AI Hedge Fund is a proof of concept for an AI-powered hedge fund that explores the use of AI to make trading decisions. The project is for educational purposes only and simulates trading decisions without actual trading. It employs agents like Market Data Analyst, Valuation Agent, Sentiment Agent, Fundamentals Agent, Technical Analyst, Risk Manager, and Portfolio Manager to gather and analyze data, calculate risk metrics, and make trading decisions.

Open CUAK (Computer Use Agent) is a platform for managing automation agents at scale, designed to run and manage thousands of automation agents with reliability. It allows for abundant productivity by ensuring scalability and profitability. The project aims to usher in a new era of work with equally distributed productivity, making it open-sourced for real businesses and real people. The core features include running operator-like automation workflows locally, vision-based automation, turning any browser into an operator-companion, utilizing a dedicated remote browser, and more.

Humanoid-Gym is a reinforcement learning framework designed for training locomotion skills for humanoid robots, focusing on zero-shot transfer from simulation to real-world environments. It integrates a sim-to-sim framework from Isaac Gym to Mujoco for verifying trained policies in different physical simulations. The codebase is verified with RobotEra's XBot-S and XBot-L humanoid robots. It offers comprehensive training guidelines, step-by-step configuration instructions, and execution scripts for easy deployment. The sim2sim support allows transferring trained policies to accurate simulated environments. The upcoming features include Denoising World Model Learning and Dexterous Hand Manipulation. Installation and usage guides are provided along with examples for training PPO policies and sim-to-sim transformations. The code structure includes environment and configuration files, with instructions on adding new environments. Troubleshooting tips are provided for common issues, along with a citation and acknowledgment section.

Cosdata is a cutting-edge AI data platform designed to power the next generation search pipelines. It features immutability, version control, and excels in semantic search, structured knowledge graphs, hybrid search capabilities, real-time search at scale, and ML pipeline integration. The platform is customizable, scalable, efficient, enterprise-grade, easy to use, and can manage multi-modal data. It offers high performance, indexing, low latency, and high requests per second. Cosdata is designed to meet the demands of modern search applications, empowering businesses to harness the full potential of their data.

The AI Scientist is a comprehensive system for fully automatic scientific discovery, enabling Foundation Models to perform research independently. It aims to tackle the grand challenge of developing agents capable of conducting scientific research and discovering new knowledge. The tool generates papers on various topics using Large Language Models (LLMs) and provides a platform for exploring new research ideas. Users can create their own templates for specific areas of study and run experiments to generate papers. However, caution is advised as the codebase executes LLM-written code, which may pose risks such as the use of potentially dangerous packages and web access.

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.

LLM-on-Ray is a comprehensive solution for building, customizing, and deploying Large Language Models (LLMs). It simplifies complex processes into manageable steps by leveraging the power of Ray for distributed computing. The tool supports pretraining, finetuning, and serving LLMs across various hardware setups, incorporating industry and Intel optimizations for performance. It offers modular workflows with intuitive configurations, robust fault tolerance, and scalability. Additionally, it provides an Interactive Web UI for enhanced usability, including a chatbot application for testing and refining models.

Polymarket Agents is a developer framework and set of utilities for building AI agents to trade autonomously on Polymarket. It integrates with Polymarket API, provides AI agent utilities for prediction markets, supports local and remote RAG, sources data from various services, and offers comprehensive LLM tools for prompt engineering. The architecture features modular components like APIs and scripts for managing local environments, server set-up, and CLI for end-user commands.

Arch is an intelligent Layer 7 gateway designed to protect, observe, and personalize AI agents with APIs. It handles tasks related to prompts, including detecting jailbreak attempts, calling backend APIs, routing between LLMs, and managing observability. Built on Envoy Proxy, it offers features like function calling, prompt guardrails, traffic management, and observability. Users can build fast, observable, and personalized AI agents using Arch to improve speed, security, and personalization of GenAI apps.

VerifyWise is an open-source AI governance platform designed to help businesses harness the power of AI safely and responsibly. The platform ensures compliance and robust AI management without compromising on security. It offers additional products like MaskWise for data redaction, EvalWise for AI model evaluation, and FlagWise for security threat monitoring. VerifyWise simplifies AI governance for organizations, aiding in risk management, regulatory compliance, and promoting responsible AI practices. It features options for on-premises or private cloud hosting, open-source with AGPLv3 license, AI-generated answers for compliance audits, source code transparency, Docker deployment, user registration, role-based access control, and various AI governance tools like risk management, bias & fairness checks, evidence center, AI trust center, and more.

TokenFormer is a fully attention-based neural network architecture that leverages tokenized model parameters to enhance architectural flexibility. It aims to maximize the flexibility of neural networks by unifying token-token and token-parameter interactions through the attention mechanism. The architecture allows for incremental model scaling and has shown promising results in language modeling and visual modeling tasks. The codebase is clean, concise, easily readable, state-of-the-art, and relies on minimal dependencies.

