memora
Active·★ 407·MIT·Updated 2026-05-28
Give your AI agents persistent memory.
Memora provides AI agents with a persistent memory layer, featuring structured storage, semantic retrieval, and graph relations for cross-session context. It allows agents to absorb work into a durable knowledge graph and retrieve relevant information, TODOs, and related edges using `memory_digest`.
#AI Agents#Persistent Memory#Knowledge Graph#Semantic Search#RAG#SQLite#Vector Embeddings#LLM Tools
01
Features
01Persistent Storage: SQLite with optional cloud sync (S3, R2, D1)
02Semantic Search: Vector embeddings for advanced queries (TF-IDF, sentence-transformers, OpenAI)
03Knowledge Graph: Interactive visualization with Mermaid rendering and cluster detection
04Chat with Memories: RAG-powered chat panel with LLM tool calling for search, create, update, delete
05LLM Deduplication: AI-powered semantic comparison to find and merge duplicate memories
02
Compatibility
Python
Runtime
Verified via docs
SQLite
Database
Verified via docs
Cloudflare D1
Cloud Database
Verified via docs
AWS S3 / Cloudflare R2
Cloud Storage
Verified via docs
OpenAI API
AI Service
Verified via docs
Neovim
Editor Integration
Verified via docs
03
Quick start
1
$ pip install git+https://github.com/agentic-box/memora.git
04
Use cases
↳AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks.
↳Knowledge Base for Agents: Building a retrievable knowledge base that agents can query semantically and contextually.
↳Project Management & Task Tracking: Automating the creation and tracking of TODOs and issues, and surfacing insights for project progress.
↳Document Analysis & Retrieval: Storing and searching structured documents (e.g., reports, plans) at a granular fragment level.
↳Contextual Conversation for AI: Powering RAG-based chat panels that allow agents or users to converse deeply about stored memories.
05
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