936541 * Minimize Python release lockfile updates Regenerate uv.lock with the pre-commit hook pinned uv version so the release changes only workspace package versions while preserving platform markers and agentlightning 0.3.0. Copilot-Session: e234a28b-c2fd-4ff4-a51d-3d8917936541 --------- Copilot-Session: e234a28b-c2fd-4ff4-a51d-3d8917936541
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