AgentIndex icon
AgentIndex
mori logo
mori
★ 32
vs
enquire-mcp logo
enquire-mcp
★ 34

mori vs enquire-mcp

mori: Mori is a shared memory layer for AI coding agents that allows multiple instances to learn from each other, ensuring coherence and preventing 'cold starts'. It distills session activity into durable knowledge, enabling agents to start informed and benefit from collective intelligence.; 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

mori logo
moriMemory & Context · ★ 32

Enabling AI coding agents across different machines or teams to share learned knowledge and maintain consistency.

Read mori 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
mori logomori
enquire-mcp logoenquire-mcp
Category
Memory & Context
Memory & Context
Stars
★ 32
★ 34
License
AGPL-3.0
MIT
Install Command
git clone https://github.com/fjwood69/mori.git && cd mori && cp deploy/homelab/.env.example deploy/homelab/.env && docker compose -f deploy/homelab/docker-compose.yml up -d
npm install -g @oomkapwn/enquire-mcp
Updated
3d ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
AI Agents, Shared Memory, Knowledge Base
AI Agents, Long-term Memory, RAG
03

Features

mori logomori
01Multi-Instance Coherence: Shared memory ensures all AI agent instances are informed by others' decisions.
02Dream Pipeline: Automatically distills session events into structured memories.
03Universal Ingestion: Feed various data types (PDFs, images, git history) into the memory store.
04Strategic Consultation: Provides LLM guidance grounded in project context and team standards.
05Inter-Agent Messaging: Enables agents to send tasks, questions, and decisions across the network.
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

mori logomori
↳Enabling AI coding agents across different machines or teams to share learned knowledge and maintain consistency.
↳Onboarding new AI agents or team members by rapidly bootstrapping the memory store with existing project documentation and history.
↳Obtaining context-aware strategic advice and reviews (e.g., architecture, security) from LLMs based on project specifics.
↳Orchestrating complex tasks by delegating work and sharing decisions among multiple AI agent instances.
↳Persistent knowledge base for AI development projects, preventing repetitive learning and ensuring continuity.
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.
FAQ

FAQ

What is the difference between mori and enquire-mcp?
Both mori and enquire-mcp are in the Memory & Context category. mori has 32 stars, while enquire-mcp has 34 stars.
Which is better, mori or enquire-mcp?
The best choice depends on your use case. Choose mori if Enabling AI coding agents across different machines or teams to share learned knowledge and maintain consistency., 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 mori free or open source?
Yes, mori is open source on GitHub (AGPL-3.0).
Is enquire-mcp free or open source?
Yes, enquire-mcp is open source on GitHub (MIT).
Cloud Infrastructure
Ad

Deploy on DigitalOcean

Host your MCP servers, Docker containers, and AI agents with $200 free credit over 60 days.

Claim $200 Free Credit
→

Related

Alternatives to mori →Alternatives to enquire-mcp →mori details →enquire-mcp details →