Best AI tools for< Build Backend >

Amplication is an AI-powered platform for .NET and Node.js app development, offering the world's fastest way to build backend services. It empowers developers by providing customizable, production-ready backend services without vendor lock-ins. Users can define data models, extend and customize with plugins, generate boilerplate code, and modify the generated code freely. The platform supports role-based access control, microservices architecture, continuous Git sync, and automated deployment. Amplication is SOC-2 certified, ensuring data security and compliance.

Rowy is a low-code backend platform that allows users to manage their database on a spreadsheet-like interface and build powerful backend cloud functions without leaving their browser. It offers a variety of features such as derivative fields, action fields, extensions, webhooks, and integrations with popular tools like Google Vision, GPT-3, Figma, and Webflow. Rowy is designed to be accessible to both developers and non-technical users, making it a versatile tool for building and managing backend applications.

Goptimise is a no-code AI-powered scalable backend builder that helps developers craft scalable, seamless, powerful, and intuitive backend solutions. It offers a solid foundation with robust and scalable infrastructure, including dedicated infrastructure, security, and scalability. Goptimise simplifies software rollouts with one-click deployment, automating the process and amplifying productivity. It also provides smart API suggestions, leveraging AI algorithms to offer intelligent recommendations for API design and accelerating development with automated recommendations tailored to each project. Goptimise's intuitive visual interface and effortless integration make it easy to use, and its customizable workspaces allow for dynamic data management and a personalized development experience.

ZeroCactus is an AI-assisted NoCode platform that allows users to build complex backends using plain English via prompting. It is a strategic choice for businesses looking for a world-class development platform that is built for performance and delivers to the highest standard every time. ZeroCactus offers a fluid experience, starting as NoCode in MVP and continuing with code as the business grows. Its no-code solution is seamless and easy to use, and it provides endless possibilities. ZeroCactus is also easily customized, and its team of experts is available to deliver what users need now. ZeroCactus uses advanced AI technology to maximize the NoCode experience while ensuring personalized support from expert coders when users need it most. It provides powerful solutions at all times, and its craft is powered by advanced AI technology combined with a hint of human touch. Users can start by building their solutions and MVP with the NoCode experience, and the control is always in their hands. ZeroCactus has no limits and is suited to many businesses, providing creative freedom.

React Native Starter AI is an all-in-one development kit designed to help users quickly launch their mobile apps with AI functionality. The boilerplate template includes integrations such as AI tools, Firebase functions, analytics, authentication, in-app purchases, and more. It aims to save developers time by providing pre-built components and screens for building AI mobile applications. With React Native Starter AI, users can easily customize and publish their apps on mobile app stores, catering to both beginner and experienced developers.

Backend.AI is an enterprise-scale cluster backend for AI frameworks that offers scalability, GPU virtualization, HPC optimization, and DGX-Ready software products. It provides a fast and efficient way to build, train, and serve AI models of any type and size, with flexible infrastructure options. Backend.AI aims to optimize backend resources, reduce costs, and simplify deployment for AI developers and researchers. The platform integrates seamlessly with existing tools and offers fractional GPU usage and pay-as-you-play model to maximize resource utilization.

Convex is a fullstack TypeScript development platform that serves as an open-source backend for application builders. It offers a comprehensive set of APIs and tools to build, launch, and scale applications efficiently. With features like real-time collaboration, optimized transactions, and over 80 OAuth integrations, Convex simplifies backend operations and allows developers to focus on delivering value to customers. The platform enables developers to write backend logic in TypeScript, perform database operations with strong consistency, and integrate with various third-party services seamlessly. Convex is praised for its reliability, simplicity, and developer experience, making it a popular choice for modern software development projects.

BuildShip is a low-code visual backend builder that allows users to create powerful APIs in minutes. It is powered by AI and offers a variety of features such as pre-built nodes, multimodal flows, and integration with popular AI models. BuildShip is suitable for a wide range of users, from beginners to experienced developers. It is also a great tool for teams who want to collaborate on backend development projects.

