Projects sharing features with FinRobot

This project is a comprehensive market data toolkit and financial analysis system specifically designed for China A-shares. It serves as a data pipeline for retrieving real-time quotes, aggregating corporate financial statements, and automating equity research. The system distinguishes itself through specialized monitors for institutional capital movements, including Northbound fund flows, margin trading balances, and large block transactions. It also features a dedicated options Greeks calculator for ETF derivatives and tools to gauge market sentiment via retail popularity rankings and trend

Agency Swarm is a multi-agent orchestration framework and development kit designed to coordinate specialized AI agents through defined communication patterns and handoffs. It functions as a system for managing agent swarms, providing an API gateway to expose these coordinated collectives as production-ready HTTP endpoints. The project distinguishes itself through its Model Context Protocol integration layer, which connects agents to external data sources and capabilities. It implements specialized orchestration patterns, such as the orchestrator-worker model and role-based delegation, to tran

Youtu Agent is an open-source framework for building, running, and evaluating autonomous agents powered by large language models. It provides the core infrastructure for creating agents that follow reasoning loops, use toolkits, and coordinate with other agents to solve complex tasks, all managed through YAML-driven configuration files. The framework distinguishes itself through its support for multi-agent orchestration, where a planner agent decomposes tasks and coordinates specialized worker agents, and through its integration with the Model Context Protocol for connecting to external toolk

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering, and algorithmic trading. It serves as a centralized library of documentation covering mathematical models, financial instruments, and systematic trading strategies. The project integrates AI-driven capabilities through a modular retrieval-augmented generation framework that extracts structured data from research papers and news. It features a multi-agent workflow engine designed to discover and validate predictive alpha factors, alongside tools for local large language model

This project is a financial market data API and quantitative analysis tool designed to aggregate metrics, scrape web data, and monitor market sentiment. It functions as a financial indicator aggregator and stock market web scraper that provides a programmatic interface for retrieving stock prices, indices, and ETF metadata from multiple data providers. The system differentiates itself through a dedicated market sentiment monitor and investment risk assessment capabilities. It tracks investor behavior via northbound capital flows, dragon-tiger lists, popularity rankings, and security margin ba

The FinanceToolkit is an open-source Python library for quantitative finance that provides a unified framework for financial analysis, asset valuation, and risk management. It serves as a comprehensive platform for computing over 200 financial metrics and ratios, with capabilities spanning financial ratio analysis, fixed income analytics, macroeconomic data aggregation, options pricing, and portfolio risk management. The toolkit distinguishes itself through a modular architecture that separates data retrieval from computation, with stateless engines for financial models like Black-Scholes, GA

mcp-agent is a framework for building AI agents that integrate with Model Context Protocol servers to execute tools and access data. It functions as a multi-agent orchestrator and protocol-compliant server, enabling the creation of agents that can discover and invoke tools from connected external servers. The project distinguishes itself through a durable workflow engine that supports long-running tasks capable of pausing, resuming, and surviving restarts. It implements complex orchestration patterns, including iterative evaluator-optimizer loops, hierarchical workflow nesting, and specialist

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

CrewAI is a multi-agent orchestration framework and autonomous agent workflow engine. It provides a system for coordinating autonomous AI agents with specific roles and goals to solve complex tasks through collaborative intelligence. The framework distinguishes itself through a collaborative AI agent system that enables multiple language model instances to share intelligence and execute multi-step objectives via role-playing. It incorporates human-in-the-loop mechanisms, allowing for manual review checkpoints to validate decisions and refine outcomes within autonomous execution paths. The pl

Nexent is an enterprise AI control plane and LLM agent orchestration platform. It provides a zero-code environment for designing, deploying, and managing production AI agents through a multi-agent collaboration framework that coordinates specialized autonomous agents using standardized messaging protocols. The platform integrates the Model Context Protocol to connect agents with external tools, plugins, and services via a universal communication interface. It further distinguishes itself with a dedicated RAG knowledge base manager that imports unstructured documents and utilizes hybrid search

ECC is an LLM agent orchestration framework and cross-platform AI tooling suite designed to coordinate multi-model workflows. It provides a system for managing specialized agent roles, reusable skills, and structured planning to execute complex software development tasks across different AI-powered code editors. The project distinguishes itself as a Model Context Protocol manager, providing a configuration layer to integrate external servers and audit tool execution. It further implements an agentic security sandbox that restricts sensitive file access and scans for secret leakage to secure a

This project is a quantitative trading platform and algorithmic trading bot designed for market data aggregation, strategy backtesting, and trade execution. It functions as a comprehensive system for collecting financial data via APIs and web sources, simulating investment strategies against historical records, and programmatically managing investment positions through brokerage interfaces. The platform distinguishes itself through institutional sentiment analysis and market intelligence tools. It monitors institutional fund activity, tracks corporate actions like equity pledges, and crawls f

This framework provides a development environment for building collaborative systems where autonomous agents interact to solve complex tasks through conversational workflows. It functions as a conversational workflow engine and event-driven runtime, coordinating multi-step processes by translating high-level goals into structured dialogue sequences between specialized agents. The system distinguishes itself through its message-passing orchestration, which manages state transitions and task delegation between independent participants. It supports dynamic conversation state management to provid

