RAG & embeddings

Vector stores, embeddings, and retrieval-augmented generation pipelines. 214 passende Einträge im Forge-Registry — angezeigt werden die obersten 150.

• thedotmack/claude-memPersistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, com
• ruvnet/ruflo🌊 The leading agent meta-harness for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous wor
• chopratejas/headroomCompress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answer
• 1Panel-dev/MaxKB🔥 MaxKB is an open-source platform for building enterprise-grade agents. 强大易用的开源企业级智能体平台。
• topoteretes/cogneeCognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory acros
• Upsonic/UpsonicBuild autonomous AI agents in Python.
• the-open-agent/openagent⚡️next-generation personal AI assistant powered by LLM, RAG and agent loops, supporting computer-use, browser-
• MinishLab/sembleFast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read
• WenyuChiou/awesome-agentic-ai-zhA trilingual (繁中 / English / 简中) learning roadmap for agentic AI: from LLM basics to multi-agent systems, with
• genieincodebottle/generative-aiComprehensive resources on Generative AI, including a detailed roadmap, projects, use cases, interview prepara
• agentset-ai/agentsetThe open-source RAG platform: built-in citations, deep research, 22+ file formats, partitions, MCP server, and
• chrisryugj/korean-law-mcp국가법령정보MCP v4.4 | 법제처 42개 API → 9개 MCP 도구. 법령·판례·조례·조약 + 다단계 리서치(legal_research) + 정밀분석(legal_analysis: 인용검증·판례
• doobidoo/mcp-memory-serviceOpen-source persistent memory for AI agent pipelines (LangGraph, CrewAI, AutoGen) and Claude. REST API + knowl
• MicrosoftDocs/mcpOfficial Microsoft Learn MCP Server and CLI tool – powering LLMs and AI agents with real-time, trusted Microso
• qdrant/mcp-server-qdrantAn official Qdrant Model Context Protocol (MCP) server implementation
• trpc-group/trpc-agent-goA Go framework for building production agent systems with graph workflows, tools, memory, A2A, AG-UI, MCP, eva
• study8677/antigravity-workspace-templateMulti-agent knowledge engine (ag-refresh / ag-ask) that turns any codebase into a queryable AI assistant.Works
• Dataojitori/nocturne_memoryA lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents. Say goodbye to Vector RAG and
• dmayboroda/minimaOn-premises conversational RAG with configurable containers
• nduckmink/arkonArkon: Enterprise AI Knowledge Hub & MCP Server. Self-hosted knowledge base for teams to manage RAG contexts,
• jerry-ai-dev/MODULAR-RAG-MCP-SERVERA modular RAG (Retrieval-Augmented Generation) system with MCP Server architecture. Using Skill to make AI fol
• elastic/mcp-server-elasticsearchOfficial Elastic MCP server for interacting with Elasticsearch clusters
• AmeNetwork/aserAser is a lightweight, self-assembling AI Agent frame.
• osovv/grace-marketplace GRACE (Graph-RAG Anchored Code Engineering): open Agent Skills for contract-driven AI code generation with se
• AgentToolkit/altk-evolveSelf improving agents through iterations
• RubensZimbres/Multi-Agent-System-A2A-ADK-MCPMulti-Agent Systems with Google's Agent Development Kit + A2A + MCP
• CrewForm/crewformBuild your AI team with Crewform. Orchestrate specialized, autonomous agents to collaborate on complex tasks a
• redhat-community-ai-tools/UnifAIProduction-grade multi-agent orchestration engine. Compose agentic workflows from a pluggable catalog of Agent
• elbruno/ElBruno.ModelContextProtocolSemantic routing for MCP tools - .NET library that indexes MCP tool definitions and returns the most relevant
• IntunoAI/intunoOpen platform for AI agent networks — semantic discovery, a secure broker, and multidirectional agent-to-agent
• @laskarks/mcp-rag-nodeSimple MCP RAG server using @modelcontextprotocol/sdk
• integrated-semantics/flexible-graphragFlexible GraphRAG: Python, LlamaIndex (LangChain also coming) Docker Compose: 8 Property Graph dbs, 3 RDF grap
• shebe-oss/shebeFast BM25 full-text search for code repositories with CLI and MCP integration for AI coding agents.
• io-devel/rag-mcp-goA Go-based Open Source Universal Memory server that exposes a Model Context Protocol (MCP) interface for LLM c
• io.github.Br0ski777/vector-searchIn-memory vector search with TF-IDF and cosine similarity. x402 micropayment.
