OpenVoiceChat

OpenVoiceChat is an open-source tool designed for having natural voice conversations with an LLM model. It supports various speech-to-text (STT), text-to-speech (TTS), and large language model (LLM) models. The tool aims to provide an alternative to closed commercial implementations, with well-abstracted APIs that are easy to use and extend. Users can install base and functionality-specific packages using pip, and the tool supports interruptions during conversations. The project encourages contributions through bounties and has a detailed roadmap available for reference.

Agent Development Kit (ADK) is an open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control. It is a flexible and modular framework optimized for Gemini and the Google ecosystem, but also compatible with other frameworks. ADK aims to make agent development feel more like software development, enabling developers to create, deploy, and orchestrate agentic architectures ranging from simple tasks to complex workflows.

Zep is a long-term memory service for AI Assistant apps. With Zep, you can provide AI assistants with the ability to recall past conversations, no matter how distant, while also reducing hallucinations, latency, and cost. Zep persists and recalls chat histories, and automatically generates summaries and other artifacts from these chat histories. It also embeds messages and summaries, enabling you to search Zep for relevant context from past conversations. Zep does all of this asyncronously, ensuring these operations don't impact your user's chat experience. Data is persisted to database, allowing you to scale out when growth demands. Zep also provides a simple, easy to use abstraction for document vector search called Document Collections. This is designed to complement Zep's core memory features, but is not designed to be a general purpose vector database. Zep allows you to be more intentional about constructing your prompt: 1. automatically adding a few recent messages, with the number customized for your app; 2. a summary of recent conversations prior to the messages above; 3. and/or contextually relevant summaries or messages surfaced from the entire chat session. 4. and/or relevant Business data from Zep Document Collections.

Studio B3 (B-3 Bomber) is a sophisticated editor designed for content creation, catering to various formats such as blogs, articles, user stories, and more. It provides an immersive content generation experience with local AI capabilities for intelligent search and recommendation functions. Users can define custom actions and variables for flexible content generation. The editor includes interactive tools like Bubble Menu, Slash Command, and Quick Insert for enhanced user experience in editing, searching, and navigation. The design principles focus on intelligent embedding of AI, local optimization for efficient writing experience, and context flexibility for better control over AI-generated content.

BitMat is a Python package designed to optimize matrix multiplication operations by utilizing custom kernels written in Triton. It leverages the principles outlined in the "1bit-LLM Era" paper, specifically utilizing packed int8 data to enhance computational efficiency and performance in deep learning and numerical computing tasks.

KAG is a logical reasoning and Q&A framework based on the OpenSPG engine and large language models. It is used to build logical reasoning and Q&A solutions for vertical domain knowledge bases. KAG supports logical reasoning, multi-hop fact Q&A, and integrates knowledge and chunk mutual indexing structure, conceptual semantic reasoning, schema-constrained knowledge construction, and logical form-guided hybrid reasoning and retrieval. The framework includes kg-builder for knowledge representation and kg-solver for logical symbol-guided hybrid solving and reasoning engine. KAG aims to enhance LLM service framework in professional domains by integrating logical and factual characteristics of KGs.

Woodpecker is a tool designed to correct hallucinations in Multimodal Large Language Models (MLLMs) by introducing a training-free method that picks out and corrects inconsistencies between generated text and image content. It consists of five stages: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction. Woodpecker can be easily integrated with different MLLMs and provides interpretable results by accessing intermediate outputs of the stages. The tool has shown significant improvements in accuracy over baseline models like MiniGPT-4 and mPLUG-Owl.

TapeAgents is a framework that leverages a structured, replayable log of the agent session to facilitate all stages of the LLM Agent development lifecycle. The agent reasons by processing the tape and the LLM output to produce new thoughts, actions, control flow steps, and append them to the tape. Key features include building agents as low-level state machines or high-level multi-agent team configurations, debugging agents with TapeAgent studio or TapeBrowser apps, serving agents with response streaming, and optimizing agent configurations using successful tapes. The Tape-centric design of TapeAgents provides ultimate flexibility in project development, allowing access to tapes for making prompts, generating next steps, and controlling agent behavior.

Craftium is an open-source platform based on the Minetest voxel game engine and the Gymnasium and PettingZoo APIs, designed for creating fast, rich, and diverse single and multi-agent environments. It allows for connecting to Craftium's Python process, executing actions as keyboard and mouse controls, extending the Lua API for creating RL environments and tasks, and supporting client/server synchronization for slow agents. Craftium is fully extensible, extensively documented, modern RL API compatible, fully open source, and eliminates the need for Java. It offers a variety of environments for research and development in reinforcement learning.

