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Memorizer

Memory serverOfficial Petabridge project

Created by Dario Griffo

Published in petabridge/memorizer. Publisher page

221 GitHub stars

What it is

Vector-search powered agent memory MCP server
— the description by Dario Griffo

AutoGineer overview, from the brain’s own files

Memorizer is a dedicated memory service designed to give AI agents persistent, long-term storage capabilities. It functions as an MCP server, allowing agents to store, retrieve, and semantically search through structured memories. Built on .NET and PostgreSQL with the pgvector extension, it supports advanced features like versioning, audit trails, relationship mapping for knowledge graphs, and workspace-based organization. It includes a web-based UI for manual management and provides a robust framework for agents to proactively manage their own knowledge base, ensuring that insights and context are preserved across sessions.

How it works

Memory model: Hybrid

AutoGineer overview, from the brain’s own files

Memorizer runs as a service that connects to an AI agent via the Model Context Protocol. It stores data in a PostgreSQL database, utilizing vector embeddings for semantic similarity searches. The system organizes information into workspaces and projects, supports metadata tagging, and tracks version history for all memory entries. Agents interact with the service through a set of defined tools for CRUD operations, metadata updates, and relationship creation, enabling the construction of an interconnected knowledge graph.

Folders
FolderRoleFiles
.githubagent-config4
docsdocs37
scriptsscripts3
srcsource-code195
.configfolder1

The agent writes

Requirements

Databases: PostgreSQL

Works with: Claude Code, OpenCode, Devin (ex-Windsurf)

Set it up

  1. Not scanned by AutoGineerThe author’s README also describes installing it with Docker Compose. Read its install section: AutoGineer did not scan that path. https://github.com/petabridge/memorizer/blob/d3e90fa8d6b50254d65a3ffa3acc35e0204c5c90/README.md#-quick-start-public-image

  2. Created by Dario Griffo. Published in petabridge/memorizer. Licence: MIT License.

Usage

AutoGineer overview, from the brain’s own files

  • Connects to AI agents via the Model Context Protocol (MCP) to provide persistent memory.
  • Enables semantic search to retrieve relevant context based on task descriptions.
  • Supports proactive memory management where agents store, update, and relate information autonomously.
  • Provides a web interface for human users to manage, audit, and organize memory entries.
  • Facilitates knowledge graph construction by creating explicit relationships between memory nodes.
“Search for previous instructions on how to configure the build pipeline.”
The agent queries the memory server using semantic similarity to find relevant 'how-to' notes and returns the specific configuration steps.
“Store this new insight about the project architecture as a reference.”
The agent saves the markdown content to the memory server with appropriate tags and metadata for future retrieval.
“Link this new task to the related project requirements memory.”
The agent creates a relationship edge between the new memory ID and the existing requirements memory ID.

Documentation by Dario Griffo

Memorizer

Image: Docker Pulls Image: GitHub License Image: GitHub Actions Workflow Status Image: GitHub Release

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.

Image: Memorizer - Memory List

Key features:

  • Workspaces & Projects - Organize memories into hierarchical workspaces and projects with status tracking

  • 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

  • Provider Settings - Configure embedding and LLM providers through the UI (Ollama, OpenAI, and compatible APIs)

  • MCP (Model Context Protocol) integration for easy use with AI agents

  • Light & Dark theme support

Image: Memorizer - Workspace Detail

Technologies

  • .NET 10.0

  • 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

📖 Documentation

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:

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

🚀 Local Development Builds

If you want to build and run from source:

Prerequisites

  • Docker and Docker Compose

  • .NET 10.0 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.

Upgrading to Memorizer 2.0

NOTE
The upgrade from Memorizer 1.x to 2.0 is designed to work automatically. All schema migrations run on startup and have been thoroughly tested. Your existing memories will be preserved and continue to work as expected.

CAUTION
We recommend backing up your database before upgrading, as a standard best practice. Memorizer 2.0 runs automatic schema migrations on startup that alter tables and add new columns. While we've tested these migrations extensively, having a backup ensures you can recover in the unlikely event that something goes wrong.

Backup Instructions

NOTE
The container names below (e.g., memorizer-postgres) are based on the default docker-compose.yml. If you've customized your Docker Compose file, adjust the container name accordingly.

