1---2name: 2560-applications-f9b340d63description: App and Agent4---5# App and Agent67## **Contents**8 9 - [RAG (Retrieval-Augmented Generation)](#rag-retrieval-augmented-generation)10 - [Advanced RAG](#advanced-rag)11 - [GraphRAG](#graphrag)12 - [RAG Application](#rag-application)13 - [Vector Database & Embedding](#vector-database--embedding)14 - [AI Application](#ai-application)15 - [Top Agent Frameworks](#top-agent-frameworks)16 - [Orchestration Framework](#orchestration-framework)17 - [Frameworks / SDKs / Building Blocks](#frameworks--sdks--building-blocks)18 - [Multi-Agent & Swarm Frameworks](#multi-agent--swarm-frameworks)19 - [General Agent Frameworks](#general-agent-frameworks)20 - [Official Vendor SDKs](#official-vendor-sdks)21 - [Tool Integration & Infrastructure](#tool-integration--infrastructure)22 - [Monitoring, Training & Optimization](#monitoring-training--optimization)23 - [NLP & ML Libraries](#nlp--ml-libraries)24 - [Apps / Demos / Ready-to-use Agents](#apps--demos--ready-to-use-agents)25 - [General Autonomous Agents](#general-autonomous-agents)26 - [Coding & Development](#coding--development)27 - [Research, Web & Knowledge Tools](#research-web--knowledge-tools)28 - [Productivity & Personal Assistants](#productivity--personal-assistants)29 - [Data, Finance & Analytics](#data-finance--analytics)30 - [Specialized Agents](#specialized-agents)31 - [No Code & User Interface](#no-code--user-interface)32 - [Personal AI assistant & desktop](#personal-ai-assistant--desktop)33 - [Infrastructure & Backend Services](#infrastructure--backend-services)34 - [Caching](#caching)35 - [Data Processing](#data-processing)36 - [Gateway](#gateway)37 - [Memory](#memory)38 - [Agent Protocol](#agent-protocol)39 - [Model Context Protocol (MCP)](#model-context-protocol-mcp)40 - [A2A](#a2a)41 - [Computer use](#computer-use)42 - [Coding & Research](#coding--research)43 - [Coding](#coding)44 - [Skill](#skill)45 - [Domain-Specific Agents](#domain-specific-agents)46 - [Deep Research](#deep-research)4748## **RAG (Retrieval-Augmented Generation)**4950- RAG integrates retrieval (searching) into LLM text generation, enabling models to access external information. [✍️](https://towardsdatascience.com/rag-vs-finetuning-which-is-the-best-tool-to-boost-your-llm-application-94654b1eaba7) [25 Aug 2023]51- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks📑](https://arxiv.org/abs/2005.11401): Meta's 2020 framework for giving LLMs access to information beyond training data. [22 May 2020]52 - RAG-sequence — Retrieve k documents to generate all output tokens.53 - RAG-token— Retrieve k documents per token generation.54 - RAG-sequence is the industry standard due to lower cost and simplicity. [✍️](https://towardsdatascience.com/add-your-own-data-to-an-llm-using-retrieval-augmented-generation-rag-b1958bf56a5a) [30 Sep 2023]5556### **Advanced RAG**5758- [9 Effective Techniques To Boost Retrieval Augmented Generation (RAG) Systems✍️](https://towardsdatascience.com/9-effective-techniques-to-boost-retrieval-augmented-generation-rag-systems-210ace375049) [🗄️](9-effective-rag-techniques.png): ReRank, Prompt Compression, Hypothetical Document Embedding (HyDE), Query Rewrite and Expansion, Enhance Data Quality, Optimize Index Structure, Add Metadata, Align Query with Documents, Mixed Retrieval (Hybrid Search) [2 Jan 2024]59- Advanced RAG Patterns: How to improve RAG peformance [✍️](https://cloudatlas.me/why-do-rag-pipelines-fail-advanced-rag-patterns-part1-841faad8b3c2) / [✍️](https://cloudatlas.me/how-to-improve-rag-peformance-advanced-rag-patterns-part2-0c84e2df66e6) [17 