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FinRobot

Multi-agent equity research where the numbers are code-calculated and the narrative is LLM-written

GitHub★ 8.4kWebsitefinrobot.ai
Finance & Market AgentsOpen source
Language
Python
License
Apache-2.0
IN SHORT

What is FinRobot?

FinRobot is Multi-agent equity research where the numbers are code-calculated and the narrative is LLM-written. It sits in the Finance & Market Agents category of the Agents & Automation tools and is open-source, written in Python and licensed Apache-2.0. To get started: configure API keys.

Markdown version ↗

Overview

FinRobot is an open-source AI agent platform for financial applications from the AI4Finance Foundation, the same group behind FinGPT. Where FinGPT centred on a single fine-tuned model, FinRobot unifies several techniques — large language models, reinforcement learning and quantitative analytics — behind an agent layer aimed at investment research automation, trading strategies and risk assessment.

FinRobot ecosystem diagram: financial AI agents, financial LLM algorithms, LLMOps and DataOps, and multi-source foundation models
The four-layer FinRobot ecosystemFinRobot README ↗

Its defining design choice is the split between computation and narration. Every financial figure is produced by pure-Python compute operators — DCF, DDM, LBO, WACC, comparable-company analysis and Monte Carlo simulation all run through deterministic code paths with full provenance — while the language model is used only for reasoning, synthesis, explanation and report writing. The project states the rule plainly: numbers are code-calculated, narratives are LLM-assisted, and every output is provenance-tracked.

The architecture is a Lead Agent orchestrating a pipeline of specialists: five role-based sub-agents for data, analysis, modelling, synthesis and report generation, plus three debate agents that argue the bull case, the bear case and a judge-style verdict. FinRobot is deliberately not bound to one agent stack — the original open-source framework (V0) is built on AutoGen, the open-source `finrobot_equity` project (V1) on the OpenAI Agents SDK, and later generations move to PydanticAI and DeepSeek-Harness while keeping the financial domain layer constant.

What it does

FinRobot agent workflow diagram with Perception, Brain and Action modules
Agent workflow: Perception, Brain, ActionFinRobot README ↗
  • Lead Agent orchestrating five role-based sub-agents (data, analysis, modelling, synthesis, reporting) plus bull, bear and judge debate agents
  • 30 pure-Python compute operators and 7 coordinators covering DCF, DDM, LBO, comps, WACC and Monte Carlo valuation
  • Seven data providers with failover — FMP, Finnhub, yfinance, SEC EDGAR, Adanos, NewsAggregator and FX
  • Analyst-style output: 13-chapter research reports, IC memos, evidence links and numeric provenance for every figure
  • A web interface plus a scriptable CLI path, and a native macOS desktop build (FinRobot Desktop, Apple Silicon only)
  • Framework-agnostic by design: AutoGen, the OpenAI Agents SDK and PydanticAI generations all share the same financial domain layer

Getting started

FinRobot's open-source equity-research stack lives in the finrobot_equity directory. You supply API keys for a market-data provider and a model provider, then start either the web app or the command-line pipeline.

VideoFinRobot Pro demoByFinTech ↗

Configure API keys

Copy the example config and fill in your keys. FMP supplies the financial statements; the model key drives the agents.

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

Start the web interface

The deploy script installs dependencies and starts the app on http://127.0.0.1:8001. It also takes stop, restart and status.

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

Or run it manually

If deploy.sh does not work on your machine, create a virtualenv and start the web app directly.

bashbash
python3 -m venv venv
source venv/bin/activate
pip install -r requirements-equity.txt
python run_web_app.py

Generate a report from the command line

Two steps: run the financial analysis for a ticker and its peers, then turn the resulting CSVs into an equity report.

bashbash
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

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

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

When to use it

  • Reach for it when you want an equity-research report where every valuation number can be traced back to deterministic code rather than model output
  • Reach for it to automate the grind of a coverage workflow — statements, three-year forecasts, DCF and peer comparison — before an analyst reviews it
  • Reach for it when you want bull-case and bear-case arguments generated and adjudicated rather than a single one-sided thesis
  • Reach for it as a reference architecture for splitting deterministic financial computation from LLM narration in your own agent system
FinRobot Pro coverage screen with ticker cards showing price, valuation ratios and buy/hold ratings
FinRobot Pro, the hosted equity-research product built on the frameworkFinRobot README ↗

How FinRobot compares

FinRobot alongside other open-source finance & market agents tools AI/TLDR tracks, ranked by GitHub stars.

ToolStarsWhat it does
TradingAgents★ 120kLangGraph framework that mirrors a trading firm: LLM analyst, bull/bear researcher, trader and risk-management agents debate before a decision. For research, not advice.
Daily Stock Analysis★ 67.5kLLM research pipeline for A-share, Hong Kong, US, Japanese, Korean and Taiwanese tickers: pulls quotes, indicators and news, writes a decision dashboard and pushes it to chat or email.
Vibe-Trading★ 37.5kAn open-source trading research workspace from HKUDS: natural-language market research, multi-agent analyst teams, cross-market backtests, and an MCP server for Claude, Cursor and other clients.
AI Berkshire★ 17kValue-investing research skills for Claude Code and Codex that turn the methods of Buffett, Munger, Duan Yongping and Li Lu into structured, multi-agent research workflows.
QuantDinger★ 13.4kA self-hosted trading stack that carries an idea from AI market research to a Python strategy, a server-side backtest and paper or live execution on crypto exchanges and brokers, driven from the web, an API or MCP.
a-stock-data★ 11.4kA single-file agent skill that gives AI coding assistants China A-share market data (quotes, research, fund flow, filings, macro, futures and more) from 34 mostly keyless sources.
ValueCell★ 11kA multi-agent platform for financial work, pairing deep-research, news and strategy agents over US, Hong Kong, China and crypto market data, with credentials kept on the local device.
FinRobot★ 8.4kMulti-agent equity research where the numbers are code-calculated and the narrative is LLM-written