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 Anthropic
Please install this package via pip:
pip install agent-framework-anthropic --pre
Anthropic Integration
The Anthropic integration enables communication with the Anthropic API, allowing your Agent Framework applications to leverage Anthropic's capabilities.
The package also includes Anthropic-hosted transport wrappers for:
- Azure AI Foundry via
AnthropicFoundryClient - Amazon Bedrock via
AnthropicBedrockClient - Google Vertex AI via
AnthropicVertexClient
Basic Usage Example
See the Anthropic agent examples which demonstrate:
- Connecting to a Anthropic endpoint with an agent
- Streaming and non-streaming responses
Structured system blocks for prompt caching
Use instructions with Anthropic-native system blocks when you need structured system prompt content, such as
prompt-cache cache_control metadata. Do not combine structured instructions blocks with a leading system message.
from anthropic.types.beta import BetaTextBlockParam
from agent_framework_anthropic import AnthropicClient
client = AnthropicClient()
system_blocks: list[BetaTextBlockParam] = [
{"type": "text", "text": "Stable instructions", "cache_control": {"type": "ephemeral", "ttl": "1h"}},
]
response = await client.get_response("Hello", options={"instructions": system_blocks})