AgentIndex icon
AgentIndex
kaeru logo
kaeru
★ 40
vs
remem logo
remem
★ 34

kaeru vs remem

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.; remem: `remem` is a local-first memory solution for AI coding agents like Claude Code and OpenAI Codex, preventing repetitive explanations by automatically capturing and injecting project context. It distills, searches, and stores project decisions, bug fixes, and preferences locally using SQLite, accessible via CLI, hooks, and a REST API.

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 →
remem logo
rememMemory & Context · ★ 34

Avoiding repetitive project explanations to AI coding agents.

Read remem full review →
02

Side-by-Side Comparison

Field
kaeru logokaeru
remem logoremem
Category
Memory & Context
Memory & Context
Stars
★ 40
★ 34
License
MIT
MIT
Install Command
git clone https://github.com/kaerulabs/kaeru.git && cd kaeru && cargo build --release
curl -fsSL https://raw.githubusercontent.com/majiayu000/remem/main/install.sh | env REMEM_NO_CONFIG=1 sh && ~/.local/bin/remem install --target codex
Updated
2d ago
2d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
LLM Agents, Knowledge Graph, Bi-temporal Database
AI Agent Memory, Context Management, Coding Assistant
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.
remem logoremem
01Automatic memory capture and injection for coding agents.
02Searchable history of project decisions, bug fixes, and rationale.
03Local-first memory storage with SQLite and SQLCipher encryption.
04Accessible via CLI, coding agent hooks, MCP tools, and REST API.
05Single Rust binary with no external database dependencies.
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.
remem logoremem
↳Avoiding repetitive project explanations to AI coding agents.
↳Searching and recalling past project decisions and bug-fix rationale.
↳Maintaining an auditable and project-scoped history of engineering memory.
↳Reducing the need for manual upkeep of large memory documentation files.
FAQ

FAQ

What is the difference between kaeru and remem?
Both kaeru and remem are in the Memory & Context category. kaeru has 40 stars, while remem has 34 stars.
Which is better, kaeru or remem?
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 remem if Avoiding repetitive project explanations to AI coding agents..
Is kaeru free or open source?
Yes, kaeru is open source on GitHub (MIT).
Is remem free or open source?
Yes, remem is open source on GitHub (MIT).
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Alternatives to kaeru →Alternatives to remem →kaeru details →remem details →