Best AI tools for< Build Streaming Ai Pipelines >

SingleStore is a real-time data platform designed for apps, analytics, and gen AI. It offers faster hybrid vector + full-text search, fast-scaling integrations, and a free tier. SingleStore can read, write, and reason on petabyte-scale data in milliseconds. It supports streaming ingestion, high concurrency, first-class vector support, record lookups, and more.

Encord is a complete data development platform designed for AI applications, specifically tailored for computer vision and multimodal AI teams. It offers tools to intelligently manage, clean, and curate data, streamline labeling and workflow management, and evaluate model performance. Encord aims to unlock the potential of AI for organizations by simplifying data-centric AI pipelines, enabling the building of better models and deploying high-quality production AI faster.

Lume AI is an AI-powered data mapping suite that automates the process of mapping, cleaning, and validating data in various workflows. It offers a comprehensive solution for building pipelines, onboarding customer data, and more. With AI-driven insights, users can streamline data analysis, mapper generation, deployment, and maintenance. Lume AI provides both a no-code platform and API integration options for seamless data mapping. Trusted by market leaders and startups, Lume AI ensures data security with enterprise-grade encryption and compliance standards.

Taylor is a deterministic AI tool that empowers Business & Engineering teams to enrich and automate text data at scale. It allows users to structure freeform text, customize enrichments, and build classification models for real-time data pipelines. With easy customization and integration capabilities, Taylor brings powerful machine learning to streamline business operations and product features.

Glozo is an AI-powered sourcing platform designed to streamline recruitment processes by matching job requirements to suitable candidates efficiently. The platform utilizes AI to enhance sourcing accuracy, reduce time-to-hire, and minimize costs for recruiters. Glozo offers features such as AI-powered candidate search, automated screening, and access to a vast talent database from various sources. The platform caters to recruiters in the tech industry, finance sector, and other fields, helping them build stronger talent pipelines and make informed hiring decisions.

Momentic is a purpose-built AI tool for modern software testing, offering automation for E2E, UI, API, and accessibility testing. It leverages AI to streamline testing processes, from element identification to test generation, helping users shorten development cycles and enhance productivity. With an intuitive editor and the ability to describe elements in plain English, Momentic simplifies test creation and execution. It supports local testing without the need for a public URL, smart waiting for in-flight requests, and integration with CI/CD pipelines. Momentic is trusted by numerous companies for its efficiency in writing and maintaining end-to-end tests.

RunPod is a cloud platform specifically designed for AI development and deployment. It offers a range of features to streamline the process of developing, training, and scaling AI models, including a library of pre-built templates, efficient training pipelines, and scalable deployment options. RunPod also provides access to a wide selection of GPUs, allowing users to choose the optimal hardware for their specific AI workloads.

Fetcher is an AI candidate sourcing tool designed for recruiters to streamline the talent acquisition process. It leverages advanced AI technology and expert teams to efficiently source high-quality candidate profiles that match specific hiring requirements. The tool also provides smart recruitment analytics, personalized diversity search criteria, and verified contact information to enhance candidate engagement and streamline the recruitment process. With robust technology integrations and a focus on automating processes, Fetcher aims to help companies of all sizes attract top talent and optimize their recruitment strategies.

Bizware AI is an all-in-one AI-powered platform designed to help businesses automate their sales and marketing processes, attract and close more customers, and build long-lasting client relationships on autopilot. The platform offers a wide range of features such as social media management, website chat widget, forms & surveys, automated nurture sequences, email & SMS marketing, phone system & autodialer, CRM & sales pipeline management, invoicing & payments, review & reputation management, and reporting & analytics. Bizware AI aims to streamline sales, generate more leads, build brand reputation, impress existing customers, and automate various tasks to help businesses grow efficiently.

