ИИ-агенты
OpenViking
A self-evolving context database unifying agent memory, RAG, and skills.
★ 39,4 тыс. звёздAGPL-3.0
Смотреть руководство →Agent_Memory_Techniques — проект с открытым исходным кодом из категории «ИИ-агенты» от NirDiamant (NirDiamant/Agent_Memory_Techniques на GitHub). У него 1 090 звёзд на GitHub и 141 форк, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 21 сентября 2026.
30 runnable notebooks teaching everything about agent memory.
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.
A full curriculum, runnable top to bottom.
MemGPT, Mem0, Letta, Zep, Graphiti, and more.
LoCoMo evaluations hands-on.
Beyond theory: how teams ship it.
Learn by executing, not just reading.
A class worth paying for, at no cost.
Download the notebooks and move into the folder.
git clone https://github.com/NirDiamant/Agent_Memory_Techniques.git
cd Agent_Memory_TechniquesGet JupyterLab plus the repo's Python requirements.
pip install jupyterlab
pip install -r requirements.txtLaunch JupyterLab and work through the notebooks in order.
jupyter labIt is an open-source project at NirDiamant/Agent_Memory_Techniques with 141 forks. Check the contributors tab and recent commits to judge how active maintenance is.
Yes — Apache 2.0 allows commercial use and modification. Keep the license and notice files, and note it includes a patent grant.
No — these are notebooks about memory techniques; a laptop with Python and Jupyter is enough.
Yes — it's structured as a course, from buffers to production patterns.
Clone the repo and follow the README quickstart — the README is the best starting point.
A self-evolving context database unifying agent memory, RAG, and skills.
A memory agent for your agent fleet: it curates, reconciles, and expires shared memories across tools.
A benchmark for comparing AI agent memory systems head-to-head.
DeepSeek's plugin-based agent harness where everything is a plugin.
Agent memory that learns — so your agents get smarter over time.
Osaurus — own your AI: a native macOS agent harness with any model, memory and fully offline execution.
Другие проекты из категории «ИИ-агенты» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если Agent_Memory_Techniques вам не подходит.
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.
Соседние проекты из категории «ИИ-агенты» в каталоге, ближайшие по числу звёзд к Agent_Memory_Techniques — продолжайте изучать полку по обе стороны от него.
Talorys — your personal AI agent in your own Cloudflare account: chat, memory, tasks, notes and scheduled routines, deployed with one command
ByteDance's SandboxFusion: a secure sandbox for running and judging LLM-generated code in 20+ languages.
A toolkit for developing AI-powered apps, from the Weights & Biases team.
Official SDKs for tracing with LangSmith.
A lean, HuggingFace-native RL framework for agentic research.
Fast, best-practice evaluations for AI model outputs.
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.