MemNet is a self‑improving memory layer for LLM applications for .NET developer.
$ dotnet add package MemNetMemNet is a "self-improving" memory layer designed for .NET developers, providing long-term and short-term memory management, similarity search, memory consolidation/refinement, and persistence for LLM-based applications. It facilitates context reuse in conversations, recommendations, personalization, and context-aware applications.
In simple terms: LLMs are stateless, and MemNet helps you remember users' previous behaviors and conversation content, making your applications smarter and more personalized.
Using dotnet CLI:
dotnet add package MemNet
Using NuGet Package Manager Console:
Install-Package MemNet
First, configure the Embedder, LLM, and VectorStore in your project's appsettings.json file:
{
"MemNet": {
"Embedder": {
"Endpoint": "https://api.openai.com/v1/",
"Model": "text-embedding-3-large",
"ApiKey": "your-embedding-api-key"
},
"LLM": {
"Endpoint": "https://api.openai.com/v1/",
"Model": "gpt-4",
"ApiKey": "your-llm-api-key"
}
}
}
Tip: For security purposes, it's recommended to use more secure methods to store API keys rather than writing them directly in the configuration file.
Register MemNet services in Program.cs:
using MemNet;
using MemNet.Abstractions;
using MemNet.Models;
using Microsoft.Extensions.Configuration;
using Microsoft.Extensions.DependencyInjection;
var configuration = new ConfigurationBuilder()
.AddJsonFile("appsettings.json") // Requires NuGet Package: Microsoft.Extensions.Configuration.Json
.Build();
var services = new ServiceCollection();
services.AddMemNet(configuration);
await using var serviceProvider = services.BuildServiceProvider();
var memoryService = serviceProvider.GetRequiredService<IMemoryService>();
await memoryService.InitializeAsync();
await memoryService.AddAsync(new AddMemoryRequest
{
Messages =
[
new MessageContent
{
Role = "User",
Content = "My name is Zack. I love programming."
},
new MessageContent
{
Role = "User",
Content = "As a 18-years-old boy, I'm into Chinese food."
},
new MessageContent
{
Role = "User",
Content = "I'm allergic to nuts."
}
],
UserId = "user001"
});UserId is used to distinguish memory data between different users. It also supports finer-grained differentiation through dimensions such as AgentId and RunId.
var searchResults = await memoryService.SearchAsync(new SearchMemoryRequest
{
Query = "Please recommend some food.",
UserId = "user001"
});
Console.WriteLine("Search Results:");
foreach (var item in searchResults)
{
Console.WriteLine($"- {item.Memory.Data}");
}Execution result:
Search Results:
- Cuisine preference: Chinese food
- Allergy: nuts
MemNet uses in-memory vector storage by default, which is suitable for development and testing environments. For production environments, it's recommended to use persistent vector storage backends to ensure data persistence and scalability.
MemNet supports multiple vector storage backends:
Add vector database configuration to appsettings.json (replace the values with your actual values):
"VectorStore": {
"Endpoint": "your-Qdrant-endpoint, e.g., http://localhost:6333",
"ApiKey": "your-Qdrant-apikey (optional)",
"CollectionName": "your-collection-name (optional, default is 'memnet_collection')"
}
Then modify the registration code:
services.AddMemNet(configuration).WithQdrant();Add vector database configuration to appsettings.json (replace the values with your actual values):
"VectorStore": {
"Endpoint": "your-Chroma-endpoint, e.g., http://localhost:8000",
"ApiKey": "your-Chroma-apikey (optional)",
"CollectionName": "your-collection-name (optional, default is 'memnet_collection')",
"Database": "YourDatabaseName",
"Tenant": "YourTenantName"
}
Then modify the registration code:
services.AddMemNet(configuration).WithChromaV2();Add vector database configuration to appsettings.json (replace the values with your actual values):
"VectorStore": {
"Endpoint": "your-Milvus-endpoint, e.g., http://localhost:19530",
"ApiKey": "your-Milvus-apikey (optional)",
"CollectionName": "your-collection-name (optional, default is 'memnet_collection')"
}
Then modify the registration code:
services.AddMemNet(configuration).WithMilvusV2();Add vector database configuration to appsettings.json (replace the values with your actual values):
"VectorStore": {
"Endpoint": "your-Redis-address, e.g., localhost:6379",
"ApiKey": "your-Redis-username-and-password (optional), e.g. user:password",
"CollectionName": "your-collection-name (optional, default is 'memnet_collection')"
}
services.AddMemNet(configuration).WithMemNetRedis("connection-string, e.g., localhost:6379");Please refer to the MemNet.Redis project to customize support for other vector stores. Contributions via pull requests to add new integrations are welcome.