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spokV

memora

by spokV
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Features

Core Storage

  • 💾 Persistent Storage - SQLite with optional cloud sync (S3, R2, D1)

  • 📂 Hierarchical Organization - Section/subsection structure with auto-hierarchy assignment

  • 📦 Export/Import - Backup and restore with merge strategies

Search & Intelligence

  • 🔍 Semantic Search - Vector embeddings (TF-IDF, sentence-transformers, OpenAI)

  • 🎯 Advanced Queries - Full-text, date ranges, tag filters (AND/OR/NOT), hybrid search

  • 🔀 Cross-references - Auto-linked related memories based on similarity

  • 🤖 LLM Deduplication - Find and merge duplicates with AI-powered comparison

  • 🔗 Memory Linking - Typed edges, importance boosting, and cluster detection

Tools & Visualization

  • ⚡ Memory Automation - Structured tools for TODOs, issues, and sections

  • 🕸️ Knowledge Graph - Interactive visualization with Mermaid rendering and cluster overlays

  • 🌐 Live Graph Server - Built-in HTTP server with cloud-hosted option (D1/Pages)

  • 💬 Chat with Memories - RAG-powered chat panel that searches relevant memories and streams LLM responses

  • 📡 Event Notifications - Poll-based system for inter-agent communication

  • 📊 Statistics & Analytics - Tag usage, trends, and connection insights

  • 🧠 Memory Insights - Activity summary, stale detection, consolidation suggestions, and LLM-powered pattern analysis

  • 📜 Action History - Track all memory operations (create, update, delete, merge, boost, link) with grouped timeline view

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: openai (default), sentence-transformers, or tfidf

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)

CHAT_MODEL

Model for the chat panel (default: deepseek/deepseek-chat, falls back to MEMORA_LLM_MODEL)

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

  • History Panel - Action log of all operations with grouped consecutive entries and clickable memory references (deleted memories shown as strikethrough)

  • Chat Panel - Ask questions about your memories using RAG-powered LLM chat with streaming responses and clickable [Memory #ID] references

  • Time Slider - Filter memories by date range, drag to explore history

  • Real-time Updates - Graph, timeline, and history 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.

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:

  1. Create D1 database:

    npx wrangler d1 create memora-graph npx wrangler d1 execute memora-graph --file=memora-graph/schema.sql

  2. Deploy Pages:

    cd memora-graph npx wrangler pages deploy ./public --project-name=memora-graph

  3. Configure bindings in Cloudflare Dashboard:

    • Pages → memora-graph → Settings → Bindings

    • Add D1: DB_MEMORA → your database

    • Add R2: R2_MEMORA → your bucket (for images)

  4. 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:

  1. Cloudflare Dashboard → Zero Trust → Access → Applications

  2. Add application for memora-graph.pages.dev

  3. Create policy with allowed emails

  4. Pages → Settings → Enable Access Policy

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

Ask questions about your knowledge base directly from the graph UI. The chat panel uses RAG (Retrieval-Augmented Generation) to search relevant memories and stream LLM responses.

  • Toggle via the floating chat icon at bottom-right

  • Semantic search finds the most relevant memories as context

  • Streaming responses with clickable [Memory #ID] references that focus the graph node

  • Works on both the local server and Cloudflare Pages deployment

Configure the chat model:

Backend

Variable

Default

Local server

CHAT_MODEL env var

Falls back to MEMORA_LLM_MODEL

Cloudflare Pages

CHAT_MODEL in wrangler.toml

deepseek/deepseek-chat

Requires an OpenAI-compatible API (OPENAI_API_KEY + OPENAI_BASE_URL for local, OPENROUTER_API_KEY secret for Cloudflare).

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")

Analyze stored memories and surface actionable insights:

# Full analysis with LLM-powered pattern detection memory_insights(period="7d", include_llm_analysis=True) # Quick summary without LLM (faster, no API key needed) memory_insights(period="1m", include_llm_analysis=False)

Returns:

  • Activity summary — memories created in the period, grouped by type and tag

  • Open items — open TODOs and issues with stale detection (configurable via MEMORA_STALE_DAYS, default 14)

  • Consolidation candidates — similar memory pairs that could be merged

  • LLM analysis — themes, focus areas, knowledge gaps, and a summary (requires OPENAI_API_KEY)

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)

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.

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.

This server cannot be installed

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security - not tested
A
license - permissive license
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quality - not tested

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