Dexter 🤖

Dexter is an autonomous financial research agent that thinks, plans, and learns as it works. It performs analysis using task planning, self-reflection, and real-time market data. Think Claude Code, but built specifically for financial research.

This repository is a fork of https://github.com/michaelh03/dexter-free. Multiple LLM providers are supported (claude/gemini/openai).

Overview

Dexter takes complex financial questions and turns them into clear, step-by-step research plans. It runs those tasks using live market data, checks its own work, and refines the results until it has a confident, data-backed answer.

Key Capabilities:

• Intelligent Task Planning: Automatically decomposes complex queries into structured research steps
• Autonomous Execution: Selects and executes the right tools to gather financial data
• Self-Validation: Checks its own work and iterates until tasks are complete
• Real-Time Financial Data: Access to income statements, balance sheets, and cash flow statements
• Real-Time Search Engine: Access to real time macroenomic news, company sentiment and news via Tavily
• Safety Features: Built-in loop detection and step limits to prevent runaway execution

Prerequisites

• Python 3.13 or higher
• uv package manager
• One of OpenAI/Gemini/Claude API key (get here)
• Financial Datasets API key [Optional] (get here)
• Langsmith API key (get here)
• Tavily API key [Optional] (get here)

Installation

1. Clone the repository:

git clone https://github.com/rodionlim/dexter.git
cd dexter

2. Install dependencies with uv:

uv sync

3. Set up your environment variables:

# Copy the example environment file
cp env.example .env

# Edit .env and add your API keys
# OPENAI_API_KEY=your-openai-api-key
# FINANCIAL_DATASETS_API_KEY=your-financial-datasets-api-key

Usage

Run Dexter in interactive mode (defaults to Yahoo Finance):

uv run dexter-agent

Switch to FinancialDatasets provider when you need premium data:

uv run dexter-agent --provider financialdatasets

Example Queries

Try asking Dexter questions like:

• "What was Apple's revenue growth over the last 4 quarters?"
• "Compare Microsoft and Google's operating margins for 2023"
• "Analyze Tesla's cash flow trends over the past year"
• "What is Amazon's debt-to-equity ratio based on recent financials?"

Dexter will automatically:

1. Break down your question into research tasks
2. Fetch the necessary financial data
3. Perform calculations and analysis
4. Provide a comprehensive, data-rich answer

Architecture

Dexter uses a multi-agent architecture with specialized components:

• Planning Agent: Analyzes queries and creates structured task lists
• Action Agent: Selects appropriate tools and executes research steps
• Validation Agent: Verifies task completion and data sufficiency
• Answer Agent: Synthesizes findings into comprehensive responses

Project Structure

dexter/
├── src/
│   ├── dexter/
│   │   ├── agent.py      # Main agent orchestration logic
│   │   ├── model.py      # LLM interface
│   │   ├── tools.py      # Financial data tools
│   │   ├── prompts.py    # System prompts for each component
│   │   ├── schemas.py    # Pydantic models
│   │   ├── utils/        # Utility functions
│   │   └── cli.py        # CLI entry point
├── pyproject.toml
└── uv.lock

Configuration

Dexter supports configuration via the Agent class initialization:

from dexter.agent import Agent

agent = Agent(
    max_steps=20,              # Global safety limit
    max_steps_per_task=5       # Per-task iteration limit
)

How to Contribute

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

Important: Please keep your pull requests small and focused. This will make it easier to review and merge.

License

This project is licensed under the MIT License.