Oct 2023]60 - Data quality: Clean, standardize, deduplicate, segment, annotate, augment, and update data to make it clear, consistent, and context-rich.61 - Embeddings fine-tuning: Fine-tune embeddings to domain specifics, adjust them according to context, and refresh them periodically to capture evolving semantics.62 - Retrieval optimization: Refine chunking, embed metadata, use query routing, multi-vector retrieval, re-ranking, hybrid search, recursive retrieval, query engine, [HyDE📑](https://arxiv.org/abs/2212.10496) [20 Dec 2022], and vector search algorithms to improve retrieval efficiency and relevance.63 - Synthesis techniques: Query transformations, prompt templating, prompt conditioning, function calling, and fine-tuning the generator to refine the generation step.64 - HyDE: Implemented in [LangChain: HypotheticalDocumentEmbedder✨](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb). A query generates hypothetical documents, which are then embedded and retrieved to provide the most relevant results. `query -> generate n hypothetical documents -> documents embedding - (avg of embeddings) -> retrieve -> final result.` [✍️](https://www.jiang.jp/posts/20230510_hyde_detailed/index.html)65- [Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG📑](https://arxiv.org/abs/2501.09136) [15 Jan 2025]66- [Azure RAG with Vision Application Framework✨](https://github.com/Azure-Samples/rag-as-a-service-with-vision) [Mar 2024] 67- [Contextual Retrieval✍️](https://www.anthropic.com/news/contextual-retrieval): Contextual Retrieval enhances traditional RAG by using Contextual Embeddings and Contextual BM25 to maintain context during retrieval. [19 Sep 2024]68- Demystifying Advanced RAG Pipelines: An LLM-powered advanced RAG pipeline built from scratch [✨](https://github.com/pchunduri6/rag-demystified) [19 Oct 2023]69 70- [Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAG📑](https://arxiv.org/abs/2411.07688): Ultra High Resolution (UHR) remote sensing imagery, such as satellite imagery and medical imaging. [12 Nov 2024]71- [Evaluation with Ragas✍️](https://towardsdatascience.com/visualize-your-rag-data-evaluate-your-retrieval-augmented-generation-system-with-ragas-fc2486308557): UMAP (often used to reduce the dimensionality of embeddings) with Ragas metrics for visualizing RAG results. [Mar 2024] / `Ragas provides metrics`: Context Precision, Context Relevancy, Context Recall, Faithfulness, Answer Relevance, Answer Semantic Similarity, Answer Correctness, Aspect Critique [✨](https://github.com/explodinggradients/ragas) [May 2023]72 73- From Simple to Advanced RAG (LlamaIndex) [✍️](https://twitter.com/jerryjliu0/status/1711419232314065288) / [🗄️](../files/archive/LlamaIndexTalk_PyDataGlobal.pdf) /💡[✍️](https://aiconference.com/speakers/jerry-liu-2023/) [10 Oct 2023]74 <!-- <img src="../files/advanced-rag.png" width="430"> -->75- [How to improve RAG Piplines](https://www.linkedin.com/posts/damienbenveniste_how-to-improve-rag-pipelines-activity-7241497046631776256-vwOc?utm_source=li_share&utm_content=feedcontent&utm_medium=g_dt_web&utm_campaign=copy): LangGraph implementation with Self-RAG, Adaptive-RAG, Corrective RAG. [Oct 2024]76- How to optimize RAG pipeline: [Indexing optimization](https://newsletter.theaiedge.io/p/how-to-optimize-your-rag-pipelines) [24 Oct 2023]77- [localGPT-Vision✨](https://github.com/PromtEngineer/localGPT-Vision): an end-to-end vision-based Retrieval-Augmented Generation (RAG) system. [Oct 2024] 78- [Multi-Modal RAG System✍️](https://machinelearningmastery.com/implementing-multi-modal-rag-systems/): Building a knowledge base with both image and audio data. [12 Feb 2025]79- [🗣️](https://twitter.com/yi_ding/status/1721728060876300461) [7 Nov 2023] `OpenAI has put together a pretty good roadmap for building a production RAG system.