AgentIndex icon
AgentIndex
kaeru logo
kaeru
★ 40
vs
projectmem logo
projectmem
★ 868

kaeru vs projectmem

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.; projectmem: projectmem is an open-source, local-first memory and judgment layer for AI coding agents, designed to capture persistent records of project development, including issues, attempts, fixes, and decisions. It aims to make AI experienced, preventing repetition of past failures and significantly reducing token usage by providing distilled 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 →
projectmem logo
projectmemMemory & Context · ★ 868

Preventing AI agents from repeating past debugging attempts or failed architectural approaches.

Read projectmem full review →
02

Side-by-Side Comparison

Field
kaeru logokaeru
projectmem logoprojectmem
Category
Memory & Context
Memory & Context
Stars
★ 40
★ 868
License
MIT
MIT
Install Command
git clone https://github.com/kaerulabs/kaeru.git && cd kaeru && cargo build --release
pip install projectmem && cd your-project && pjm init
Updated
1d ago
2w ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
LLM Agents, Knowledge Graph, Bi-temporal Database
AI Agent Memory, Local-First, Developer Tooling
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.
projectmem logoprojectmem
01Pre-Commit Warnings: Warns developers before committing if they're repeating failed approaches, modifying high-churn files, or touching unresolved issues.
02Local-First and 100% Private: All memory and data reside locally in a `.projectmem/` directory with no cloud, accounts, or telemetry.
03Global Dashboard: Provides a unified view of all `pjm init`-ed projects, showing grades, issues, savings, and a 'needs attention' list, aggregated at read-time.
04Code Structure + Judgment: Overlays failure heat from the event log directly onto the codebase's import graph, highlighting files with repeated failed attempts.
05Smart Context Injection & Token Efficiency: Distills raw event logs into compact, AI-readable summaries and provides token-budgeted memory blocks, significantly reducing token cost.
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.
projectmem logoprojectmem
↳Preventing AI agents from repeating past debugging attempts or failed architectural approaches.
↳Improving AI agent efficiency and reducing token costs by providing distilled, context-aware project memory instead of re-reading raw files.
↳Visualizing project development history, including issues, fixes, and decisions, to understand codebase evolution and identify problem areas.
↳Onboarding new developers or bringing AI agents up to speed on a project quickly by injecting pre-existing project knowledge and decisions.
↳Providing a persistent, auditable record of AI agent interactions and learning within a project.
05

Best For

kaeru logokaeru
—
projectmem logoprojectmem
Hidden Gem
FAQ

FAQ

What is the difference between kaeru and projectmem?
Both kaeru and projectmem are in the Memory & Context category. kaeru has 40 stars, while projectmem has 868 stars.
Which is better, kaeru or projectmem?
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 projectmem if Preventing AI agents from repeating past debugging attempts or failed architectural approaches..
Is kaeru free or open source?
Yes, kaeru is open source on GitHub (MIT).
Is projectmem free or open source?
Yes, projectmem is open source on GitHub (MIT).
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