Multi-Agent Financial Planner

What This Does

This playbook creates a virtual financial advisory firm with multiple specialized agents: a budget analyst, a conservative retirement planner, an aggressive investment advisor, and a lead partner who coordinates them all. The agents analyze your financial data, debate strategies, and produce a unified action plan with quarterly check-ins. Inspired by a Reddit user who built this exact setup and connected it to their YNAB budgeting software via API.

Prerequisites

• Claude Code installed and configured
• Your financial data in markdown or CSV format (assets, liabilities, income, spending)
• Optional: API access to your budgeting software (YNAB, Monarch, etc.)

Step-by-Step Setup

Step 1: Create the project structure

mkdir -p ~/financial-planner/{data,agents,reports,scripts}
cd ~/financial-planner

Step 2: Document your financial data

Create data/assets.md:

# Assets
- 401(k): $XXX,XXX
- Roth IRA: $XX,XXX
- Brokerage: $XX,XXX
- Savings: $XX,XXX
- Home equity: $XXX,XXX

Create data/liabilities.md:

# Liabilities
- Mortgage: $XXX,XXX at X.X%
- Student loans: $XX,XXX at X.X%
- Car loan: $XX,XXX at X.X%

Create data/income.md:

# Income
- Gross annual: $XXX,XXX
- Net monthly: $X,XXX
- Side income: $X,XXX/month

Step 3: Add transaction data

Export transactions from your bank or budgeting app as CSV files into data/transactions/. Claude can analyze CSV directly.

Step 4: Save the CLAUDE.md and launch

cd ~/financial-planner
claude

Try: "Interview me about my financial goals, then have the budget analyst review my spending data"

Example Usage

Start with the intake interview:

"Act as the lead advisor. Interview me about my short-term goals (1-2 years), medium-term goals (3-5 years), and retirement vision. Then dispatch the appropriate agents."

Deep spending analysis:

"Have the budget analyst review my last 12 months of transactions. Identify the top 3 areas where I'm overspending and calculate my true savings rate."

Investment strategy debate:

"Have the conservative and aggressive advisors each propose an investment allocation for my brokerage account. Then have the lead advisor synthesize their recommendations."

Retirement modeling:

"Model three retirement scenarios: retire at 55 (lean), 60 (comfortable), and 65 (generous). Factor in my current savings rate and expected market returns."

Quarterly review:

"Generate a Q1 quarterly review comparing my actual spending and investment performance against the plan from last quarter."

Tips

• Use Obsidian for data entry: If you already use Obsidian, keep your financial data there and point Claude Code at the vault folder.
• API integration: If you use YNAB, their API is free. Have Claude write a Python script to pull transaction summaries — this way the script is reusable without burning tokens each time.
• Agent debate produces better plans: The value comes from agents with different philosophies challenging each other. Don't skip the aggressive advisor even if you're conservative.
• Keep secrets out of files: Store API tokens in environment variables or a .json file you add to .gitignore. Reference them by name in your markdown docs.
• Automate the repetitive parts: Have Claude write scripts for data fetching and transaction categorization so you can re-run them without LLM involvement.

Troubleshooting

Problem: Claude's financial advice seems generic

Solution: Provide more specific data. The more detailed your income, spending, and goal information, the more personalized the analysis. Include actual numbers, not ranges.

Problem: Transaction data is too large for context

Solution: Have Claude write a Python script that pre-processes your CSV into a spending summary by category and month. Feed the summary to the agents instead of raw transactions.

Problem: Recommendations conflict between agents

Solution: That's by design. The lead advisor's job is to synthesize competing recommendations into a balanced plan. If you want more conservative output, weight the Boglehead advisor's input higher in your prompt.