agentic-mcp-tools/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 · Multi-DB · Containers · 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)
- 🗄️ **Multi-database routing** - One process serves many stores; a workspace reaches its own at /mcp/ (see Multi-database routing)
- 📂 **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
Two paths. **pip** is a local stdio child the client spawns. A **container** is a detached HTTP service you start with up; with MEMORA_DATABASES it serves multiple stores from one process. The LaunchAgent supervises the **proxy**, not the container — after a host restart the listener can come back while its upstream is still stopped. If you are running memora as a service, the container path *is* the install.
pip (local / stdio)
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.