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Kernel Memory

An archived C# research repository offering a reference implementation for AI and LLM memory, indexing, semantic search, and retrieval-augmented generation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Kernel Memory — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Kernel Memory when the goal is to learn from or prototype an indexing, retrieval, semantic-search, or RAG workflow in a C# and .NET-oriented environment.

Who it is for

  • Developers prototyping LLM applications with document retrieval
  • Researchers and learners studying memory and RAG workflows
  • Teams exploring semantic search and indexed knowledge architectures

Documented uses

  • Index documents and generate embeddings
  • Search indexed information semantically
  • Retrieve context for RAG prompts
  • Attach persistent memory and filtering to an AI application
  • Study a reference implementation for LLM memory
Before you choose

Requirements & limits

The repository is publicly visible and lists an MIT license. It is documented as an archived research project without production support.

  • The project is written in C# and provides .NET embedded-library examples.
  • Document ingestion covers text extraction, chunking, embedding generation, and vector-index storage.
  • Supported examples include a web service, Docker container, plugins, and an embedded .NET component.
  • Tags and filters can organize documents and restrict retrieval queries.
  • The README cautions that the code is a demonstration and not production software.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Index a tagged document through the embedded .NET component

Import a document into a serverless Kernel Memory instance with collection and fiscal-year tags.

Before you start

  • A .NET project using the documented Kernel Memory APIs.
  • An OpenAI API key available through the OPENAI_API_KEY environment variable.
  • A document file such as meeting-transcript.docx or business-plan.docx.

Steps

  1. Build a MemoryServerless instance with KernelMemoryBuilder and the documented OpenAI defaults.

  2. Call ImportDocumentAsync with the document path.

  3. Optionally assign a document ID and tags such as collection and fiscalYear.

  4. Use the resulting memory instance for the documented retrieval workflow.

Expected outcome

The document is imported into the embedded memory component with metadata available for organization and filtering.

Guide limitations

  • The repository describes the code as an archived research project and not production software.
  • The documented example depends on OpenAI defaults and does not establish operational support.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
1 October 2026
Original website title
microsoft/kernel-memory · Official homepage title · retained in its source language
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