HireList.io is an AI-driven recruitment software designed to streamline the hiring process for startups. It offers a range of features to help businesses target the right talent, connect with the right candidates, and build their dream team quickly and efficiently. HireList's AI-powered candidate filter narrows down the pool of applicants, highlighting only those who fit the bill. The platform also provides a streamlined hiring pipeline, taking businesses from job posting to welcoming their new hire. With HireList, businesses can create and tailor job postings, review and comment on candidates, and track their progress through the hiring process. The platform also offers communication automation, structured interviews, and performance tracking to help businesses refine their hiring practices.

Valohai is a scalable MLOps platform that enables Continuous Integration/Continuous Deployment (CI/CD) for machine learning and pipeline automation on-premises and across various cloud environments. It helps streamline complex machine learning workflows by offering framework-agnostic ML capabilities, automatic versioning with complete lineage of ML experiments, hybrid and multi-cloud support, scalability and performance optimization, streamlined collaboration among data scientists, IT, and business units, and smart orchestration of ML workloads on any infrastructure. Valohai also provides a knowledge repository for storing and sharing the entire model lifecycle, facilitating cross-functional collaboration, and allowing developers to build with total freedom using any libraries or frameworks.

Wedo AI is an all-in-one AI-powered platform designed to help businesses attract customers, convert leads, and manage various aspects of online marketing, sales, and delivery. It offers a range of tools such as AI ads, chat bots, social media planner, websites, ecommerce store, memberships, CRM, email marketing, analytics, and more. Wedo AI aims to streamline processes, increase efficiency, and drive revenue growth for entrepreneurs, startups, influencers, non-profits, coaches, contractors, freelancers, and consultants. The platform provides features for managing finances, automating billing, creating funnels, building websites, selling products, engaging with customers, and analyzing data to make informed decisions.

SubSync is the #1 CRM for Home Services that leverages AI technology to help home service businesses accelerate their revenue by generating, engaging, and converting new leads. It offers a comprehensive all-in-one platform for lead prospecting, marketing automation, and sales management. SubSync's innovative features streamline the process of finding and connecting with potential customers, empowering sales teams to focus on quality interactions. With SubSync, users can automate time-consuming workflows, track campaign performance, and optimize their sales funnel, ultimately leading to increased efficiency and higher ROI.

The AI SDK is a free open-source library designed to empower developers to build AI-powered products. It offers a unified Provider API, allowing users to easily switch between AI providers with a single line of code. The SDK enables the creation of dynamic, AI-powered user interfaces and supports various frameworks like React, Next, Vue, Nuxt, and SvelteKit. It also provides the ability to stream AI responses instantly, enhancing user experience. The AI SDK has received high praise from developers for its ease of use, speed of development, and comprehensive documentation.

Outspeed is a platform for Realtime Voice and Video AI applications, providing networking and inference infrastructure to build fast, real-time voice and video AI apps. It offers tools for intelligence across industries, including Voice AI, Streaming Avatars, Visual Intelligence, Meeting Copilot, and the ability to build custom multimodal AI solutions. Outspeed is designed by engineers from Google and MIT, offering robust streaming infrastructure, low-latency inference, instant deployment, and enterprise-ready compliance with regulations such as SOC2, GDPR, and HIPAA.

MarsX is an AI-powered development tool that combines AI, NoCode, Code, and MicroApps to revolutionize software development. It offers a wide range of features such as AI-powered landing page builder, Micro-AppStore, NFT marketplace, Uber for X, social network creation, No-Code builder, peer-to-peer marketplace, video streaming portal, photo-sharing app, and over 1000 micro-apps for various purposes. The platform enables developers to save time and resources by leveraging AI technology and pre-built tools for different tasks.

Symbl.ai is a real-time voice AI platform that enables businesses to extract insights from unstructured live calls. It offers a range of features, including real-time transcription, sentiment analysis, question detection, and topic tracking. Symbl.ai's platform is powered by Nebula, a proprietary LLM that is specialized in understanding human interactions in streaming mode. This allows Symbl.ai to provide accurate and low-latency insights that can be used to improve customer service, sales, and compliance.

