FinRobot: An Open-Source AI Agent Platform for Financial Analysis using Large Language Models

License

FinRobot is an AI Agent platform tailored for financial applications, surpassing FinGPT's single-model approach. It unifies multiple AI technologies—including LLMs, reinforcement learning, and quantitative analytics—to power investment research automation, algorithmic trading strategies, and risk assessment, delivering a full-stack intelligent solution for the financial industry.

Concept of AI Agent: an AI Agent is an intelligent entity that uses large language models as its brain to perceive its environment, make decisions, and execute actions. Unlike traditional artificial intelligence, AI Agents possess the ability to independently think and utilize tools to progressively achieve given objectives.

Whitepaper of FinRobot

Visitors

🎬 FinRobot Pro — Your Personal AI-Powered Equity Research Assistant

🌐 https://finrobot.ai/

<div align="center"> <a href="https://www.youtube.com/watch?v=ebgPiJINi-k" target="_blank"> <img src="https://github.com/user-attachments/assets/de3b9f9c-50aa-49f0-82c6-3d2b938f4670" width="90%" /> </a> </div> <p align="center"> ▶️ Click the image above to watch the demo video, or see the short preview below. </p>

A locally-deployed AI assistant that fetches financial data, runs multi-agent LLM analysis, and generates professional equity research reports.

1. Configure API Keys

cp finrobot_equity/core/config/config.ini.example finrobot_equity/core/config/config.ini

Edit config.ini with your keys:

[API_KEYS]
fmp_api_key = YOUR_FMP_API_KEY          # https://financialmodelingprep.com/developer
openai_api_key = YOUR_OPENAI_API_KEY    # https://platform.openai.com/account/api-keys

2. One-Command Deploy (Web Interface)

chmod +x deploy.sh
./deploy.sh start

#if deploy.sh not working then
python3 -m venv venv                                                                                                                                           
source venv/bin/activate
pip install -r requirements-equity.txt                                                                                                                         
python run_web_app.py

Access at http://127.0.0.1:8001

| Command | Description | |:---|:---| | ./deploy.sh start | Start the web app (auto-installs dependencies) | | ./deploy.sh stop | Stop the application | | ./deploy.sh restart | Restart the application | | ./deploy.sh status | Check running status |

3. Or Run via Command Line

# Step 1: Financial analysis
python finrobot_equity/core/src/generate_financial_analysis.py \
    --company-ticker NVDA \
    --company-name "NVIDIA Corporation" \
    --config-file finrobot_equity/core/config/config.ini \
    --peer-tickers AMD INTC \
    --generate-text-sections

# Step 2: Generate report
python finrobot_equity/core/src/create_equity_report.py \
    --company-ticker NVDA \
    --company-name "NVIDIA Corporation" \
    --analysis-csv output/NVDA/analysis/financial_metrics_and_forecasts.csv \
    --ratios-csv output/NVDA/analysis/ratios_raw_data.csv \
    --config-file finrobot_equity/core/config/config.ini

Pipeline:

Fetch Financial Data: income statements, balance sheets, cash flows via FMP API
Process & Forecast: 3-year financial projections, DCF valuation, peer comparison
AI Agent Analysis: 8 specialized agents generate investment thesis, risk assessment, valuation overview, etc.
Report Generation: professional multi-page HTML/PDF with 15+ chart types

Example Reports

For full documentation, see finrobot_equity/README.md.

What is FinRobot Pro?

https://github.com/user-attachments/assets/93ec0f1e-e28b-4474-a0bf-a79e0c12f0ff

FinRobot Pro is an AI-powered equity research platform that automates professional stock analysis using Large Language Models (LLMs) and AI Agents.

Key Features:

Automated Report Generation – Generate professional equity research reports instantly
Financial Analysis – Deep dive into income statements, balance sheets, and cash flows
Valuation Analysis – P/E ratio, EV/EBITDA multiples, and peer comparison
Risk Assessment – Comprehensive investment risk evaluation

FinRobot Ecosystem

The overall framework of FinRobot is organized into four distinct layers, each designed to address specific aspects of financial AI processing and application:

Financial AI Agents Layer: The Financial AI Agents Layer now includes Financial Chain-of-Thought (CoT) prompting, enhancing complex analysis and decision-making capacity. Market Forecasting Agents, Document Analysis Agents, and Trading Strategies Agents utilize CoT to dissect financial challenges into logical steps, aligning their advanced algorithms and domain expertise with the evolving dynamics of financial markets for precise, actionable insights.
Financial LLMs Algorithms Layer: The Financial LLMs Algorithms Layer configures and utilizes specially tuned models tailored to specific domains and global market analysis.
LLMOps and DataOps Layers: The LLMOps layer implements a multi-source integration strategy that selects the most suitable LLMs for specific financial tasks, utilizing a range of state-of-the-art models.
Multi-source LLM Foundation Models Layer: This foundational layer supports the plug-and-play functionality of various general and specialized LLMs.

FinRobot: Agent Workflow

Perception: This module captures and interprets multimodal financial data from market feeds, news, and economic indicators, using sophisticated techniques to structure the data for thorough analysis.
Brain: Acting as the core processing unit, this module perceives data from the Perception module with LLMs and utilizes Financial Chain-of-Thought (CoT) processes to generate structured instructions.
Action: This module executes instructions from the Brain module, applying tools to translate analytical insights into actionable outcomes. Actions include trading, portfolio adjustments, generating reports, or sending alerts, thereby actively influencing the financial environment.

FinRobot: Smart Scheduler

The Smart Scheduler is central to ensuring model diversity and optimizing the integration and selection of the most appropriate LLM for each task.

Director Agent: This component orchestrates the task assignment process, ensuring that tasks are allocated to agents based on their performance metrics and suitability for specific tasks.
Agent Registration: Manages the registration and tracks the availability of agents within the system, facilitating an efficient task allocation process.
Agent Adaptor: Tailor agent functionalities to specific tasks, enhancing their performance and integration within the overall system.
Task Manager: Manages and stores different general and fine-tuned LLMs-based agents tailored for various financial tasks, updated periodically to ensure relevance and efficacy.

File Structure

The main folder finrobot has three subfolders agents, data_source, functional.

FinRobot
├── finrobot (main folder)
│   ├── agents
│   	├── agent_library.py
│   	└── workflow.py
│   ├── data_source
│   	├── fin

FinRobot

Install / Use

README