ИИ-агенты
eko
Build production-ready agentic workflows from natural language — agents that operate browsers and computers.
★ 5,0 тыс. звёздMIT
Смотреть руководство →R2R — проект с открытым исходным кодом из категории «ИИ-агенты» от SciPhi-AI (SciPhi-AI/R2R на GitHub). У него 8 011 звёзд на GitHub и 649 форков, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 7 ноября 2025.
Production-ready agentic RAG behind a clean REST API.
Be clear about the problem you are solving before installing.
Clone the repository and skim the docs for hardware or key requirements.
Install dependencies, then add API keys, endpoints, or local-model settings.
Launch the example flow and verify the output matches expectations.
Retrieval with agent-style reasoning.
Consume retrieval as a service.
Positioned for deployment, not just demos.
Ingestion through generation in one system.
Hackable Python implementation.
Permissive licensing.
Download the source and move into the project folder.
git clone https://github.com/SciPhi-AI/R2R.git
cd R2RInstall the package and its dependencies.
pip install r2rConfigure provider keys and launch the API per the README.
# verify the install still works — last push was Nov 2025It is maintained by SciPhi-AI at SciPhi-AI/R2R, with 649 forks. Note the last push was November 2025 — check for recent activity before building on it.
Yes. It is released under the MIT license, which permits commercial use, modification, and redistribution. Read the LICENSE file for the exact terms.
You need LLM and embedding provider access; the system itself is free.
Compare on features and maintenance status — its API-first design is the differentiator.
Follow the README install, but first verify the pinned dependencies still install cleanly given the quiet period.
Build production-ready agentic workflows from natural language — agents that operate browsers and computers.
Turn any website into clean, agent-ready data with one API.
A production-grade platform for building agentic chat bots on every IM platform
Production-ready headless browser for AI agents, built in Rust - structure first, pixels only on demand.
An open-source platform for AI-native application development — assistants, tools, RAG and models in one API.
Convert PDFs, DOCX, and HTML into clean, AI-ready documents.
Другие проекты из категории «ИИ-агенты» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если R2R 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 R2R — продолжайте изучать полку по обе стороны от него.
Osaurus — own your AI: a native macOS agent harness with any model, memory and fully offline execution.
Upsonic — build autonomous AI agents in Python, with prebuilt agents, custom tools, MCP and OCR built in.
All your agents and workspaces in one place — on every device you own.
ZCode — Z.ai's AI coding workspace: desktop app, browser interface and terminal agent in one open-source harness
Stream's open Vision Agents — build real-time voice and vision agents with any model or video provider.
Flexible and powerful framework for managing multiple AI agents and handling complex conversations
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.