` Naive RAG -> Tune Chunks -> Rerank & Classify -> Prompt Engineering. In `llama_index`... [📺](https://www.youtube.com/watch?v=ahnGLM-RC1Y) <br/>80 <img src="../files/oai-rag-success-story.jpg" width="500">81- [Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answering📑](https://arxiv.org/abs/2411.17073): Using HistoCartography to improve pathology image analysis and boost PathVQA-Open performance. [26 Nov 2024]82- [RAG Hallucination Detection Techniques✍️](https://machinelearningmastery.com/rag-hallucination-detection-techniques/): Hallucination metrics using the DeepEval, G-Eval. [10 Jan 2025] - [UniversalRAG✨](https://github.com/wgcyeo/UniversalRAG) [29 Apr 2025] 83- [VideoRAG📑](https://arxiv.org/abs/2501.05874): Not only does it retrieve relevant videos from a large video corpus, but it also integrates both the visual and textual elements of videos into the answer-generation process using Large Video Language Models (LVLMs). [10 Jan 2025]84- [Visual RAG over PDFs with Vespa✍️](https://blog.vespa.ai/visual-rag-in-practice/): a demo showcasing Visual RAG over PDFs using ColPali embeddings in Vespa [✨](https://github.com/vespa-engine/sample-apps/tree/master/visual-retrieval-colpali) [19 Nov 2024]85- [What is Agentic RAG](https://weaviate.io/blog/what-is-agentic-rag): The article published by Weaviate. [5 Nov 2024]8687### **GraphRAG**8889- [Fast GraphRAG✨](https://github.com/circlemind-ai/fast-graphrag): 6x cost savings compared to `graphrag`, with 20% higher accuracy. Combines PageRank and GraphRAG. [Oct 2024] 90- [FalkorDB✨](https://github.com/FalkorDB/FalkorDB): Graph Database. Knowledge Graph for LLM (GraphRAG). OpenCypher (query language in Neo4j). [Jul 2023] 91- [GitNexus✨](https://github.com/abhigyanpatwari/GitNexus): Indexes codebases into a knowledge graph and exposes it via MCP tools for AI agents. [Aug 2025] 92- [Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!📑](https://arxiv.org/abs/2405.11706): using a knowledge graph, the Text-to-SQL accuracy improved from 16% to 54%. [20 May 2024]93- [Graph RAG (by NebulaGraph)](https://medium.com/@nebulagraph/graph-rag-the-new-llm-stack-with-knowledge-graphs-e1e902c504ed): NebulaGraph proposes the concept of Graph RAG, which is a retrieval enhancement technique based on knowledge graphs. [demo](https://www.nebula-graph.io/demo) [8 Sep 2023]94- [GraphRAG (by Microsoft)📑](https://arxiv.org/abs/2404.16130):🏆1. Global search: Original Documents -> Knowledge Graph (Community Summaries generated by LLM) -> Partial Responses -> Final Response. 2. Local Search: Utilizes vector-based search to find the nearest entities and relevant information.95[✍️](https://microsoft.github.io/graphrag) / [✨](https://github.com/microsoft/graphrag) [24 Apr 2024]9697 - [DRIFT Search✍️](https://www.microsoft.com/en-us/research/blog/introducing-drift-search-combining-global-and-local-search-methods-to-improve-quality-and-efficiency/): DRIFT search (Dynamic Reasoning and Inference with Flexible Traversal) combines global and local search methods to improve query relevance by generating sub-questions and refining the context using HyDE (Hypothetical Document Embeddings). [31 