AgentIndex icon
AgentIndex
enquire-mcp logo
enquire-mcp
★ 34
vs
borhan logo
borhan
★ 39

enquire-mcp vs borhan

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.; borhan: Borhan provides a robust long-term memory solution for AI agents, enabling them to store important information and retrieve it months later through a powerful, structured query language. Unlike simple grep or vector databases, it offers advanced matching, ranking, and multilingual capabilities for thousands of memories, helping agents learn and recall complex concepts.

01

TL;DR

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 →
borhan logo
borhanMemory & Context · ★ 39

Provide long-term memory for AI agents to retain critical information and past insights across conversations.

Read borhan full review →
02

Side-by-Side Comparison

Field
enquire-mcp logoenquire-mcp
borhan logoborhan
Category
Memory & Context
Memory & Context
Stars
★ 34
★ 39
License
MIT
MIT
Install Command
npm install -g @oomkapwn/enquire-mcp
curl -fsSL https://raw.githubusercontent.com/pouriya/borhan/master/install.sh | sh
Updated
1d ago
1mo ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
AI Agents, Long-term Memory, RAG
AI Agents, Long-term Memory, Search Engine
03

Features

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.
borhan logoborhan
01Agent-driven content storage and search query generation
02Structured memory organization (notes, sessions, messages) with descriptions
03Advanced, multilingual text matching with normalization and linguistic flexibility
04Intelligent ranking of search results based on idea coverage, proximity, and recency
05Flexible, powerful query language supporting complex ideas and boolean logic
04

Use Cases

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.
borhan logoborhan
↳Provide long-term memory for AI agents to retain critical information and past insights across conversations.
↳Store operational knowledge such as project decisions, code explanations, and debugging paths for future agent reference.
↳Integrate as a memory and knowledge retrieval tool for existing AI agents via MCP protocol or REST API.
↳Establish a shared knowledge base for collaborative AI agent environments across multiple users or machines.
FAQ

FAQ

What is the difference between enquire-mcp and borhan?
Both enquire-mcp and borhan are in the Memory & Context category. enquire-mcp has 34 stars, while borhan has 39 stars.
Which is better, enquire-mcp or borhan?
The best choice depends on your use case. Choose 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., and borhan if Provide long-term memory for AI agents to retain critical information and past insights across conversations..
Is enquire-mcp free or open source?
Yes, enquire-mcp is open source on GitHub (MIT).
Is borhan free or open source?
Yes, borhan is open source on GitHub (MIT).
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