• io.github.CSOAI-ORG/rag-knowledge-graph-mcprag-knowledge-graph-mcp MCP server by MEOK AI Labs
• io.github.CSOAI-ORG/rag-knowledge-mcprag-knowledge-mcp MCP server by MEOK AI Labs
• io.github.CSOAI-ORG/vector-knowledge-graph-mcpAI-powered vector knowledge graph MCP server for agents. Supports add node, add edge, semantic node
• io.github.Evozim/repo-to-ragGitHub repository vectorizer and context synthesizer for RAG pipelines.
• io.github.RobThePCGuy/rag-vaultLocal RAG MCP server with hybrid search, PDF/DOCX support, and zero-config setup
• io.github.c64dos-png/vector-mirrorDeterministic SVG perception for LLM agents — measures, never guesses. MCP server.
• io.github.calypso-so/multimodal-rag-mcp-serverCalypso multimodal RAG for grounded answers from docs, images, charts, and knowledge.
• io.github.fieldcure/ragMCP RAG server with hybrid search, multi-KB support, and AI-powered chunk contextualization.
• io.github.ggozad/haiku-ragOpinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling
• io.github.kimsb2429/internal-knowledge-baseProduction-ready RAG + MCP demo: eval-in-CI merge gate, Langfuse traces, structure-aware chunking.
• io.github.kroq86/vector-db-mcpAssembly-accelerated DuckDB MCP server for code search, indexing, and symbol lookup.
• io.github.lazymac2x/embedding-searchCloudflare Workers MCP server: embedding-search
• io.github.linggen/linggenLocal, privacy-focused RAG service for code search via MCP. https://linggen.dev
• io.github.mameshivaa/x-archive-ragLocal-first MCP tools for searching and drafting from your X/Twitter archive.
• io.github.maxi-moss/medical-rag-mcp-testMedical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
• io.github.maxi-moss/test-mcpMedical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
• io.github.meloncafe/chromadb-remote-mcpRemote ChromaDB vector database MCP server with streamable HTTP transport
• io.github.msjsc001/omniclip-rag-mcpRead-only local-first MCP server for private Markdown, PDF, and Tika-backed search on Windows.
• io.github.shinpr/mcp-local-ragEasy-to-setup local RAG server with minimal configuration
• rag-securityDetects RAG pipelines that ingest external documents into LLM context without
• dspy-3-retrieval-augmented-generationSub-skill of dspy: 3. Retrieval-Augmented Generation.
• venice-embeddingsCall POST /embeddings on Venice. Covers request shape (input, model, encoding_format, dimensions, user), OpenA
• astra-vector-backendBuild Astra DB Data API vector retrieval adapters and LangChain-compatible backend contracts.
• wshobson-rag-implementationA curated guide to convention files AI agents read, write, and act on: AGENTS.md, CLAUDE.md, SKILL.md, llms.tx
• rag-pipeline-designerDesigns end-to-end retrieval-augmented generation (RAG) systems by making principled choices for chunking, emb
• rag-engineerProvides Retrieval-Augmented Generation patterns covering embedding models, vector databases, chunking strateg
• rag-specialistBuild Retrieval Augmented Generation (RAG) pipelines with vector databases, embeddings, and context-aware resp
• rag-architectureDesign production retrieval-augmented generation systems — the full ingest→chunk→embed→index→retrieve→rerank→a
• rag-architectDesigns and implements production-grade RAG systems by chunking documents, generating embeddings, configuring
• ragUse when building retrieval-augmented generation. Covers chunking, embedding and hybrid search, reranking, gro
• rag-builder端到端构建 RAG（检索增强生成）管道。当需要为 LLM 接入本地知识库、做文档问答、控制幻觉、选型 embedding/检索/评估方案时使用。
• ai-science-esm2-embeddingsGenerate ESM2 protein embeddings (fair-esm/transformers) and predict structure with ESMFold. Use when embeddin
• ai-science-vision-ragColPali-style Vision RAG: embed rendered PDF pages, retrieve via ColBERT MaxSim, feed top-k pages to Qwen2-VL,
• pineconeProduction-ready AI coding skills for APIs. Installable skills for Claude Code, Cursor, Codex CLI, Gemini CLI,
• qdrant-vector-searchHigh-performance vector similarity search engine for RAG and semantic search. Use when building production RAG
• vector-spacesProblem-solving strategies for vector spaces in linear algebra
• hunt-rag-vectorHunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses)
• render-flat-vector-explainerAssemble the FREE steps of the flat-vector-explainer video format — a flat-illustration creator-character walk
• embedding-optimizationOptimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and perfor
• using-vector-databasesVector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building cha
• agentdb-vector-searchImplement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and con
• langchain4j-rag-implementation-patternsProvides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates doc
• langchain4j-vector-stores-configurationProvides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic
• qdrantProvides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity s
• dspy-embedding-retrievalUse for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hoste
• dspy-rag-pipelineUse for RAG pipelines, retrieval augmented generation, ColBERTv2, context retrieval, multi-hop RAG, and ground
• vector-styleVector-clean art direction for Blender with flat fills, sharp silhouettes, and UI-like graphic clarity.
