AgentIndex icon
AgentIndex
kaeru logo
kaeru
★ 40
vs
Acontext logo
Acontext
★ 3.7k

kaeru vs Acontext

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.; Acontext: Acontext is a Context Data Platform designed to help build scalable cloud-native AI agents by managing context storage, retrieval, and state. It solves common problems like inefficient LLM message storage, complex long-running agent management, and lack of agent observability and learning capabilities.

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 →
Acontext logo
AcontextMemory & Context · ★ 3.7k

Building scalable and robust cloud-native AI agents for large user bases.

Read Acontext full review →
02

Side-by-Side Comparison

Field
kaeru logokaeru
Acontext logoAcontext
Category
Memory & Context
Memory & Context
Stars
★ 40
★ 3.7k
License
MIT
Apache-2.0
Install Command
git clone https://github.com/kaerulabs/kaeru.git && cd kaeru && cargo build --release
pip install acontext
Updated
2d ago
3mo ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
LLM Agents, Knowledge Graph, Bi-temporal Database
AI Agents, Context Management, Data Platform
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.
Acontext logoAcontext
01Unified multi-modal message storage
02Artifact management with file paths
03Context window editing API
04Real-time agent task observation
05Agent self-learning for Standard Operating Procedures (SOPs)
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.
Acontext logoAcontext
↳Building scalable and robust cloud-native AI agents for large user bases.
↳Managing and observing long-running, stateful AI agents with built-in context engineering.
↳Enabling AI agents to self-learn and adapt, improving consistency and success rates.
05

Best For

kaeru logokaeru
—
Acontext logoAcontext
Essential
FAQ

FAQ

What is the difference between kaeru and Acontext?
Both kaeru and Acontext are in the Memory & Context category. kaeru has 40 stars, while Acontext has 3.7k stars.
Which is better, kaeru or Acontext?
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 Acontext if Building scalable and robust cloud-native AI agents for large user bases..
Is kaeru free or open source?
Yes, kaeru is open source on GitHub (MIT).
Is Acontext free or open source?
Yes, Acontext is open source on GitHub (Apache-2.0).
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Alternatives to kaeru →Alternatives to Acontext →kaeru details →Acontext details →