# dexter **Repository Path**: mirrors_trending/dexter ## Basic Information - **Project Name**: dexter - **Description**: An autonomous agent for deep financial research - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-10-17 - **Last Updated**: 2026-02-14 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 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. Screenshot 2026-01-21 at 5 25 10 PM ## Table of Contents - [👋 Overview](#-overview) - [✅ Prerequisites](#-prerequisites) - [💻 How to Install](#-how-to-install) - [🚀 How to Run](#-how-to-run) - [📊 How to Evaluate](#-how-to-evaluate) - [🐛 How to Debug](#-how-to-debug) - [📱 How to Use with WhatsApp](#-how-to-use-with-whatsapp) - [🤝 How to Contribute](#-how-to-contribute) - [📄 License](#-license) ## 👋 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 - **Safety Features**: Built-in loop detection and step limits to prevent runaway execution [![Twitter Follow](https://img.shields.io/twitter/follow/virattt?style=social)](https://twitter.com/virattt) [![Discord](https://img.shields.io/badge/Discord-Join%20Server-5865F2?style=social&logo=discord)](https://discord.gg/jpGHv2XB6T) Screenshot 2026-01-21 at 5 22 19 PM ## ✅ Prerequisites - [Bun](https://bun.com) runtime (v1.0 or higher) - OpenAI API key (get [here](https://platform.openai.com/api-keys)) - Financial Datasets API key (get [here](https://financialdatasets.ai)) - Exa API key (get [here](https://exa.ai)) - optional, for web search #### Installing Bun If you don't have Bun installed, you can install it using curl: **macOS/Linux:** ```bash curl -fsSL https://bun.com/install | bash ``` **Windows:** ```bash powershell -c "irm bun.sh/install.ps1|iex" ``` After installation, restart your terminal and verify Bun is installed: ```bash bun --version ``` ## 💻 How to Install 1. Clone the repository: ```bash git clone https://github.com/virattt/dexter.git cd dexter ``` 2. Install dependencies with Bun: ```bash bun install ``` 3. Set up your environment variables: ```bash # Copy the example environment file cp env.example .env # Edit .env and add your API keys (if using cloud providers) # OPENAI_API_KEY=your-openai-api-key # ANTHROPIC_API_KEY=your-anthropic-api-key (optional) # GOOGLE_API_KEY=your-google-api-key (optional) # XAI_API_KEY=your-xai-api-key (optional) # OPENROUTER_API_KEY=your-openrouter-api-key (optional) # Institutional-grade market data for agents; AAPL, NVDA, MSFT are free # FINANCIAL_DATASETS_API_KEY=your-financial-datasets-api-key # (Optional) If using Ollama locally # OLLAMA_BASE_URL=http://127.0.0.1:11434 # Web Search (Exa preferred, Tavily fallback) # EXASEARCH_API_KEY=your-exa-api-key # TAVILY_API_KEY=your-tavily-api-key ``` ## 🚀 How to Run Run Dexter in interactive mode: ```bash bun start ``` Or with watch mode for development: ```bash bun dev ``` ## 📊 How to Evaluate Dexter includes an evaluation suite that tests the agent against a dataset of financial questions. Evals use LangSmith for tracking and an LLM-as-judge approach for scoring correctness. **Run on all questions:** ```bash bun run src/evals/run.ts ``` **Run on a random sample of data:** ```bash bun run src/evals/run.ts --sample 10 ``` The eval runner displays a real-time UI showing progress, current question, and running accuracy statistics. Results are logged to LangSmith for analysis. ## 🐛 How to Debug Dexter logs all tool calls to a scratchpad file for debugging and history tracking. Each query creates a new JSONL file in `.dexter/scratchpad/`. **Scratchpad location:** ``` .dexter/scratchpad/ ├── 2026-01-30-111400_9a8f10723f79.jsonl ├── 2026-01-30-143022_a1b2c3d4e5f6.jsonl └── ... ``` Each file contains newline-delimited JSON entries tracking: - **init**: The original query - **tool_result**: Each tool call with arguments, raw result, and LLM summary - **thinking**: Agent reasoning steps **Example scratchpad entry:** ```json {"type":"tool_result","timestamp":"2026-01-30T11:14:05.123Z","toolName":"get_income_statements","args":{"ticker":"AAPL","period":"annual","limit":5},"result":{...},"llmSummary":"Retrieved 5 years of Apple annual income statements showing revenue growth from $274B to $394B"} ``` This makes it easy to inspect exactly what data the agent gathered and how it interpreted results. ## 📱 How to Use with WhatsApp Chat with Dexter through WhatsApp by linking your phone to the gateway. Messages you send to yourself are processed by Dexter and responses are sent back to the same chat. **Quick start:** ```bash # Link your WhatsApp account (scan QR code) bun run gateway:login # Start the gateway bun run gateway ``` Then open WhatsApp, go to your own chat (message yourself), and ask Dexter a question. For detailed setup instructions, configuration options, and troubleshooting, see the [WhatsApp Gateway README](src/gateway/channels/whatsapp/README.md). ## 🤝 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.