Oct 2024]98 - ["From Local to Global" GraphRAG with Neo4j and LangChain](https://neo4j.com/developer-blog/global-graphrag-neo4j-langchain/) [09 Jul 2024]99 - [GraphRAG Implementation with LlamaIndex✨](https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/cookbooks/GraphRAG_v1.ipynb) [15 Jul 2024]100 - [Improving global search via dynamic community selection✍️](https://www.microsoft.com/en-us/research/blog/graphrag-improving-global-search-via-dynamic-community-selection/): Dynamic Community Selection narrows the scope by selecting the most relevant communities based on query relevance, utilizing Map-reduce search, reducing costs by 77% without sacrificing output quality [15 Nov 2024]101 - [LazyGraphRAG✍️](https://www.microsoft.com/en-us/research/blog/lazygraphrag-setting-a-new-standard-for-quality-and-cost/): Reduces costs to 0.1% of full GraphRAG through efficient use of best-first (vector-based) and breadth-first (global search) retrieval and deferred LLM calls [25 Nov 2024]102 - [LightRAG✨](https://github.com/HKUDS/LightRAG): Utilizing graph structures for text indexing and retrieval processes. [8 Oct 2024] 103 - [nano-graphrag✨](https://github.com/gusye1234/nano-graphrag): A simple, easy-to-hack GraphRAG implementation [Jul 2024]104105- [Graphiti✨](https://github.com/getzep/graphiti)106- [GraphSearch✨](https://github.com/DataArcTech/GraphSearch): An Agentic Workflow for Graph RAG. [Oct 2025]107- [HippoRAG✨](https://github.com/OSU-NLP-Group/HippoRAG):💡RAG + Knowledge Graphs + Personalized PageRank. [23 May 2024] 108- [How to Build a Graph RAG App✍️](https://towardsdatascience.com/how-to-build-a-graph-rag-app-b323fc33ba06): Using knowledge graphs and AI to retrieve, filter, and summarize medical journal articles [30 Dec 2024]109- [HybridRAG📑](https://arxiv.org/abs/2408.04948): Integrating VectorRAG and GraphRAG with financial earnings call transcripts in Q&A format. [9 Aug 2024]110- [Neo4j GraphRAG Package for Python✨](https://github.com/neo4j/neo4j-graphrag-python) [Feb 2024] 111112### **RAG Application**1131141. [AutoRAG✨](https://github.com/Marker-Inc-Korea/AutoRAG): RAG AutoML tool for automatically finds an optimal RAG pipeline for your data. [Jan 2024] 1151. [Canopy✨](https://github.com/pinecone-io/canopy): open-source RAG framework and context engine built on top of the Pinecone vector database. [Aug 2023] 1161. [Chonkie✨](https://github.com/SecludedCoder/chonkie): RAG chunking library [Nov 2024]  <!--old: https://github.com/chonkie-ai/chonkie -->1171. [Cognita✨](https://github.com/truefoundry/cognita): RAG (Retrieval Augmented Generation) Framework for building modular, open-source applications [Jul 2023] 1181. [Danswer✨](https://github.com/danswer-ai/danswer): Ask Questions in natural language and get Answers backed by private sources: Slack, GitHub, Confluence, etc. [Apr 2023] 1191. [Fireplexity✨](https://github.com/mendableai/fireplexity): AI search engine by Firecrawl's search API [Jun 2025] 1201. [FlashRAG✨](https://github.com/RUC-NLPIR/FlashRAG): A Python Toolkit for Efficient RAG Research [Mar 2024] 1211. [Gemini-Search✨](https://github.com/ammaarreshi/Gemini-Search): Perplexity style AI Search engine clone built with Gemini [Jan 2025] 1221. [Haystack✨](https://github.com/deepset-ai/haystack): LLM orchestration framework to build customizable, production-ready LLM applications. [5 May 2020] 1231. [KAG✨](https://github.com/OpenSPG/KAG): Knowledge Augmented Generation. a logical reasoning and Q&A framework based on the OpenSPG(Semantic-enhanced