This project is a Python quantitative finance library designed for gathering, manipulating, and analyzing stock market data. It provides a suite of tools for quantitative stock analysis, including an equity screening framework for filtering stocks based on technical and fundamental criteria. The library features a machine learning price predictor for classifying stock movements and forecasting future price directions. It also includes a financial technical analysis tool to calculate indicators such as Bollinger Bands, RSI, and MACD, alongside an algorithmic trading simulator for testing portf

The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building autonomous AI agents. It provides a tool integration layer for binding external functions, APIs, and sandboxed code as executable tools for language models. The framework distinguishes itself through a graph-based system for designing sequential and parallel task flows, featuring state management and checkpointing for long-running processes. It implements comprehensive conversational state management and an observability suite that uses telemetry to trace execution flows and monit

Agent Zero is an LLM agent framework and multi-agent orchestrator that provides an AI-powered interface for operating system tasks. It functions as a containerized AI workspace, allowing large language models to interact with a filesystem and terminal within an isolated Linux environment. The system distinguishes itself through a hierarchical orchestration model that decomposes complex goals by spawning specialized sub-agents to collaborate and consolidate results. It features a plugin-based architecture for extending capabilities via a community plugin hub, a custom skills system, and extern

DeepAnalyze is an autonomous data science agent and research pipeline designed to transform raw datasets into comprehensive analysis reports. It operates by generating and executing Python code to perform data preparation, modeling, and visualization. The system utilizes a secure, containerized execution environment to run generated scripts in isolation from the host system. It includes a benchmarking tool to evaluate the accuracy and performance of large language models against standardized data science tasks and a standardized API gateway for managing model completions and file uploads. Th

LazyLLM is a multi-agent framework and orchestration engine designed for building complex AI applications. It provides a system for chaining large language models into sequential or parallel pipelines, utilizing a tool registry to convert standard functions into discoverable tools that models can invoke via reasoning. The project features an application deployment kit that enables hosting model workflows as web services with integrated chat interfaces and API gateways. It includes an infrastructure abstraction layer that allows users to switch between bare-metal servers, clusters, and public

Swarms is a multi-agent orchestration framework and autonomous agent toolkit designed to coordinate large language model agents. It serves as a workflow engine for managing agent relationships, providing the infrastructure to build autonomous agents with integrated memory, tool-calling capabilities, and reasoning loops. The framework is distinguished by its multi-agent consensus systems, which utilize voting, adversarial debates, and judge agents to synthesize high-quality responses. It supports a variety of collaboration patterns, including director-worker hierarchies, expert synthesis, and

Agentverse is a multi-agent framework and orchestrator designed for deploying and managing multiple large language model agents. It provides a simulation environment where agents interact based on custom personas and defined interaction rules to solve tasks or simulate social dynamics. The system features a tool integration layer that connects agents to external functional plugins and specialized tools, extending their capabilities beyond text generation. It uses a combination of persona-based prompt injection and state-managed memory to maintain agent consistency and specialized skill sets d

Hive is an artificial intelligence workflow automation engine and development platform designed for building and deploying autonomous agents. It provides a framework for orchestrating complex, multi-step business processes by coordinating tasks across multiple specialized agents using directed graph structures. The platform distinguishes itself through a focus on production-grade reliability and state management. It maintains persistent execution context and conversation history on disk, enabling crash recovery and continuity for long-running automated sessions. Furthermore, it incorporates a

GPT Researcher is an autonomous agent framework designed to automate the process of gathering, synthesizing, and documenting information from diverse web and local sources. It functions as a research-oriented execution environment that orchestrates specialized agents to perform complex, multi-branch research tasks, transforming raw data into structured, factual, and cited reports. The project distinguishes itself through a graph-based orchestration layer that manages state transitions and information flow between specialized agents. It employs recursive tree-search execution to explore comple

This project is a comprehensive framework for developing, orchestrating, and deploying autonomous agents. It provides a structured environment for building agents that utilize reasoning loops to perform multi-step tasks, manage state through graph-based workflows, and interact with external tools. By mapping unstructured model outputs into typed schemas, the framework ensures reliable integration with downstream application logic. The platform distinguishes itself through a focus on production-grade reliability and security. It incorporates hybrid memory systems that combine vector embeddings

PydanticAI is a Python framework designed for building production-grade autonomous agents. It provides a unified interface for interacting with diverse language models, enabling developers to construct agents that perform complex tasks through structured data validation, tool execution, and multi-turn conversation management. The library centers on type-safe schema enforcement, ensuring that model inputs and outputs remain consistent and reliable throughout the agent's lifecycle. The framework distinguishes itself through a robust architecture that emphasizes modularity and testability. It ut

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

Owl is a framework for agentic workflow automation and multi-agent orchestration. It functions as a system for coordinating autonomous large language model agents to decompose and execute complex tasks through shared communication and collaborative planning. The project distinguishes itself through a multi-modal toolset for processing images, audio, and video, alongside a synthetic data generator that produces domain-specific datasets using self-instruct and verifier loops. It further incorporates a retrieval-augmented generation pipeline framework that integrates long-term memory and real-ti