Memorizer
This MCP server provides AI assistants with persistent memory storage and semantic search capabilities through PostgreSQL with pgvector, built by Petabridge using .NET 9 and Akka.NET actors for background processing. The implementation offers vector-based memory storage with dual embeddings (full content and metadata-only), relationship tracking between memories, and asynchronous title generation and metadata embedding processing using Ollama for LLM operations. Built with web UI for memory management, OpenTelemetry integration, and Docker containerization, it serves AI agents needing long-term memory persistence, developers building conversational AI systems that require context retention across sessions, and teams wanting semantic search capabilities with tag-based filtering and similarity scoring for knowledge management workflows.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately.
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/petabridge/memorizer
- Author type
- human
- Updated
- 2026-05-05
Use via MCP
Resolve Memorizer from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.