AgentIndex icon
AgentIndex
kaeru logo
kaeru
★ 40
vs
deja-vu logo
deja-vu
★ 1.3k

kaeru vs deja-vu

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.; 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

kaeru logo
kaeruMemory & Context · ★ 40

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

Read kaeru 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
kaeru logokaeru
deja-vu logodeja-vu
Category
Memory & Context
Memory & Context
Stars
★ 40
★ 1.3k
License
MIT
MIT
Install Command
git clone https://github.com/kaerulabs/kaeru.git && cd kaeru && cargo build --release
curl -fsSL https://raw.githubusercontent.com/vshulcz/deja-vu/main/install.sh | sh
Updated
2d ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
LLM Agents, Knowledge Graph, Bi-temporal Database
AI Agent Memory, Session Search, Code 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.
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

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.
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 kaeru and deja-vu?
Both kaeru and deja-vu are in the Memory & Context category. kaeru has 40 stars, while deja-vu has 1.3k stars.
Which is better, kaeru or deja-vu?
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 deja-vu if Reusing solutions from past agent conversations to avoid re-debugging or re-implementing..
Is kaeru free or open source?
Yes, kaeru is open source on GitHub (MIT).
Is deja-vu free or open source?
Yes, deja-vu is open source on GitHub (MIT).
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Related

Alternatives to kaeru →Alternatives to deja-vu →kaeru details →deja-vu details →