# 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. ## 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. It’s not just another chatbot. It’s an agent that plans ahead, verifies its progress, and keeps iterating until the job is done. **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 - **Safety Features**: Built-in loop detection and step limits to prevent runaway execution [](https://twitter.com/virattt) ### Prerequisites - Python 3.10 or higher - [uv](https://github.com/astral-sh/uv) package manager - OpenAI API key (get [here](https://platform.openai.com/api-keys)) - Financial Datasets API key (get [here](https://financialdatasets.ai)) ### Installation 1. Clone the repository: \`\`\`bash git clone https://github.com/virattt/dexter.git cd dexter \`\`\` 2. Install dependencies with uv: \`\`\`bash uv sync \`\`\` 3. Set up your environment variables: \`\`\`bash # 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: \`\`\`bash uv run dexter-agent \`\`\` ### 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: \`\`\`python 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.