Search Results for "ai applications"
Sort By:
Showing 34 open source projects for "ai applications"
View related business solutions-
Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
$300 in Free Credit Towards Top Cloud ServicesStart your project in minutes. After credits run out, 20+ products include free monthly usage. Only pay when you're ready to scale.
-
1
Spring AI
An Application Framework for AI Engineering
Spring AI is a framework within the Spring ecosystem that simplifies the integration of artificial intelligence capabilities into Java applications by providing abstractions over various AI services and models. It is designed to align with familiar Spring development patterns, allowing developers to incorporate AI features such as chat, embeddings, and prompt handling using consistent APIs. -
2
Lobe UI
An open-source UI component library for building AIGC web apps
...It is optimized for modern frameworks such as Next.js and supports server-side rendering with proper configuration, making it suitable for production-grade applications. Lobe UI is part of a broader ecosystem that includes tools for charts, icons, and AI-related utilities, enabling developers to build complete AI products with consistent design. -
3
LLPhant
A comprehensive PHP Generative AI Framework
LLPhant is a PHP-based generative AI framework designed to bring large language model capabilities into modern PHP applications with a structure inspired by frameworks like LangChain. It provides developers with a set of abstractions for building AI-powered features such as chat systems, retrieval pipelines, and automated workflows while remaining simple enough to integrate into existing Symfony or Laravel projects. -
4
LangChain.dart
Build LLM-powered Dart/Flutter applications
LangChain.dart is a Dart and Flutter implementation of the LangChain framework, designed to enable developers to build LLM-powered applications for mobile, web, and cross-platform environments. It provides a structured way to integrate language models into applications by offering abstractions for chains, agents, embeddings, and vector stores. The framework supports multiple providers and includes packages for integrating with services such as OpenAI, Google AI, and Firebase, allowing developers to build both local and cloud-based AI workflows. ... -
Compliant and Reliable File Transfers Backed by Top Security CertificationsStop relying on non-certified, legacy file transfer tools that creak under the weight of modern security demands. Get full audit trails, advanced access controls and more supported by an award-winning team of experts. Start your free 25-day trial today.
-
5
Cursor Cookbook
This repo contains small examples for building with Cursor
Cursor Cookbook is a curated collection of examples, patterns, and best practices for building applications with Cursor and AI-assisted coding tools. It provides practical recipes that demonstrate how to integrate AI into development workflows effectively. The repository focuses on real-world use cases such as automation, code generation, and system design. It helps developers understand how to structure prompts and workflows for consistent results. -
6
RuVector
Self-Learning, Vector Graph Neural Network, and Database built in Rust
...The repository reflects a research-forward approach that blends practical utilities with experimental agentic concepts, encouraging exploration of emerging AI design patterns. It is intended for developers building sophisticated AI-powered applications who need flexible vector handling and integration capabilities. -
7
Google Kubernetes Engine (GKE) Samples
Sample applications for Google Kubernetes Engine (GKE)
...The repository is organized into multiple categories such as AI and machine learning, autoscaling, networking, observability, security, and cost optimization, allowing developers to explore specific use cases and architectural patterns. It includes both simple quickstart examples, like basic “hello world” applications, and more advanced scenarios such as migrating monolithic applications to microservices, implementing service meshes, and configuring custom autoscaling metrics. -
8
nuwa-skill
Mental models, decision heuristics, expressing DNA
...Nuwa Skill also supports orchestration, enabling multiple skills to work together to accomplish more complex objectives. The architecture is typically designed for flexibility, making it suitable for applications in conversational AI, automation, or intelligent assistants. Overall, it serves as a foundation for building scalable and extensible AI-driven systems. -
9
PromptingTools.jl
Streamline your life using PromptingTools.jl
...It focuses on reducing the complexity of prompt creation by introducing templating systems, macros, and reusable functions that standardize how prompts are constructed and executed. The library provides a family of ai* functions that handle tasks such as generation, embeddings, classification, and data extraction, all following a consistent structure. It supports multiple backends, including OpenAI-compatible APIs and local models such as those served through Ollama, allowing users to switch providers without rewriting prompts. The toolkit also includes advanced capabilities such as asynchronous execution, routing, OCR, and experimental agent workflows, making it suitable for both simple and complex AI applications. -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
-
10
LLM Tornado
The .NET library to build AI agents with 30+ built-in connectors
...It supports multimodal inputs and outputs, including text, images, audio, and documents, making it suitable for a wide range of AI applications. LLMTornado also integrates advanced protocols such as Model Context Protocol and agent-to-agent communication, enabling complex interactions between systems. With built-in support for local deployments, enterprise guardrails, and observability features. -
11
Writer Framework
No-code in the front, Python in the back. An open-source framework
Writer Framework is an open source platform designed to help developers build AI-powered applications by combining a visual interface builder with a Python-based backend architecture. It follows a hybrid approach where user interfaces are created using a drag-and-drop editor while business logic is implemented in Python, allowing teams to balance speed and flexibility without sacrificing control. The framework is particularly focused on AI use cases, enabling developers to integrate large language models, knowledge graphs, and custom machine learning workflows into user-facing applications. -
12
assistant-ui
Typescript/React Library for AI Chat