Koxy AI is an AI-powered serverless back-end platform that allows users to build globally distributed, fast, secure, and scalable back-ends with no code required. It offers features such as live logs, smart errors handling, integration with over 80,000 AI models, and more. Koxy AI is designed to help users focus on building the best service possible without wasting time on security and latency concerns. It provides a No-SQL JSON-based database, real-time data synchronization, cloud functions, and a drag-and-drop builder for API flows.

DocDriven is an AI-powered documentation-driven API development tool that provides a shared workspace for optimizing the API development process. It helps in designing APIs faster and more efficiently, collaborating on API changes in real-time, exploring all APIs in one workspace, generating AI code, maintaining API documentation, and much more. DocDriven aims to streamline communication and coordination among backend developers, frontend developers, UI designers, and product managers, ensuring high-quality API design and development.

AgentLabs is a frontend-as-a-service platform that allows developers to build and share AI-powered chat-based applications in minutes, without any front-end experience. It provides a range of features such as real-time and asynchronous communication, background task management, backend agnosticism, and support for Markdown, files, and more.

Reachat is an open-source UI building library for creating chat interfaces in ReactJS. It offers highly customizable components and theming options, rich media support for file uploads and markdown formatting, an intuitive API for building custom chat experiences, and the ability to seamlessly switch between different AI models. Reachat is battle-tested and used in production across various enterprise products. It is a powerful, flexible, and user-friendly AI chat interface library that allows developers to easily integrate conversational AI capabilities into their applications without the need to spend weeks building custom components. Reachat is not tied to any specific backend or LLM, providing the freedom to use it with any backend or LLM of choice.

Works is a platform that connects enterprises with the top 1% of remote tech talent. It uses advanced AI technology to ensure precision-matching of talent to project requirements, saving time and resources. Works offers transparent pricing with a flat 10% transaction fee and provides risk-free hiring with payment only when the work is completed to satisfaction.

Eventual is a platform that simplifies the process of building and operating resilient event-driven applications. It offers code-first APIs, Events, and Workflows to create durable, scalable, and event-driven systems with end-to-end type safety. The platform supports composable microservices that are fully serverless, evolve naturally, and have minimal operational complexity. Eventual runs in your cloud environment, adhering to your security and privacy policies, and integrates with your preferred Infrastructure as Code (IaC) framework.

DocuHelp is an AI-powered platform that enables businesses to effortlessly create professional-grade documents, reports, proposals, and sales pitches in minutes. It facilitates real-time collaboration among team members, ensuring accuracy and efficiency. The tool eliminates the need for email chains and confusion, providing a seamless writing experience. DocuHelp AI is industry-focused, offering backend prompts tailored to specific industries for precise results. Additionally, it allows access to backend systems, enables training models on company data, and provides analytics for fine-tuning based on specific use cases.

Oncora Medical is a healthcare technology company that provides software and data solutions to oncologists and cancer centers. Their products are designed to improve patient care, reduce clinician burnout, and accelerate clinical discoveries. Oncora's flagship product, Oncora Patient Care, is a modern, intelligent user interface for oncologists that simplifies workflow, reduces documentation burden, and optimizes treatment decision making. Oncora Analytics is an adaptive visual and backend software platform for regulatory-grade real world data analytics. Oncora Registry is a platform to capture and report quality data, treatment data, and outcomes data in the oncology space.

Treblle is an End to End APIOps Platform that helps engineering and product teams build, ship, and understand their REST APIs in one single place. It offers features such as API Observability, API Documentation, API Governance, API Security, and API Analytics. With a focus on empowering API producers and consumers, Treblle provides actionable data in real-time, customizable dashboards, and automated API development. The platform aims to improve API release times, enhance developer experience, and ensure API quality and security.

Dify is an open-source platform for building AI applications that combines Backend-as-a-Service and LLMOps to streamline the development of generative AI solutions. It integrates support for mainstream LLMs, an intuitive Prompt orchestration interface, high-quality RAG engines, a flexible AI Agent framework, and easy-to-use interfaces and APIs. Dify allows users to skip complexity and focus on creating innovative AI applications that solve real-world problems. It offers a comprehensive, production-ready solution with a user-friendly interface.

