LLM-TradeBot is an Intelligent Multi-Agent Quantitative Trading Bot based on the Adversarial Decision Framework (ADF). It achieves high win rates and low drawdown in automated futures trading through market regime detection, price position awareness, dynamic score calibration, and multi-layer physical auditing. The bot prioritizes judging 'IF we should trade' before deciding 'HOW to trade' and offers features like multi-agent collaboration, agent configuration, agent chatroom, AUTO1 symbol selection, multi-LLM support, multi-account trading, async concurrency, CLI headless mode, test/live mode toggle, safety mechanisms, and full-link auditing. The system architecture includes a multi-agent architecture with various agents responsible for different tasks, a four-layer strategy filter, and detailed data flow diagrams. The bot also supports backtesting, full-link data auditing, and safety warnings for users.

Lynkr is a self-hosted proxy server that unlocks various AI coding tools like Claude Code CLI, Cursor IDE, and Codex Cli. It supports multiple LLM providers such as Databricks, AWS Bedrock, OpenRouter, Ollama, llama.cpp, Azure OpenAI, Azure Anthropic, OpenAI, and LM Studio. Lynkr offers cost reduction, local/private execution, remote or local connectivity, zero code changes, and enterprise-ready features. It is perfect for developers needing provider flexibility, cost control, self-hosted AI with observability, local model execution, and cost reduction strategies.

Pluely is a versatile and user-friendly tool for managing tasks and projects. It provides a simple interface for creating, organizing, and tracking tasks, making it easy to stay on top of your work. With features like task prioritization, due date reminders, and collaboration options, Pluely helps individuals and teams streamline their workflow and boost productivity. Whether you're a student juggling assignments, a professional managing multiple projects, or a team coordinating tasks, Pluely is the perfect solution to keep you organized and efficient.

Legacy Modernization Agents is an open source migration framework developed to demonstrate AI Agents capabilities for converting legacy COBOL code to Java or C# .NET. The framework uses Microsoft Agent Framework with a dual-API architecture to analyze COBOL code and dependencies, then convert to either Java Quarkus or C# .NET. The web portal provides real-time visualization of migration progress, dependency graphs, and AI-powered Q&A.

Tandem is a local-first, privacy-focused AI workspace that runs entirely on your machine. It is inspired by early AI coworking research previews, open source, and provider-agnostic. Tandem offers privacy-first operation, provider agnosticism, zero trust model, true cross-platform support, open-source licensing, modern stack, and developer superpowers for everyone. It provides folder-wide intelligence, multi-step automation, visual change review, complete undo, zero telemetry, provider freedom, secure design, cross-platform support, visual permissions, full undo, long-term memory, skills system, document text extraction, workspace Python venv, rich themes, execution planning, auto-updates, multiple specialized agent modes, multi-agent orchestration, project management, and various artifacts and outputs.

General Bots is a self-hosted AI automation platform and LLM conversational platform focused on convention over configuration and code-less approaches. It serves as the core API server handling LLM orchestration, business logic, database operations, and multi-channel communication. The platform offers features like multi-vendor LLM API, MCP + LLM Tools Generation, Semantic Caching, Web Automation Engine, Enterprise Data Connectors, and Git-like Version Control. It enforces a ZERO TOLERANCE POLICY for code quality and security, with strict guidelines for error handling, performance optimization, and code patterns. The project structure includes modules for core functionalities like Rhai BASIC interpreter, security, shared types, tasks, auto task system, file operations, learning system, and LLM assistance.

The MCP Memory Service is a universal memory service designed for AI assistants, providing semantic memory search and persistent storage. It works with various AI applications and offers fast local search using SQLite-vec and global distribution through Cloudflare. The service supports intelligent memory management, universal compatibility with AI tools, flexible storage options, and is production-ready with cross-platform support and secure connections. Users can store and recall memories, search by tags, check system health, and configure the service for Claude Desktop integration and environment variables.

XLN (Cross-Local Network) is a platform that enables instant off-chain settlement with on-chain finality. It combines Byzantine consensus, Bloomberg Terminal functionalities, and VR capabilities to run economic simulations in the browser without the need for a backend. The architecture includes layers for jurisdictions, entities, and accounts, with features like Solidity contracts, BFT consensus, and bilateral channels. The tool offers a panel system similar to Bloomberg Terminal for workspace organization and visualization, along with support for offline blockchain simulations in the browser and VR/Quest compatibility.

