Beyond Black-Box Prompts: Building an Explainable Multi-Agent Financial Intelligence Platform with FastAPI & React

In quantitative finance and retail investing, treating Large Language Models as a single "oracle" prompt is dangerous. When you ask a single LLM whether a stock is a buy, it frequently exhibits confirmation bias, hallucinations with financial figures, and a total lack of calibrated risk management.

Real-world institutional investment committees don't rely on a single analyst. They rely on specialized, competing personas:

To solve this problem, I built Vibe-Finance: an end-to-end multi-agent financial intelligence and explainability platform built with a Python/FastAPI backend, vector RAG over corporate filings, and a modern React/Vite/Tailwind client.

Here is a breakdown of how the architecture works, how the adversarial debate engine resolves conflict, and the engineering trade-offs made along the way.

Instead of feeding everything into one monolithic prompt, Vibe-Finance separates data ingestion, analysis, adversarial cross-examination, and synthesis into dedicated layers:

flowchart TD
    UI[Frontend: React 18 + Vite + Tailwind + TradingView Charts] -->|REST / JSON| API[Backend: FastAPI]

    subgraph MultiAgentEngine [Multi-Agent Intelligence Layer]
        QA[Quant Agent\nWilder RSI-14, 50-EMA, 200-SMA, MACD]
        FA[Fundamental RAG Agent\nSEC 10-K Filings & Polarity Extraction]
        BM[Behavioral Risk Manager\nDrawdown Caps & Beta Exposure Limits]
        DEB[Adversarial Debate Engine\nCross-Examination & Calibration]
        SYN[Synthesis Agent\nHybrid Gemini 1.5 Flash + Deterministic Fallback]
    end

    API --> MultiAgentEngine
    QA --> DEB
    FA --> DEB
    BM --> DEB
    DEB --> SYN
    SYN --> API

    subgraph Storage [Persistence & Ingestion Layer]
        SQLITE[(SQLite: Profiles, Holdings, Decision Memos)]
        CACHE[(Market Cache: Live Feed + Offline Snapshot Fallback)]
    end
    API --- Storage

The Quant Agent operates purely on hard mathematical indicators. It executes:

This agent parses regulatory corporate filings (SEC 10-Ks, 10-Qs, earnings conference transcripts) using vector embeddings.

The Risk Manager protects the investor against behavioral pitfalls. It evaluates:

Conservative, Moderate, or Aggressive). The most critical component of the platform is the Debate Engine. When the Quant Agent sees a strong breakout signal (Bullish) but the Fundamental RAG Agent detects margin compression in recent filings (Bearish), the system detects agent friction.

Rather than averaging their scores, the engine runs a structured adversarial exchange:

class AdversarialDebateEngine:
    """Detects agent friction and calibrates overall decision confidence."""

    def evaluate_consensus(self, quant_signal, rag_signal, risk_profile):
        friction_detected = quant_signal.polarity != rag_signal.polarity

        if friction_detected:
            calibrated_confidence = min(quant_signal.confidence, rag_signal.confidence) * 0.85
            debate_summary = (
                f"Friction Detected: Technical momentum is {quant_signal.polarity}, "
                f"but corporate disclosures indicate {rag_signal.polarity} risks."
            )
        else:
            calibrated_confidence = (quant_signal.confidence + rag_signal.confidence) / 2.0
            debate_summary = "Agents aligned on directional momentum."

        return {
            "friction": friction_detected,
            "calibrated_confidence": round(calibrated_confidence, 2),
            "debate_summary": debate_summary
        }

The resulting debate log is passed to a hybrid Gemini 1.5 Flash synthesis engine that returns structured JSON. If the LLM provider experiences network latency or rate-limiting, the system automatically falls back to an algorithmic deterministic engine—ensuring 100% operational uptime.

On the client side (React 18 + Vite + Tailwind CSS), technical analysis is visualized using TradingView Lightweight Charts (lightweight-charts v5.2.1):

Building autonomous agents in finance demands strict testing. The repository includes an end-to-end automated test suite verifying 21 critical scenarios (python -u backend/test_suite.py):

The entire codebase is open-source under the Apache 2.0 License:

  git clone https://github.com/Khare69/Vibe-Finance.git
  cd Vibe-Finance
  docker compose up --build

I’d love to hear your thoughts, feedback, and architectural questions in the comments below! If you find the project interesting, feel free to drop a ⭐ on the repository!