Summary FinChain-Agent simulates a decentralized financial analysis market, addressing the need for automated, verifiable, and incentivized insights. It targets engineers and researchers by providing a multi-agent framework for generating high-quality, auditable financial reports through parallel competition and a token economy, offering a novel approach to collaborative AI analysis with built-in accountability.

How It Works This project utilizes LangChain and LangGraph to orchestrate a multi-agent system. Three AI financial analysts concurrently research and draft reports. A Chief Auditor provides critical feedback, guiding a second round of report refinement. The Auditor then judges the optimized reports, selecting a winner. This iterative competition and AI auditing ensure analytical rigor. The system incorporates a token economy, rewarding the winning analyst with FCA tokens, and records the validated report's hash on a simulated blockchain for immutability.

Quick Start & Requirements Setup requires creating a Conda environment (e.g., conda create -n FinchainAgent python=3.11, conda activate FinchainAgent) and installing dependencies (pip install -r requirements.txt). Users must configure API keys for DeepSeek and Tavily by creating a .env file with DEEPSEEK_API_KEY=your_deepseek_api_key and TAVILY_API_KEY=your_tavily_api_key. The system is initiated via python main.py.

Highlighted Details

• Iterative Competition: Multi-round process: analysts draft, receive auditor critique, and refine reports.
• AI Judge & Auditor: Chief Auditor provides feedback and selects the best report.
• Token Economy: Integrates FCA Token system, rewarding winners and maintaining token_ledger.json.
• Automated HTML Reports: Generates detailed HTML reports for winning analyses, including blockchain hashes.
• Blockchain Mock: Secures winning report hashes on a simulated ledger (blockchain_ledger.json) for tamper-proof records.

Maintenance & Community No specific details regarding project maintainers, community channels, or a public roadmap were found in the provided README.

Licensing & Compatibility The README does not specify a software license. Compatibility for commercial use or closed-source linking cannot be determined without further information.

Limitations & Caveats The parallel execution of three analysts significantly increases API token consumption, requiring careful monitoring. The system is also dependent on the availability and performance of the DeepSeek and Tavily APIs.