Programmable Graph). By Ant Group. [Oct 2024] 1241. [Khoj✨](https://github.com/khoj-ai/khoj): Open-source, personal AI agents. Cloud or Self-Host, Multiple Interfaces. Python Django based [Aug 2021] 1251. [kotaemon✨](https://github.com/Cinnamon/kotaemon): Open-source clean & customizable RAG UI for chatting with your documents. [Mar 2024] 1261. [llm-answer-engine✨](https://github.com/developersdigest/llm-answer-engine): Build a Perplexity-Inspired Answer Engine Using Next.js, Groq, Mixtral, LangChain, OpenAI, Brave & Serper [Mar 2024] 1271. [llmware✨](https://github.com/llmware-ai/llmware): Building Enterprise RAG Pipelines with Small, Specialized Models [Sep 2023] 1281. [Marqo✨](https://github.com/marqo-ai/marqo): Tensor search for humans [Aug 2022] 1291. [MedGraphRAG📑](https://arxiv.org/abs/2408.04187): MedGraphRAG outperforms the previous SOTA model, [Medprompt📑](https://arxiv.org/abs/2311.16452), by 1.1%. [✨](https://github.com/medicinetoken/medical-graph-rag) [8 Aug 2024] 1301. [Meilisearch✨](https://github.com/meilisearch/meilisearch): A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications. [Apr 2018] 1311. [MemFree✨](https://github.com/memfreeme/memfree): Hybrid AI Search Engine + AI Page Generator. [Jun 2024] 1321. [MindSearch✨](https://github.com/InternLM/MindSearch): An open-source AI Search Engine Framework [Jul 2024] 1331. [MiniRAG✨](https://github.com/HKUDS/MiniRAG): RAG through heterogeneous graph indexing and lightweight topology-enhanced retrieval. [Jan 2025] 1341. [Morphic✨](https://github.com/miurla/morphic): An AI-powered search engine with a generative UI [Apr 2024] 1351. [PageIndex✨](https://github.com/VectifyAI/PageIndex): a vectorless, reasoning-based RAG system that builds a hierarchical tree index [Apr 2025] 1361. [PaperQA2✨](https://github.com/Future-House/paper-qa): High accuracy RAG for answering questions from scientific documents with citations [Feb 2023] 1371. [Perplexica✨](https://github.com/ItzCrazyKns/Perplexica):💡Open source alternative to Perplexity AI [Apr 2024] 1381. [PrivateGPT✨](https://github.com/imartinez/privateGPT): 100% privately, no data leaks. The API is built using FastAPI and follows OpenAI's API scheme. [May 2023] 1391. [Pyversity✨](https://github.com/Pringled/pyversity): A rerank library for search results [Oct 2025] 1401. [quivr✨](https://github.com/QuivrHQ/quivr): A personal productivity assistant (RAG). Chat with your docs (PDF, CSV, ...) [May 2023] 1411. [R2R (Reason to Retrieve)✨](https://github.com/SciPhi-AI/R2R): Agentic Retrieval-Augmented Generation (RAG) with a RESTful API. [Feb 2024] 1421. [RAG Builder✨](https://github.com/KruxAI/ragbuilder): Automatically create an optimal production-ready Retrieval-Augmented Generation (RAG) setup for your data. [Jun 2024] 1431. [RAG capabilities of LlamaIndex to QA about SEC 10-K & 10-Q documents✨](https://github.com/run-llama/sec-insights): A real world full-stack application using LlamaIndex [Sep 2023] 1441. [RAG-Anything✨](https://github.com/HKUDS/RAG-Anything): "RAG-Anything: All-in-One RAG System". [Jun 2025] 1451. [RAGApp✨](https://github.com/ragapp/ragapp): Agentic RAG. Custom GPTs, but deployable in your own cloud infrastructure using Docker. [Apr 2024] 1461. [RAGChecker📑](https://arxiv.org/abs/2408.08067): A Fine-grained Framework For Diagnosing RAG [✨](https://github.com/amazon-science/RAGChecker) [15 Aug 2024] 1471. [RAGflow✨](https://github.com/infiniflow/ragflow):💡Streamlined RAG workflow. Focusing on Deep document understanding [Dec 2023] 