Memoria — Persistent Context for Claude Desktop

Created By
leszini2 months ago
Persistent memory MCP server for Claude Desktop — lets Chat, Cowork, and Code share a single self-maintained memory across sessions.
Overview

memoria-mcp — Persistent memory and context awareness for Claude Desktop (Chat, Cowork, and Code share a single self-maintained memory across sessions.)

It's an MCP server that gives Claude a file-based, persistent memory system that works across sessions, surfaces, and time. When Claude starts a conversation, it calls get_context() and instantly knows who you are, what you're working on, what happened yesterday, and what's due tomorrow. When the conversation ends, Claude logs a structured summary. The next session picks up where the last one left off.

The problem it solves

Claude is stateless by design. Every new conversation is a blank slate — no memory of what you discussed, decided, or left unfinished. If you work with Claude regularly on ongoing projects, studies, or work tasks, you end up repeating yourself constantly. memoria-mcp eliminates that friction by giving Claude a structured, self-maintaining memory.

How it works

The memory lives as plain markdown files in a local folder on your machine. No database, no cloud service, no API keys — just files you can read, edit, and version-control yourself. Claude reads and writes to this folder through 17 tools organized into six categories: Context and awareness: get_context() returns a full situation overview (deadlines, projects, recent activity) in one call. get_deadlines() extracts dates from your context file and sorts them by urgency. update_context() keeps the living context model fresh. Session continuity: log_session_summary() creates lightweight, structured session traces at the end of each conversation. get_recent_activity() shows what happened in the last N days. Together, they bridge the gap between conversations so context is never lost. Reflection and intelligence: reflect("topic") synthesizes everything the memory system knows about a topic — pulling from context files, session logs, project docs, and archives into a single structured overview. suggest_context_updates() compares recent sessions against the living context model and flags stale information, unprocessed changes, and open items. acknowledge_updates() marks items as handled. Search: search_memory() does full-text search across all active files. search_archive() searches archived content including monthly digests and raw files. File management: read_memory_file(), write_memory_file(), and list_memory_files() provide direct access to the memory filesystem for creating project docs, research notes, or any structured content. Archival: archive() consolidates old sessions and logs into monthly digests, moves originals to a raw backup folder, and keeps the active file count manageable. archive_project() handles completed project files. Nothing is ever deleted — archived content remains searchable.

Key design decisions

The system uses structure over search. Instead of embedding-based retrieval, it relies on a living context document that Claude actively maintains — a markdown file describing your current situation, updated continuously. This means Claude doesn't need to "find" relevant context through semantic search; the most important information is always right there in get_context(). Session summaries are intentionally lightweight: type, highlights, context changes, open items. They're optimized for quick scanning by the next session, not for human reading. Detailed narrative logs go in log_entry() when needed. Everything is local and transparent. Your memory is a folder of markdown files. You can read them, edit them, back them up, or delete them. There's no lock-in, no proprietary format, no cloud dependency.

What you need

Python 3.10+, the mcp pip package, and Claude Desktop (or any MCP-compatible client). Setup takes about 5 minutes. Full configuration is optional — the defaults work out of the box, with environment variables available for customizing directory names, file names, and retention periods.

Server Config

{
  "mcpServers": {
    "memoria": {
      "command": "python",
      "args": [
        "C:/path/to/memoria-mcp/server.py"
      ],
      "env": {
        "MEMORIA_ROOT": "C:/path/to/your/claude-memoria"
      }
    }
  }
}
Project Info
Created At
2 months ago
Updated At
2 months ago
Author Name
leszini
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