Before pulling the new Memorizer 2.0 image, create a PostgreSQL dump of your existing database:

# Create a full database backup
docker exec postgmem-postgres pg_dump -U postgres postgmem > memorizer-backup-$(date +%Y%m%d).sql

# Or use pg_dump with compression
docker exec postgmem-postgres pg_dump -U postgres -Fc postgmem > memorizer-backup-$(date +%Y%m%d).dump

To restore from a backup if needed:

# Restore from SQL dump
docker exec -i postgmem-postgres psql -U postgres postgmem < memorizer-backup-20250207.sql

# Or restore from compressed dump
docker exec -i postgmem-postgres pg_restore -U postgres -d postgmem memorizer-backup-20250207.dump
Updating
# make sure you have created a backup before starting here
git pull
docker-compose down
docker-compose pull memorizer
docker-compose up -d
Breaking Changes in 2.0

  • MCP endpoint moved from / to /mcp. Update your MCP client configurations from http://localhost:5000 to http://localhost:5000/mcp.

  • Web UI moved from /ui to /. The Web UI is now at the root path.

  • New database tables: Workspaces, projects, provider settings, data migrations, and archetype tracking are added automatically on first startup.

  • Existing memories are preserved: All your V1 memories will continue to work. They will appear as "Unfiled" until you organize them into workspaces and projects.

After Upgrading

TIP
Once the upgrade is complete, ask your AI agent to suggest workspaces and projects to organize your existing memories into. Memorizer 2.0's workspace and project system helps you keep memories structured and easy to find - and your agent can analyze your existing memories to recommend a good organizational scheme.

🔌 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/mcp"
  }
}

This uses the modern Streamable HTTP transport (MCP spec 2025-03-26+). Note that the MCP endpoint is at /mcp (changed from / in 2.0).

🖥️ 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/

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 /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

Originally forked from Dario Griffo's postg-mem server

docs/security.md

Security Configuration

This document covers security-related configuration options for Memorizer.

CORS (Cross-Origin Resource Sharing)

CORS is always enabled by default to allow MCP clients like Claude Code to connect to the server. The default configuration is permissive to ensure the server works out of the box.

Default Configuration

By default, Memorizer uses permissive CORS settings:

  • All origins are allowed (*)

  • All HTTP methods are allowed (GET, POST, PUT, DELETE, etc.)

  • All headers are allowed

  • Credentials are not allowed (cookies, authorization headers)

This configuration works for most development and internal network deployments.

Configuring CORS

You can customize CORS settings in appsettings.json or via environment variables:

appsettings.json
{
  "Cors": {
    "AllowedOrigins": ["*"],
    "AllowedMethods": ["*"],
    "AllowedHeaders": ["*"],
    "AllowCredentials": false
  }
}
Environment Variables

When using environment variables (recommended for production), prefix with MEMORIZER_:

# Allow specific origins only
MEMORIZER_Cors__AllowedOrigins__0=https://app.example.com
MEMORIZER_Cors__AllowedOrigins__1=https://admin.example.com

# Allow specific methods
MEMORIZER_Cors__AllowedMethods__0=GET
MEMORIZER_Cors__AllowedMethods__1=POST

# Allow specific headers
MEMORIZER_Cors__AllowedHeaders__0=Content-Type
MEMORIZER_Cors__AllowedHeaders__1=Authorization

# Enable credentials (only works with specific origins, not "*")
MEMORIZER_Cors__AllowCredentials=true
Production Recommendations

For production deployments, especially when exposed to the internet:

  1. Restrict origins to specific domains that need access:

    {
  "Cors": {
    "AllowedOrigins": [
      "https://your-app.example.com",
      "https://trusted-client.example.com"
    ]
  }
}

  2. Limit methods to only what's needed:

    {
  "Cors": {
    "AllowedMethods": ["GET", "POST", "DELETE"]
  }
}

  3. Enable credentials only if you're using authentication:

    {
  "Cors": {
    "AllowedOrigins": ["https://your-app.example.com"],
    "AllowCredentials": true
  }
}

    Note: You cannot use AllowedOrigins: ["*"] with AllowCredentials: true. You must specify exact origins.

