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memora
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memora

Active·★ 744·MIT·Updated 2026-10-21
★ Trending★ Hidden Gem

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`.

memora sits in the Memory & Context category, so it is usually better judged by workflow fit, deployment style, and team needs than by isolated features alone. Based on the available metadata, it leans most clearly toward Persistent Storage: SQLite with optional cloud sync (S3, R2, D1). A representative use case is AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks.. The listed license is MIT. GitHub stars are currently 744. Relevant tags include AI Agents, Persistent Memory.

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This write-up is based on the project's README and public repository data. We have not hand-tested the tool.

#AI Agents#Persistent Memory#Knowledge Graph#Semantic Search#RAG#SQLite#Vector Embeddings#LLM Tools
$ Install
$ pip install git+https://github.com/agentic-box/memora.git
↗ Visit site★ GitHub
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

Why choose it

+Persistent Storage: SQLite with optional cloud sync (S3, R2, D1)
+Semantic Search: Vector embeddings for advanced queries (TF-IDF, sentence-transformers, OpenAI)
+AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks.
+Supports 6 platforms or runtime environments.
03

Trade-offs

!The final decision should still be made through side-by-side comparison with similar tools.
04

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
05

Quick start

1
$ pip install git+https://github.com/agentic-box/memora.git
06

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.
07

Alternatives

⚡Top VS:
VSmcp-local-memory★60→VSmontycat-mcp★28→VSokf-agent-memory★827→

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On this page
01Features02Why choose it03Trade-offs04Compatibility05Quick start06Use cases
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07
Alternatives
agentic-box /
memora
↗

Give your AI agents persistent memory.

744MIT
Last commit1d ago
StatusActive
CategoryMemory & Context
Links
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