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.

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type `@` followed by the MCP server name and your instructions, e.g., "`@memora` search for meeting notes about the Q3 project using semantic search" That's it! The server will respond to your query, and you can continue using it as needed. Here is a [step-by-step guide with screenshots](https://glama.ai/blog/2025-07-08-how-to-install-and-use-mcp-servers).

Memora

Version License GitHub stars

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

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.

Graph Color Scheme

Type	Color	Meaning
Tags	Purple shades	Different tags get different purple tones
Issues	Red	Open
	Orange	In Progress
	Green	Resolved
	Gray	Won't Fix
TODOs	Blue	Open
	Orange	In Progress
	Green	Completed
	Red	Blocked

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.

memora