CORS and MCP Clients

MCP (Model Context Protocol) clients connect using the Streamable HTTP transport. The MCP endpoint requires CORS to be properly configured to work with external clients.

If you're experiencing connection issues with Claude Code or other MCP clients:

  1. Verify CORS is enabled (it should be by default)

  2. Check that your AllowedOrigins includes the client's origin

  3. For local development, using ["*"] is recommended

  4. For production, add specific origins as needed

Common CORS Configurations
Development (Default)

Permissive settings for local development:

{
  "Cors": {
    "AllowedOrigins": ["*"],
    "AllowedMethods": ["*"],
    "AllowedHeaders": ["*"],
    "AllowCredentials": false
  }
}
Internal Network

Restrict to specific internal applications:

{
  "Cors": {
    "AllowedOrigins": [
      "http://internal-app:8080",
      "http://192.168.1.100:3000"
    ],
    "AllowedMethods": ["*"],
    "AllowedHeaders": ["*"],
    "AllowCredentials": false
  }
}
Production (Locked Down)

Restrict to specific production domains with credentials:

{
  "Cors": {
    "AllowedOrigins": [
      "https://app.example.com",
      "https://admin.example.com"
    ],
    "AllowedMethods": ["GET", "POST", "PUT", "DELETE"],
    "AllowedHeaders": ["Content-Type", "Authorization"],
    "AllowCredentials": true
  }
}

Database Security

Connection Strings

Always use secure connection strings in production:

# Use SSL/TLS for database connections
MEMORIZER_ConnectionStrings__Storage="Host=db.example.com;Port=5432;Database=postgmem;Username=app_user;Password=<secure-password>;SSL Mode=Require;Trust Server Certificate=false"
Credentials

  • Never commit database passwords to source control

  • Use environment variables or secrets management (e.g., Azure Key Vault, AWS Secrets Manager)

  • Follow the principle of least privilege for database users

  • Create application-specific database users with minimal required permissions

Network Security

Reverse Proxy

For production deployments, run Memorizer behind a reverse proxy (e.g., nginx, Caddy, Traefik):

  • TLS termination: Handle HTTPS at the proxy level

  • Rate limiting: Protect against abuse

  • Request filtering: Block malicious requests

  • IP restrictions: Limit access to known networks if appropriate

Firewall

  • Only expose necessary ports (typically just the web server port)

  • Use network segmentation to isolate the database

  • Consider using a VPN for administrative access

General Best Practices

  1. Keep dependencies updated: Regularly update NuGet packages for security patches

  2. Use HTTPS: Always use TLS in production (configure at reverse proxy)

  3. Monitor logs: Watch for unusual access patterns

  4. Backup data: Regular database backups with secure storage

  5. Environment isolation: Separate development, staging, and production environments

Security Reporting

If you discover a security vulnerability, please report it by:

  1. Opening a security advisory on GitHub (preferred)

  2. Contacting the maintainers directly (see README.md for contact info)

Do not open public issues for security vulnerabilities.

260 files · 244 not served here

Advantages

  • Provides persistent, long-term memory that survives agent session resets. evidence: README.md
  • Supports semantic search through vector embeddings, allowing for context-aware retrieval. evidence: README.md
  • Includes full versioning and audit trails for all memory modifications. evidence: README.md
  • Allows for the creation of knowledge graphs by linking related memories. evidence: README.md

Limitations

  • Requires external infrastructure including PostgreSQL with pgvector and an embedding provider. evidence: README.md
  • Requires manual configuration of MCP clients to point to the correct endpoint. evidence: README.md
  • Upgrades may require database backups and potential schema migrations. evidence: README.md

Security

Risk level: No risk found

This brain runs

This brain runs nothing: AutoGineer found no hooks, scripts, plugin code or MCP servers in its files.

Attribution and licence

Created by
Dario Griffo
Publisher
Petabridge (Official Petabridge project)
Source
petabridge/memorizer · commit d3e90fa
Licence
MIT License (files served here)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Dario Griffo or Petabridge.

Install from source

244 of its files are not served here, so there is no ZIP. petabridge/memorizer at d3e90fa