AgentIndex icon
AgentIndex
memo logo
memo
★ 24
vs
squish-memory logo
squish-memory
★ 12

memo vs squish-memory

memo: Memo provides a 100% local, persistent, and searchable memory solution for AI coding agents, eliminating amnesia across sessions. It uniquely offers token-efficient context management, historical querying, contradiction detection, and autonomous maintenance, saving costs and improving agent performance.; squish-memory: Squish is a local-first, open-source memory system designed for AI agents, preventing them from forgetting past decisions, constraints, and preferences. It integrates with various coding agents and offers CLI, an MCP server, and a desktop app for managing and recalling relevant context.

01

TL;DR

memo logo
memoMemory & Context · ★ 24

Providing continuous memory and context for AI coding agents across multiple sessions to prevent 'amnesia'.

Read memo full review →
squish-memory logo
squish-memoryMemory & Context · ★ 12

Enhancing AI Agent Performance by Providing Persistent Memory

Read squish-memory full review →
02

Side-by-Side Comparison

Field
memo logomemo
squish-memory logosquish-memory
Category
Memory & Context
Memory & Context
Stars
★ 24
★ 12
License
MIT
NOASSERTION
Install Command
curl -fsSL https://raw.githubusercontent.com/jagoff/memo/v4.9.0/install.sh | bash
npm install -g squish-memory && squish install --all
Updated
Today
2w ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
AI Memory, Local-first, Coding Agent
AI Agent Memory, Context Management, Local-first
03

Features

memo logomemo
01100% local, persistent, and searchable memory for various AI coding agents (e.g., Claude Code, Cursor), requiring no cloud or external databases.
02Token-efficient context management designed to reduce overhead and save costs for AI interactions.
03Time-machine capability to query knowledge as it was at any historical point, aiding in understanding past decisions.
04Contradiction radar that detects and helps resolve conflicting facts within the knowledge corpus.
05Autonomous nightly maintenance pipeline to optimize memory, resolve conflicts, and pre-warm embeddings.
squish-memory logosquish-memory
01Unified Memory System for AI Agents
02Local-first Storage with SQLite and Cloud Sync Option
03Automatic Context Injection for Agents (50-200 tokens)
04Multiple Interfaces: CLI, MCP Server, and Desktop App
05Compatibility with Various Coding Agents via MCP Standard
04

Use Cases

memo logomemo
↳Providing continuous memory and context for AI coding agents across multiple sessions to prevent 'amnesia'.
↳Storing and retrieving important decisions, facts, or project-specific knowledge for AI agents.
↳Analyzing the evolution of project knowledge and understanding the rationale behind past decisions using historical queries.
↳Managing changing project requirements by detecting and resolving contradictions in stored knowledge.
↳Enhancing AI agent responses by injecting relevant, token-optimized memory before model answers.
squish-memory logosquish-memory
↳Enhancing AI Agent Performance by Providing Persistent Memory
↳Managing and Recalling Development-Specific Knowledge and Preferences
↳Injecting Relevant Context into Coding Assistants like Claude Code or Codex
FAQ

FAQ

What is the difference between memo and squish-memory?
Both memo and squish-memory are in the Memory & Context category. memo has 24 stars, while squish-memory has 12 stars.
Which is better, memo or squish-memory?
The best choice depends on your use case. Choose memo if Providing continuous memory and context for AI coding agents across multiple sessions to prevent 'amnesia'., and squish-memory if Enhancing AI Agent Performance by Providing Persistent Memory.
Is memo free or open source?
Yes, memo is open source on GitHub (MIT).
Is squish-memory free or open source?
Yes, squish-memory is open source on GitHub (NOASSERTION).
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Related

Alternatives to memo →Alternatives to squish-memory →memo details →squish-memory details →