AgentIndex icon
AgentIndex
memora logo
memora
★ 744
vs
enquire-mcp logo
enquire-mcp
★ 34

memora vs enquire-mcp

memora: 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`.; enquire-mcp: enquire-mcp transforms your Obsidian vault into persistent, queryable long-term memory for various AI agents, eliminating the need to re-explain context in every session. It provides best-in-class hybrid retrieval, including semantic search, reranking, and HNSW, all running locally with zero cloud calls during serving.

01

TL;DR

memora logo
memoraMemory & Context · ★ 744

AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks.

Read memora full review →
enquire-mcp logo
enquire-mcpMemory & Context · ★ 34

Long-term memory for AI agents: Provides durable, semantic recall for agents across sessions, models, and providers, grounded in your Obsidian notes.

Read enquire-mcp full review →
02

Side-by-Side Comparison

Field
memora logomemora
enquire-mcp logoenquire-mcp
Category
Memory & Context
Memory & Context
Stars
★ 744
★ 34
License
MIT
MIT
Install Command
pip install git+https://github.com/agentic-box/memora.git
npm install -g @oomkapwn/enquire-mcp
Updated
2d ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
AI Agents, Persistent Memory, Knowledge Graph
AI Agents, Long-term Memory, RAG
03

Features

memora logomemora
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
enquire-mcp logoenquire-mcp
01Vendor-neutral memory: Your memory lives in .md files, ensuring portability across different AI agents and providers.
02Best-in-class hybrid retrieval: Features BM25, multilingual embeddings, BGE cross-encoder reranker, HNSW, and int8 quantization for superior search quality and performance.
03Zero cloud calls during serve: All models are cached locally, and vault content never leaves your machine, ensuring privacy and air-gap safety.
04Freshness-aware recall: Search hits report note age and a `stale` flag, with optional recency-weighted ranking to prioritize fresh knowledge.
05PDFs with OCR & GraphRAG-light: Integrates PDFs (including OCR for scanned docs) into hybrid search and offers GraphRAG-light for wikilink community detection.
04

Use Cases

memora logomemora
↳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.
enquire-mcp logoenquire-mcp
↳Long-term memory for AI agents: Provides durable, semantic recall for agents across sessions, models, and providers, grounded in your Obsidian notes.
↳Personal knowledge base / second brain: Offers hybrid retrieval across 50+ languages, wikilink graph-boost, and GraphRAG-light to surface relevant notes and discover connections.
↳Agentic RAG / context engineering: Exposes per-signal scores, HyDE pre-rewrites, and sub-question decomposition for advanced retrieval, plus a built-in evaluation harness.
05

Best For

memora logomemora
TrendingHidden Gem
enquire-mcp logoenquire-mcp
—
FAQ

FAQ

What is the difference between memora and enquire-mcp?
Both memora and enquire-mcp are in the Memory & Context category. memora has 744 stars, while enquire-mcp has 34 stars.
Which is better, memora or enquire-mcp?
The best choice depends on your use case. Choose memora if AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks., and enquire-mcp if Long-term memory for AI agents: Provides durable, semantic recall for agents across sessions, models, and providers, grounded in your Obsidian notes..
Is memora free or open source?
Yes, memora is open source on GitHub (MIT).
Is enquire-mcp free or open source?
Yes, enquire-mcp is open source on GitHub (MIT).
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