FinAgents

💡 Inspiration

Modern mobile banking and finance applications often lack customization, usability, and flexibility. Users struggle to efficiently navigate bank statements, bills, documents, and financial insights.

While some apps attempt to solve personal finance tracking, they rarely address the needs of business professionals who require assistance with administrative and analytical tasks such as audit report generation, compliance checks, policy validation, and business intelligence.

We built FinAgents to bridge this gap by providing secure, intelligent AI agents capable of handling any personal or business finance task through natural language.

🧠 What it does

FinAgents is a multi-agent AI system that connects to a user’s financial ecosystem—databases, documents, and other data sources—and allows intelligent agents to securely interact with them.

Users can delegate complex tasks such as:

• Managing and analyzing spending
• Generating financial insights and reports
• Auditing databases and detecting anomalies
• Identifying compliance or security issues
• Performing business intelligence and analytics

In short, FinAgents acts as a universal AI financial operations assistant for both individuals and organizations.

⚙️ How we built it

At the core of FinAgents is a multi-agent architecture powered by a central orchestrator agent that delegates tasks to specialized sub-agents.

• Each sub-agent is exposed as a tool via an MCP (Model Context Protocol) server
• Sub-agents have controlled, secure access to specific resources and tools
• Authentication and user management are handled using Supabase
• The frontend is built with Next.js
• Backend services are powered by FastAPI
• We leverage open-source LLMs running locally via Ollama

This architecture ensures modularity, security, and scalability across financial workflows.

🚧 Challenges we ran into

• Designing a reliable multi-agent orchestration system
• Handling latency and concurrency in asynchronous agent workflows
• Managing performance constraints from locally hosted LLMs
• Ensuring secure and controlled access to sensitive financial data

🏆 Accomplishments that we're proud of

• Successfully implemented a working multi-agent system with orchestration
• Achieved smooth collaboration between specialized finance agents
• Built a secure pipeline for connecting LLMs to real financial resources
• Demonstrated end-to-end task execution across complex workflows

📚 What we learned

• How to securely connect LLMs to databases and external tools
• Practical challenges of multi-agent coordination in real systems
• Tradeoffs between local LLM performance and system responsiveness
• Designing modular AI systems using tool-based architectures (MCP)

🚀 What's next for FinAgents

• Expanding the library of specialized finance sub-agents
• Allowing users to fully customize and configure their own agents
• Improving the latency and scalability of multi-agent workflows
• Adding enterprise-grade compliance and audit tooling
• Building a richer analytics dashboard for financial insights

Built With

• docker
• fastapi
• langchain
• nextjs
• postgresql
• python