ИИ-агенты
langmem
LangMem: long-term memory primitives for LangGraph agents that learn and adapt from conversations.
★ 1,7 тыс. звёздMIT
Смотреть руководство →cognee — проект с открытым исходным кодом из категории «ИИ-агенты» от topoteretes (topoteretes/cognee на GitHub). У него 31 591 звезда на GitHub и 3 246 форков, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 8 октября 2026.
Open-source AI memory that gives agents persistent long-term recall.
Write down the task or workflow you want the agent to handle.
Clone the repository and read the README's prerequisites.
Install dependencies and configure keys or endpoints.
Run the sample script and check the output against the docs.
Facts stored as connected graphs, not flat text.
Memory that survives across sessions.
Designed to work well without giant models.
Focus on how memory is structured for retrieval.
Permissive open-source license.
Large star count and steady pushes.
Download the source and move into the project folder.
git clone https://github.com/topoteretes/cognee.git
cd cogneeCreate 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.pyThe project lives at topoteretes/cognee and has attracted 3246 forks. Browse its commit history and contributor list for a sense of maintenance pace.
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.
mem0 is a drop-in memory API; cognee is a knowledge-graph memory platform with a cognitive-architecture angle. If graph-structured recall appeals to you, start with cognee.
The README and the official docs site are the fastest path: run the quickstart example first, then adapt it to your use case.
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