ИИ-агенты
agentops
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI …
★ 5,9 тыс. звёздMIT
Смотреть руководство →nanobot — проект с открытым исходным кодом из категории «ИИ-агенты» от HKUDS (HKUDS/nanobot на GitHub). У него 48 763 звезды на GitHub и 8 600 форков, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 3 октября 2026.
Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat…
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.
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.
Approval gates for actions that matter.
Download the source and move into the project folder.
git clone https://github.com/HKUDS/nanobot.git
cd nanobotEvery 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 commandAlso on this shelf: gpqa — a graduate-level benchmark even Google can't crack. The AI Agents shelf also holds MMLU-Pro — the tougher, more robust successor to the MMLU benchmark. The AI Agents shelf also holds RAMPART — Safety testing for agents, right in pytest.
It is an open-source project at HKUDS/nanobot with 8,600 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.
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI …
AdalFlow: The library to build & auto-optimize LLM applications.
100+ AI Agents, Agent Skills and RAG Apps - Free and Open Source.
A lightweight, powerful framework for multi-agent workflows
OpenAI's lightweight TypeScript framework for multi-agent workflows and voice agents.
AI agent framework for plan-first development workflows with approval-based execution. Multi-language support (TypeScript, Python, Go, Rust) with automatic t…
Другие проекты из категории «ИИ-агенты» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если nanobot 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 nanobot — продолжайте изучать полку по обе стороны от него.
LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.
Agent memory that learns — so your agents get smarter over time.
Make humans and AI agents work as one team — open-source and self-hostable.
An open-source, privacy-first, self-hosted knowledge workspace where humans and AI agents work together 开源、隐私优先、自托管的知识工作空间，让人与智能体在此协作
LlamaIndex is the document processing platform for AI
The cloud-native vector database for planet-scale similarity search.
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.