Which integrations are available for this server?

Supports cloud storage sync via [Cloudflare](/mcp/servers/integrations/cloudflare) R2 for storing and syncing the SQLite database, with support for encryption and compression. Renders [Mermaid](/mcp/servers/integrations/mermaid) diagrams within the interactive knowledge graph visualization when viewing memory content. Supports S3-compatible storage sync via [MinIO](/mcp/servers/integrations/minio) for storing and syncing the SQLite database. Provides a Telescope plugin for browsing and searching memories directly in [Neovim](/mcp/servers/integrations/neovim) with fuzzy search and preview capabilities. Supports [OpenAI](/mcp/servers/integrations/openai) embeddings for high-quality semantic search and cross-referencing of memories using text-embedding models. Uses [SQLite](/mcp/servers/integrations/sqlite) as the persistent storage backend for memories, with support for local storage and cloud sync.

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": { "memory": { "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": { "memory": { "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.memory] 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

Variable	Description
`MEMORA_DB_PATH`	Local SQLite database path (default: `~/.local/share/memora/memories.db` )
`MEMORA_STORAGE_URI`	Cloud storage URI for S3/R2 (e.g., `s3://bucket/memories.db` )
`MEMORA_CLOUD_ENCRYPT`	Encrypt database before uploading to cloud ( `true` / `false` )
`MEMORA_CLOUD_COMPRESS`	Compress database before uploading to cloud ( `true` / `false` )
`MEMORA_CACHE_DIR`	Local cache directory for cloud-synced database
`MEMORA_ALLOW_ANY_TAG`	Allow any tag without validation against allowlist ( `1` to enable)
`MEMORA_TAG_FILE`	Path to file containing allowed tags (one per line)
`MEMORA_TAGS`	Comma-separated list of allowed tags
`MEMORA_GRAPH_PORT`	Port for the knowledge graph visualization server (default: `8765` )
`MEMORA_EMBEDDING_MODEL`	Embedding backend: `tfidf` (default), `sentence-transformers` , or `openai`
`SENTENCE_TRANSFORMERS_MODEL`	Model for sentence-transformers (default: `all-MiniLM-L6-v2` )
`OPENAI_API_KEY`	API key for OpenAI embeddings (required when using `openai` backend)
`OPENAI_EMBEDDING_MODEL`	OpenAI embedding model (default: `text-embedding-3-small` )
`AWS_PROFILE`	AWS credentials profile from `~/.aws/credentials` (useful for R2)
`AWS_ENDPOINT_URL`	S3-compatible endpoint for R2/MinIO
`R2_PUBLIC_DOMAIN`	Public domain for R2 image URLs

Semantic Search & Embeddings

Memora supports three embedding backends for semantic search:

Backend	Install	Quality	Speed
`tfidf` (default)	None	Basic keyword matching	Fast
`sentence-transformers`	`pip install sentence-transformers`	True semantic understanding	Medium
`openai`	`pip install openai`	High quality	API latency

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.

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.

memora