Memora

A lightweight Model Context Protocol (MCP) server that persists shared memories in SQLite. Compatible with Claude Code, Codex CLI, and other MCP-aware clients.

Features

• Persistent Storage - SQLite-backed database with optional cloud sync (S3, GCS, Azure)
• Semantic Search - Vector embeddings (TF-IDF, sentence-transformers, or OpenAI)
• Event Notifications - Poll-based system for inter-agent communication
• Advanced Queries - Full-text search, date ranges, tag filters (AND/OR/NOT)
• Cross-references - Auto-linked related memories based on similarity
• Hierarchical Organization - Explore memories by section/subsection
• Export/Import - Backup and restore with merge strategies
• Knowledge Graph - Interactive HTML visualization with filtering
• Live Graph Server - Auto-starts HTTP server for remote access via SSH
• Statistics & Analytics - Tag usage, trends, and connection insights
• Zero Dependencies - Works out-of-box with Python stdlib (optional backends available)

Install

# From GitHub
pip install git+https://github.com/agentic-mcp-tools/memora.git

# With extras
pip install -e ".[cloud]"       # S3/R2/GCS cloud storage (boto3)
pip install -e ".[embeddings]"  # semantic search (sentence-transformers)
pip install -e ".[all]"         # cloud + embeddings + dev tools

Usage

The server runs automatically when configured in Claude Code. Manual invocation:

# Default (stdio mode for MCP)
memora-server

# With graph visualization server
memora-server --graph-port 8765

# HTTP transport (alternative to stdio)
memora-server --transport streamable-http --host 127.0.0.1 --port 8080

Claude Code Config

Add to .mcp.json in your project root:

Local DB

{
  "mcpServers": {
    "memora": {
      "command": "memora-server",
      "args": [],
      "env": {
        "MEMORA_DB_PATH": "~/.local/share/memora/memories.db",
        "MEMORA_ALLOW_ANY_TAG": "1",
        "MEMORA_GRAPH_PORT": "8765"
      }
    }
  }
}

Cloud DB (S3/R2)

{
  "mcpServers": {
    "memora": {
      "command": "memora-server",
      "args": [],
      "env": {
        "AWS_PROFILE": "memora",
        "AWS_ENDPOINT_URL": "https://<account-id>.r2.cloudflarestorage.com",
        "MEMORA_STORAGE_URI": "s3://memories/memories.db",
        "MEMORA_CLOUD_ENCRYPT": "true",
        "MEMORA_ALLOW_ANY_TAG": "1",
        "MEMORA_GRAPH_PORT": "8765"
      }
    }
  }
}

Codex CLI Config

Add to ~/.codex/config.toml:

[mcp_servers.memora]
  command = "memora-server"  # or full path: /path/to/bin/memora-server
  args = ["--no-graph"]
  env = {
    AWS_PROFILE = "memora",
    AWS_ENDPOINT_URL = "https://<account-id>.r2.cloudflarestorage.com",
    MEMORA_STORAGE_URI = "s3://memories/memories.db",
    MEMORA_CLOUD_ENCRYPT = "true",
    MEMORA_ALLOW_ANY_TAG = "1",
  }

Environment Variables

Semantic Search & Embeddings

Memora supports three embedding backends for semantic search:

Automatic: Embeddings and cross-references are computed automatically when you memory_create, memory_update, or memory_create_batch.

Manual rebuild required when:

• Changing MEMORA_EMBEDDING_MODEL after memories exist
• Switching to a different sentence-transformers model

# After changing embedding model, rebuild all embeddings
memory_rebuild_embeddings

# Then rebuild cross-references to update the knowledge graph
memory_rebuild_crossrefs

Neovim Integration

Browse memories directly in Neovim with Telescope. Copy the plugin to your config:

# For kickstart.nvim / lazy.nvim
cp nvim/memora.lua ~/.config/nvim/lua/kickstart/plugins/

Usage: Press <leader>sm to open the memory browser with fuzzy search and preview.

Requires: telescope.nvim, plenary.nvim, and memora installed in your Python environment.

Knowledge Graph Export

Export memories as an interactive HTML knowledge graph visualization:

# Via MCP tool
memory_export_graph(output_path="~/memories_graph.html", min_score=0.25)

Interactive vis.js graph with tag/section filtering, memory tooltips, Mermaid diagram rendering, and auto-resized image thumbnails. Click nodes to view content, drag to explore.

Graph Color Scheme

Node size reflects connection count (more connections = larger node).

Live Graph Server

A built-in HTTP server starts automatically with the MCP server, serving the graph visualization on-demand.

Access locally:

http://localhost:8765/graph

Remote access via SSH:

ssh -L 8765:localhost:8765 user@remote
# Then open http://localhost:8765/graph in your browser

Configuration:

{
  "env": {
    "MEMORA_GRAPH_PORT": "8765"
  }
}

Use different ports on different machines to avoid conflicts when forwarding multiple servers.

To disable: add "--no-graph" to args in your MCP config.