Ragpi is an open-source AI assistant that answers questions using your documentation, GitHub issues, and READMEs. It combines LLMs with intelligent search to provide relevant, documentation-backed answers through a simple API. It supports multiple providers like OpenAI, Ollama, and Deepseek, and has built-in integrations with Discord and Slack. Ragpi builds knowledge bases from docs, GitHub issues, and READMEs, with an agentic RAG system for dynamic document retrieval. It has an API-first design with Docker deployment.

AIScript is a unique programming language and web framework written in Rust, designed to help developers effortlessly build AI applications. It combines the strengths of Python, JavaScript, and Rust to create an intuitive, powerful, and easy-to-use tool. The language features first-class functions, built-in AI primitives, dynamic typing with static type checking, data validation, error handling inspired by Rust, a rich standard library, and automatic garbage collection. The web framework offers an elegant route DSL, automatic parameter validation, OpenAPI schema generation, database modules, authentication capabilities, and more. AIScript excels in AI-powered APIs, prototyping, microservices, data validation, and building internal tools.

Iceburg CRM is a metadata driven CRM with AI abilities that allows users to quickly prototype any CRM. It offers features like metadata creations, import/export in multiple formats, field validation, themes, role permissions, calendar, audit logs, API, workflow, field level relationships, module level relationships, and more. Created with Vue 3 for the frontend, Laravel 10 for the backend, Tailwinds with DaisyUI plugin, and Inertia for routing. Users can install default, admin panel, core, custom, or AI versions. The tool supports AI Assist for module data suggestions and provides API endpoints for CRM modules, search, specific module data, record updates, and deletions. Iceburg CRM also includes themes, custom field types, calendar, datalets, workflow, roles and permissions, import/export functionality, and custom seeding options.

Quivr is a personal assistant powered by Generative AI, designed to be a second brain for users. It offers fast and efficient access to data, ensuring security and compatibility with various file formats. Quivr is open source and free to use, allowing users to share their brains publicly or keep them private. The marketplace feature enables users to share and utilize brains created by others, boosting productivity. Quivr's offline mode provides anytime, anywhere access to data. Key features include speed, security, OS compatibility, file compatibility, open source nature, public/private sharing options, a marketplace, and offline mode.

MAIAR is a composable, plugin-based AI agent framework designed to abstract data ingestion, decision-making, and action execution into modular plugins. It enables developers to define triggers and actions as standalone plugins, while the core runtime handles decision-making dynamically. This framework offers extensibility, composability, and model-driven behavior, allowing seamless addition of new functionality. MAIAR's architecture is influenced by Unix pipes, ensuring highly composable plugins, dynamic execution pipelines, and transparent debugging. It remains declarative and extensible, allowing developers to build complex AI workflows without rigid architectures.

Promptwright is a Python library designed for generating large synthetic datasets using a local LLM and various LLM service providers. It offers flexible interfaces for generating prompt-led synthetic datasets. The library supports multiple providers, configurable instructions and prompts, YAML configuration for tasks, command line interface for running tasks, push to Hugging Face Hub for dataset upload, and system message control. Users can define generation tasks using YAML configuration or Python code. Promptwright integrates with LiteLLM to interface with LLM providers and supports automatic dataset upload to Hugging Face Hub.

Promptwright is a Python library designed for generating large synthetic datasets using local LLM and various LLM service providers. It offers flexible interfaces for generating prompt-led synthetic datasets. The library supports multiple providers, configurable instructions and prompts, YAML configuration, command line interface, push to Hugging Face Hub, and system message control. Users can define generation tasks using YAML configuration files or programmatically using Python code. Promptwright integrates with LiteLLM for LLM providers and supports automatic dataset upload to Hugging Face Hub. The library is not responsible for the content generated by models and advises users to review the data before using it in production environments.

The Neo4j GraphRAG package for Python is an official repository that provides features for creating and managing vector indexes in Neo4j databases. It aims to offer developers a reliable package with long-term commitment, maintenance, and fast feature updates. The package supports various Python versions and includes functionalities for creating vector indexes, populating them, and performing similarity searches. It also provides guidelines for installation, examples, and development processes such as installing dependencies, making changes, and running tests.

AgentScheduler is an Automatic/Vladmandic Stable Diffusion Web UI extension designed to enhance image generation workflows. It allows users to enqueue prompts, settings, and controlnets, manage queued tasks, prioritize, pause, resume, and delete tasks, view generation results, and more. The extension offers hidden features like queuing checkpoints, editing queued tasks, and custom checkpoint selection. Users can access the functionality through HTTP APIs and API callbacks. Troubleshooting steps are provided for common errors. The extension is compatible with latest versions of A1111 and Vladmandic. It is licensed under Apache License 2.0.

RAG-Powered Odoo Documentation Assistant is a comprehensive documentation processing and chat system that converts Odoo's documentation to a searchable knowledge base with an AI-powered chat interface. It supports multiple Odoo versions (16.0, 17.0, 18.0) and provides semantic search capabilities powered by OpenAI embeddings. The tool automates the conversion of RST to Markdown, offers real-time semantic search, context-aware AI-powered chat responses, and multi-version support. It includes a Streamlit-based web UI, REST API for programmatic access, and a CLI for document processing and chat. The system operates through a pipeline of data processing steps and an interface layer for UI and API access to the knowledge base.

