Sentience is a tool that allows developers to create autonomous AI agents on-chain with verifiable proofs. It leverages a Trusted Execution Environment (TEE) architecture to ensure secure execution of AI calls and provides transparency through cryptographic attestations posted on Solana's blockchain. The tool enhances market potential by transforming agents into cryptographically verifiable entities, addressing the need for trust in AI development. Sentience offers features like OpenAI compatibility, on-chain verifiability, an explorer for agent history, and an easy-to-use developer experience. The repository includes SDKs for Python and JavaScript, along with components for verified inference and instructions for verifying the TEE architecture.

OpenDAN is an open source Personal AI OS that consolidates various AI modules for personal use. It empowers users to create powerful AI agents like assistants, tutors, and companions. The OS allows agents to collaborate, integrate with services, and control smart devices. OpenDAN offers features like rapid installation, AI agent customization, connectivity via Telegram/Email, building a local knowledge base, distributed AI computing, and more. It aims to simplify life by putting AI in users' hands. The project is in early stages with ongoing development and future plans for user and kernel mode separation, home IoT device control, and an official OpenDAN SDK release.

AutoGPT is a revolutionary tool that empowers everyone to harness the power of AI. With AutoGPT, you can effortlessly build, test, and delegate tasks to AI agents, unlocking a world of possibilities. Our mission is to provide the tools you need to focus on what truly matters: innovation and creativity.

Mastra is an opinionated Typescript framework designed to help users quickly build AI applications and features. It provides primitives such as workflows, agents, RAG, integrations, syncs, and evals. Users can run Mastra locally or deploy it to a serverless cloud. The framework supports various LLM providers, offers tools for building language models, workflows, and accessing knowledge bases. It includes features like durable graph-based state machines, retrieval-augmented generation, integrations, syncs, and automated tests for evaluating LLM outputs.

Neo.mjs is a revolutionary Application Engine for the web that offers true multithreading and context engineering, enabling desktop-class UI performance and AI-driven runtime mutation. It is not a framework but a complete runtime and toolchain for enterprise applications, excelling in single page apps and browser-based multi-window applications. With a pioneering Off-Main-Thread architecture, Neo.mjs ensures butter-smooth UI performance by keeping the main thread free for flawless user interactions. The latest version, v11, introduces AI-native capabilities, allowing developers to work with AI agents as first-class partners in the development process. The platform offers a suite of dedicated Model Context Protocol servers that give agents the context they need to understand, build, and reason about the code, enabling a new level of human-AI collaboration.

This repository contains the official codebase for Bittensor Subnet 1 (SN1) v1.0.0+, released on 22nd January 2024. It defines an incentive mechanism to create a distributed conversational AI for Subnet 1. Validators and miners are based on large language models (LLM) using internet-scale datasets and goal-driven behavior to drive human-like conversations. The repository requires python3.9 or higher and provides compute requirements for running validators and miners. Users can run miners or validators using specific commands and are encouraged to run on the testnet before deploying on the main network. The repository also highlights limitations and provides resources for understanding the architecture and methodology of SN1.

Eternal AI is an open source AI protocol for fully onchain agents, enabling developers to create various types of onchain AI agents without middlemen. It operates on a decentralized infrastructure with state-of-the-art models and omnichain interoperability. The protocol architecture includes components like ai-kernel, decentralized-agents, decentralized-inference, decentralized-compute, agent-as-a-service, and agent-studio. Ongoing research projects include cuda-evm, nft-ai, and physical-ai. The system requires Node.js, npm, Docker Desktop, Go, and Ollama for installation. The 'eai' CLI tool allows users to create agents, fetch agent info, list agents, and chat with agents. Design principles focus on decentralization, trustlessness, production-grade quality, and unified agent interface. Featured integrations can be quickly implemented, and governance will be overseen by EAI holders in the future.

EdgeChains is an open-source chain-of-thought engineering framework tailored for Large Language Models (LLMs)- like OpenAI GPT, LLama2, Falcon, etc. - With a focus on enterprise-grade deployability and scalability. EdgeChains is specifically designed to **orchestrate** such applications. At EdgeChains, we take a unique approach to Generative AI - we think Generative AI is a deployment and configuration management challenge rather than a UI and library design pattern challenge. We build on top of a tech that has solved this problem in a different domain - Kubernetes Config Management - and bring that to Generative AI. Edgechains is built on top of jsonnet, originally built by Google based on their experience managing a vast amount of configuration code in the Borg infrastructure.

crewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It provides a flexible and structured approach to AI collaboration, enabling users to define agents with specific roles, goals, and tools, and assign them tasks within a customizable process. crewAI supports integration with various LLMs, including OpenAI, and offers features such as autonomous task delegation, flexible task management, and output parsing. It is open-source and welcomes contributions, with a focus on improving the library based on usage data collected through anonymous telemetry.