Dora is a no-code 3D animated website design platform that allows users to create stunning 3D and animated visuals without writing a single line of code. With Dora, designers, freelancers, and creative professionals can focus on what they do best: designing. The platform is tailored for professionals who prioritize design aesthetics without wanting to dive deep into the backend. Dora offers a variety of features, including a drag-and-connect constraint layout system, advanced animation capabilities, and pixel-perfect usability. With Dora, users can create responsive 3D and animated websites that translate seamlessly across devices.

Prisms is a no-code platform for building AI-powered apps. It allows users to harness the power of AI without having to write any code. Prisms is built on top of Large Language models including GPT3, DALL-E, and Stable Diffusion. Users can connect the pieces in Prisms to stack together data sources, user inputs, and off-the-shelf building blocks to create their own AI-powered apps. Prisms also makes it easy to deploy AI-powered apps directly from the platform with its pre-built UI. Alternatively, users can build their own frontend and use Prisms as a backend for their AI logic.

Cool-admin-java is an open-source backend permission management system with features like Ai coding, flow arrangement, modularity, and plugin support. It is used to quickly build backend applications. The system offers a modern development experience by providing functionalities such as one-click generation of API interfaces to frontend pages, drag-and-drop flow arrangement, modularized code for easy maintenance, and extensibility through plugin installation for features like payments, SMS, and emails.

Superplatform is a microservices platform focused on distributed AI management and development. It enables users to self-host AI models, build backendless AI apps, develop microservices-based AI applications, and deploy third-party AI apps easily. The platform supports running open-source AI models privately, building apps leveraging AI models, and utilizing a microservices-based communal backend for diverse projects.

This repository contains separate packages for Grafana LLM Plugin and the @grafana/llm package for interfacing with it. The packages are tightly coupled and developed together with identical dependencies. The repository provides instructions for developing the packages, including backend and frontend development, testing, and release processes.

Singulatron is an AI Superplatform that runs on your computer(s) and server(s) without using third party APIs, providing complete control over data and privacy. It offers AI functionality, user management, supports different database backends, collaboration, and mini-apps. It aims to be a desktop app for local usage and a distributed daemon for servers, with a web app frontend client. The tool is stack-based on Electron, Angular, and Go, and currently dual-licensed under AGPL-3.0-or-later and a commercial license.

The llama-recipes repository provides a scalable library for fine-tuning Llama 2, along with example scripts and notebooks to quickly get started with using the Llama 2 models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama 2 and other tools in the LLM ecosystem. The examples here showcase how to run Llama 2 locally, in the cloud, and on-prem.

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LLM Foundry is a codebase for training, finetuning, evaluating, and deploying LLMs for inference with Composer and the MosaicML platform. It is designed to be easy-to-use, efficient _and_ flexible, enabling rapid experimentation with the latest techniques. You'll find in this repo: * `llmfoundry/` - source code for models, datasets, callbacks, utilities, etc. * `scripts/` - scripts to run LLM workloads * `data_prep/` - convert text data from original sources to StreamingDataset format * `train/` - train or finetune HuggingFace and MPT models from 125M - 70B parameters * `train/benchmarking` - profile training throughput and MFU * `inference/` - convert models to HuggingFace or ONNX format, and generate responses * `inference/benchmarking` - profile inference latency and throughput * `eval/` - evaluate LLMs on academic (or custom) in-context-learning tasks * `mcli/` - launch any of these workloads using MCLI and the MosaicML platform * `TUTORIAL.md` - a deeper dive into the repo, example workflows, and FAQs

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

ML-Bench is a tool designed to evaluate large language models and agents for machine learning tasks on repository-level code. It provides functionalities for data preparation, environment setup, usage, API calling, open source model fine-tuning, and inference. Users can clone the repository, load datasets, run ML-LLM-Bench, prepare data, fine-tune models, and perform inference tasks. The tool aims to facilitate the evaluation of language models and agents in the context of machine learning tasks on code repositories.