Preswald is a full-stack platform for building, deploying, and managing interactive data applications in Python. It simplifies the process by combining ingestion, storage, transformation, and visualization into one lightweight SDK. With Preswald, users can connect to various data sources, customize app themes, and easily deploy apps locally. The platform focuses on code-first simplicity, end-to-end coverage, and efficiency by design, making it suitable for prototyping internal tools or deploying production-grade apps with reduced complexity and cost.

otto-m8 is a flowchart based automation platform designed to run deep learning workloads with minimal to no code. It provides a user-friendly interface to spin up a wide range of AI models, including traditional deep learning models and large language models. The tool deploys Docker containers of workflows as APIs for integration with existing workflows, building AI chatbots, or standalone applications. Otto-m8 operates on an Input, Process, Output paradigm, simplifying the process of running AI models into a flowchart-like UI.

LangManus is a community-driven AI automation framework that combines language models with specialized tools for tasks like web search, crawling, and Python code execution. It implements a hierarchical multi-agent system with agents like Coordinator, Planner, Supervisor, Researcher, Coder, Browser, and Reporter. The framework supports LLM integration, search and retrieval tools, Python integration, workflow management, and visualization. LangManus aims to give back to the open-source community and welcomes contributions in various forms.

AutoMathText is an extensive dataset of around 200 GB of mathematical texts autonomously selected by the language model Qwen-72B. It aims to facilitate research in mathematics and artificial intelligence, serve as an educational tool for learning complex mathematical concepts, and provide a foundation for developing AI models specialized in processing mathematical content.

The 'bot-on-anything' repository allows developers to integrate various AI models into messaging applications, enabling the creation of intelligent chatbots. By configuring the connections between models and applications, developers can easily switch between multiple channels within a project. The architecture is highly scalable, allowing the reuse of algorithmic capabilities for each new application and model integration. Supported models include ChatGPT, GPT-3.0, New Bing, and Google Bard, while supported applications range from terminals and web platforms to messaging apps like WeChat, Telegram, QQ, and more. The repository provides detailed instructions for setting up the environment, configuring the models and channels, and running the chatbot for various tasks across different messaging platforms.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

ONNX Runtime Generative AI is a library that provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. Users can call a high level `generate()` method, or run each iteration of the model in a loop. It supports greedy/beam search and TopP, TopK sampling to generate token sequences, has built in logits processing like repetition penalties, and allows for easy custom scoring.

Jupyter AI connects generative AI with Jupyter notebooks. It provides a user-friendly and powerful way to explore generative AI models in notebooks and improve your productivity in JupyterLab and the Jupyter Notebook. Specifically, Jupyter AI offers: * An `%%ai` magic that turns the Jupyter notebook into a reproducible generative AI playground. This works anywhere the IPython kernel runs (JupyterLab, Jupyter Notebook, Google Colab, Kaggle, VSCode, etc.). * A native chat UI in JupyterLab that enables you to work with generative AI as a conversational assistant. * Support for a wide range of generative model providers, including AI21, Anthropic, AWS, Cohere, Gemini, Hugging Face, NVIDIA, and OpenAI. * Local model support through GPT4All, enabling use of generative AI models on consumer grade machines with ease and privacy.

Khoj is an open-source, personal AI assistant that extends your capabilities by creating always-available AI agents. You can share your notes and documents to extend your digital brain, and your AI agents have access to the internet, allowing you to incorporate real-time information. Khoj is accessible on Desktop, Emacs, Obsidian, Web, and Whatsapp, and you can share PDF, markdown, org-mode, notion files, and GitHub repositories. You'll get fast, accurate semantic search on top of your docs, and your agents can create deeply personal images and understand your speech. Khoj is self-hostable and always will be.

LangChain.dart is a Dart port of the popular LangChain Python framework created by Harrison Chase. LangChain provides a set of ready-to-use components for working with language models and a standard interface for chaining them together to formulate more advanced use cases (e.g. chatbots, Q&A with RAG, agents, summarization, extraction, etc.). The components can be grouped into a few core modules: * **Model I/O:** LangChain offers a unified API for interacting with various LLM providers (e.g. OpenAI, Google, Mistral, Ollama, etc.), allowing developers to switch between them with ease. Additionally, it provides tools for managing model inputs (prompt templates and example selectors) and parsing the resulting model outputs (output parsers). * **Retrieval:** assists in loading user data (via document loaders), transforming it (with text splitters), extracting its meaning (using embedding models), storing (in vector stores) and retrieving it (through retrievers) so that it can be used to ground the model's responses (i.e. Retrieval-Augmented Generation or RAG). * **Agents:** "bots" that leverage LLMs to make informed decisions about which available tools (such as web search, calculators, database lookup, etc.) to use to accomplish the designated task. The different components can be composed together using the LangChain Expression Language (LCEL).

Danswer is an open-source Gen-AI Chat and Unified Search tool that connects to your company's docs, apps, and people. It provides a Chat interface and plugs into any LLM of your choice. Danswer can be deployed anywhere and for any scale - on a laptop, on-premise, or to cloud. Since you own the deployment, your user data and chats are fully in your own control. Danswer is MIT licensed and designed to be modular and easily extensible. The system also comes fully ready for production usage with user authentication, role management (admin/basic users), chat persistence, and a UI for configuring Personas (AI Assistants) and their Prompts. Danswer also serves as a Unified Search across all common workplace tools such as Slack, Google Drive, Confluence, etc. By combining LLMs and team specific knowledge, Danswer becomes a subject matter expert for the team. Imagine ChatGPT if it had access to your team's unique knowledge! It enables questions such as "A customer wants feature X, is this already supported?" or "Where's the pull request for feature Y?"

Infinity is an AI-native database designed for LLM applications, providing incredibly fast full-text and vector search capabilities. It supports a wide range of data types, including vectors, full-text, and structured data, and offers a fused search feature that combines multiple embeddings and full text. Infinity is easy to use, with an intuitive Python API and a single-binary architecture that simplifies deployment. It achieves high performance, with 0.1 milliseconds query latency on million-scale vector datasets and up to 15K QPS.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.