KI-Agenten
langmem
LangMem: long-term memory primitives for LangGraph agents that learn and adapt from conversations.
★ 1,7 Tsd. SterneMIT
Anleitung ansehen →memobase ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von memodb-io (memodb-io/memobase auf GitHub). Es hat 2.925 Sterne auf GitHub und 238 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 11. Januar 2026 auf GitHub aktualisiert.
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.
LangMem: long-term memory primitives for LangGraph agents that learn and adapt from conversations.
Multi-agent platform where every agent gets its own computer and long-term memory.
Self-managing, unbounded context and long-term memory for coding agents: one session, for life.
Long-term memory runtime for AI agents — plain Markdown as truth, local ranked retrieval, no API key
Open-source AI memory that gives agents persistent long-term recall.
A memory operating system for long-term, personalized AI agents.
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls memobase nicht das Richtige ist.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Verwandte Projekte der Kategorie KI-Agenten im Katalog, die memobase bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
Blazing-fast business intelligence built for humans and AI agents alike.
Fork() for AI agent microVMs — spawn 100 KVM-isolated children in ~100ms from a warm parent
Cross-platform desktop panel for managing OpenClaw and Hermes AI agents.
Living architecture documentation that both your team and AI agents can read.
Write coding-agent rules once, then apply them to every AI coding tool you use.
Self-hosted AI pentesting agent that finds flaws and opens PRs to fix them.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.