Projects sharing features with Agents

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

Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading strategies. It provides a comprehensive environment for quantitative finance, allowing users to simulate trading logic against historical market data or connect directly to brokerage platforms for automated real-time trading. The project distinguishes itself through a unified event-driven architecture that treats backtesting and live trading with the same API. This consistency is supported by a flexible data-feed abstraction layer that normalizes diverse financial sources, ena

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

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 structured educational resource and technical guide for designing and implementing autonomous systems using large language models. It provides a comprehensive curriculum and code samples focused on agentic design patterns, autonomous development, and the creation of systems capable of planning and executing multi-step tasks. The resource details the implementation of agentic retrieval-augmented generation, where models autonomously plan and refine data searches. It covers a wide array of orchestrators and design patterns, including metacognitive reflection for self-correctin

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

This library is a Python-based tool for retrieving historical and real-time financial market data from public sources. It functions as a programmatic interface for downloading stock prices, dividends, financial statements, and corporate calendars, allowing users to perform automated research and analysis on various market assets. The project distinguishes itself by structuring retrieved financial time series directly into tabular data frames, which facilitates mathematical analysis and manipulation of market metrics. It supports efficient data retrieval through multi-threaded batch downloadin

Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across long-term interactions. It provides a comprehensive suite of primitives for defining agents with configurable personas, modular memory blocks, and tool-use capabilities, enabling them to retain user preferences and conversation history over extended sessions. The platform distinguishes itself through its advanced memory management and orchestration capabilities. It allows agents to autonomously update their own memory, perform retrieval-augmented generation, and coordinate com

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

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

Hikyuu is a quantitative trading framework designed for developing, backtesting, and executing systematic trading strategies. It functions as a high-speed system that combines a financial time-series library, a multi-factor analysis tool, and a quantitative backtesting engine to support comprehensive trading research. The framework is distinguished by its high-speed computing core, which utilizes multi-threaded execution to process large volumes of market data for technical indicator generation. It supports a modular strategy composition model where signal, risk, and fund management component

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

iflow-cli is a command-line interface and suite of AI tools designed for software engineering, workflow orchestration, and multimodal data analysis. It functions as an LLM command line interface that enables users to execute AI workflows, analyze codebase structures, and interact with large language models directly from the terminal. The project features a plugin-based agent architecture that allows for the integration of specialized domain experts and custom instruction sets from an external marketplace. It distinguishes itself through a multimodal AI terminal capable of processing visual da

PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo

Abu is an algorithmic trading framework designed for the development, backtesting, and optimization of automated trading strategies. It functions as a quantitative financial analysis library that processes time-series data to identify market trends, volatility patterns, and key price levels. The platform distinguishes itself through a modular architecture that integrates diverse financial data sources and a rule-based engine for automated risk management. It enables users to construct complex trading signals by layering technical indicators and machine learning models, while simultaneously en

This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg

gs-quant is a quantitative finance library and financial data analytics toolkit. It serves as a framework for analyzing financial data, developing systematic trading strategies, and managing risk exposure for derivative products in global markets. The project provides tools for quantitative financial analysis, quantitative portfolio modeling, and the development of systematic trading strategies. It enables the calculation of risk for derivative products to structure and hedge positions across markets.

aiquanttrade is an AI-driven quantitative trading platform that enables the development, backtesting, and deployment of trading strategies powered by machine learning and artificial intelligence. It provides a complete local environment for quantitative research, simulation, and automated live trading through brokerage APIs, supporting both historical backtesting and real-time paper trading without capital risk. The platform distinguishes itself through a modular, event-driven architecture that separates strategy logic from execution, allowing rule-based and machine learning models to be co

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

This project is a Python-based stock market tracker and financial data dashboard designed to fetch and visualize real-time and historical market data. It functions as a containerized financial application and analysis tool for monitoring price trends and calculating financial indicators. The system includes a metadata-driven reporting engine that transforms database tables into interactive web reports without manual page development. It further extends its analysis capabilities by embedding third-party financial interfaces and indicator calculators directly into the user interface via frames.

The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents that coordinate reasoning, tool execution, and complex workflows. It functions as a structured AI output controller and RAG integration library, providing a unified interface to manage multiple language model providers. The framework is distinguished by its implementation of the Model Context Protocol, allowing agents, tools, and models to be shared between different AI platforms and hosted as agentic tooling servers. It enables the design of collaborative agent teams through dec

This project is an AI-powered IDE extension and LLM coding assistant that provides a conversational interface for generating, refactoring, and debugging code. It functions as an AI agent framework and a Model Context Protocol client, connecting AI models to external data sources and tools to automate complex development tasks. The system is distinguished by its use of autonomous AI agents capable of multi-step task execution, including the ability to read files, modify code, and run terminal commands iteratively. It supports recursive agent orchestration through subagent delegation and employ

This project provides technical documentation and reference guides for spot trading, including specifications for REST, WebSocket, and FIX protocols. It serves as a comprehensive resource for integrating with spot trading endpoints to execute trades, query account data, and fetch market statistics. The project distinguishes itself by supporting institutional-grade connectivity through the Financial Information eXchange standard and simple binary encoding to reduce latency and payload size. It also includes a dedicated sandbox environment for validating trading logic and strategies without fin

FinRL is a financial reinforcement learning framework and quantitative trading library. It provides a specialized system for developing, training, and simulating autonomous agents designed to automate financial trading and portfolio management. The project serves as an automated portfolio optimizer and financial market simulator. It enables the creation of decision-making policies to balance asset allocations, maximize potential returns, and minimize financial risk through reinforcement learning. The framework includes capabilities for financial market data engineering, algorithmic trading s

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

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

FinanceDatabase is a system of data repositories and interfaces providing a corporate fundamental database, a financial market data API, and an SEC filings aggregator. It functions as a financial valuation engine and a macroeconomic indicator feed, offering a programmatic way to access market quotes, corporate fundamentals, and official regulatory disclosures. The project distinguishes itself through an institutional ownership tracker that monitors fund holdings, insider trading activity, and political financial disclosures. It also includes a dedicated tool for extracting and analyzing offic