ИИ-агенты
langmem
LangMem: long-term memory primitives for LangGraph agents that learn and adapt from conversations.
★ 1,7 тыс. звёздMIT
Смотреть руководство →memobase — проект с открытым исходным кодом из категории «ИИ-агенты» от memodb-io (memodb-io/memobase на GitHub). У него 2 925 звёзд на GitHub и 238 форков, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 11 января 2026.
User-profile-based long-term memory for AI chatbot applications
Decide what your chatbot should remember about each user across sessions — preferences, facts and profile traits — rather than treating every chat as new.
Clone the repo and check its documentation at memobase.io for the current deployment options.
Follow the project's docs to run the memory backend and connect your LLM, since profile extraction is LLM-driven.
Send a few conversations for one user, then start a new session and confirm the chatbot recalls the stored profile.
Builds long-term memory around user profiles rather than only raw message logs, per the project description.
Designed specifically as memory for AI chatbot applications.
Memory is meant to survive across sessions so returning users are recognized.
Implemented in Python, fitting the typical LLM application stack.
Apache-2.0 licensed and developed in the open by memodb-io.
Documentation and product information are published at memobase.io.
Clone Memobase and enter the directory.
git clone https://github.com/memodb-io/memobase.git
cd memobaseThe README could not be fetched when this entry was generated — use the documented quickstart in the repo/docs for the exact install command.
# see README for the exact commandThe project's site links current docs and deployment guidance.
# open https://memobase.iomemodb-io maintains it; the project site is memobase.io.
Yes, it is Apache-2.0 licensed open source. Running it still needs an LLM for profile extraction, whose costs depend on your provider.
Profile-based memory systems typically call an LLM to extract and update profiles, so expect to supply your own model key or a local model setup; check the current docs for supported providers.
Storing history replays raw messages. Memobase distills conversations into user profiles designed for long-term recall.
Clone the repo and follow the quickstart in its documentation at memobase.io.
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