KISS AI is a lightweight and powerful multi-agent evolutionary framework that simplifies building AI agents. It uses native function calling for efficiency and accuracy, making building AI agents as straightforward as possible. The framework includes features like multi-agent orchestration, agent evolution and optimization, relentless coding agent for long-running tasks, output formatting, trajectory saving and visualization, GEPA for prompt optimization, KISSEvolve for algorithm discovery, self-evolving multi-agent, Docker integration, multiprocessing support, and support for various models from OpenAI, Anthropic, Gemini, Together AI, and OpenRouter.

AutoAgents is a cutting-edge multi-agent framework built in Rust that enables the creation of intelligent, autonomous agents powered by Large Language Models (LLMs) and Ractor. Designed for performance, safety, and scalability. AutoAgents provides a robust foundation for building complex AI systems that can reason, act, and collaborate. With AutoAgents you can create Cloud Native Agents, Edge Native Agents and Hybrid Models as well. It is so extensible that other ML Models can be used to create complex pipelines using Actor Framework.

Callytics is an advanced call analytics solution that leverages speech recognition and large language models (LLMs) technologies to analyze phone conversations from customer service and call centers. By processing both the audio and text of each call, it provides insights such as sentiment analysis, topic detection, conflict detection, profanity word detection, and summary. These cutting-edge techniques help businesses optimize customer interactions, identify areas for improvement, and enhance overall service quality. When an audio file is placed in the .data/input directory, the entire pipeline automatically starts running, and the resulting data is inserted into the database. This is only a v1.1.0 version; many new features will be added, models will be fine-tuned or trained from scratch, and various optimization efforts will be applied.

GitVizz is an AI-powered repository analysis tool that helps developers understand and navigate codebases quickly. It transforms complex code structures into interactive documentation, dependency graphs, and intelligent conversations. With features like interactive dependency graphs, AI-powered code conversations, advanced code visualization, and automatic documentation generation, GitVizz offers instant understanding and insights for any repository. The tool is built with modern technologies like Next.js, FastAPI, and OpenAI, making it scalable and efficient for analyzing large codebases. GitVizz also provides a standalone Python library for core code analysis and dependency graph generation, offering multi-language parsing, AST analysis, dependency graphs, visualizations, and extensibility for custom applications.

OpenOutreach is a self-hosted, open-source LinkedIn automation tool designed for B2B lead generation. It automates the entire outreach process in a stealthy, human-like way by discovering and enriching target profiles, ranking profiles using ML for smart prioritization, sending personalized connection requests, following up with custom messages after acceptance, and tracking everything in a built-in CRM with web UI. It offers features like undetectable behavior, fully customizable Python-based campaigns, local execution with CRM, easy deployment with Docker, and AI-ready templating for hyper-personalized messages.

Vibe Remote is a tool that allows developers to code using AI agents through Slack or Discord, eliminating the need for a laptop or IDE. It provides a seamless experience for coding tasks, enabling users to interact with AI agents in real-time, delegate tasks, and monitor progress. The tool supports multiple coding agents, offers a setup wizard for easy installation, and ensures security by running locally on the user's machine. Vibe Remote enhances productivity by reducing context-switching and enabling parallel task execution within isolated workspaces.

ClaraVerse is a privacy-first AI assistant and agent builder that allows users to chat with AI, create intelligent agents, and turn them into fully functional apps. It operates entirely on open-source models running on the user's device, ensuring data privacy and security. With features like AI assistant, image generation, intelligent agent builder, and image gallery, ClaraVerse offers a versatile platform for AI interaction and app development. Users can install ClaraVerse through Docker, native desktop apps, or the web version, with detailed instructions provided for each option. The tool is designed to empower users with control over their AI stack and leverage community-driven innovations for AI development.

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.

TradeCat is a comprehensive data analysis and trading platform designed for cryptocurrency, stock, and macroeconomic data. It offers a wide range of features including multi-market data collection, technical indicator modules, AI analysis, signal detection engine, Telegram bot integration, and more. The platform utilizes technologies like Python, TimescaleDB, TA-Lib, Pandas, NumPy, and various APIs to provide users with valuable insights and tools for trading decisions. With a modular architecture and detailed documentation, TradeCat aims to empower users in making informed trading decisions across different markets.

