kaeru
★ 45
vs
letta
★ 25.2k
kaeru vs letta
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.; letta: Letta is a powerful platform for building stateful AI agents equipped with advanced memory, enabling them to learn and self-improve over time. It provides both a command-line interface for local agent execution and a comprehensive API with Python and TypeScript SDKs for seamless integration into applications.
01
TL;DR
kaeruMemory & Context · ★ 45
An agent needs to recall its thought process and project context from a previous session.
lettaMemory & Context · ★ 25.2k
When to choose:
- You want a long-lived agent that remembers past work and rewrites its own memory and context over time.
- You want one agent reachable from CLI, desktop, browser, mobile and messaging apps, with subagents, schedules and hooks.
- You want memory you can inspect: the README says context is tracked in git and can sync to your own repository.
When to skip:
- You only need to add memory to an agent you already use through MCP; a small MCP memory server is a lighter fit.
- You rely on older Letta tutorials or the V1 API server: the project restructured, and the V1 server is retired.
- You want everything to work without signing in: Letta Cloud is the default backend and some features need it.
02
Side-by-Side Comparison
Field
kaerulettaCategory
Memory & Context
Memory & Context
Stars
★ 45
★ 25.2k
License
MIT
Apache-2.0
Install Command
git clone https://github.com/kaerulabs/kaeru.git && cd kaeru && cargo build --release
npm install -g @letta-ai/letta-code
Updated
2d ago
5d ago
Open Source
Yes
Yes
Tags
LLM Agents, Knowledge Graph, Bi-temporal Database
Stateful AI, Agent Platform, Advanced Memory
03
Features
kaeru01Two-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.
letta01Build stateful AI agents with advanced memory
02Agents capable of learning and self-improvement over time
03CLI tool for running agents locally in your terminal
04Comprehensive API for integrating agents into applications
05Support for skills and subagents; fully model-agnostic
04
Use Cases
kaeru↳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.
letta↳Running AI agents locally in your terminal for coding and general tasks.
↳Integrating stateful AI agents into custom applications using the API and SDKs.
↳Developing self-improving AI systems that adapt and learn continually.
05
Best For
kaeru—
lettaMost PopularEssentialTrending
FAQ
FAQ
What is the difference between kaeru and letta?
Both kaeru and letta are in the Memory & Context category. kaeru has 45 stars, while letta has 25.2k stars.
Which is better, kaeru or letta?
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 letta if Running AI agents locally in your terminal for coding and general tasks..
Is kaeru free or open source?
Yes, kaeru is open source on GitHub (MIT).
Is letta free or open source?
Yes, letta is open source on GitHub (Apache-2.0).
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