Projects sharing features with TradingAgents CN

FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models. The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow

This project is a Python library designed for the programmatic retrieval and analysis of diverse financial datasets. It functions as a comprehensive toolkit for quantitative research, providing a unified interface to fetch historical and real-time market data across asset classes including equities, futures, bonds, cryptocurrencies, and foreign exchange. By abstracting complex network requests into simple, parameter-driven functions, it enables users to integrate financial data into research workflows and automated trading systems. The library distinguishes itself through its scraper-based ag

This project provides a comprehensive framework for building, deploying, and orchestrating autonomous agents within a decentralized network. It serves as a collection of patterns and examples for developing intelligent software entities capable of performing complex tasks, making decisions, and interacting with other agents to achieve shared goals. The framework distinguishes itself through its focus on multi-agent orchestration and decentralized communication. It enables the coordination of specialized agent teams that collaborate on workflows through structured messaging protocols, allowing

This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for orchestrating language models, autonomous agents, and interactive user interfaces. It serves as a central library for managing the entire lifecycle of AI interactions, from initial prompt generation and model provider abstraction to complex, multi-step reasoning and tool execution. The framework distinguishes itself through its deep integration with frontend development, specifically by enabling generative user interfaces that render dynamic components directly from model outputs. I

This project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability. The framework distinguishes itself through its hierarchical agent registry and policy-driven

This project is an automated trading and agentic workflow platform designed to orchestrate complex financial tasks through state-based graphs. It provides a comprehensive framework for building, deploying, and managing autonomous agents that execute multi-step analytical processes, monitor real-time market conditions, and perform high-speed trade execution. The platform distinguishes itself through a robust agentic plugin ecosystem that integrates directly with popular AI-powered development environments and command-line interfaces. It features a specialized financial analysis engine capable

This project is a comprehensive framework for engineering financial data pipelines, designed to automate the collection, cleaning, and synchronization of large-scale market datasets. It functions as a quantitative trading data engine, providing the infrastructure necessary to manage historical and real-time asset pricing information for research and machine learning workflows. The system distinguishes itself through a configuration-driven approach to orchestration, allowing users to manage complex data acquisition tasks across multiple financial providers. It features resilient middleware tha

Kilocode is an autonomous engineering platform designed to orchestrate AI agents for complex software development tasks. It functions as a comprehensive system for automating coding, testing, and repository management by integrating directly with your codebase and terminal. The platform provides a unified gateway for model orchestration, allowing for the management of agentic workflows, event-driven automation, and persistent session state across distributed development environments. The platform distinguishes itself through its federated task management and policy-based access control, which

pybroker is a Python algorithmic trading framework and quantitative technical analysis library designed for developing, testing, and optimizing trading strategies using historical market data. It functions as a trading strategy backtester and a financial performance evaluator, providing a structured environment to simulate trading rules and analyze their statistical reliability. The framework distinguishes itself through a market data integration layer that handles the fetching and caching of historical price data from external providers. It incorporates an event-driven backtesting engine and

Hummingbot is an open-source framework designed for building, backtesting, and deploying autonomous trading agents and algorithmic strategies across centralized and decentralized cryptocurrency exchanges. It provides a modular environment where users can orchestrate containerized bots to execute complex market-making, grid trading, and arbitrage operations. The platform distinguishes itself through a skill-based architecture that integrates large language models, enabling users to monitor market conditions and control trading operations via natural language commands. It features a unified con

Suna is an orchestration platform designed for the deployment, management, and governance of autonomous AI agents. It provides a centralized system for defining agent behaviors and tool integrations, enabling the automation of complex business processes through a unified interface. The platform distinguishes itself by applying infrastructure-as-code principles to AI, utilizing version-controlled repositories to manage agent configurations, skills, and guardrails. It ensures secure and predictable operations by spawning ephemeral, isolated virtual machines for every individual task, preventing

AI-Trader is a framework for managing autonomous trading agents and executing simulated financial operations. It provides a structured environment for registering and authenticating agents, tracking their reputation, and managing simulated capital balances within a competitive market ecosystem. The platform distinguishes itself through integrated social trading and collaborative investment capabilities. Users can follow experienced participants to automatically mirror their market positions, or organize into teams to execute shared strategies, vote on collective investment proposals, and comp

DeepCode is an agentic development framework designed to orchestrate autonomous AI agents for software engineering tasks. It functions as a multi-agent workflow orchestrator that translates natural language requirements into functional codebases by coordinating specialized agents for architectural planning, intent analysis, and implementation. The platform integrates multiple language models to power these automated routines, providing a unified environment for complex development projects. The system distinguishes itself through its ability to transform academic research papers into executab

Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI applications. It serves as a multi-modal integration layer that connects diverse local and remote language models with an agentic retrieval-augmented generation system. The project distinguishes itself through a collaborative message-exchange paradigm, allowing specialized agents to delegate tasks hierarchically and coordinate via structured communication. It features an advanced state management system for conversational AI, including the ability to rewind and prune conversation hist

Stock is an algorithmic trading framework designed for the development, backtesting, and execution of automated investment strategies. It provides a comprehensive environment for quantitative market analysis, enabling users to build systems that connect to brokerage interfaces for order placement based on predefined technical rules. The platform distinguishes itself through integrated data acquisition and analysis capabilities, including a financial data collection engine that utilizes proxy rotation and session persistence to maintain stable connectivity and bypass rate limits. It supports h

This project is a Python framework for building autonomous, event-driven agent systems. It provides a unified runtime for orchestrating multi-agent workflows, managing persistent conversation state, and executing code within secure, isolated sandbox environments. The framework is designed to handle complex task delegation, allowing agents to invoke other agents as tools while maintaining context across multi-turn interactions. The framework distinguishes itself through its deep integration with the Model Context Protocol, enabling agents to connect to external data sources and remote services

QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models with mathematical analysis to automate market insights and trading strategies. It functions as a system for building, backtesting, and executing strategies using both historical and real-time market data. The framework is distinguished by its use of large language models for financial analysis and sentiment extraction from news and social media. It utilizes autonomous agents with chain-of-thought reasoning to generate market intelligence and strategic reports, while employing

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut

RD-Agent is an autonomous framework designed to orchestrate multi-step software engineering and data science workflows. By leveraging large language models, the system decomposes complex technical requirements into actionable research, planning, and execution phases, ultimately generating and running code to solve specific development tasks. The platform distinguishes itself through a containerized execution sandbox that ensures secure dependency management and system stability for all autonomously generated code. It employs multi-agent orchestration to manage iterative feedback loops, allowi

This project serves as a comprehensive framework and registry for managing extensions within autonomous assistant environments. It provides the infrastructure necessary to integrate third-party tools, configure diverse language model backends, and deploy persistent agent instances across local or cloud-hosted platforms. The ecosystem distinguishes itself through a modular architecture that prioritizes security and interoperability. It features a centralized system for managing sensitive credentials and scoped permissions, ensuring that external integrations operate within defined access bound

Awesome-quant is a curated directory of open-source software libraries and tools designed for quantitative finance, algorithmic trading, and financial data analysis. It serves as a central hub for discovering resources that support the entire lifecycle of financial modeling, from raw data ingestion to complex statistical research. The repository organizes specialized tools into categorized collections, enabling users to identify solutions for high-performance numerical computing, technical indicator calculation, and derivative pricing. It highlights frameworks that facilitate the construction

This project is a framework for developing multimodal AI agents that function as programmable participants in real-time communication rooms. It enables the construction of agents that can see, hear, and speak by integrating speech-to-text, large language models, and text-to-speech pipelines to facilitate low-latency, natural conversations. The system is distinguished by its advanced orchestration of real-time media and conversational flow, including support for full-duplex speech, preemptive response generation, and sophisticated interruption management. It further differentiates itself throu

Agent-skills is a collection of structured instructions and behavioral personas designed to standardize how AI coding agents perform engineering tasks. It functions as a workflow orchestrator that maps natural language intent to repeatable technical sequences and verification checklists. The project distinguishes itself through the use of specialized markdown-defined roles, such as security auditors or test engineers, to apply targeted domain expertise. It employs an evidence-based verification model that requires runtime data or passing tests as mandatory exit criteria to ensure AI-generated

Cube is a semantic data layer that provides a unified framework for defining business metrics, dimensions, and relationships across diverse data sources. By acting as a headless business intelligence engine, it transforms raw data into a governed model that can be queried via SQL, REST, and GraphQL interfaces. This architecture ensures consistent data definitions and logic across all downstream analytical applications and reporting tools. The platform distinguishes itself through its integrated conversational AI capabilities, which allow users to explore data using natural language. It orches

Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project

This project is an LLM autonomous agent framework and orchestration tool designed to build goal-driven agents that automate complex workflows. It functions as a system for converting high-level objectives into a series of autonomous actions and managing the coordination of multiple specialized agents to solve multi-step problems. The framework features a tool integration layer that parses structured model outputs into executable functions and external API calls. It utilizes a non-blocking execution pipeline to manage task orchestration through recursive loops and asynchronous event handling.

Wiseflow is an AI business process automation suite and an orchestration framework for coordinating multiple specialized AI agents. It functions as a multi-agent collaboration platform where autonomous agents can spawn new instances and request assistance to resolve complex technical blockers and business workflows. The system distinguishes itself through a browser-based task executor that uses anti-detection techniques to scrape data and perform actions on websites that block bots. It further differentiates its setup process by using a chat-based conversational interface to collect user cont