mori
★ 32
vs
mex
★ 1.8k
mori vs mex
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.; mex: mex provides persistent, navigable project memory for AI coding agents, allowing every session to start with the right context instead of a cold prompt. It structures agent memory through a markdown scaffold, offers CLI tools for drift detection against the real codebase, and synchronizes context to keep agents aligned and reduce token usage.
01
TL;DR
moriMemory & Context · ★ 32
Enabling AI coding agents across different machines or teams to share learned knowledge and maintain consistency.
mexMemory & Context · ★ 1.8k
When to choose:
- Your AGENTS.md or CLAUDE.md has grown too big and you want a small anchor plus routed context files.
- You want memory as plain markdown in the repo, reviewable in pull requests and diffable.
- You want drift checks that run without AI tokens and flag missing paths, stale files and broken scripts.
When to skip:
- You want recall across unrelated projects or conversations; mex lives inside one repository.
- You have a small project: another set of files to maintain may cost more than it saves.
- You expect the AST code-graph feature today: the README marks it as an unreleased preview.
02
Side-by-Side Comparison
Field
morimexCategory
Memory & Context
Memory & Context
Stars
★ 32
★ 1.8k
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
npx mex-agent setup
Updated
2d ago
3d ago
Open Source
Yes
Yes
Tags
AI Agents, Shared Memory, Knowledge Base
AI Agents, Persistent Memory, Context Management
03
Features
mori01Multi-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.
mex01Creates a structured markdown scaffold for AI agent memory (e.g., AGENTS.md, ROUTER.md, context/, patterns/).
02CLI tools for drift detection, validating scaffold against the actual codebase without using AI tokens.
03Automatically generates targeted prompts for AI agents to fix stale or broken context.
04Supports task-specific context routing, loading only relevant files for the current task.
05Includes an append-only event log to store decisions, notes, risks, and todos.
04
Use Cases
mori↳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.
mex↳Maintaining consistent project context for AI coding agents across multiple sessions.
↳Significantly reducing token consumption in LLM interactions by providing only relevant context.
↳Onboarding new AI agents or developers to complex codebases with structured, up-to-date project knowledge.
↳Developing persistent AI agents for operational environments (homelab scenarios, system administration) with structured memory.
↳Automating the detection and correction of documentation or contextual drift from the actual codebase.
05
Best For
mori—
mexEssentialTrending
FAQ
FAQ
What is the difference between mori and mex?
Both mori and mex are in the Memory & Context category. mori has 32 stars, while mex has 1.8k stars.
Which is better, mori or mex?
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 mex if Maintaining consistent project context for AI coding agents across multiple sessions..
Is mori free or open source?
Yes, mori is open source on GitHub (AGPL-3.0).
Is mex free or open source?
Yes, mex is open source on GitHub (MIT).
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