{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Provides RAG (Retrieval Augmented Generation) context providers for Azure AI Search, supporting both semantic hybrid search and agentic multi-hop reasoning modes within the Microsoft Agent Framework.","skillfed_tags":["rag-retrieval","azure-integration","agent-framework"],"use_cases":["Build RAG agents that retrieve context from Azure AI Search indexes using hybrid vector + keyword search with semantic ranking.","Implement multi-hop reasoning agents that iteratively refine queries against Knowledge Bases to answer complex questions.","Enforce document-level access control in agent queries by passing user-specific credentials to retrieve only authorized results.","Migrate between stable and preview Azure AI Search API versions without code changes\u2014just update the install command.","Integrate Azure AI Search retrieval into larger agent-framework applications for context-aware decision-making."],"what_it_does":"This package integrates Azure AI Search into the Microsoft Agent Framework, enabling retrieval-augmented generation (RAG) for agent applications. It offers two retrieval modes: Semantic Mode performs fast hybrid search combining vector and keyword matching with semantic ranking, while Agentic Mode supports multi-hop reasoning over Knowledge Bases for complex queries that require iterative refinement. The package automatically detects which version of azure-search-documents is installed and adapts its behavior accordingly\u2014stable builds use extractive output with minimal reasoning, while preview builds unlock advanced features like answer synthesis and extended reasoning effort levels.\n\nThe integration handles query-time user identity by forwarding caller-specific Azure AI Search authorization tokens, enabling document-level access control when the index uses permission fields. It supports both API key and managed identity authentication, and configuration is typically done via environment variables. The package is in active development (beta status) and requires Python 3.10 or later.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, and offers low install friction. It's permissively licensed (MIT) and supports current Python versions. The integration is well-suited if you're building agents that need RAG capabilities with Azure AI Search, especially if you require either semantic hybrid search or multi-hop reasoning. The auto-detection of API versions is a practical design choice that reduces maintenance burden."},"id":"agent-framework-azure-ai-search","links":{"html":"https://skillfed.io/packages/agent-framework-azure-ai-search","md":"https://skillfed.io/packages/agent-framework-azure-ai-search.md","pypi":"https://pypi.org/project/agent-framework-azure-ai-search/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"agent-framework-azure-ai-search","python_support":"supports_current","summary":"Azure AI Search integration for Microsoft Agent Framework."},"popularity":{"monthly_downloads":477949,"position":6441,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.0b260813"}