ИИ-агенты
PraisonAI
PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous self-improving agents that research, plan, code, and execute t…
★ 9,1 тыс. звёздMIT
Смотреть руководство →ruby_llm — проект с открытым исходным кодом из категории «ИИ-агенты» от crmne (crmne/ruby_llm на GitHub). У него 4 428 звёзд на GitHub и 510 форков, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 3 октября 2026.
The Ruby-native AI framework. Chats, agents, tools, images, audio, and video through one consistent API, in plain Ruby or Rails.
Research, coding, browsing, or ops — agents work best with a clear goal.
From no-code builders to code-first SDKs, match complexity to the task.
Web search, code execution, and APIs are what make agents useful.
Watch the trace, approve consequential actions, iterate.
Large language models drive its smartest features, locally or via API.
Agents call real tools: browsers, terminals, APIs.
Benchmarks keep agent quality measurable.
Run agents on your infra with your keys.
Swap frontier models as better ones appear.
Traces show exactly what the agent did and why.
Download the source and move into the project folder.
git clone https://github.com/crmne/ruby_llm.git
cd ruby_llmEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandIt is an open-source project at crmne/ruby_llm with 510 forks. Check the repo's contributors and recent commits to see how active maintenance is.
MIT licensed — use it commercially, no strings attached.
For bounded tasks with supervision, yes. Fully autonomous money-moves still need guardrails.
Sandboxing, approval gates, and least-privilege credentials.
Builders exist, but code-first frameworks go further.
PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous self-improving agents that research, plan, code, and execute t…
A visual playground for agentic workflows: Iterate over your agents 10x faster
A股多Agent投研框架 — 适配A股数据源(龙虎榜/游资/解禁等)，7位分析师基于A股规则的辩论决策，基于TradingAgents深度改造，适配大A。A-share multi-agent investment research framework — 7 AI analysts, bull/bear deb…
A TypeScript toolkit for building agents: unified LLM API, loop, TUI, and CLI.
An open-source platform for AI-native application development — assistants, tools, RAG and models in one API.
Framework-agnostic, git-native standard for defining and sharing AI agents.
Другие проекты из категории «ИИ-агенты» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если ruby_llm 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 ruby_llm — продолжайте изучать полку по обе стороны от него.
Memory-LanceDB-Pro gives OpenClaw agents hybrid search, smart extraction, and intelligent forgetting
nono: zero-trust, zero-setup, zero-latency sandboxing and runtime security for AI agents
Agent framework for the JVM. Pronounced Em-BAY-bel /ɛmˈbeɪbəl/
Archestra: the enterprise AI platform — chat UI, LLM gateway, and MCP gateway with guardrails
Pydantic Logfire: Python and AI observability on OpenTelemetry — SQL-queryable traces, metrics, and logs
Monid — OpenRouter for agent tools: one base URL and one key give an agent 2,000+ tools across 72+ providers
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.