LMNT is an ultrafast lifelike AI speech pricing API that offers low latency streaming for conversational apps, agents, and games. It provides lifelike voices through studio-quality voice clones and instant voice clones. Engineered by an ex-Google team, LMNT ensures reliable performance under pressure with consistent low latency and high availability. The platform enables real-time conversation, content creation at scale, and product marketing through captivating voiceovers. With a user-friendly interface and developer API, LMNT simplifies voice cloning and synthesis for both beginners and professionals.

OpenLIT is an AI application designed as an Observability tool for GenAI and LLM applications. It empowers model understanding and data visualization through an interactive Learning Interpretability Tool. With OpenTelemetry-native support, it seamlessly integrates into projects, offering features like fine-tuning performance, real-time data streaming, low latency processing, and visualizing data insights. The tool simplifies monitoring with easy installation and light/dark mode options, connecting to popular observability platforms for data export. Committed to OpenTelemetry community standards, OpenLIT provides valuable insights to enhance application performance and reliability.

JFrog ML is an AI platform designed to streamline AI development from prototype to production. It offers a unified MLOps platform to build, train, deploy, and manage AI workflows at scale. With features like Feature Store, LLMOps, and model monitoring, JFrog ML empowers AI teams to collaborate efficiently and optimize AI & ML models in production.

The Holoscan SDK is part of NVIDIA Holoscan, the AI sensor processing platform that combines hardware systems for low-latency sensor and network connectivity, optimized libraries for data processing and AI, and core microservices to run streaming, imaging, and other applications, from embedded to edge to cloud. It can be used to build streaming AI pipelines for a variety of domains, including Medical Devices, High Performance Computing at the Edge, Industrial Inspection and more.

Pathway is a Python data processing framework for analytics and AI pipelines over data streams. It's the ideal solution for real-time processing use cases like streaming ETL or RAG pipelines for unstructured data. Pathway comes with an **easy-to-use Python API** , allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: **you can use it in both development and production environments, handling both batch and streaming data effectively**. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a **scalable Rust engine** based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with **Docker and Kubernetes**. You can install Pathway with pip: `pip install -U pathway` For any questions, you will find the community and team behind the project on Discord.

Jina is a tool that allows users to build multimodal AI services and pipelines using cloud-native technologies. It provides a Pythonic experience for serving ML models and transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Users can build and serve models for any data type and deep learning framework, design high-performance services with easy scaling, serve LLM models while streaming their output, integrate with Docker containers via Executor Hub, and host on CPU/GPU using Jina AI Cloud. Jina also offers advanced orchestration and scaling capabilities, a smooth transition to the cloud, and easy scalability and concurrency features for applications. Users can deploy to their own cloud or system with Kubernetes and Docker Compose integration, and even deploy to JCloud for autoscaling and monitoring.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

AITreasureBox is a comprehensive collection of AI tools and resources designed to simplify and accelerate the development of AI projects. It provides a wide range of pre-trained models, datasets, and utilities that can be easily integrated into various AI applications. With AITreasureBox, developers can quickly prototype, test, and deploy AI solutions without having to build everything from scratch. Whether you are working on computer vision, natural language processing, or reinforcement learning projects, AITreasureBox has something to offer for everyone. The repository is regularly updated with new tools and resources to keep up with the latest advancements in the field of artificial intelligence.

NNStreamer is a set of Gstreamer plugins that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.

Ax is a Typescript library that allows users to build intelligent agents inspired by agentic workflows and the Stanford DSP paper. It seamlessly integrates with multiple Large Language Models (LLMs) and VectorDBs to create RAG pipelines or collaborative agents capable of solving complex problems. The library offers advanced features such as streaming validation, multi-modal DSP, and automatic prompt tuning using optimizers. Users can easily convert documents of any format to text, perform smart chunking, embedding, and querying, and ensure output validation while streaming. Ax is production-ready, written in Typescript, and has zero dependencies.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

Jina-Serve is a framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets. It provides native support for major ML frameworks and data types, high-performance service design with scaling and dynamic batching, LLM serving with streaming output, built-in Docker integration and Executor Hub, one-click deployment to Jina AI Cloud, and enterprise-ready features with Kubernetes and Docker Compose support. Users can create gRPC-based AI services, build pipelines, scale services locally with replicas, shards, and dynamic batching, deploy to the cloud using Kubernetes, Docker Compose, or JCloud, and enable token-by-token streaming for responsive LLM applications.

