agentic-box/memora

Memora

“You never truly know the value of a moment until it becomes a memory.”

Give your AI agents persistent collective memory An MCP memory layer for agents: structured storage, semantic retrieval, graph relations, and source-backed cross-session context.

Absorb agent work into durable graph memory, then use memory_digest(topic) to retrieve relevant memories, TODOs/issues, related edges, and source IDs.

Features · Preview · Install · Usage · Config · Live Graph · Cloud Graph · Chat · Semantic Search · Documents · LLM Dedup · Linking · Neovim

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

Absorb & Lineage

• 🧬 Absorb - Feed facts in; an LLM classifies each against the store (duplicate / update / contradiction / related / new), skips duplicates, links relations, and consolidates related facts — with dry_run preview
• 🌱 Supersession Lineage - Updates supersede old knowledge instead of deleting it; retrieval follows the chain to the current version by default (follow modes: active, latest, full_history)
• 🗞️ Topic Digest - memory_digest(topic) bundles relevant memories, open TODOs/issues, related edges, and source IDs into one retrieval

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

Document Storage

• 📄 Structured Documents - Store markdown documents as searchable fragment trees (claims, plan items, references, risks)
• 🔒 Fragment Integrity - Guards against accidental delete/merge/absorb of document fragments
• 🔍 Granular Search - Individual claims and findings are semantically searchable while the full document remains retrievable as a unit

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 with LLM tool calling to search, create, update, and delete memories via streaming chat
• 📡 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

Preview

Install

pip install memora-mcp

The PyPI package is memora-mcp (bare memora on PyPI is an unrelated project). Includes cloud storage (S3/R2) and OpenAI embeddings out of the box.

# Optional: local embeddings (offline, ~2GB for PyTorch)
pip install "memora-mcp[local]"

# Latest development version straight from git
pip install "git+https://github.com/agentic-box/memora.git"