EasyDist is an automated parallelization system and infrastructure designed for multiple ecosystems. It offers usability by making parallelizing training or inference code effortless with just a single line of change. It ensures ecological compatibility by serving as a centralized source of truth for SPMD rules at the operator-level for various machine learning frameworks. EasyDist decouples auto-parallel algorithms from specific frameworks and IRs, allowing for the development and benchmarking of different auto-parallel algorithms in a flexible manner. The architecture includes MetaOp, MetaIR, and the ShardCombine Algorithm for SPMD sharding rules without manual annotations.

POML (Prompt Orchestration Markup Language) is a novel markup language designed to bring structure, maintainability, and versatility to advanced prompt engineering for Large Language Models (LLMs). It addresses common challenges in prompt development, such as lack of structure, complex data integration, format sensitivity, and inadequate tooling. POML provides a systematic way to organize prompt components, integrate diverse data types seamlessly, and manage presentation variations, empowering developers to create more sophisticated and reliable LLM applications.

searchGPT is an open-source project that aims to build a search engine based on Large Language Model (LLM) technology to provide natural language answers. It supports web search with real-time results, file content search, and semantic search from sources like the Internet. The tool integrates LLM technologies such as OpenAI and GooseAI, and offers an easy-to-use frontend user interface. The project is designed to provide grounded answers by referencing real-time factual information, addressing the limitations of LLM's training data. Contributions, especially from frontend developers, are welcome under the MIT License.

The Codebase Context Specification (CCS) project aims to standardize embedding contextual information within codebases to enhance understanding for both AI and human developers. It introduces a convention similar to `.env` and `.editorconfig` files but focused on documenting code for both AI and humans. By providing structured contextual metadata, collaborative documentation guidelines, and standardized context files, developers can improve code comprehension, collaboration, and development efficiency. The project includes a linter for validating context files and provides guidelines for using the specification with AI assistants. Tooling recommendations suggest creating memory systems, IDE plugins, AI model integrations, and agents for context creation and utilization. Future directions include integration with existing documentation systems, dynamic context generation, and support for explicit context overriding.

AI Edge Torch is a Python library that supports converting PyTorch models into a .tflite format for on-device applications on Android, iOS, and IoT devices. It offers broad CPU coverage with initial GPU and NPU support, closely integrating with PyTorch and providing good coverage of Core ATen operators. The library includes a PyTorch converter for model conversion and a Generative API for authoring mobile-optimized PyTorch Transformer models, enabling easy deployment of Large Language Models (LLMs) on mobile devices.

Transtation is an easy-to-use and powerful translation software for Android/Desktop based on Kotlin Multiplatform + Compose Multiplatform. It allows users to translate one item using multiple engines simultaneously, utilize advanced Large Language Models for translation, chat with LLMs for translation, translate long text, support plugin development, image translation, and screen translation. The application is designed for Chinese users and serves as a reference for learning Jetpack Compose or Compose Multiplatform. It features Kotlin Multiplatform, Compose Multiplatform, MVVM, Kotlin Coroutine, Flow, SqlDelight, synchronized translation with multiple engines, plugin development, and makes use of Kotlin language features like lazy loading, Coroutine, sealed classes, and reflection. The application gradually adapts to Android13 with features like setting application language separately and supporting Monet icon.

The AI Fairness 360 toolkit is an open-source library designed to detect and mitigate bias in machine learning models. It provides a comprehensive set of metrics, explanations, and algorithms for bias mitigation in various domains such as finance, healthcare, and education. The toolkit supports multiple bias mitigation algorithms and fairness metrics, and is available in both Python and R. Users can leverage the toolkit to ensure fairness in AI applications and contribute to its development for extensibility.

SakuraLLM is a project focused on building large language models for Japanese to Chinese translation in the light novel and galgame domain. The models are based on open-source large models and are pre-trained and fine-tuned on general Japanese corpora and specific domains. The project aims to provide high-performance language models for galgame/light novel translation that are comparable to GPT3.5 and can be used offline. It also offers an API backend for running the models, compatible with the OpenAI API format. The project is experimental, with version 0.9 showing improvements in style, fluency, and accuracy over GPT-3.5.

OpenVoiceChat is an open-source tool designed for having natural voice conversations with an LLM model. It supports various speech-to-text (STT), text-to-speech (TTS), and large language model (LLM) models. The tool aims to provide an alternative to closed commercial implementations, with well-abstracted APIs that are easy to use and extend. Users can install base and functionality-specific packages using pip, and the tool supports interruptions during conversations. The project encourages contributions through bounties and has a detailed roadmap available for reference.

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.

Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

LoLLMs Server is a text generation server based on large language models. It provides a Flask-based API for generating text using various pre-trained language models. This server is designed to be easy to install and use, allowing developers to integrate powerful text generation capabilities into their applications.

LlamaIndex.TS is a data framework for your LLM application. Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.

Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code. What makes Semantic Kernel _special_ , however, is its ability to _automatically_ orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.

Botpress is a platform for building next-generation chatbots and assistants powered by OpenAI. It provides a range of tools and integrations to help developers quickly and easily create and deploy chatbots for various use cases.

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.

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.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.