kaeru
Active·★ 40·MIT·Updated 2026-10-21
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.
#LLM Agents#Knowledge Graph#Bi-temporal Database#Multi-Agent Memory#Cognitive Engine#Local-first#CozoDB#Context Management
01
Features
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.
02
Compatibility
Rust
Runtime
Verified via docs
CozoDB
Database
Verified via docs
LLM Agents
Integration
Verified via docs
Claude Code
Client
Verified via docs
Opencode
Client
Verified via docs
Rig
Framework
Verified via docs
03
Quick start
1
$ git clone https://github.com/kaerulabs/kaeru.git
2
$ cd kaeru
3
$ cargo build --release
04
Use cases
↳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.
05
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