LionAGI is a powerful intelligent workflow automation framework that introduces advanced ML models into any existing workflows and data infrastructure. It can interact with almost any model, run interactions in parallel for most models, produce structured pydantic outputs with flexible usage, automate workflow via graph based agents, use advanced prompting techniques, and more. LionAGI aims to provide a centralized agent-managed framework for "ML-powered tools coordination" and to dramatically lower the barrier of entries for creating use-case/domain specific tools. It is designed to be asynchronous only and requires Python 3.10 or higher.

Instructor is a popular Python library for managing structured outputs from large language models (LLMs). It offers a user-friendly API for validation, retries, and streaming responses. With support for various LLM providers and multiple languages, Instructor simplifies working with LLM outputs. The library includes features like response models, retry management, validation, streaming support, and flexible backends. It also provides hooks for logging and monitoring LLM interactions, and supports integration with Anthropic, Cohere, Gemini, Litellm, and Google AI models. Instructor facilitates tasks such as extracting user data from natural language, creating fine-tuned models, managing uploaded files, and monitoring usage of OpenAI models.

The Scale AI Python SDK is a tool that provides a Python interface for interacting with the Scale API. It allows users to easily create tasks, manage projects, upload files, and work with evaluation tasks, training tasks, and Studio assignments. The SDK handles error handling and provides detailed documentation for each method. Users can also manage teammates, project groups, and batches within the Scale Studio environment. The SDK supports various functionalities such as creating tasks, retrieving tasks, canceling tasks, auditing tasks, updating task attributes, managing files, managing team members, and working with evaluation and training tasks.

Sparkle is a tool that streamlines the process of building AI-driven features in applications using Large Language Models (LLMs). It guides users through creating and managing agents, defining tools, and interacting with LLM providers like OpenAI. Sparkle allows customization of LLM provider settings, model configurations, and provides a seamless integration with Sparkle Server for exposing agents via an OpenAI-compatible chat API endpoint.

Flow Prompt is a dynamic library for managing and optimizing prompts for large language models. It facilitates budget-aware operations, dynamic data integration, and efficient load distribution. Features include CI/CD testing, dynamic prompt development, multi-model support, real-time insights, and prompt testing and evolution.

Fastc is a tool focused on CPU execution, using efficient models for embedding generation and cosine similarity classification. It allows for efficient multi-classifier execution without extra overhead. Users can easily train text classifiers, export models, publish to HuggingFace, load existing models, make class predictions, use instruct templates, and launch an inference server. The tool provides an HTTP API for text classification with JSON payloads and supports multiple languages for language identification.

The Python Redis Vector Library (RedisVL) is a tailor-made client for AI applications leveraging Redis. It enhances applications with Redis' speed, flexibility, and reliability, incorporating capabilities like vector-based semantic search, full-text search, and geo-spatial search. The library bridges the gap between the emerging AI-native developer ecosystem and the capabilities of Redis by providing a lightweight, elegant, and intuitive interface. It abstracts the features of Redis into a grammar that is more aligned to the needs of today's AI/ML Engineers or Data Scientists.

Hezar is an all-in-one AI library designed specifically for the Persian community. It brings together various AI models and tools, making it easy to use AI with just a few lines of code. The library seamlessly integrates with Hugging Face Hub, offering a developer-friendly interface and task-based model interface. In addition to models, Hezar provides tools like word embeddings, tokenizers, feature extractors, and more. It also includes supplementary ML tools for deployment, benchmarking, and optimization.

AwaDB is an AI native database designed for embedding vectors. It simplifies database usage by eliminating the need for schema definition and manual indexing. The system ensures real-time search capabilities with millisecond-level latency. Built on 5 years of production experience with Vearch, AwaDB incorporates best practices from the community to offer stability and efficiency. Users can easily add and search for embedded sentences using the provided client libraries or RESTful API.

This repository contains the code and dataset for LDB, a novel debugging framework that enables Large Language Models (LLMs) to refine their generated programs by tracking the values of intermediate variables throughout the runtime execution. LDB segments programs into basic blocks, allowing LLMs to concentrate on simpler code units, verify correctness block by block, and pinpoint errors efficiently. The tool provides APIs for debugging and generating code with debugging messages, mimicking how human developers debug programs.

LLMClient is a JavaScript/TypeScript library that simplifies working with large language models (LLMs) by providing an easy-to-use interface for building and composing efficient prompts using prompt signatures. These signatures enable the automatic generation of typed prompts, allowing developers to leverage advanced capabilities like reasoning, function calling, RAG, ReAcT, and Chain of Thought. The library supports various LLMs and vector databases, making it a versatile tool for a wide range of applications.

Instructor is a Python library that makes it a breeze to work with structured outputs from large language models (LLMs). Built on top of Pydantic, it provides a simple, transparent, and user-friendly API to manage validation, retries, and streaming responses. Get ready to supercharge your LLM workflows!

SynCode is a novel framework for the grammar-guided generation of Large Language Models (LLMs) that ensures syntactically valid output with respect to defined Context-Free Grammar (CFG) rules. It supports general-purpose programming languages like Python, Go, SQL, JSON, and more, allowing users to define custom grammars using EBNF syntax. The tool compares favorably to other constrained decoders and offers features like fast grammar-guided generation, compatibility with HuggingFace Language Models, and the ability to work with various decoding strategies.

SenseVoice is a speech foundation model focusing on high-accuracy multilingual speech recognition, speech emotion recognition, and audio event detection. Trained with over 400,000 hours of data, it supports more than 50 languages and excels in emotion recognition and sound event detection. The model offers efficient inference with low latency and convenient finetuning scripts. It can be deployed for service with support for multiple client-side languages. SenseVoice-Small model is open-sourced and provides capabilities for Mandarin, Cantonese, English, Japanese, and Korean. The tool also includes features for natural speech generation and fundamental speech recognition tasks.

Redis Vector Library (RedisVL) is a Python client library for building AI applications on top of Redis. It provides a high-level interface for managing vector indexes, performing vector search, and integrating with popular embedding models and providers. RedisVL is designed to make it easy for developers to build and deploy AI applications that leverage the speed, flexibility, and reliability of Redis.

Parea AI provides a SDK to evaluate & monitor AI applications. It allows users to test, evaluate, and monitor their AI models by defining and running experiments. The SDK also enables logging and observability for AI applications, as well as deploying prompts to facilitate collaboration between engineers and subject-matter experts. Users can automatically log calls to OpenAI and Anthropic, create hierarchical traces of their applications, and deploy prompts for integration into their applications.

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.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

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.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.