AI Agents
Autonomous AI agents that finance teams can deploy for research, reporting, and anomaly detection.
FinRL
Built on: Deep Reinforcement Learning
The original open-source framework for financial reinforcement learning, organized around three core layers, where a trading agent interacts with a market environment and learns sequential decision-making policies. Supports multiple DRL algorithms including A2C, DDPG, PPO, TD3, and SAC.
Best for: Quant researchers, Algorithmic traders, ML engineers
FinGPT
Built on: GPT + LoRA Fine-tuning
Open-source financial large language models with a data source layer for real-time market coverage, a data engineering layer for NLP processing, an LLMs layer focused on fine-tuning methods like LoRA, and a task layer for financial benchmarks like sentiment analysis and market forecasting.
Best for: Data scientists, Finance researchers, Quant teams
AI Financial Agent
Built on: Financial Datasets API + LLMs
A proof-of-concept AI financial agent for investment research using the Financial Datasets API — providing real-time and historical stock market data, financial statements, options data, insider trades, and institutional ownership covering 100% of the US market over 30 years.
Best for: Investment analysts, Portfolio managers, Corporate development
AgenticTrading
Built on: DAG-based Multi-Agent + LLMs
A framework bridging quantitative finance and cognitive AI, enabling adaptive research loops where agents propose, test, and refine hypotheses autonomously, with dynamic factor generation, memory-augmented learning, and DAG-based transparent multi-agent execution. Presented at NeurIPS 2025.
Best for: Quant researchers, Algo trading teams, AI engineers
LLMs in Finance
Built on: AutoGen + LlamaIndex + CrewAI
A collection of examples using frameworks like AutoGen, LlamaIndex, and CrewAI to create collaborative agents for financial analysis, including RAG for retrieving and parsing financial documents, and multimodal evaluation of how models interpret charts in earnings reports.
Best for: Finance teams, Data scientists, FP&A analysts
Deep Research Agent for Finance
Built on: MCP Framework + LLMs
Leverages AI to perform automated financial research and analysis, gathering data from multiple reliable financial sources to create structured investment reports. Uses the MCP (Model Context Protocol) agent framework and a deep research architecture that allows the LLM to thoroughly research and revise its findings until a comprehensive report is complete.
Best for: Investment researchers, Analysts, Strategy teams
FinRobot
Built on: LLMs + Reinforcement Learning
An AI agent platform tailored for financial applications that unifies multiple AI technologies — including LLMs, reinforcement learning, and quantitative analytics — to power investment research automation, algorithmic trading strategies, and risk assessment.
Best for: Investment researchers, Risk teams, Quant analysts
TradingAgents
Built on: LangChain + LLMs
A multi-agent trading framework that mirrors real-world trading firms, deploying specialized LLM-powered agents — fundamental analysts, sentiment experts, technical analysts, traders, and a risk management team — that collaboratively evaluate market conditions and make trading decisions through dynamic debates.
Best for: Quant traders, Hedge funds, Trading desks
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