工具说明为英文。
RAG 与搜索
检索增强、向量数据库、嵌入和语义搜索。
用途
活跃度
排序方式
147
工具排名
混合排名:实测增长优先。 原始值保留各自的数据窗口;计算变化需要 7 天内至少两次测量。
- 1已放弃
vibe
智能体Open-Source AI-powered web browser. Browse the web with your own LLM API key. Alternative to Dia / Comet.
github实测增长打开来源 ↗
94GitHub 星标稳定 - 2活跃
Cortex
MCPCross-platform persistent memory MCP for Codex, Gemini CLI, Claude Code, and other local MCP hosts. 36 cited neuroscience mechanisms, local-first SQLite/PostgreSQL, hybrid retrieval, decay-based consolidation, and reproducible benchmarks.…
github实测增长打开来源 ↗
71GitHub 星标稳定 - 3活跃
Semantic Scholar API wrapper for agents — search 200M+ papers, traverse citations, find authors. Python, rate-limited, multi-agent.
github实测增长打开来源 ↗
安装
git clone https://github.com/Agents365-ai/semanticscholar-skill ~/.claude/skills/semanticscholar-skill68GitHub 星标稳定 - 4活跃
The official Pinecone marketplace for Claude Code Plugins
github实测增长打开来源 ↗
安装
/plugin marketplace add pinecone-io/pinecone-claude-code-plugin68GitHub 星标稳定 - 5活跃
Token-efficient code review knowledge graph: semantic search and call-graph resolution.
mcp实测增长打开来源 ↗
安装
claude mcp add better-code-review-graph -- uvx better-code-review-graph66GitHub 星标稳定 - 6活跃
Persistent memory for Claude Code & Codex CLI. Auto-extracted knowledge graph, multi-representation embeddings, 3D WebGL visualization. LongMemEval R@5=97.45%. Self-hosted, Ollama-optional
github实测增长打开来源 ↗
66GitHub 星标稳定 - 7活跃
ruvnet-brain
MCPRuvNet Brain — a downloadable, source-grounded brain for Claude Code over Reuven Cohen's (rUv's) RuvNet stack: RuVector/RVF, Ruflo, AgentDB, RuLake, SPARC + 21 building blocks. Grounds Claude in real source via one MCP tool (search_ruvnet),…
github实测增长打开来源 ↗
64GitHub 星标稳定 - 8活跃
Agent skills for LandingAI's Agentic Document Extraction (ADE) — production-ready document AI for agentic coding assistants
github实测增长打开来源 ↗
安装
git clone https://github.com/landing-ai/ade-document-processing-skills ~/.claude/skills/ade-document-processing-skills64GitHub 星标稳定 - 936 578GitHub 星标—
学习与参考资源
按实测增长排序,并在不同来源间归一化。 这些资源可单独访问,不参与主要排名。
- 1休眠打开来源 ↗
membrane
技能A selective learning and memory substrate for agentic systems — typed, revisable, decayable memory with competence learning and trust-aware retrieval.
github安装
资源实测增长git clone https://github.com/BennettSchwartz/membrane ~/.claude/skills/membrane94GitHub 星标稳定 - 2活跃打开来源 ↗
Build LLM systems you actually control. A free, open engineering book + course — from tokenization to serving your own models. Mechanisms, trade-offs, and numbers, not prompt tips
github资源实测增长46GitHub 星标稳定 - 3活跃打开来源 ↗
Official Claude Documentation - Converted to 2000+ Markdown Files - All documentation sourced from first party sources- 24 categories - refreshed daily, multi-agent orchestration
github安装
资源实测增长/plugin marketplace add johnzfitch/claude-wiki21GitHub 星标稳定 - 4活跃打开来源 ↗
My complete journey to becoming an Agentic AI Engineer through structured learning, projects, experiments, and production-ready implementations of modern AI systems.
github资源实测增长18GitHub 星标稳定 - 5休眠打开来源 ↗
Persistent memory for Claude Code. Auto-capture decisions, learnings, client info. Cloud backup to Supabase. Never lose context again. Free alternative to Mem.ai, Rewind AI, Personal AI.
github资源实测增长14GitHub 星标稳定 - 6活跃打开来源 ↗
Spring AI 实战教程 🚀 ChatClient → RAG → MCP → Agent → A2A,24 个手写模块循序渐进(Spring Boot 4.1 + Spring AI 2.0 + Java 21)
github资源实测增长3GitHub 星标稳定 - 72GitHub 星标稳定
- 8活跃打开来源 ↗
Curated Agentic AI resources: AI agent frameworks, tutorials, courses, projects, MCP tools, research papers, evaluation, security, and production guides.
github资源实测增长2GitHub 星标稳定 - 9活跃打开来源 ↗
Comprehensive collection of AI concepts, research findings, and practical implementations. From machine learning fundamentals to cutting-edge LLMs, agents, and emerging technologies.
github资源实测增长1GitHub 星标稳定 - 10活跃打开来源 ↗
Detect underpriced products by scraping deals and using AI models to estimate fair prices for profit opportunity identification.
github资源实测增长1GitHub 星标稳定 - 11活跃打开来源 ↗
Handbook técnico aberto sobre engenharia de IA em produção: ML tradicional, LLMs, RAG, agentes, segurança, observabilidade, FinOps e deployment.
github资源实测增长1GitHub 星标稳定 - 12休眠打开来源 ↗
Interactive roadmap for becoming an Agentic AI Engineer — 11 stages from Python & FastAPI to multi-agent systems, MCP, RAG, Docker, and context engineering
github资源实测增长1GitHub 星标稳定 - 13休眠打开来源 ↗
50+ AI agent apps you can actually run — MCP, A2A, LangGraph, SAP, RAG, and multi-agent patterns
github资源实测增长0GitHub 星标稳定