An LLM-powered multi-agent system that generates structured market intelligence reports by combining real-time data, financial metrics, and retrieval-augmented knowledge.
Given a company name, the system:
- Fetches real-time news
- Retrieves financial metrics
- Augments context using RAG (FAISS)
- Applies LLM reasoning
- Generates:
- Market summary
- Risks & opportunities
- Investment strategy + confidence
UI → FastAPI → LangGraph → Agents → LLM → Response
➡️ For detailed architecture, design, tradeoffs :
- Refer to: system_design.md
- Backend: FastAPI
- Orchestration: LangGraph
- LLM: Groq (LLaMA3)
- Embeddings: HuggingFace
- Vector DB: FAISS
- Frontend: Streamlit
app/
├── agents/
├── api/
├── config/
├── prompts/
├── schemas/
├── services/
├── tools/
├── workflows/
└── main.py
data/
docker/
system_design.md
- Clone repo
git clone https://github.com/Nithin00614/Market-Intelligence-Layer
cd Market-intelligence-agent
- Setup environment
python -m venv venv
Activate:
- Windows → "venv\Scripts\activate"
- Mac/Linux → "source venv/bin/activate"
- Install dependencies
pip install -r requirements.txt
- Configure environment variables
Create ".env" file:
- GROQ_API_KEY=your_api_key
- MODEL_NAME=llama3-8b-8192
- NEWS_API_KEY = your_api_key
- FINANCIAL_API_KEY = your_api_key
- Run backend
uvicorn app.main:app --reload
- Run UI
streamlit run app.py
📤 Example Request
{ "company": "Apple", "question": "Analyze market position" }
📈 Example Output
{ "analysis": { "summary": "...", "key_risks": [...], "opportunities": [...] }, "strategy": { "decision": "Hold", "confidence": "High" } }
- Relies on external APIs (latency dependent)
- LLM outputs are probabilistic
- FAISS is not distributed (local setup)
🎯 Key Highlights
- Multi-agent AI system using LangGraph
- Tool-augmented LLM reasoning
- Retrieval-Augmented Generation (RAG)
- Modular, production-style architecture