Is the memorizer MCP server actively maintained?

The memorizer MCP server was last committed 18d ago and has 6 contributors. Issue health: 40% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

memorizer MCP Server

Name: petabridge/memorizer
Rating: 3.8 (1 reviews)
Author: petabridge

petabridge/memorizer

Score: 69.4 ↓ 4.1% Rank #4180 MCP Server

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Vector-search powered agent memory MCP server

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Overview

petabridge/memorizer is a C# MCP server licensed under MIT. Vector-search powered agent memory MCP server

Ranked #4180 out of 24846 indexed tools.

Ecosystem

C# MIT

Score Breakdown

Stars 15% 216

216 stars → growing community

Freshness 25% 18d ago

Last commit 18d ago → recently updated

Issue Health 25% 40%

12/30 issues closed → many open issues

Contributors 10% 6

6 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 33

Description Brief

License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

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Issue Health high impact

You have 18 open vs 12 closed issues — triaging stale issues improves health

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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Details

Stars: 216
Forks: 33
Open Issues: 18
Closed Issues: 12
Contributors: 6
Dependents: —
Language: C#
License: MIT
Last Commit: 18d ago

Matched Queries

"mcp server""mcp-server"

From the README

# 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:
- **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, a

Read full README on GitHub →