Financial Coach (LangGraph)

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What it does

• Ask questions about their spending in natural language
• Get breakdowns by category, merchant, or time period
• Receive personalized recommendations and savings opportunities
• Have multi-turn conversations with conversation memory

You: Show me my spending by category

╔══════════════════════════════════════════════════════════╗
║           💡 FINANCIAL COACHING INSIGHTS                 ║
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📊 YOUR SPENDING ANALYSIS
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  💰 Real Monthly Spending: $2,543.22
  🔴 Highest Expense: Rent @ $1,200.00 (47.2% of total)
  💡 Found 3 optimization opportunities totaling $425/month

💡 RECOMMENDATIONS FOR YOU
──────────────────────────────────────────────────────────────
  1. Your Rent is your largest expense (47.2%). This should be priority #1.
  2. Meal prep 2x/week could save $127/month (Highest impact)

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Architecture

1. Enrich query - Adds context about time period, categories, and merchants
2. Query Snow Leopard - Converts natural language to SQL and retrieves data
3. Analyze and coach - Generates insights, recommendations, and follow-up questions
4. Format response - Creates formatted CLI output with Rich

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Key components

• LangGraph orchestrates the multi-step workflow
• Snow Leopard handles natural language to SQL conversion
• Coaching analyzer generates personalized financial insights
• Memory manager maintains conversation context across turns
• Rich CLI provides formatted terminal output

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Run the example

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Prerequisites

• Python 3.10+
• OpenAI API key
• Snow Leopard API key
• A datafile uploaded to Snow Leopard Playground

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Setup

git clone https://github.com/SnowLeopard-AI/snowy-examples.git
cd snowy-examples/agent_examples/financial_coach_langchain

pip install -r requirements.txt

python data/create_sample_data.py

OPENAI_API_KEY=your-openai-key
SNOWLEOPARD_API_KEY=your-snowleopard-key
SNOWLEOPARD_DATAFILE_ID=your-datafile-id

python main.py

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Example queries

• “Show me my spending by category”
• “Which merchants did I spend the most at?”
• “Compare this month vs last month”
• “How much did I spend on groceries?”

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Next steps

• View the full source code on GitHub
• Learn more about LangGraph
• Explore the LangGraph quickstart