AutoGroq is a revolutionary tool that dynamically generates tailored teams of AI agents based on project requirements, eliminating manual configuration. It enables users to effortlessly tackle questions, problems, and projects by creating expert agents, workflows, and skillsets with ease and efficiency. With features like natural conversation flow, code snippet extraction, and support for multiple language models, AutoGroq offers a seamless and intuitive AI assistant experience for developers and users.

Rakis is a decentralized verifiable AI network in the browser where nodes can accept AI inference requests, run local models, verify results, and arrive at consensus without servers. It is open-source, functional, multi-model, multi-chain, and browser-first, allowing anyone to participate in the network. The project implements an embedding-based consensus mechanism for verifiable inference. Users can run their own node on rakis.ai or use the compiled version hosted on Huggingface. The project is meant for educational purposes and is a work in progress.

DeerFlow is a community-driven Deep Research framework that combines language models with specialized tools for tasks like web search, crawling, and Python code execution. It supports FaaS deployment and one-click deployment based on Volcengine. The framework includes core capabilities like LLM integration, search and retrieval, RAG integration, MCP seamless integration, human collaboration, report post-editing, and content creation. The architecture is based on a modular multi-agent system with components like Coordinator, Planner, Research Team, and Text-to-Speech integration. DeerFlow also supports interactive mode, human-in-the-loop mechanism, and command-line arguments for customization.

The Artificial Intelligence Incident Database (AIID) is a collection of incidents involving the development and use of artificial intelligence (AI). The database is designed to help researchers, policymakers, and the public understand the potential risks and benefits of AI, and to inform the development of policies and practices to mitigate the risks and promote the benefits of AI. The AIID is a collaborative project involving researchers from the University of California, Berkeley, the University of Washington, and the University of Toronto.

Aide is an Open Source AI-native code editor that combines the powerful features of VS Code with advanced AI capabilities. It provides a combined chat + edit flow, proactive agents for fixing errors, inline editing widget, intelligent code completion, and AST navigation. Aide is designed to be an intelligent coding companion, helping users write better code faster while maintaining control over the development process.

CrewAI is a cutting-edge framework designed to orchestrate role-playing autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It enables AI agents to assume roles, share goals, and operate in a cohesive unit, much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions. With features like role-based agent design, autonomous inter-agent delegation, flexible task management, and support for various LLMs, CrewAI offers a dynamic and adaptable solution for both development and production workflows.

Vikit.ai SDK is a software development kit that enables easy development of video generators using generative AI and other AI models. It serves as a langchain to orchestrate AI models and video editing tools. The SDK allows users to create videos from text prompts with background music and voice-over narration. It also supports generating composite videos from multiple text prompts. The tool requires Python 3.8+, specific dependencies, and tools like FFMPEG and ImageMagick for certain functionalities. Users can contribute to the project by following the contribution guidelines and standards provided.

A curated list of Machine Learning (ML) and Generative AI (GenAI) packages and resources for the Elixir programming language. It includes core tools for data exploration, traditional machine learning algorithms, deep learning models, computer vision libraries, generative AI tools, livebooks for interactive notebooks, and various resources such as books, videos, and articles. The repository aims to provide a comprehensive overview for experienced Elixir developers and ML/AI practitioners exploring different ecosystems.

Sentience is a tool that allows developers to create autonomous AI agents on-chain with verifiable proofs. It leverages a Trusted Execution Environment (TEE) architecture to ensure secure execution of AI calls and provides transparency through cryptographic attestations posted on Solana's blockchain. The tool enhances market potential by transforming agents into cryptographically verifiable entities, addressing the need for trust in AI development. Sentience offers features like OpenAI compatibility, on-chain verifiability, an explorer for agent history, and an easy-to-use developer experience. The repository includes SDKs for Python and JavaScript, along with components for verified inference and instructions for verifying the TEE architecture.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.