Which integrations are available for this server?

Supports cloud storage sync via Cloudflare R2 for storing and syncing the SQLite database, with support for encryption and compression. Renders Mermaid diagrams within the interactive knowledge graph visualization when viewing memory content. Supports S3-compatible storage sync via MinIO for storing and syncing the SQLite database. Provides a Telescope plugin for browsing and searching memories directly in Neovim with fuzzy search and preview capabilities. Supports OpenAI embeddings for high-quality semantic search and cross-referencing of memories using text-embedding models. Uses 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.

memora by spokV

Features

💾 Persistent Storage - SQLite-backed database with optional cloud sync (S3, GCS, Azure)
🔍 Semantic Search - Vector embeddings (TF-IDF, sentence-transformers, or OpenAI)
🤖 LLM Deduplication - Find and merge duplicate memories with AI-powered comparison
⚡ Memory Automation - Structured tools for TODOs, issues, and section placeholders
🔗 Memory Linking - Typed edges, importance boosting, and cluster detection
📡 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 Mermaid diagram rendering
🌐 Live Graph Server - Auto-starts HTTP server for remote access via SSH
📊 Statistics & Analytics - Tag usage, trends, and connection insights

Install

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

Includes cloud storage (S3/R2) and OpenAI embeddings out of the box.

# Optional: local embeddings (offline, ~2GB for PyTorch) pip install "memora[local]" @ git+https://github.com/agentic-mcp-tools/memora.git

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

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 (Cloudflare D1) - Recommended:

{ "mcpServers": { "memora": { "command": "memora-server", "args": ["--no-graph"], "env": { "MEMORA_STORAGE_URI": "d1://<account-id>/<database-id>", "CLOUDFLARE_API_TOKEN": "<your-api-token>", "MEMORA_ALLOW_ANY_TAG": "1" } } } }

With D1, use --no-graph to disable the local visualization server. Instead, use the hosted graph at your Cloudflare Pages URL (see Cloud Graph).

Cloud DB (S3/R2) - Sync mode:

{ "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

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", }

Variable	Description
`MEMORA_DB_PATH`	Local SQLite database path (default: `~/.local/share/memora/memories.db`)
`MEMORA_STORAGE_URI`	Storage URI: `d1://<account>/<db-id>` (D1) or `s3://bucket/memories.db` (S3/R2)
`CLOUDFLARE_API_TOKEN`	API token for D1 database access (required for `d1://` URI)
`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 and LLM deduplication
`OPENAI_BASE_URL`	Base URL for OpenAI-compatible APIs (OpenRouter, Azure, etc.)
`OPENAI_EMBEDDING_MODEL`	OpenAI embedding model (default: `text-embedding-3-small`)
`MEMORA_LLM_ENABLED`	Enable LLM-powered deduplication comparison (`true`/`false`, default: `true`)
`MEMORA_LLM_MODEL`	Model for deduplication comparison (default: `gpt-4o-mini`)
`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

Memora supports three embedding backends:

Backend	Install	Quality	Speed
`openai` (default)	Included	High quality	API latency
`sentence-transformers`	`pip install memora[local]`	Good, runs offline	Medium
`tfidf`	Included	Basic keyword matching	Fast

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

A built-in HTTP server starts automatically with the MCP server, serving an interactive knowledge graph visualization.

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" } }

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

Graph UI Features

Details Panel - View memory content, metadata, tags, and related memories
Timeline Panel - Browse memories chronologically, click to highlight in graph
Time Slider - Filter memories by date range, drag to explore history
Real-time Updates - Graph and timeline update via SSE when memories change
Filters - Tag/section dropdowns, zoom controls
Mermaid Rendering - Code blocks render as diagrams

Node Colors

🟣 Tags - Purple shades by tag
🔴 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.

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.

For offline viewing, export memories as a static HTML file:

memory_export_graph(output_path="~/memories_graph.html", min_score=0.25)

This is optional - the Live Graph Server provides the same visualization with real-time updates.

When using Cloudflare D1 as your database, the graph visualization is hosted on Cloudflare Pages - no local server needed.

Benefits:

Access from anywhere (no SSH tunneling)
Real-time updates via WebSocket
Multi-database support via ?db= parameter
Secure access with Cloudflare Zero Trust

Setup:

Create D1 database:
npx wrangler d1 create memora-graph npx wrangler d1 execute memora-graph --file=memora-graph/schema.sql
Deploy Pages:
cd memora-graph npx wrangler pages deploy ./public --project-name=memora-graph
Configure bindings in Cloudflare Dashboard:
- Pages → memora-graph → Settings → Bindings
- Add D1: DB_MEMORA → your database
- Add R2: R2_MEMORA → your bucket (for images)
Configure MCP with D1 URI:
{ "env": { "MEMORA_STORAGE_URI": "d1://<account-id>/<database-id>", "CLOUDFLARE_API_TOKEN": "<your-token>" } }

Access: https://memora-graph.pages.dev

Secure with Zero Trust:

Cloudflare Dashboard → Zero Trust → Access → Applications
Add application for memora-graph.pages.dev
Create policy with allowed emails
Pages → Settings → Enable Access Policy

See memora-graph/ for detailed setup and multi-database configuration.

Find and merge duplicate memories using AI-powered semantic comparison:

# Find potential duplicates (uses cross-refs + optional LLM analysis) memory_find_duplicates(min_similarity=0.7, max_similarity=0.95, limit=10, use_llm=True) # Merge duplicates (append, prepend, or replace strategies) memory_merge(source_id=123, target_id=456, merge_strategy="append")

LLM Comparison analyzes memory pairs and returns:

verdict: "duplicate", "similar", or "different"
confidence: 0.0-1.0 score
reasoning: Brief explanation
suggested_action: "merge", "keep_both", or "review"

Works with any OpenAI-compatible API (OpenAI, OpenRouter, Azure, etc.) via OPENAI_BASE_URL.

Structured tools for common memory types:

# Create a TODO with status and priority memory_create_todo(content="Implement feature X", status="open", priority="high", category="backend") # Create an issue with severity memory_create_issue(content="Bug in login flow", status="open", severity="major", component="auth") # Create a section placeholder (hidden from graph) memory_create_section(content="Architecture", section="docs", subsection="api")

Manage relationships between memories:

# Create typed edges between memories memory_link(from_id=1, to_id=2, edge_type="implements", bidirectional=True) # Edge types: references, implements, supersedes, extends, contradicts, related_to # Remove links memory_unlink(from_id=1, to_id=2) # Boost memory importance for ranking memory_boost(memory_id=42, boost_amount=0.5) # Detect clusters of related memories memory_clusters(min_cluster_size=2, min_score=0.3)

memora

Features

Install

Claude Code

Codex CLI

Graph UI Features

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