AgentIndex icon
AgentIndex
kaeru logo
kaeru
★ 40
vs
squish-memory logo
squish-memory
★ 12

kaeru vs squish-memory

kaeru: Kaeru is a local-first, cross-agent cognitive engine for LLM agents, providing a typed property graph and a recollection layer for long-term memory. It enables multi-session and multi-agent continuity, allowing agents to maintain full context, follow provenance, and build on shared knowledge over time.; 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

kaeru logo
kaeruMemory & Context · ★ 40

An agent needs to recall its thought process and project context from a previous session.

Read kaeru 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
kaeru logokaeru
squish-memory logosquish-memory
Category
Memory & Context
Memory & Context
Stars
★ 40
★ 12
License
MIT
NOASSERTION
Install Command
git clone https://github.com/kaerulabs/kaeru.git && cd kaeru && cargo build --release
npm install -g squish-memory && squish install --all
Updated
2d ago
3w ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
LLM Agents, Knowledge Graph, Bi-temporal Database
AI Agent Memory, Context Management, Local-first
03

Features

kaeru logokaeru
01Two-tier graph (operational/hippocampus and archival/cortex) for active thinking and settled knowledge.
02Bi-temporal nature with native assertion/retraction history for time-travel queries and non-destructive conflict resolution.
03Per-initiative scoping and layered re-entry to restore project context across sessions and access archival knowledge on demand.
04Optional cross-agent sharing through a cloud tier, allowing trusted teams to share settled knowledge with safety gates.
05Structural recall based on exact name lookup, typed graph traversal, and FTS fuzzy fallback, rather than primarily vector 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

kaeru logokaeru
↳An agent needs to recall its thought process and project context from a previous session.
↳A team of agents needs to share and build upon a collective, long-term knowledge base.
↳An agent wants to capture new observations or hypotheses within a specific project scope.
↳Tracing the provenance and history of a piece of knowledge or a decision made by an agent.
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 kaeru and squish-memory?
Both kaeru and squish-memory are in the Memory & Context category. kaeru has 40 stars, while squish-memory has 12 stars.
Which is better, kaeru or squish-memory?
The best choice depends on your use case. Choose kaeru if An agent needs to recall its thought process and project context from a previous session., and squish-memory if Enhancing AI Agent Performance by Providing Persistent Memory.
Is kaeru free or open source?
Yes, kaeru 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 kaeru →Alternatives to squish-memory →kaeru details →squish-memory details →