Le descrizioni degli strumenti sono in inglese.

RAG e ricerca

Recupero, database vettoriali, embedding e ricerca semantica.

Utilizzo

Attività

Ordina per

141

Classifica degli strumenti

Classifica mista: la crescita misurata ha la precedenza. I valori grezzi mantengono la propria finestra; una variazione richiede almeno due rilevazioni in 7 giorni.

  1. 1
    Attivo

    The official Pinecone marketplace for Claude Code Plugins

    githubcrescita misurataApri fonte ↗

    Installa /plugin marketplace add pinecone-io/pinecone-claude-code-plugin

    68stelle GitHubstabile
  2. 2
    Attivo

    Token-efficient code review knowledge graph: semantic search and call-graph resolution.

    mcpcrescita misurataApri fonte ↗

    Installa claude mcp add better-code-review-graph -- uvx better-code-review-graph

    66stelle GitHubstabile
  3. 3
    Attivo

    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

    githubcrescita misurataApri fonte ↗

    66stelle GitHubstabile
  4. 4
    Attivo

    RuvNet 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),…

    githubcrescita misurataApri fonte ↗

    64stelle GitHubstabile
  5. 5
    Attivo

    Agent skills for LandingAI's Agentic Document Extraction (ADE) — production-ready document AI for agentic coding assistants

    githubcrescita misurataApri fonte ↗

    Installa git clone https://github.com/landing-ai/ade-document-processing-skills ~/.claude/skills/ade-document-processing-skills

    64stelle GitHubstabile
  6. 6

    Agente
    Attivo

    Vane is an AI-powered answering engine.

    githubmomentum stimatoApri fonte ↗

    36 578stelle GitHub

Risorse didattiche e di riferimento

Classifica per crescita misurata e normalizzata tra le fonti. Queste risorse restano accessibili separatamente e non partecipano alla classifica principale.

  1. 1
    AttivoApri fonte ↗

    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

    githubRisorsacrescita misurata
    46stelle GitHubstabile
  2. 2
    AttivoApri fonte ↗

    Official Claude Documentation - Converted to 2000+ Markdown Files - All documentation sourced from first party sources- 24 categories - refreshed daily, multi-agent orchestration

    github

    Installa /plugin marketplace add johnzfitch/claude-wiki

    Risorsacrescita misurata
    21stelle GitHubstabile
  3. 3
    AttivoApri fonte ↗

    My complete journey to becoming an Agentic AI Engineer through structured learning, projects, experiments, and production-ready implementations of modern AI systems.

    githubRisorsacrescita misurata
    18stelle GitHubstabile
  4. 4
    AttivoApri fonte ↗

    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)

    githubRisorsacrescita misurata
    3stelle GitHubstabile
  5. 5
    AttivoApri fonte ↗

    Curated Agentic AI resources: AI agent frameworks, tutorials, courses, projects, MCP tools, research papers, evaluation, security, and production guides.

    githubRisorsacrescita misurata
    2stelle GitHubstabile
  6. 6
    AttivoApri fonte ↗

    Comprehensive collection of AI concepts, research findings, and practical implementations. From machine learning fundamentals to cutting-edge LLMs, agents, and emerging technologies.

    githubRisorsacrescita misurata
    1stelle GitHubstabile
  7. 7
    AttivoApri fonte ↗

    Detect underpriced products by scraping deals and using AI models to estimate fair prices for profit opportunity identification.

    githubRisorsacrescita misurata
    1stelle GitHubstabile
  8. 8
    AttivoApri fonte ↗

    Handbook técnico aberto sobre engenharia de IA em produção: ML tradicional, LLMs, RAG, agentes, segurança, observabilidade, FinOps e deployment.

    githubRisorsacrescita misurata
    1stelle GitHubstabile
  9. 9
    DormienteApri fonte ↗

    Interactive roadmap for becoming an Agentic AI Engineer — 11 stages from Python & FastAPI to multi-agent systems, MCP, RAG, Docker, and context engineering

    githubRisorsacrescita misurata
    1stelle GitHubstabile
  10. 10
    DormienteApri fonte ↗

    50+ AI agent apps you can actually run — MCP, A2A, LangGraph, SAP, RAG, and multi-agent patterns

    githubRisorsacrescita misurata
    0stelle GitHubstabile