Content
# Memorizer
[](https://hub.docker.com/r/petabridge/memorizer)   
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
- Edit memory content with automatic versioning and change tracking
- Update memory metadata (title, type, tags, confidence) independently
- Revert memories to previous versions with full audit trail
- Create relationships between memories to form knowledge graphs
- Web UI for manually adding, editing, deleting, viewing memories, and managing versions
- 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](https://getakka.net/) for background jobs, such as re-embedding memories if you change algorithms
- Npgsql for PostgreSQL connectivity
---
## 📖 Documentation
- [Configuration & Advanced Setup](docs/configuration.md)
- [Security Configuration](docs/security.md)
- [Embedding Models & Dimensions](docs/embedding-models.md)
- [Embedding Dimension Migration](docs/embedding-migration.md)
- [Local Development](docs/local-development.md)
- [Schema Migrations](docs/schema-migrations.md)
- [Architecture Decision Records](docs/adr/README.md)
---
## 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`](docker-compose.yml) file:
```bash
docker-compose up -d
```
This will:
- Download and run the latest [`petabridge/memorizer` image from Docker Hub](https://hub.docker.com/r/petabridge/memorizer)
- 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
```bash
# 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
```bash
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`):
```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 with full CRUD operations
- **Version Control**: View version history, compare versions with visual diffs, and revert to previous versions
- **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:
>
> **Storage & Retrieval:**
> - `store`: Store a new memory. Parameters: `type`, `text` (markdown), `source`, `title`, `tags`, `confidence`, `relatedTo` (optional, memory ID), `relationshipType` (optional).
> - `searchMemories`: Search for similar memories using semantic similarity. Parameters: `query`, `limit`, `minSimilarity`, `filterTags`.
> - `get`: Retrieve a memory by ID. Parameters: `id`, `includeVersionHistory`, `versionNumber`.
> - `getMany`: Retrieve multiple memories by their IDs. Parameter: `ids` (list of IDs).
> - `delete`: Delete a memory by ID. Parameter: `id`.
>
> **Editing & Updates:**
> - `edit`: Edit memory content using find-and-replace (ideal for checking off to-do items, updating sections). Parameters: `id`, `old_text`, `new_text`, `replace_all`.
> - `updateMetadata`: Update memory metadata (title, type, tags, confidence) without changing content. Parameters: `id`, `title`, `type`, `tags`, `confidence`.
>
> **Relationships & Versioning:**
> - `createRelationship`: Create a relationship between two memories. Parameters: `fromId`, `toId`, `type` (e.g., 'example-of', 'explains', 'related-to').
> - `revertToVersion`: Revert a memory to a previous version. Parameters: `id`, `versionNumber`, `changedBy`.
>
> All edits and updates are automatically versioned, allowing you to track changes and revert if needed. Use these tools to remember, recall, edit, relate, and manage information as needed to assist the user.
---
## License
MIT
---
## 💖 Attribution
Made with ❤️ by [Petabridge](https://petabridge.com/)
Originally forked from [Dario Griffo](https://dario.griffo.io/)'s [`postg-mem`](https://github.com/dariogriffo/postg-mem) server
