#typescript #agent #ai #anthropic #backend_as_a_service #chatbot #gemini #genai #gpt #gpt_4 #llama3 #llm #llmops #nextjs #openai #orchestration #python #rag #workflow #workflows

Dify is an open-source platform for developing AI applications, especially those using Large Language Models (LLMs). It offers a user-friendly interface to build and test AI workflows, integrate various LLMs, and manage models. Key features include a visual workflow builder, comprehensive model support (including GPT, Mistral, and more), a prompt IDE for crafting and testing prompts, RAG pipeline capabilities for document ingestion and retrieval, and agent capabilities with pre-built tools like Google Search and DALL·E.

Using Dify, you can quickly move from prototyping to production with features like observability to monitor application performance and backend-as-a-service for easy integration into your business logic. You can deploy Dify via their cloud service or self-host it in your environment. This makes it highly versatile and beneficial for developers looking to leverage AI efficiently in their projects.

https://github.com/langgenius/dify

#python #agent #agents #ai_search #chatbot #chatgpt #data_pipelines #deep_learning #document_parser #document_understanding #genai #graph #graphrag #llm #nlp #pdf_to_text #preprocessing #rag #retrieval_augmented_generation #table_structure_recognition #text2sql

RAGFlow is an open-source tool that helps businesses answer questions accurately using large language models and deep document understanding. It extracts information from various complex data formats, such as Word documents, Excel files, and web pages, and provides grounded citations to support its answers. You can try a demo online or set it up on your own server using Docker. The setup is relatively straightforward, requiring a few steps like cloning the repository, building the Docker image, and configuring the system settings. RAGFlow offers key features like template-based chunking, reduced hallucinations, and compatibility with multiple data sources, making it a powerful tool for truthful question-answering capabilities. This benefits users by providing reliable and explainable answers, streamlining their workflow, and supporting integration with their business systems.

https://github.com/infiniflow/ragflow

#typescript #agent #agents #ai #chatgpt #genai #genaistack #gpt #gpt4 #javascript #llm #prompt_engineering #scripting #typescript #vscode_extension

GenAIScript is a powerful tool that helps you work with large language models (LLMs) using JavaScript. It allows you to create and manage prompts, include files and data, and extract structured output all in one script. You can write JavaScript code to generate prompts, analyze data, and save results in files. It integrates well with Visual Studio Code, making it easy to edit, debug, and run your scripts. This tool also supports various file types like PDFs, DOCX, CSV, and XLSX, and you can even reuse and share your scripts. The benefit is that it simplifies the process of working with LLMs, making it more efficient and productive for developers.

https://github.com/microsoft/genaiscript

#python #ai_agents #customer_service #customer_success #genai #llm

Parlant is a tool that helps you control how AI agents behave when talking to customers. It lets you set specific rules, called guidelines, that the agent must follow in different situations. This means you can ensure the agent's responses are consistent and align with your business needs. With Parlant, you can easily update these rules without retraining the AI, and it also helps you detect any conflicts in the rules. This makes it easier to manage and improve the agent's behavior, ensuring reliable and efficient customer interactions. Additionally, Parlant supports multiple AI providers and offers a user-friendly interface for testing and debugging, making it easier to deploy and maintain your AI agents.

https://github.com/emcie-co/parlant

#python #elevenlabs #gemini #genai #notebooklm #openai #podcast

Podcastfy is an open-source tool that turns different types of content (like text, images, websites, and YouTube videos) into engaging audio conversations using AI. It allows you to customize the audio style, language, and format, making it useful for various purposes. For example, content creators can convert blog posts into podcasts, educators can make lectures more accessible, and researchers can turn complex papers into easy-to-listen audio. This tool helps make information more accessible to everyone, especially those with visual impairments or different learning preferences. You can use it through Python code or a command-line interface, and it supports multiple languages and advanced text-to-speech models.

https://github.com/souzatharsis/podcastfy

#jupyter_notebook #agents #ai #genai #langchain #langgraph #llm #llms #openai #tutorials

This repository offers a comprehensive collection of tutorials and implementations for building Generative AI (GenAI) agents. It helps users learn how to create simple conversational bots to complex multi-agent systems. By using this resource, you can improve your skills in developing AI solutions that automate tasks, enhance decision-making, and provide personalized experiences. The benefits include increased efficiency, better customer interactions, and the ability to innovate faster than competitors.

https://github.com/NirDiamant/GenAI_Agents

#python #genai #gpt #gpt_4 #graphrag #knowledge_graph #large_language_models #llm #rag #retrieval_augmented_generation

LightRAG is a system that helps computers understand and answer questions better by using a special way of organizing information called a "graph." This graph shows how different pieces of information are connected, making it easier for the system to find related answers. It works fast and can handle complex questions by combining two types of searches: one that looks at specific details and another that looks at broader topics. This makes it very useful for answering questions that need both specific and general information. Users benefit from getting accurate and relevant answers quickly, which is helpful in many applications like customer service and document retrieval.

https://github.com/HKUDS/LightRAG

#rust #ai #ai_engineering #anthropic #artificial_intelligence #deep_learning #genai #generative_ai #gpt #large_language_models #llama #llm #llmops #llms #machine_learning #ml #ml_engineering #mlops #openai #python #rust

TensorZero is a free, open-source tool that helps you build and improve large language model (LLM) applications by using real-world data and feedback. It gives you one simple API to connect with all major LLM providers, collects data from your app’s use, and lets you easily test and improve prompts, models, and strategies. You can see how your LLMs perform, compare different options, and make them smarter, faster, and cheaper over time—all while keeping your data private and under your control. This means you get better results with less effort and cost, and your apps keep improving as you use them[1][2][3].

https://github.com/tensorzero/tensorzero

#typescript #agents #agi #ai #api #backend #developer_tools #framework #genai #javascript #python #ruby

Motia is a modern backend framework that helps simplify complex systems by combining APIs, background jobs, events, and AI agents into one unified system. It allows developers to write code in multiple languages like JavaScript, TypeScript, and Python, all within the same project. This makes it easier to manage and deploy applications, reducing complexity and errors. With Motia, you get built-in observability and one-click deployments, making it easier to monitor and debug your workflows. This means you can focus on your business logic without worrying about the underlying infrastructure.

https://github.com/MotiaDev/motia

#go #databases #genai #llms #mcp

The MCP Toolbox for Databases helps developers connect AI agents to databases more easily and securely. It simplifies the process by handling complex tasks like connection pooling and authentication, allowing you to integrate databases with AI agents using minimal code. This toolbox supports the Model Context Protocol (MCP), which standardizes how AI interacts with external tools. By using MCP Toolbox, you can automate database tasks, query databases using natural language, and generate context-aware code, all of which save time and improve development efficiency.

https://github.com/googleapis/genai-toolbox