Open Alice is an AI trading agent that provides users with their own research desk, quant team, trading floor, and risk management, all running on their laptop 24/7. It is file-driven, reasoning-driven, and OS-native, allowing interactions with the operating system. Features include dual AI providers, crypto trading, securities trading, market analysis, cognitive state, event log, cron scheduling, evolution mode, and a web UI. The architecture includes providers, core components, extensions, tasks, and interfaces. Users can quickly start by setting up prerequisites, AI providers, crypto trading, securities trading, environment variables, and running the tool. Configuration files, project structure, and license information are also provided.

LLM-TradeBot is an Intelligent Multi-Agent Quantitative Trading Bot based on the Adversarial Decision Framework (ADF). It achieves high win rates and low drawdown in automated futures trading through market regime detection, price position awareness, dynamic score calibration, and multi-layer physical auditing. The bot prioritizes judging 'IF we should trade' before deciding 'HOW to trade' and offers features like multi-agent collaboration, agent configuration, agent chatroom, AUTO1 symbol selection, multi-LLM support, multi-account trading, async concurrency, CLI headless mode, test/live mode toggle, safety mechanisms, and full-link auditing. The system architecture includes a multi-agent architecture with various agents responsible for different tasks, a four-layer strategy filter, and detailed data flow diagrams. The bot also supports backtesting, full-link data auditing, and safety warnings for users.

Qlib is an open-source, AI-oriented quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It covers the entire chain of quantitative investment, from alpha seeking to order execution. The platform empowers researchers to explore ideas and implement productions using AI technologies in quantitative investment. Qlib collaboratively solves key challenges in quantitative investment by releasing state-of-the-art research works in various paradigms. It provides a full ML pipeline for data processing, model training, and back-testing, enabling users to perform tasks such as forecasting market patterns, adapting to market dynamics, and modeling continuous investment decisions.

Jupyter Quant is a dockerized environment tailored for quantitative research, equipped with essential tools like statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, numpy, pandas, sci-py, scikit-learn, yellowbricks, shap, optuna, ib_insync, Cython, Numba, bottleneck, numexpr, jedi language server, jupyterlab-lsp, black, isort, and more. It does not include conda/mamba and relies on pip for package installation. The image is optimized for size, includes common command line utilities, supports apt cache, and allows for the installation of additional packages. It is designed for ephemeral containers, ensuring data persistence, and offers volumes for data, configuration, and notebooks. Common tasks include setting up the server, managing configurations, setting passwords, listing installed packages, passing parameters to jupyter-lab, running commands in the container, building wheels outside the container, installing dotfiles and SSH keys, and creating SSH tunnels.

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.

This repository provides a hands-on lab for learning about Neo4j and Google Cloud Vertex AI. It is intended for data scientists and data engineers to deploy Neo4j and Vertex AI in a Google Cloud account, work with real-world datasets, apply generative AI, build a chatbot over a knowledge graph, and use vector search and index functionality for semantic search. The lab focuses on analyzing quarterly filings of asset managers with $100m+ assets under management, exploring relationships using Neo4j Browser and Cypher query language, and discussing potential applications in capital markets such as algorithmic trading and securities master data management.

Jupyter Quant is a dockerized environment tailored for quantitative research, equipped with essential tools like statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, numpy, pandas, sci-py, scikit-learn, yellowbricks, shap, optuna, and more. It provides Interactive Broker connectivity via ib_async and includes major Python packages for statistical and time series analysis. The image is optimized for size, includes jedi language server, jupyterlab-lsp, and common command line utilities. Users can install new packages with sudo, leverage apt cache, and bring their own dot files and SSH keys. The tool is designed for ephemeral containers, ensuring data persistence and flexibility for quantitative analysis tasks.

Qbot is an AI-oriented automated quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It provides a full closed-loop process from data acquisition, strategy development, backtesting, simulation trading to live trading. The platform emphasizes AI strategies such as machine learning, reinforcement learning, and deep learning, combined with multi-factor models to enhance returns. Users with some Python knowledge and trading experience can easily utilize the platform to address trading pain points and gaps in the market.

This repository contains the Python source code for FINMEM, a Performance-Enhanced Large Language Model Trading Agent with Layered Memory and Character Design. It introduces FinMem, a novel LLM-based agent framework devised for financial decision-making, encompassing three core modules: Profiling, Memory with layered processing, and Decision-making. FinMem's memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. The framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. It presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns.

This repository focuses on the application of Large Language Models (LLMs) in the field of finance. It provides insights and knowledge about how LLMs can be utilized in various scenarios within the finance industry, particularly in generating AI agents. The repository aims to explore the potential of LLMs to enhance financial processes and decision-making through the use of advanced natural language processing techniques.