OpenKBS Apps provides a unified interface to upload and process documents, generate semantic embeddings, and configure multiple LLMs for retrieval-augmented generation. Users can fine-tune query workflows, set access controls, and integrate agents into web or messaging channels. The platform offers analytics on user interactions, continuous learning from feedback, and support for multilingual content, enabling rapid creation of intelligent assistants tailored to organizational data.

LangDB AI is an AI-powered knowledge management platform designed to convert scattered documentation into a searchable, interactive assistant. Users upload documents—such as PDFs, Word files, or web pages—and LangDB’s AI parses and indexes content using natural language processing and embeddings. Its semantic search engine retrieves relevant passages, while a chatbot interface lets team members ask questions in plain language. The platform supports multi-channel deployment via chat widgets, Slack, and API integrations. Administrators can configure user roles, track usage analytics, and update document versions seamlessly. By automating content ingestion, tagging, and conversational support, LangDB AI reduces time spent searching for information and enhances collaboration across customer support, engineering, and training departments.

TiDB is an integrated database solution tailored for AI applications. It supports vector search, semantic knowledge graph search, and operational data management. Its serverless architecture ensures reliability and scalability, eliminating the need for manual data synchronization and management of multiple data stores. With enterprise-grade features such as role-based access control, encryption, and high availability, TiDB is ideal for production-ready AI applications that demand performance, security, and ease of use. TiDB's platform compatibility spans both cloud-based and local deployments, making it versatile for various infrastructure needs.

Crawlr is an open-source CLI AI agent built to streamline the process of ingesting web-based information into structured knowledge bases. Utilizing OpenAI's GPT-3.5/4 models, it traverses specified URLs, cleans and chunks raw HTML into meaningful text segments, generates concise summaries, and creates vector embeddings for efficient semantic search. The tool supports configuration of crawl depth, domain filters, and chunk sizes, allowing users to tailor ingestion pipelines to project needs. By automating link discovery and content processing, Crawlr reduces manual data collection efforts, accelerates creation of FAQ systems, chatbots, and research archives, and seamlessly integrates with vector databases like Pinecone, Weaviate, or local SQLite setups. Its modular design enables easy extension for custom parsers and embedding providers.

Financial Agentic RAG combines document ingestion, embedding-based retrieval, and GPT-powered generation to deliver an interactive financial analysis assistant. The agent pipelines balance search and generative AI: PDFs, spreadsheets, and reports are vectorized, enabling contextual retrieval of relevant content. When a user submits a question, the system fetches top-matching segments and conditions the language model to produce concise, accurate financial insights. Deployable locally or in the cloud, it supports custom data connectors, prompt templating, and vector stores like Pinecone or FAISS.

Granite Retrieval Agent provides developers with a flexible platform to build retrieval-augmented generative AI agents that combine semantic search and large language models. Users can ingest documents from diverse sources, create vector embeddings, and configure Azure Cognitive Search indexes or alternative vector stores. When a query arrives, the agent retrieves the most relevant passages, constructs context windows, and calls LLM APIs for precise answers or summaries. It supports memory management, chain-of-thought orchestration, and custom plugins for pre- and post-processing. Deployable with Docker or directly via Python, Granite Retrieval Agent accelerates the creation of knowledge-driven chatbots, enterprise assistants, and Q&A systems with reduced hallucinations and enhanced factual accuracy.

Haystack is designed to help developers easily create custom search solutions that leverage the latest advancements in machine learning. With its components like document stores, retrievers, and readers, Haystack can connect to various data sources and effectively process queries. Its modular architecture supports mixed search strategies, including semantic search and traditional keyword-based search, making it a versatile tool for enterprises looking to enhance their search capabilities.

The E-commerce Landing Page AI Agent is a specialized solution designed to streamline the creation of marketing landing pages for online stores. By leveraging LangChain’s pipeline orchestration capabilities and Qdrant’s vector database for semantic data retrieval, the agent transforms raw product catalogs, brand guidelines, and customer profiles into fully formatted, persuasive landing page content. It generates attention-grabbing headlines, detailed product descriptions, optimized call-to-action elements, and layout suggestions, while optionally integrating AI-driven image recommendations. Developers can customize prompts, tweak style parameters, and incorporate A/B test variants directly within the agent’s workflow. Seamless integration with popular CMS platforms enables automated publishing, reducing manual work by up to 80%. This AI-driven approach accelerates time-to-market, enhances personalization, and boosts conversion performance for e-commerce marketers and small business owners.

Memonto functions as a middleware library for AI agents, orchestrating the complete memory lifecycle. During each conversation turn, it records user and AI messages, distills salient details, and generates concise summaries. These summaries are converted into embeddings and stored in vector databases or file-based stores. When constructing new prompts, Memonto performs semantic searches to retrieve the most relevant historical memories, enabling agents to maintain context, recall user preferences, and provide personalized responses. It supports multiple storage backends (SQLite, FAISS, Redis) and offers configurable pipelines for embedding, summarization, and retrieval. Developers can seamlessly integrate Memonto into existing agent frameworks, boosting coherence and long-term engagement.