• ai-ragDesigns retrieval-augmented generation and search systems. Use when choosing retrieval, chunking, hybrid searc
• ai-vector-brainBuilds vector-brain implementations for repos, docs hubs, and compliance corpora. Use when creating pgvector r
• rag-and-memoryPatterns for Retrieval-Augmented Generation (RAG) and agent memory systems. Retrieves only relevant context, p
• assessing-vector-and-embedding-weaknessesTest vector stores for embedding inversion, cross-tenant leakage, and poisoning.
• weaviate-collection-managerCreate, view, update, and delete Weaviate collections with schema management (for local Weaviate)
• weaviate-connectionConnect to local Weaviate vector database and verify connection health
• weaviate-data-ingestionUpload and process data into local Weaviate collections with support for single objects, batch uploads, and mu
• weaviate-local-setupSet up and manage a local Weaviate instance using Docker
• weaviate-query-agentSearch and retrieve data from local Weaviate using semantic search, filters, RAG, and hybrid queries
• monitor-rag-qualityUse this to measure and monitor the quality of a RAG (retrieval-augmented generation) pipeline - whether it re
• laravel-vector-searchUse when implementing semantic/vector search in Laravel 13 with PostgreSQL + pgvector.
• swmm-rag-memoryRetrieve relevant Agentic SWMM modeling memory from audited runs, modeling-memory summaries, and Obsidian-comp
• api-vector-db-chromaChroma vector database -- collection management, automatic embedding, metadata filtering, document storage, qu
• api-vector-db-pineconePinecone serverless vector database -- index management, vector operations, metadata filtering, namespaces, hy
• api-vector-db-qdrantQdrant vector database -- collection management, point operations, payload filtering, named vectors, quantizat
• api-vector-db-weaviateWeaviate vector database patterns with weaviate-client v3 -- collection management, vectorizer modules, hybrid
• google-gemini-embeddingsCovers the Google Gemini embeddings API (gemini-embedding-001) for RAG, semantic search, document clustering,
• goldenwing-360-rag-securitySecure the trust boundaries RAG adds beyond a plain LLM app. Covers retrieval-time document authorization, ten
• preset-embeddingInspect embedded dashboard configuration, trusted domains, origins, guest-token routing, and embedded RLS rout
• embedding-strategiesSelect and optimize embedding models for semantic search and RAG applications. Use when choosing embedding mod
• rag-implementationBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic sea
• vector-index-tuningOptimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting
• vectorUse when installing or configuring the WizTelemetry Data Pipeline (vector) extension for KubeSphere, which pro
• vector-forgeMutation-driven test vector generation. Finds implementations of a cryptographic algorithm or protocol, runs m
• rag-evalProfessional RAG development skills for Claude Code - audit, evaluate, optimize, and scaffold RAG pipelines
• mcp-local-ragSearches, saves, and maintains a local document index through a local RAG MCP server. Use when user says "sear
• qdrant-advisorDiagnose, troubleshoot, and advise on any Qdrant deployment by loading the latest official Qdrant skills live
• qdrant-clients-sdkQdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deploymen
• qdrant-deployment-optionsGuides Qdrant deployment selection. Use when someone asks 'how to deploy Qdrant', 'Docker vs Cloud', 'local mo
• qdrant-edgeGuides building on Qdrant Edge, the embedded in-process shard. Use when someone asks 'how to sync Edge with th
• qdrant-model-migrationGuides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding mo
• qdrant-monitoringGuides Qdrant monitoring and observability setup. Use when someone asks 'how to monitor Qdrant', 'what metrics
• qdrant-monitoring-debuggingDiagnoses Qdrant production issues using metrics and observability tools. Use when someone reports 'optimizer
• qdrant-monitoring-setupGuides Qdrant monitoring setup including Prometheus scraping, health probes, Hybrid Cloud metrics, alerting, a
