Memorizer

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Memorizer is a .NET-based service that allows AI agents to store, retrieve, and search through memories using vector embeddings. It leverages PostgreSQL with the pgvector extension to provide efficient similarity search capabilities.

Key features:

Store structured memories with vector embeddings
Retrieve memories by ID
Semantic search through memories using vector similarity
Filter search results using tags
Create relationships between memories to form knowledge graphs
UI for manually adding, editing, deleting, or viewing memories
MCP (Model Context Protocol) integration for easy use with AI agents

Technologies

.NET 9.0
PostgreSQL with pgvector extension
Model Context Protocol (MCP)
ASP.NET Core
Akka.NET for background jobs, such as re-embedding memories if you change algorithms
Npgsql for PostgreSQL connectivity

Installation with Docker

🐳 Quick Start (Public Image)

The easiest way to get started is using the pre-built Docker image and our docker-compose.yml file:

docker-compose up -d

This will:

Download and run the latest petabridge/memorizer image from Docker Hub
Start PostgreSQL with pgvector (port 5432)
Start PgAdmin (port 5050)
Start Ollama (port 11434)
Start Memorizer API (port 5000)

View the Memorizer Web UI on http://localhost:5000/ui.

🚀 Local Development Builds

If you want to build and run from source:

Prerequisites

Docker and Docker Compose
.NET 9.0 SDK

1. Build and Publish Local Container

# From solution root directory
# Build and publish the .NET container
dotnet publish -c Release /t:PublishContainer

This creates a container image named memorizer:latest.

2. Start Infrastructure and Application

docker-compose -f docker-compose.local.yml up -d

This starts the same services but uses your locally built image.

🔌 MCP Configuration Example

To use Memorizer with any MCP-compatible client, add the following to your configuration (e.g., mcp.json):

{
  "memorizer": {
    "url": "http://localhost:5000"
  }
}

This uses the modern Streamable HTTP transport (MCP spec 2025-03-26+).

🖥️ Web UI

Memorizer includes a web-based user interface for managing memories through your browser.

Access the Web UI

Once the application is running (via docker-compose up -d), you can access the Web UI at:

http://localhost:5000/ui/

Features

Memory Management: Create, view, edit, and delete memories
Search & Filter: Search memories using semantic similarity and filter by tags
Statistics Dashboard: View memory counts, tag distributions, and system statistics
MCP Configuration: Get the MCP configuration JSON for connecting clients at /ui/mcp-config

The Web UI provides a user-friendly interface for all Memorizer functionality, making it easy to manage your AI agent's memory without needing to use the MCP tools directly.

🧠 Example System Prompt for LLMs

[!IMPORTANT] ⚡ Pro Tip: Add this system prompt to your AGENT.md, Cursor Rules files, or any AI agent configuration! This will dramatically improve how often and effectively your LLM uses the Memorizer service for persistent memory management.

You have access to a long-term memory system via the Model Context Protocol (MCP) at the endpoint memorizer. Use the following tools:

store: Store a new memory. Parameters: type, content (markdown), source, tags, confidence, relatedTo (optional, memory ID), relationshipType (optional).

search: Search for similar memories. Parameters: query, limit, minSimilarity, filterTags.

get: Retrieve a memory by ID. Parameter: id.

getMany: Retrieve multiple memories by their IDs. Parameter: ids (list of IDs).

delete: Delete a memory by ID. Parameter: id.

createRelationship: Create a relationship between two memories. Parameters: fromId, toId, type.

Use these tools to remember, recall, relate, and manage information as needed to assist the user. You can also manually retrieve or relate memories by their IDs when necessary.

License

MIT

💖 Attribution

Made with ❤️ by Petabridge

Originally forked from Dario Griffo's postg-mem server

记忆器