ToolAI.io · GitHub 채널

GitHub에서 인기 있는 AI 프로젝트

LLM, 에이전트, MCP, RAG 및 코딩 분야의 활발한 오픈 소스 AI 리포지토리를 추적하고, 스타 수와 검증된 프로젝트 정보를 제공합니다.

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130 프로젝트
OpenViking: Self-evolving Context Database for AI Agents 스크린샷
에이전트 및 멀티 에이전트

OpenViking: Self-evolving Context Database for AI Agents

volcengine/openviking

OpenViking is an open-source context database that unifies agent memory, knowledge RAG, and skills into a single virtual filesystem, enabling AI agents to browse and retrieve context deterministically using a file-system-like protocol.

★ 28K⑂ 2.2KPython
AGPL-3.0Q98
Headroom: Context Compression Layer for AI Agents 스크린샷
MCP 및 도구 호출

Headroom: Context Compression Layer for AI Agents

headroomlabs-ai/headroom

Headroom is a local-first context compression layer that reduces token usage for AI agents by compressing tool outputs, logs, files, and RAG chunks before they reach the LLM. It offers library, proxy, and MCP server modes and claims to preserve answer accuracy while cutting tokens by 15-95% depending on workload.

★ 65K⑂ 4.9KPython
Apache-2.0Q98
Potpie 스크린샷
에이전트 및 멀티 에이전트

Potpie

potpie-ai/potpie

Potpie transforms a codebase and software development lifecycle into a living context graph for AI agents, indexing code, structure, decisions, source history, and team knowledge to enable context-aware coding, planning, and debugging.

★ 5.5K⑂ 642Python
Apache-2.0Q98
Firecrawl 스크린샷
에이전트 및 멀티 에이전트

Firecrawl

firecrawl/firecrawl

An open-source web context API to search, scrape, and interact with the web at scale, converting web data into clean Markdown or structured JSON for AI agents and LLMs.

★ 161.1K⑂ 9.1KTypeScript
AGPL-3.0Q98
BISHENG 스크린샷
에이전트 및 멀티 에이전트

BISHENG

dataelement/bisheng

BISHENG is an open-source LLM application DevOps platform designed for next-generation enterprise AI applications, offering comprehensive features like GenAI workflow orchestration, RAG, Agent management, and enterprise-grade system controls.

★ 11.8K⑂ 1.9KPython
Apache-2.0Q98
promptfoo 스크린샷
에이전트 및 멀티 에이전트

promptfoo

promptfoo/promptfoo

An open-source CLI and library for evaluating, testing, and red-teaming LLM applications, RAGs, and agents. It enables side-by-side model comparison and vulnerability scanning with declarative configs and CI/CD integration.

★ 23.9K⑂ 2.2KTypeScript
MITQ98
ha-mcp: The Unofficial Home Assistant MCP Server 스크린샷
MCP 및 도구 호출

ha-mcp: The Unofficial Home Assistant MCP Server

homeassistant-ai/ha-mcp

A comprehensive Model Context Protocol (MCP) server enabling AI assistants to interact with, configure, build, and debug Home Assistant smart home setups using natural language.

★ 4.3K⑂ 177Python
MITQ98
OpenHands Agent Canvas 스크린샷
에이전트 및 멀티 에이전트

OpenHands Agent Canvas

openhands/openhands

A self-hosted developer control center for running, managing, and automating coding agents across local, remote, and cloud environments.

★ 83.1K⑂ 10.7KTypeScript
MITQ98
LlamaIndex 스크린샷
에이전트 및 멀티 에이전트

LlamaIndex

run-llama/llama_index

LlamaIndex is an open-source Python data framework for building LLM applications with private data, offering data connectors, indexing structures, and advanced retrieval interfaces. It is complemented by LlamaParse, an enterprise platform for agentic OCR, parsing, extraction, and indexing.

★ 51.4K⑂ 7.9KPython
MITQ98
Mem0: Universal Memory Layer for AI Agents 스크린샷
에이전트 및 멀티 에이전트

Mem0: Universal Memory Layer for AI Agents

mem0ai/mem0

Mem0 is an open-source memory layer for AI agents and assistants, enabling personalized, long-term memory across user sessions. It provides multi-level memory retention, hybrid retrieval, and temporal reasoning through Python and npm SDKs, a self-hosted server, or a managed cloud platform.

★ 62.5K⑂ 7.3KPython
Apache-2.0Q98
AgentScope 2.0 스크린샷
MCP 및 도구 호출

AgentScope 2.0

agentscope-ai/agentscope

A production-ready, easy-to-use agent framework designed to leverage the reasoning and tool-use abilities of increasingly agentic LLMs without constraining them with strict prompts or opinionated orchestrations.

★ 28.6K⑂ 3.3KPython
Apache-2.0Q98

7 / 11페이지 · 프로젝트 130개

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별이 가장 많은

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