island-ai is a TypeScript toolkit tailored for developers engaging with structured outputs from Large Language Models. It offers streamlined processes for handling, parsing, streaming, and leveraging AI-generated data across various applications. The toolkit includes packages like zod-stream for interfacing with LLM streams, stream-hooks for integrating streaming JSON data into React applications, and schema-stream for JSON streaming parsing based on Zod schemas. Additionally, related packages like @instructor-ai/instructor-js focus on data validation and retry mechanisms, enhancing the reliability of data processing workflows.

Vespa is an open-source search and AI engine that provides a unified platform for building and deploying search and AI applications. Vespa sample applications showcase various use cases and features of Vespa, including basic search, recommendation, semantic search, image search, text ranking, e-commerce search, question answering, search-as-you-type, and ML inference serving.

LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. Here is an attempt to keep track of the initiatives around LangChain. Subscribe to the newsletter to stay informed about the Awesome LangChain. We send a couple of emails per month about the articles, videos, projects, and tools that grabbed our attention Contributions welcome. Add links through pull requests or create an issue to start a discussion. Please read the contribution guidelines before contributing.

The LLM Twin Course is a free, end-to-end framework for building production-ready LLM systems. It teaches you how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices. The course is split into 11 hands-on written lessons and the open-source code you can access on GitHub. You can read everything and try out the code at your own pace.

SuperDuperDB is a Python framework for integrating AI models, APIs, and vector search engines directly with your existing databases, including hosting of your own models, streaming inference and scalable model training/fine-tuning. Build, deploy and manage any AI application without the need for complex pipelines, infrastructure as well as specialized vector databases, and moving our data there, by integrating AI at your data's source: - Generative AI, LLMs, RAG, vector search - Standard machine learning use-cases (classification, segmentation, regression, forecasting recommendation etc.) - Custom AI use-cases involving specialized models - Even the most complex applications/workflows in which different models work together SuperDuperDB is **not** a database. Think `db = superduper(db)`: SuperDuperDB transforms your databases into an intelligent platform that allows you to leverage the full AI and Python ecosystem. A single development and deployment environment for all your AI applications in one place, fully scalable and easy to manage.

Swiftide is a fast, streaming indexing and query library tailored for Retrieval Augmented Generation (RAG) in AI applications. It is built in Rust, utilizing parallel, asynchronous streams for blazingly fast performance. With Swiftide, users can easily build AI applications from idea to production in just a few lines of code. The tool addresses frustrations around performance, stability, and ease of use encountered while working with Python-based tooling. It offers features like fast streaming indexing pipeline, experimental query pipeline, integrations with various platforms, loaders, transformers, chunkers, embedders, and more. Swiftide aims to provide a platform for data indexing and querying to advance the development of automated Large Language Model (LLM) applications.

Flo AI is a Python framework that enables users to build production-ready AI agents and teams with minimal code. It allows users to compose complex AI architectures using pre-built components while maintaining the flexibility to create custom components. The framework supports composable, production-ready, YAML-first, and flexible AI systems. Users can easily create AI agents and teams, manage teams of AI agents working together, and utilize built-in support for Retrieval-Augmented Generation (RAG) and compatibility with Langchain tools. Flo AI also provides tools for output parsing and formatting, tool logging, data collection, and JSON output collection. It is MIT Licensed and offers detailed documentation, tutorials, and examples for AI engineers and teams to accelerate development, maintainability, scalability, and testability of AI systems.

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

AI System School is a curated list of research in machine learning systems, focusing on ML/DL infra, LLM infra, domain-specific infra, ML/LLM conferences, and general resources. It provides resources such as data processing, training systems, video systems, autoML systems, and more. The repository aims to help users navigate the landscape of AI systems and machine learning infrastructure, offering insights into conferences, surveys, books, videos, courses, and blogs related to the field.

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