...It is designed to integrate with a wide range of AI backends and providers, making it highly flexible for different architectures. The system also supports advanced features like tool rendering, attachments, and interactive UI elements, enabling rich user experiences. With strong TypeScript support and developer-friendly defaults, it accelerates the process of building and deploying AI-powered applications. -
13
Elixir LangChain
Elixir implementation of a LangChain style framework
...This makes it particularly well-suited for building scalable, real-time AI systems that benefit from Elixir’s fault tolerance and lightweight process management. The framework supports multiple providers and includes features such as streaming responses, tool calling, and structured outputs, allowing developers to create complex AI-driven workflows. -
14
Groq TypeScript / Node.s
The official Node.js / Typescript library for the Groq API
...With this SDK, developers can call Groq’s models, transcribe audio, perform file uploads — all with minimal boilerplate — which streamlines creation of AI-enabled applications in the JavaScript/TypeScript ecosystem. -
15
Ollama-rs
A simple and easy-to-use library for interacting with the Ollama API
Ollama-rs is a Rust library designed to provide a simple and efficient interface for interacting with the Ollama API, enabling developers to integrate local large language models into Rust applications. It follows the official Ollama API closely, ensuring compatibility while offering an idiomatic Rust experience with strong typing and asynchronous execution. The library supports a wide range of operations, including text generation, chat interactions, embeddings, and model management, making it suitable for both simple applications and more advanced AI workflows. ... -
16
Ollama-Laravel Package
Ollama-Laravel is a Laravel package providing seamless integration
Ollama-Laravel Package is a PHP package designed to integrate local large language models into Laravel applications through a clean and idiomatic interface. It abstracts the Ollama API into a developer-friendly facade, allowing Laravel developers to interact with models using familiar patterns such as service containers, configuration files, and fluent method chaining. The package supports a wide range of AI capabilities including text generation, chat-based interactions, embeddings, and multimodal vision analysis, making it suitable for both simple features and complex AI-driven systems. ... -
17
Ollama4j
A simple Java library for interacting with Ollama server
Ollama4j is a lightweight Java library designed to simplify interaction with the Ollama server, enabling developers to integrate locally hosted large language models into Java applications with minimal friction. It provides a clean and developer-friendly API that abstracts the underlying REST communication, allowing users to focus on building features rather than handling low-level networking details. The library supports a wide range of core capabilities, including chat-based interactions, text generation, embeddings, and model management, making it suitable for both simple applications and more advanced AI-driven systems. ... -
18
Claude Code Slash Commands
A collection of production-ready slash commands for Claude Code
...It emphasizes clarity and predictability by enforcing schemas and structured definitions for each command, reducing ambiguity in automated workflows. Developers can define custom commands tailored to their applications, making it adaptable to a wide range of use cases including automation, scripting, and AI-assisted development. It is particularly useful in environments where reliability and reproducibility of actions are critical. -
19
Groq Python
The official Python Library for the Groq API
...This makes it easy to integrate Groq-powered AI capabilities into backend services, data pipelines, research notebooks, or applications written in Python. For those building AI-based tooling, automation scripts, or ML-backed backends, groq-python abstracts away HTTP request plumbing and exposes a clean API, accelerating development and reducing boilerplate. -
20
Google Cloud Platform Go Samples
Sample apps and code written for Google Cloud
...The repository includes both simple quickstart examples and more advanced application patterns, often accompanied by documentation guides that explain how to deploy and run them in different environments. It also showcases integrations with services such as Pub/Sub, BigQuery, Cloud Functions, and Vertex AI, helping developers understand how to orchestrate distributed systems in Go. -
21
LangChain for .NET
C# implementation of LangChain
...The framework supports integration with multiple providers, including OpenAI and other compatible APIs, and includes tools for embeddings, document loading, and vector database interaction. It also provides both low-level async APIs and higher-level chain abstractions, giving developers flexibility in how they structure their applications. With built-in support for retrieval-augmented generation and structured pipelines, LangChain .NET is designed for production-ready AI systems. -
22
Cookbook (Google Gemini)
Examples and guides for using the Gemini API
...The Cookbook also includes tutorials on advanced API workflows such as grounding answers with external tools, batch-mode request handling, and live multimodal interactivity with LiveAPI. Designed as a hands-on resource, it helps developers quickly explore Gemini’s potential while serving as a reference for integrating cutting-edge multimodal AI into applications. -
23
Ollama Python
Ollama Python library
...Developers use it to load models, send prompts, manage sessions, and stream responses directly from Python code. It simplifies integration of Ollama-based models into applications, supporting synchronous and streaming modes. This tool is ideal for those building AI-driven apps with local model deployment. -
24
ollama-hpp
Modern, Header-only C++ bindings for the Ollama API
...It includes support for streaming responses, enabling applications to process output as it is generated rather than waiting for full completions. The project also evolves alongside the Ollama API, adding support for newer features such as blob handling and improved model interaction capabilities. Its design prioritizes simplicity, allowing developers to quickly integrate AI features without introducing large dependencies. -
25
Ollama Swift Client
A Swift client library for interacting with Ollama
...The library wraps the Ollama REST API into structured Swift calls, making it easy to perform tasks such as chat completion, text generation, and embeddings without dealing with raw HTTP requests. It supports streaming responses, allowing applications to display generated content progressively, which is especially useful for chat interfaces. The project emphasizes simplicity and integration with native app development, making it ideal for building AI-powered mobile or desktop applications that leverage local models.