Backend.AI Web UI is a user-friendly web and app interface designed to make AI accessible for end-users, DevOps, and SysAdmins. It provides features for session management, inference service management, pipeline management, storage management, node management, statistics, configurations, license checking, plugins, help & manuals, kernel management, user management, keypair management, manager settings, proxy mode support, service information, and integration with the Backend.AI Web Server. The tool supports various devices, offers a built-in websocket proxy feature, and allows for versatile usage across different platforms. Users can easily manage resources, run environment-supported apps, access a web-based terminal, use Visual Studio Code editor, manage experiments, set up autoscaling, manage pipelines, handle storage, monitor nodes, view statistics, configure settings, and more.

fAIr is an open AI-assisted mapping service developed by the Humanitarian OpenStreetMap Team (HOT) to improve mapping efficiency and accuracy for humanitarian purposes. It uses AI models, specifically computer vision techniques, to detect objects like buildings, roads, waterways, and trees from satellite and UAV imagery. The service allows OSM community members to create and train their own AI models for mapping in their region of interest and ensures models are relevant to local communities. Constant feedback loop with local communities helps eliminate model biases and improve model accuracy.

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.

This NVIDIA AI Workbench project is designed for developing a Retrieval Augmented Generation application with a customizable Gradio Chat app. It allows users to embed documents into a locally running vector database and run inference locally on a Hugging Face TGI server, in the cloud using NVIDIA inference endpoints, or using microservices via NVIDIA Inference Microservices (NIMs). The project supports various models with different quantization options and provides tutorials for using different inference modes. Users can troubleshoot issues, customize the Gradio app, and access advanced tutorials for specific tasks.

Kafka-ML is a framework designed to manage the pipeline of Tensorflow/Keras and PyTorch machine learning models on Kubernetes. It enables the design, training, and inference of ML models with datasets fed through Apache Kafka, connecting them directly to data streams like those from IoT devices. The Web UI allows easy definition of ML models without external libraries, catering to both experts and non-experts in ML/AI.

The Artificial Intelligence Controller Interface (AICI) lets you build Controllers that constrain and direct output of a Large Language Model (LLM) in real time. Controllers are flexible programs capable of implementing constrained decoding, dynamic editing of prompts and generated text, and coordinating execution across multiple, parallel generations. Controllers incorporate custom logic during the token-by-token decoding and maintain state during an LLM request. This allows diverse Controller strategies, from programmatic or query-based decoding to multi-agent conversations to execute efficiently in tight integration with the LLM itself.

CrewAI-GUI is a Node-Based Frontend tool designed to revolutionize AI workflow creation. It empowers users to design complex AI agent interactions through an intuitive drag-and-drop interface, export designs to JSON for modularity and reusability, and supports both GPT-4 API and Ollama for flexible AI backend. The tool ensures cross-platform compatibility, allowing users to create AI workflows on Windows, Linux, or macOS efficiently.

lm.rs is a tool that allows users to run inference on Language Models locally on the CPU using Rust. It supports LLama3.2 1B and 3B models, with a WebUI also available. The tool provides benchmarks and download links for models and tokenizers, with recommendations for quantization options. Users can convert models from Google/Meta on huggingface using provided scripts. The tool can be compiled with cargo and run with various arguments for model weights, tokenizer, temperature, and more. Additionally, a backend for the WebUI can be compiled and run to connect via the web interface.

The serverless-pdf-chat repository contains a sample application that allows users to ask natural language questions of any PDF document they upload. It leverages serverless services like Amazon Bedrock, AWS Lambda, and Amazon DynamoDB to provide text generation and analysis capabilities. The application architecture involves uploading a PDF document to an S3 bucket, extracting metadata, converting text to vectors, and using a LangChain to search for information related to user prompts. The application is not intended for production use and serves as a demonstration and educational tool.

llama.cpp is a C++ implementation of LLaMA, a large language model from Meta. It provides a command-line interface for inference and can be used for a variety of tasks, including text generation, translation, and question answering. llama.cpp is highly optimized for performance and can be run on a variety of hardware, including CPUs, GPUs, and TPUs.

The TensorRT-LLM Backend is a Triton backend designed to serve TensorRT-LLM models with Triton Inference Server. It supports features like inflight batching, paged attention, and more. Users can access the backend through pre-built Docker containers or build it using scripts provided in the repository. The backend can be used to create models for tasks like tokenizing, inferencing, de-tokenizing, ensemble modeling, and more. Users can interact with the backend using provided client scripts and query the server for metrics related to request handling, memory usage, KV cache blocks, and more. Testing for the backend can be done following the instructions in the 'ci/README.md' file.

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