AgentIndex icon
AgentIndex
squish-memory logo
squish-memory
★ 12
vs
deja-vu logo
deja-vu
★ 1.3k

squish-memory vs deja-vu

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.; deja-vu: Deja-vu is a zero-dependency, local-first binary that indexes and makes searchable the historical sessions of various AI coding agents. It acts as a universal memory layer, allowing agents to recall past solutions and context efficiently, even from before its installation.

01

TL;DR

squish-memory logo
squish-memoryMemory & Context · ★ 12

Enhancing AI Agent Performance by Providing Persistent Memory

Read squish-memory full review →
deja-vu logo
deja-vuMemory & Context · ★ 1.3k

Reusing solutions from past agent conversations to avoid re-debugging or re-implementing.

Read deja-vu full review →
02

Side-by-Side Comparison

Field
squish-memory logosquish-memory
deja-vu logodeja-vu
Category
Memory & Context
Memory & Context
Stars
★ 12
★ 1.3k
License
NOASSERTION
MIT
Install Command
npm install -g squish-memory && squish install --all
curl -fsSL https://raw.githubusercontent.com/vshulcz/deja-vu/main/install.sh | sh
Updated
3w ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
AI Agent Memory, Context Management, Local-first
AI Agent Memory, Session Search, Code Assistant
03

Features

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
deja-vu logodeja-vu
01Fast, Retroactive Session Search
02Automatic AI Agent Memory Recall
03Cross-Machine Memory Synchronization
04Seamless Agent Context Handoff
05Automated Sensitive Data Redaction
04

Use Cases

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
deja-vu logodeja-vu
↳Reusing solutions from past agent conversations to avoid re-debugging or re-implementing.
↳Debugging complex issues by accessing comprehensive past debugging sessions and design decisions.
↳Maintaining persistent context across different AI agents and development machines.
↳Sharing sanitized session digests with colleagues for onboarding or knowledge transfer.
↳Understanding file change history and decision-making via session blame.
FAQ

FAQ

What is the difference between squish-memory and deja-vu?
Both squish-memory and deja-vu are in the Memory & Context category. squish-memory has 12 stars, while deja-vu has 1.3k stars.
Which is better, squish-memory or deja-vu?
The best choice depends on your use case. Choose squish-memory if Enhancing AI Agent Performance by Providing Persistent Memory, and deja-vu if Reusing solutions from past agent conversations to avoid re-debugging or re-implementing..
Is squish-memory free or open source?
Yes, squish-memory is open source on GitHub (NOASSERTION).
Is deja-vu free or open source?
Yes, deja-vu is open source on GitHub (MIT).
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Related

Alternatives to squish-memory →Alternatives to deja-vu →squish-memory details →deja-vu details →