Agent Framework Integrations

Microsoft Agent Framework has integrations with many different services, tools and protocols.

Browse by provider

Provider	Integration areas
Microsoft Foundry	Models, managed agents, tools, RAG, memory, evaluation, observability, local models, and hosted agents
Microsoft Azure	Azure OpenAI, Azure AI Search, Azure Cosmos DB, Azure Content Understanding, Microsoft Purview, Azure Monitor, and Azure Functions
OpenAI	Model inference, hosted tools, ChatKit, and OpenAI-compatible endpoints
Anthropic	Claude models, the Claude Agent SDK, Foundry, Bedrock, and Vertex AI
Amazon Web Services	Amazon Bedrock and Anthropic Claude on Bedrock
Google	Google Gemini and Anthropic Claude on Vertex AI
Ollama	Local model inference through native and OpenAI-compatible clients
Mistral	Mistral text embeddings

See all provider ecosystems.

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• Model providers
• Agent services
• Tools
• Context providers
• Middleware
• Evaluation
• UI: AG-UI, ChatKit, and DevUI
• All component categories
• Context provider concepts

UI Framework integrations

UI Framework	Release Status
AG-UI	Preview
ChatKit	Preview
DevUI	Preview

Middleware integrations

• Microsoft Purview

Evaluation integrations

• Microsoft Foundry

Vector Stores

Microsoft Agent Framework supports integration with many different vector stores. These can be useful for doing Retrieval Augmented Generation (RAG) or storage of memories.

To integrate with vector stores, we rely on the 📦 Microsoft.Extensions.VectorData.Abstractions package which provides a unified layer of abstractions for interacting with vector stores in .NET. These abstractions let you write simple, high-level code against a single API, and swap out the underlying vector store with minimal changes to your application. Where Agent Framework components rely on a vector store, they use these abstractions to allow you to choose your preferred implementation.

Tip

See the Vector databases for .NET AI apps documentation for more information on how to ingest data into a vector store, generate embeddings, and do vector or hybrid searches.

Vector Store Abstraction Implementations

Implementation	C#	Uses officially supported SDK	Maintainer / Vendor
Azure AI Search	✅	✅	Microsoft
Cosmos DB MongoDB (vCore)	✅	✅	Microsoft
Cosmos DB No SQL	✅	✅	Microsoft
Couchbase	✅	✅	Couchbase
Elasticsearch	✅	✅	Elastic
In-Memory	✅	N/A	Microsoft
MongoDB	✅	✅	Microsoft
Neon Serverless Postgres	Use Postgres Connector	✅	Microsoft
Oracle	✅	✅	Oracle
Pinecone	✅	❌	Microsoft
Postgres	✅	✅	Microsoft
Qdrant	✅	✅	Microsoft
Redis	✅	✅	Microsoft
SQL Server	✅	✅	Microsoft
SQLite	✅	✅	Microsoft
Volatile (In-Memory)	Deprecated (use In-Memory)	N/A	Microsoft
Weaviate	✅	✅	Microsoft

Important

The vector store abstraction implementations are built by a variety of sources. Not all connectors are maintained by Microsoft. When considering an implementation, be sure to evaluate quality, licensing, support, etc. to ensure they meet your requirements. Also make sure you review each provider's documentation for detailed version compatibility information.

Important

Some implementations are internally using Database SDKs that are not officially supported by Microsoft or by the Database provider. The Uses Officially supported SDK column lists which are using officially supported SDKs and which are not.

Agent Framework supports using Semantic Kernel's VectorStore collections to provide vector storage capabilities to agents. See the vector store connectors documentation to learn how to set up different vector store collections. See Creating a search tool from a VectorStore for more information on how to use these for RAG.

Note

Go support for this feature is coming soon. See the Agent Framework Go repository for the latest status.

Next steps

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