1481. [RAGFoundry✨](https://github.com/IntelLabs/RAGFoundry): A library designed to improve LLMs ability to use external information by fine-tuning models on specially created RAG-augmented datasets. [5 Aug 2024] 1491. [RAGLite✨](https://github.com/superlinear-ai/raglite): a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite [Jun 2024] 1501. [RAGxplorer✨](https://github.com/gabrielchua/RAGxplorer): Visualizing document chunks and the queries in the embedding space. [Jan 2024] 1511. [Renumics RAG✨](https://github.com/Renumics/renumics-rag): Visualization for a Retrieval-Augmented Generation (RAG) Data [Jan 2024] 1521. [Scira (Formerly MiniPerplx)✨](https://github.com/zaidmukaddam/scira): A minimalistic AI-powered search engine [Aug 2024] 1531. [Semantica✨](https://github.com/Hawksight-AI/semantica): Semantic intelligence layer that makes your AI agents auditable, explainable, and compliant — beyond Text Similarity [Jun 2025] 1541. [Semantra✨](https://github.com/freedmand/semantra): Multi-tool for semantic search [Mar 2023] 1551. [Simba✨](https://github.com/GitHamza0206/simba): Portable KMS (knowledge management system) designed to integrate seamlessly with any Retrieval-Augmented Generation (RAG) system [Dec 2024] 1561. [smartrag✨](https://github.com/aymenfurter/smartrag): Deep Research through Multi-Agents, using GraphRAG. [Jun 2024] 1571. [SWIRL AI Connect✨](https://github.com/swirlai/swirl-search): SWIRL AI Connect enables you to perform Unified Search and bring in a secure AI Co-Pilot. [Apr 2022] 1581. [turboseek✨](https://github.com/Nutlope/turboseek): An AI search engine inspired by Perplexity [May 2024] 1591. [txtai✨](https://github.com/neuml/txtai): Semantic search and workflows powered by language models [Aug 2020] 1601. [Typesense✨](https://github.com/typesense/typesense): Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch [Jan 2017] 1611. [UltraRAG✨](https://github.com/OpenBMB/UltraRAG): A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines [Jan 2025] 1621. [UniversalRAG✨](https://github.com/wgcyeo/UniversalRAG): RAG framework that retrieves across multiple modalities. [29 Apr 2025] 1631. [Verba✨](https://github.com/weaviate/Verba): Retrieval Augmented Generation (RAG) chatbot powered by Weaviate [Jul 2023] 1641. [WeKnora✨](https://github.com/Tencent/WeKnora): LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answers using RAG paradigm. [Jul 2025] 1651. [Xyne✨](https://github.com/xynehq/xyne): an AI-first Search & Answer Engine for work. We're an OSS alternative to Glean, Gemini and MS Copilot. [Sep 2024] 166167### **Vector Database & Embedding**168169- [A Comprehensive Survey on Vector Database📑](https://arxiv.org/abs/2310.11703): Categorizes search algorithms by their approach, such as hash-based, tree-based, graph-based, and quantization-based. [18 Oct 2023]170- [A Gentle Introduction to Word Embedding and Text Vectorization✍️](https://machinelearningmastery.com/a-gentle-introduction-to-word-embedding-and-text-vectorization/): Word embedding, Text vectorization, One-hot encoding, Bag-of-words, TF-IDF, word2vec, GloVe, FastText. | [Tokenizers in Language Models✍️](https://machinelearningmastery.com/tokenizers-in-language-models/): Stemming, Lemmatization, Byte Pair Encoding (BPE), WordPiece, SentencePiece, Unigram [23 May 2025]171- Azure Open AI Embedding API, `text-embedding-ada-002`, supports 1536 dimensions. Elastic search, Lucene based engine, supports 1024 dimensions as a max. Open search can insert 16,000 dimensions as a vector storage. Open search is available to use as a vector database with Azure Open AI Embedding API.172- [A SQLite extension for efficient vector search, based on Faiss!