agent-framework-anthropic
Anthropic integration for Microsoft Agent Framework.
Decision gist · record as of 2026-08-14
Yes, if you are building agents with Microsoft Agent Framework and want to use Anthropic models. The package is actively maintained, has low install friction, carries a permissive MIT license, and offers both direct and multi-cloud transport options. Be aware it is in beta—test thoroughly before production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; package is in beta (pre-release) and depends on agent-framework-core and anthropic packages.
- Low friction install with only two runtime dependencies.
- Active maintenance—released 15 days ago with recent commits and a healthy repository signal.
License · maintenance · safety
permissive license (permissive) — MIT license (permissive) means you can use, modify, and distribute this package freely with minimal restrictions.
last release 2026-07-30 (15 days) · last repo commit 2026-08-14 · 12,804 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 543,188 downloads/mo, #6,085 on PyPI
Alternatives
Verify before relying
pip install agent-framework-anthropic --pre
from agent_framework_anthropic import AnthropicClient
client = AnthropicClient()
response = await client.get_response("Hello")- Whether streaming and non-streaming response modes have feature parity or known differences.
- Whether prompt caching with ephemeral TTL is supported across all three hosted transports (Foundry, Bedrock, Vertex).
- Performance characteristics and latency overhead of the transport wrappers compared to direct Anthropic API calls.
What it is and what it does
This package bridges Microsoft's Agent Framework with Anthropic's API, letting you build agent applications that call Anthropic models. It provides a unified client interface (AnthropicClient) for direct Anthropic endpoints and three hosted transport wrappers—AnthropicFoundryClient, AnthropicBedrockClient, and AnthropicVertexClient—that route requests through Microsoft Foundry, Amazon Bedrock, and Google Vertex AI respectively.
The package supports both streaming and non-streaming responses, and includes structured system block support for Anthropic's prompt caching feature with cache control metadata. It depends on agent-framework-core for the framework scaffolding and anthropic for API communication. The package is in beta and requires Python 3.10 or later.
Use it for
- Build multi-provider agent applications that can switch between direct Anthropic and hosted transports without changing core logic.
- Implement agents with prompt caching to reduce latency and costs on repeated or similar requests to Anthropic.
- Route agent workloads through Amazon Bedrock or Google Vertex AI for compliance, latency, or organizational requirements.
- Stream agent responses for real-time interaction while maintaining structured system prompts with cache control.
- Integrate Anthropic models into existing Microsoft Agent Framework deployments.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building agents with Microsoft Agent Framework and want to use Anthropic models.
The package is actively maintained, has low install friction, carries a permissive MIT license, and offers both direct and multi-cloud transport options. Be aware it is in beta—test thoroughly before production use.
Install
agent-framework-anthropic on PyPI
Before you install
Low friction install with only two runtime dependencies. Active maintenance—released 15 days ago with recent commits and a healthy repository signal.
Requires Python 3.10 or later; package is in beta (pre-release) and depends on agent-framework-core and anthropic packages.
License in practice
MIT license (permissive) means you can use, modify, and distribute this package freely with minimal restrictions.
Quickstart
pip install agent-framework-anthropic --pre
from agent_framework_anthropic import AnthropicClient
client = AnthropicClient()
response = await client.get_response("Hello")Verify before relying
- Whether streaming and non-streaming response modes have feature parity or known differences.
- Whether prompt caching with ephemeral TTL is supported across all three hosted transports (Foundry, Bedrock, Vertex).
- Performance characteristics and latency overhead of the transport wrappers compared to direct Anthropic API calls.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesagent-framework-coreanthropic |
| Maintenance | Actively maintained 15 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 543,188 / month, #6,085 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Typing :: Typed |
Evidence: agent_framework_anthropic-1.0.0b260730-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “anthropic agent framework integration”
- agent-framework-anthropicConnects Agent Framework applications to Anthropic's API, providing…
- livekit-plugins-anthropicIntegrates Anthropic's Claude models into LiveKit's agent framework,…
- langchainLangChain provides a framework for building agents and LLM-powered…
Give your agent the search over MCP, or paste the wish link into any chat.
More Application Frameworks packages
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Textual is a Python framework for building cross-platform user interfaces that run in the terminal or web browser using a modern, component-based API.
Install it if you're developing CLI tools, dashboards, or interactive terminal applications.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Build and connect to Model Context Protocol servers that expose tools, resources, and prompts to LLM applications over stdio, HTTP, or SSE transports.
Install it if you need to build or connect to servers.
Werkzeug is a WSGI utility library providing request/response objects, URL routing, an interactive debugger, HTTP utilities, and a development server for building web applications.
See also agent-framework-bedrock · agent-framework-core · agent-framework-foundry-local · agent-framework · agent-framework-azure-ai · agent-framework-ollama · agent-framework-claude · agent-framework-foundry · bedrock-agentcore · agent-framework-foundry-hosting