| title | Agent Framework Integrations |
|---|---|
| description | Agent Framework Integrations |
| author | westey-m |
| ms.topic | conceptual |
| ms.author | westey |
| ms.date | 01/27/2026 |
| ms.service | agent-framework |
| zone_pivot_groups | programming-languages |
Microsoft Agent Framework has integrations with many different services, tools and protocols.
| UI Framework | Release Status |
|---|---|
| AG UI | Preview |
| Agent Framework Dev UI | Preview |
| Purview | Preview |
Microsoft Agent Framework supports many different agent types with different chat history storage capabilities. In some cases agents store chat history in the AI service, while in others Agent Framework manages the storage.
To allow chat history storage to be customized when managed by Agent Framework, custom Chat History Providers may be supplied. Here is a list of existing providers that can be used.
::: zone pivot="programming-language-csharp"
| Chat History Provider | Release Status |
|---|---|
| In-Memory Chat History Provider | Released |
| Cosmos DB Chat History Provider | Preview |
::: zone-end
::: zone pivot="programming-language-python"
| Chat History Provider | Release Status |
|---|---|
| Redis History Provider | Preview |
::: zone-end
AI Context Providers are plugins for ChatClientAgent instances and can be used to add memory to an agent. This is done by extracting memories from new messages provided by the user or generated by the agent, and by searching for existing memories and providing them to the AI service with the user input.
Here is a list of existing providers that can be used.
::: zone pivot="programming-language-csharp"
| Memory AI Context Provider | Release Status |
|---|---|
| Chat History Memory Provider | Released |
::: zone-end
::: zone pivot="programming-language-python"
| Memory AI Context Provider | Release Status |
|---|---|
| Mem0 Memory Provider | Preview |
| Neo4j Memory Provider | Preview |
| Purview Context Provider | Preview |
| Redis Provider | Preview |
::: zone-end
AI Context Providers are plugins for ChatClientAgent instances and can be used to add RAG capabilities to an agent. This is done by searching for relevant data based on the user input, and passing this data to the AI service with the other inputs.
Here is a list of existing providers that can be used.
::: zone pivot="programming-language-csharp"
| RAG AI Context Provider | Release Status |
|---|---|
| Neo4j GraphRAG Provider | Preview |
| Text Search Provider | Released |
::: zone-end
::: zone pivot="programming-language-python"
| RAG AI Context Provider | Release Status |
|---|---|
| Azure AI Search Provider | Preview |
| Neo4j GraphRAG Provider | Preview |
::: zone-end
Microsoft Agent Framework supports integration with many different vector stores. These can be useful for doing Retrieval Augmented Generation (RAG) or storage of memories.
::: zone pivot="programming-language-csharp"
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.
| 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.
::: zone-end
::: zone pivot="programming-language-python"
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.
::: zone-end
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