✨](https://github.com/asg017/sqlite-vss) [Jan 2023]173 174- [Chroma✨](https://github.com/chroma-core/chroma): Open-source embedding database [Oct 2022]175 176- [Contextual Document Embedding (CDE)📑](https://arxiv.org/abs/2410.02525): Improve document retrieval by embedding both queries and documents within the context of the broader document corpus. [✍️](https://pub.aimind.so/unlocking-the-power-of-contextual-document-embeddings-enhancing-search-relevance-01abfa814c76) [3 Oct 2024]177- [Contextualized Chunk Embedding Model✍️](https://blog.voyageai.com/2025/07/23/voyage-context-3/): Rather than embedding each chunk separately, a contextualized chunk embedding model uses the whole document to create chunk embeddings that reflect the document's overall context. [✍️](https://blog.dailydoseofds.com/p/contextualized-chunk-embedding-model) [23 Jul 2025]178- [EmbedAnything✨](https://github.com/StarlightSearch/EmbedAnything): Built by Rust. Supports BERT, CLIP, Jina, ColPali, ColBERT, ModernBERT, Reranker, Qwen. Mutilmodality. [Mar 2024] 179- [Embedding Atlas✨](https://github.com/apple/embedding-atlas): Apple. a tool that provides interactive visualizations for large embeddings. [May 2025]180- [Faiss](https://faiss.ai/): Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It is used as an alternative to a vector database in the development and library of algorithms for a vector database. It is developed by Facebook AI Research. [✨](https://github.com/facebookresearch/faiss) [Feb 2017]181 182- [FalkorDB✨](https://github.com/FalkorDB/FalkorDB): Graph Database. Knowledge Graph for LLM (GraphRAG). OpenCypher (query language in Neo4j). For a sparse matrix, the graph can be queried with linear algebra instead of traversal, boosting performance. [Jul 2023] 183- [Fine-tuning Embeddings for Specific Domains✍️](https://blog.gopenai.com/fine-tuning-embeddings-for-specific-domains-a-comprehensive-guide-5e4298b42185): The guide discusses fine-tuning embeddings for domain-specific tasks using `sentence-transformers` [1 Oct 2024]184- [Is Cosine-Similarity of Embeddings Really About Similarity?📑](https://arxiv.org/abs/2403.05440): Regularization in linear matrix factorization can distort cosine similarity. L2-norm regularization on (1) the product of matrices (like dropout) and (2) individual matrices (like weight decay) may lead to arbitrary similarities. [8 Mar 2024]185- OpenAI Embedding models: `text-embedding-3`186- [lancedb✨](https://github.com/lancedb/lancedb): LanceDB's core is written in Rust and is built using Lance, an open-source columnar format. [Feb 2023] 187- [LEANN✨](https://github.com/yichuan-w/LEANN): The smallest vector database. 97% less storage. [Jun 2025] 188- Milvus (A cloud-native vector database) Embedded [✨](https://github.com/milvus-io/milvus) [Sep 2019]: Alternative option to replace PineCone and Redis Search in OSS. It offers support for multiple languages, addresses the limitations of RedisSearch, and provides cloud scalability and high reliability with Kubernetes.189 190- [MongoDB's GenAI Showcase✨](https://github.com/mongodb-developer/GenAI-Showcase): Step-by-step Jupyter Notebook examples on how to use MongoDB as a vector database, data store, memory provider [Jan 2024] 191- [Not All Vector Databases Are Made Equal✍️](https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696): Printed version for "Medium" limits. [🗄️](../files/vector-dbs.pdf) [2 Oct 2021]192- [pgvector✨](https://github.com/pgvector/pgvector): Open-source vector similarity search for Postgres [Apr 2021] / [pgvectorscale✨](https://github.com/timescale/pgvectorscale): 75% cheaper than pinecone [Jul 2023]  193- [Pinecone](https://docs.pinecone.io): A fully managed cloud Vector Database. Commercial Product [Jan 2021]194- [Qdrant✨](https://github.com/qdrant/qdrant): Written in Rust. Qdrant (read: quadrant) [May 2020]195 196- [Redis extension for vector search, RedisVL✨](https://github.com/redis/redis-vl-python): Redis Vector Library (RedisVL) [Nov 2022]197 198- [text-embedding-ada-002✍️](https://openai.com/blog/new-and-improved-embedding-model):199 Smaller embedding size. The new embeddings have only 1536 dimensions, one-eighth the size of davinci-001 embeddings,200 making the new embeddings more cost effective in working with vector databases. [15 Dec 2022]201- [The Semantic Galaxy🤗](https://huggingface.co/spaces/webml-community/semantic-galaxy): Visualize embeddings in 3D space, powered by EmbeddingGemma and Transformers.js [Sep 2025]202- [Vector Search with OpenAI Embeddings: Lucene Is All You Need📑](https://arxiv.org/abs/2308.14963): For vector search applications, Lucene's HNSW implementation is a resilient and extensible solution with performance comparable to specialized vector databases like FAISS. Our experiments used Lucene 9.5.0, which limits vectors to 1024 dimensions—insufficient for OpenAI's 1536-dimensional embeddings. A fix to make vector dimensions configurable per codec has been merged to Lucene's source [here✨](https://github.com/apache/lucene/pull/12436) but was not yet released as of August 2023. [29 Aug 2023]203- [Weaviate✨](https://github.com/weaviate/weaviate): Store both vectors and data objects. [Jan 2021]204 205- [zvec✨](https://github.com/alibaba/zvec): Lightweight vector database by Alibaba Cloud. [Dec 2025] 206207### **AI Application**208209- [900 most popular open source AI tools](https://huyenchip.com/2024/03/14/ai-oss.html):🏆What I learned from looking at 900 most popular open source AI tools [list](https://huyenchip.com/llama-police) [Mar 2024]210- [Awesome LLM Apps✨](https://github.com/Shubhamsaboo/awesome-llm-apps):💡A curated collection of awesome LLM apps built with RAG and AI agents. [Apr 2024]211 212- [Azure OpenAI Samples✨](https://github.com/kimtth/azure-openai-cookbook): 🐳 Azure OpenAI (OpenAI) Sample Collection - 🪂 100+ Code Cookbook 🧪 [Mar 2025]213- [GenAI Agents✨](https://github.com/NirDiamant/GenAI_Agents):🏆Tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. [Sep 2024]214 215- [GenAI Cookbook✨](https://github.com/dmatrix/genai-cookbook): A mixture of Gen AI cookbook recipes for Gen AI applications. [Nov 2023] 216- [Generative AI Design Patterns✍️](https://towardsdatascience.com/generative-ai-design-patterns-a-comprehensive-guide-41425a40d7d0): 9 architecture patterns for working with LLMs. [Feb 2024]217- [Open100: Top 100 Open Source achievements.](https://www.benchcouncil.org/evaluation/opencs/annual.html)218219#### Agent & Application220221##### **Top Agent Frameworks**222223- [AG2✨](https://github.com/ag2ai/ag2): Multi-agent conversational framework (formerly AutoGen)224 225- [Agno✨](https://github.com/agno-agi/agno): Agent orchestration framework226 227- [AWS Bedrock Agents✨](https://github.com/awslabs/amazon-bedrock-agent-samples): AWS-native agent framework228 229- [CrewAI✨](https://github.com/crewAIInc/crewAI): Role-based230231…(truncated)