CodiumAI Cover Agent is a tool designed to help increase code coverage by automatically generating qualified tests to enhance existing test suites. It utilizes Generative AI to streamline development workflows and is part of a suite of utilities aimed at automating the creation of unit tests for software projects. The system includes components like Test Runner, Coverage Parser, Prompt Builder, and AI Caller to simplify and expedite the testing process, ensuring high-quality software development. Cover Agent can be run via a terminal and is planned to be integrated into popular CI platforms. The tool outputs debug files locally, such as generated_prompt.md, run.log, and test_results.html, providing detailed information on generated tests and their status. It supports multiple LLMs and allows users to specify the model to use for test generation.

llm-compressor is an easy-to-use library for optimizing models for deployment with vllm. It provides a comprehensive set of quantization algorithms, seamless integration with Hugging Face models and repositories, and supports mixed precision, activation quantization, and sparsity. Supported algorithms include PTQ, GPTQ, SmoothQuant, and SparseGPT. Installation can be done via git clone and local pip install. Compression can be easily applied by selecting an algorithm and calling the oneshot API. The library also offers end-to-end examples for model compression. Contributions to the code, examples, integrations, and documentation are appreciated.

Upsonic offers a cutting-edge enterprise-ready framework for orchestrating LLM calls, agents, and computer use to complete tasks cost-effectively. It provides reliable systems, scalability, and a task-oriented structure for real-world cases. Key features include production-ready scalability, task-centric design, MCP server support, tool-calling server, computer use integration, and easy addition of custom tools. The framework supports client-server architecture and allows seamless deployment on AWS, GCP, or locally using Docker.

OpenAdapt is an open-source software adapter between Large Multimodal Models (LMMs) and traditional desktop and web Graphical User Interfaces (GUIs). It aims to automate repetitive GUI workflows by leveraging the power of LMMs. OpenAdapt records user input and screenshots, converts them into tokenized format, and generates synthetic input via transformer model completions. It also analyzes recordings to generate task trees and replay synthetic input to complete tasks. OpenAdapt is model agnostic and generates prompts automatically by learning from human demonstration, ensuring that agents are grounded in existing processes and mitigating hallucinations. It works with all types of desktop GUIs, including virtualized and web, and is open source under the MIT license.

PSAI is a PowerShell module that empowers scripts with the intelligence of OpenAI, bridging the gap between PowerShell and AI. It enables seamless integration for tasks like file searches and data analysis, revolutionizing automation possibilities with just a few lines of code. The module supports the latest OpenAI API changes, offering features like improved file search, vector store objects, token usage control, message limits, tool choice parameter, custom conversation histories, and model configuration parameters.

FinRobot is an open-source AI agent platform designed for financial applications using large language models. It transcends the scope of FinGPT, offering a comprehensive solution that integrates a diverse array of AI technologies. The platform's versatility and adaptability cater to the multifaceted needs of the financial industry. FinRobot's ecosystem is organized into four layers, including Financial AI Agents Layer, Financial LLMs Algorithms Layer, LLMOps and DataOps Layers, and Multi-source LLM Foundation Models Layer. The platform's agent workflow involves Perception, Brain, and Action modules to capture, process, and execute financial data and insights. The Smart Scheduler optimizes model diversity and selection for tasks, managed by components like Director Agent, Agent Registration, Agent Adaptor, and Task Manager. The tool provides a structured file organization with subfolders for agents, data sources, and functional modules, along with installation instructions and hands-on tutorials.

AirdropsBot2024 is an efficient and secure solution for automated trading and sniping of coins on the Solana blockchain. It supports multiple chain networks such as Solana, BTC, and Ethereum. The bot utilizes premium APIs and Chromedriver to automate trading operations through web interfaces of popular exchanges. It offers high-speed data analysis, in-depth market analysis, support for major exchanges, complete security and control, data visualization, advanced notification options, flexibility and adaptability in trading strategies, and profile management.

The gpt-bitcoin repository is focused on creating an automated trading system for Bitcoin using GPT AI technology. It provides different versions of trading strategies utilizing various data sources such as OHLCV, Moving Averages, RSI, Stochastic Oscillator, MACD, Bollinger Bands, Orderbook Data, news data, fear/greed index, and chart images. Users can set up the system by creating a .env file with necessary API keys and installing required dependencies. The repository also includes instructions for setting up the environment on local machines and AWS EC2 Ubuntu servers. The future plan includes expanding the system to support other cryptocurrency exchanges like Bithumb, Binance, Coinbase, OKX, and Bybit.

AI Hedge Fund is a proof of concept for an AI-powered hedge fund that explores the use of AI to make trading decisions. The project is for educational purposes only and simulates trading decisions without actual trading. It employs agents like Market Data Analyst, Valuation Agent, Sentiment Agent, Fundamentals Agent, Technical Analyst, Risk Manager, and Portfolio Manager to gather and analyze data, calculate risk metrics, and make trading decisions.

PredictorLLM is an advanced trading agent framework that utilizes large language models to automate trading in financial markets. It includes a profiling module to establish agent characteristics, a layered memory module for retaining and prioritizing financial data, and a decision-making module to convert insights into trading strategies. The framework mimics professional traders' behavior, surpassing human limitations in data processing and continuously evolving to adapt to market conditions for superior investment outcomes.

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.