README.md
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.
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:
If you don't have Bun installed, you can install it using curl:
macOS/Linux:
curl -fsSL https://bun.com/install | bash
Windows:
powershell -c "irm bun.sh/install.ps1|iex"
After installation, restart your terminal and verify Bun is installed:
bun --version
git clone https://github.com/virattt/dexter.git
cd dexter
bun install
# 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
# 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
Run Dexter in interactive mode:
bun start
Or with watch mode for development:
bun dev
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:
bun run src/evals/run.ts
Run on a random sample of data:
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.
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:
Example scratchpad entry:
{"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.
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:
# 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.
Important: Please keep your pull requests small and focused. This will make it easier to review and merge.
This project is licensed under the MIT License.