AIエージェント
Memori
Agent-native memory infrastructure - structured, persistent memory for production agents
★ 17.1k スターNOASSERTION
ガイドを見る →mem0は、mem0aiが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: mem0ai/mem0）。GitHubスターは66,803、フォークは7,860で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月7日です。
Drop-in persistent memory infrastructure for AI agents and apps.
Decide what you want the project to do before installing anything.
Grab the source and skim the README for setup requirements.
Install dependencies and add any API keys or settings it needs.
Launch it and follow the example workflow from the docs.
Context survives across sessions and restarts.
Add memory without rebuilding your agent.
Positioned as infrastructure, not a demo.
Fits naturally into Python agent stacks.
Permissive license for commercial deployment.
Pushed regularly with a large contributor base.
Download the source and move into the project folder.
git clone https://github.com/mem0ai/mem0.git
cd mem0Create a virtual environment and install the requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtTreat these as starting points - the README documents the exact commands.
python examples/quickstart.pyIt is an open-source project at mem0ai/mem0 with 7860 forks. Check the repo's contributors and recent commits to judge how active maintenance is.
Yes - it is Apache-2.0 licensed, so it is free for commercial use with attribution.
It depends on your setup - self-hosted storage can be free, while hosted tiers or embedding APIs may cost money. Check the README for options.
All three are agent memory systems. mem0 emphasizes drop-in production infrastructure; cognee leans on knowledge-graph memory; OpenViking adds self-evolving RAG and skills. Try each README's quickstart and pick by fit.
Clone the repo, read the README's quickstart, and run the provided example - most projects get you to a first result in minutes.
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