Ncurator is an AI-powered browser extension designed to assist you in managing and organizing your knowledge. By integrating with several platforms like Notion, Gmail, and Google Drive, it performs tasks like importing PDFs, crawling webpages, and enabling semantic searches. With its offline capabilities and focus on data security, Ncurator ensures all your information is stored locally, providing a secure and efficient way to handle your documents and knowledge base.

OpenKBS transforms corporate content—PDFs, docs, web pages—into vector embeddings stored in a knowledge graph. Users interact with an AI chatbot that retrieves precise answers by scanning the semantic index. The platform offers robust API endpoints, customizable UI widgets, and role-based access control. It accelerates internal support, documentation search, and developer onboarding through automated, context-aware responses and continuous learning from new data.

The repository provides a step-by-step tutorial and reference implementation for creating a ReAct-based AI agent that uses DeepSeek for high-dimensional vector retrieval. It covers environment setup, dependency installation, and configuration of vector stores for custom data. The agent employs the ReAct pattern to combine reasoning traces with external knowledge searches, resulting in transparent and explainable responses. Users can extend the system by integrating additional document loaders, fine-tuning prompt templates, or swapping vector databases. This flexible framework enables developers and researchers to prototype powerful conversational agents that reason, retrieve, and interact seamlessly with various knowledge sources in a few lines of Python code.

RecurSearch is an open-source Python library designed to improve Retrieval-Augmented Generation (RAG) and AI agent workflows by enabling recursive semantic search. Users define a search pipeline that embeds queries and documents into vector spaces, then iteratively refines queries based on prior results, applies metadata or keyword filters, and summarizes or aggregates findings. This step-by-step refinement yields higher precision, reduces API calls, and helps agents surface deeply nested or context-specific information from large corpora.

Shoonya develops specialized foundation models designed specifically for modern commerce. These models are multilingual, optimized for various verticals, and deeply understand local contexts and preferences. Shoonya's technology supports use-cases like catalog searches, product classification, and semantic product matching. It also integrates with platforms such as India's ONDC, providing voice shopping demos for easy product searches in multiple Indian languages. Shoonya aims to enhance commerce experiences through advanced AI models tailored for retail needs.

Tendery is an AI-powered platform that automates the discovery and management of public tenders across Europe. It offers advanced semantic search capabilities for finding relevant government tenders, comprehensive tender management tools, and personalized recommendations based on business profiles. Users can save custom search queries, receive regular updates, and access full tender documentation to make informed decisions quickly and efficiently. By streamlining the entire procurement process, Tendery enhances the chances of winning contracts and saves time for businesses.

Trieve offers advanced AI-powered search and discovery solutions, ensuring companies have a competitive edge. Features include semantic vector search, full-text search with BM25 and SPLADE models, and hybrid search capabilities. Trieve also provides relevance tuning, sub-sentence highlighting, and robust API integrations for easy data management. Companies can manage ingestion, embeddings, and analytics effortlessly, leveraging private open-source models for maximum data security. Set up industry-leading search experiences quickly and efficiently.

VisQueryPDF processes PDF files by splitting them into chunks, generating vector embeddings via OpenAI or compatible models, and storing those embeddings in a local vector store. Users can submit natural language queries to retrieve the most relevant chunks. Search hits are displayed with highlighted text on the original PDF pages and plotted in a two-dimensional embedding space, allowing interactive exploration of semantic relationships between document segments.

Voice File Agent combines voice recognition and AI document analysis to let users interact with their files conversationally. After uploading a document—such as a PDF, Word file, image, or text file—the agent transcribes voice queries via Whisper and uses OpenAI embeddings to semantically search content. It then generates precise, context-aware answers or summaries. The agent supports multi-format ingestion, real-time transcription feedback, and seamless integration with existing workflows, empowering professionals to retrieve key information without manual reading.

Whiz is designed to provide a robust foundation for developing intelligent agents that can perform complex conversational and task-oriented workflows. Using Whiz, developers define "tools"—Python functions or external APIs—that the agent can invoke when processing user queries. A built-in memory module captures and retrieves conversation context, enabling coherent multi-turn interactions. A dynamic planning engine decomposes goals into actionable steps, while a flexible interface allows injecting custom policies, tool registries, and memory backends. Whiz supports embedding-based semantic search to fetch relevant documents, logging for auditability, and asynchronous execution for scaling. Fully open-source, Whiz can be deployed anywhere Python runs, enabling rapid prototyping of customer support bots, data analysis assistants, or specialized domain agents with minimal boilerplate.

Agent-OpenAI-Java-Banking-Assistant is an open-source Java application that uses Azure OpenAI for large language model processing and vector embeddings for semantic search. It loads banking PDFs, generates embeddings, and performs conversational QA to summarize financial statements, explain loan agreements, and retrieve transaction details. The sample illustrates prompt engineering, function calling, and integration with Azure services to build a domain-specific banking assistant.