• qdrant-multitenancyGuides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications. Use when someone a
• qdrant-performance-optimizationNavigation hub linking sub-skills for proactive Qdrant tuning: search speed, indexing performance, and memory
• qdrant-indexing-performance-optimizationDiagnoses and fixes slow Qdrant indexing and data ingestion. Use when someone reports 'uploads are slow', 'ind
• qdrant-memory-usage-optimizationDiagnoses and reduces Qdrant memory usage. Use when someone reports 'memory too high', 'RAM keeps growing', 'n
• qdrant-search-speed-optimizationDiagnoses and fixes slow Qdrant search. Use when someone reports 'search is slow', 'high latency', 'queries ta
• qdrant-scalingGuides Qdrant scaling decisions. Use when someone asks 'how many nodes do I need', 'data doesn't fit on one no
• qdrant-minimize-latencyGuides Qdrant query latency optimization. Use when someone asks 'search is slow', 'how to reduce latency', 'p9
• qdrant-scaling-data-volumeGuides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much d
• qdrant-horizontal-scalingDiagnoses and guides Qdrant horizontal scaling decisions. Use when someone asks 'vertical or horizontal?', 'ho
• qdrant-sliding-time-windowGuides sliding time window scaling in Qdrant. Use when someone asks 'only recent data matters', 'how to expire
• qdrant-tenant-scalingGuides Qdrant multi-tenant scaling. Use when someone asks 'how to scale tenants', 'one collection per tenant?'
• qdrant-vertical-scalingGuides Qdrant vertical scaling decisions. Use when someone asks 'how to scale up a node', 'need more RAM', 'up
• qdrant-scaling-qpsGuides Qdrant query throughput (QPS) scaling. Use when someone asks 'how to increase QPS', 'need more throughp
• qdrant-scaling-query-volumeGuides Qdrant query volume scaling. Use when someone asks 'query returns too many results', 'scroll performanc
• qdrant-search-qualityDiagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong resu
• qdrant-search-quality-diagnosisDiagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not rele
• qdrant-search-strategiesGuides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'how to rerank?'
• qdrant-hybrid-searchExplains hybrid search in Qdrant. Use when someone asks 'how do I setup hybrid search?', 'how to combine keywo
• qdrant-hybrid-search-combiningFusing scores from multiple searches into a single ranked result (RRF, DBSF, custom fusion). Use when someone
• qdrant-hybrid-search-prefetchesConstructing prefetch queries for hybrid retrieval, including sparse/dense and multi-field setups, and choosin
• qdrant-relevance-feedbackExpanding the candidate pool via relevance feedback, as an alternative to reranking when a dense retriever is
• qdrant-sizingSizes a Qdrant deployment before it is provisioned. Use when someone asks 'how much RAM do I need', 'how many
• qdrant-version-upgradeCovers upgrading Qdrant server and SDKs without interrupting availability or losing data integrity. Use when s
• openrouter-embeddingsGenerate text embeddings via OpenRouter using Qwen3-Embedding-8B.
• xs-workspace-ragワークスペース全体をベクトル検索＋構造化ファクト管理する任意スキル。ユーザーがRAGのセットアップ・検索・ファクト操作を明示的に依頼した場合に使う。「RAGをセットアップして」「RAGで探して」「ファクト登録」で使用。
• neo4j-vector-index-skillCreate and manage Neo4j vector indexes, run vector similarity search (ANN/kNN),
• rag-observability-evalsMonitor and evaluate RAG systems with retrieval quality metrics, groundedness checks, hallucination detection,
• vector-database-opsDeploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pine
• ai-native-knowledge-ragAI Native 产品方法论——RAG与知识系统设计的实操 Skill。
• raster-vector-ingestion-starterA framework for discovering, compiling, and validating reusable skills for scientific agents.
• ansi-safe-border-caption-embeddingEmbed captions into terminal borders while preserving alignment, clipping, and color/dim semantics.
• makepad-2-0-vectorCRITICAL: Use for Makepad 2.0 Vector graphics widget. Triggers on: makepad vector, Vector widget, SVG path, ma
• doc-to-vector-dataset-generatorConverts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JS
• embedding-pipeline-builderBuilds document embedding pipelines with text chunking, embedding generation, indexing, and retrieval optimiza