Neuron AI is an end-to-end serverless platform for creating, deploying, and managing intelligent AI agents. It supports major LLM providers (OpenAI, Anthropic, Hugging Face) and enables multi-model pipelines, conversation context handling, and automated workflows via a low-code interface or SDKs. With built-in data ingestion, vector search, and plugin integration, Neuron simplifies knowledge sourcing and service orchestration. Its auto-scaling infrastructure and monitoring dashboards ensure performance and reliability, making it ideal for enterprise-grade chatbots, virtual assistants, and automated data processing bots.

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.

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.

Weaviate is an AI-native, open-source vector database designed to help developers scale and deploy AI applications. It supports lightning-fast vector similarity searches over raw vectors or data objects, enabling flexible integration with various technology stacks and model providers. Its cloud-agnostic nature allows seamless deployment, and it is equipped with extensive resources for developers to facilitate learning and integration into existing projects. Weaviate's robust developer community ensures that users obtain continuous support and insights.

AgenticRAG provides a modular architecture for creating autonomous agents that leverage retrieval-augmented generation (RAG). It offers components to index documents in vector stores, retrieve relevant context, and feed it into LLMs to generate context-aware responses. Users can integrate external APIs and tools, configure memory stores to track conversation history, and define custom workflows to orchestrate multi-step decision-making processes. The framework supports popular vector databases like Pinecone and FAISS, and LLM providers such as OpenAI, allowing seamless switching or multi-model setups. With built-in abstractions for agent loops and tool management, AgenticRAG simplifies development of agents capable of tasks like document QA, automated research, and knowledge-driven automation, reducing boilerplate code and accelerating time to deployment.

FastAPI LangGraph Agent Template offers a comprehensive foundation for developing LLM-driven agents within a FastAPI application. It includes predefined LangGraph nodes for common tasks like text completion, embedding, and vector similarity search while allowing developers to create custom nodes and pipelines. The template manages conversation history via memory modules that persist context across sessions and supports environment-based configuration for different deployment stages. Built-in Docker files and CI/CD-friendly structure ensure seamless containerization and deployment. Logging and error-handling middleware enhance observability, while the modular codebase simplifies extending functionality. By combining FastAPI's high-performance web framework with LangGraph's orchestration capabilities, this template streamlines the agent development lifecycle from prototyping to production.

This AI PDF Chatbot agent is a customizable solution that enables users to upload and parse PDF documents, store vector embeddings in a database, and query these documents through a chat interface. It integrates with OpenAI or other LLM providers to generate answers with references to the relevant content. The system utilizes LangChain for language model orchestration and LangGraph for managing agent workflows. Its architecture includes a backend service that handles ingestion and retrieval graphs, a frontend with a Next.js UI to upload files and chat, and Supabase for vector storage. It supports real-time streaming responses and allows customization of retrievers, prompts, and storage configurations.

Connery SDK is a comprehensive framework that simplifies the creation of AI agents. It provides client libraries for Node.js, Python, Deno, and the browser, enabling developers to define agent behaviors, integrate external tools and data sources, manage long-term memory, and connect to multiple LLMs. With built-in telemetry and deployment utilities, Connery SDK accelerates the entire agent lifecycle from development to production.

RAGFlow is a powerful open-source RAG (Retrieval-Augmented Generation) engine designed to streamline the development and deployment of AI agents. It combines deep document understanding with vector similarity search to ingest, preprocess, and index unstructured data from PDFs, web pages, and databases into custom knowledge bases. Developers can leverage its Python SDK or RESTful API to retrieve relevant context and generate accurate responses using any LLM model. RAGFlow supports building diverse agent workflows, such as chatbots, document summarizers, and Text2SQL generators, enabling automation of customer support, research, and reporting tasks. Its modular architecture and extension points allow seamless integration with existing pipelines, ensuring scalability and minimal hallucinations in AI-driven applications.

KoG Playground is an open-source, browser-based platform designed to simplify the development of retrieval-augmented generation (RAG) agents. It connects to popular vector stores like Pinecone or FAISS, allowing users to ingest text corpora, compute embeddings, and configure retrieval pipelines visually. The interface offers modular components to define prompt templates, LLM backends (OpenAI, Hugging Face), and chain handlers. Real-time logs display token usage and latency metrics for each API call, helping optimize performance and cost. Users can adjust similarity thresholds, re-ranking algorithms, and result fusion strategies on the fly, then export their configuration as code snippets or reproducible projects. KoG Playground streamlines prototyping for knowledge-driven chatbots, semantic search applications, and custom AI assistants with minimal coding overhead.

LangSearch offers a robust API that supports natural language processing for web searches. It provides detailed search results from a vast database of web documents including news, images, and videos. The API supports both keyword and vector searches, and utilizes a reranking model that enhances result accuracy. Easy integration into various applications and tools makes LangSearch an ideal choice for developers looking to add advanced search capabilities to their projects.

Serving as a robust Go implementation of the popular LlamaIndex framework, Llama-Index-Go offers end-to-end capabilities for constructing and querying vector-based indexes from textual data. Users can load documents via built-in or custom loaders, generate embeddings using OpenAI or other providers, and store vectors in memory or external vector databases. The library exposes a QueryEngine API that supports keyword and semantic search, boolean filters, and retrieval-augmented generation with LLMs. Developers can extend parsers for markdown, JSON, or HTML, and plug in alternative embedding models. Designed with modular components and clear interfaces, it provides high performance, easy debugging, and flexible integration in microservices, CLI tools, or web applications, enabling rapid prototyping of AI-powered search and chat solutions.

Open WebUI Pipeline for RAGFlow provides developers and data scientists with a modular pipeline to build retrieval-augmented generation (RAG) applications. It supports uploading documents, computing embeddings using various LLM APIs, and storing vectors in local databases for efficient similarity search. The framework orchestrates retrieval, summarization, and conversational flows, enabling real-time chat interfaces that reference external knowledge. With customizable prompts, multi-model compatibility, and memory management, it empowers users to create specialized QA systems, document summarizers, and personal AI assistants all within an interactive Web UI environment. The plugin architecture allows seamless integration with existing local WebUI setups like Oobabooga. It includes step-by-step configuration files and supports batch processing, conversational context tracking, and flexible retrieval strategies. Developers can extend the pipeline with custom modules for vector store selection, prompt chaining, and user memory, making it ideal for research, customer support, and specialized knowledge services.

RAG for Cybersecurity combines the power of large language models with vector-based retrieval to transform how security teams access and analyze cybersecurity information. Users begin by ingesting documents such as MITRE ATT&CK matrices, CVE entries, and security advisories. The framework then generates embeddings for each document and stores them in a vector database. When a user submits a query, RAG retrieves the most relevant document chunks, passes them to the LLM, and returns precise, context-rich responses. This approach ensures answers are grounded in authoritative sources, reducing hallucinations while improving accuracy. With customizable data pipelines and support for multiple embeddings and LLM providers, teams can tailor the system to their unique threat intelligence needs.