TrueWealth AI is an end-to-end Multi-Agent Financial Advisor AI System that delivers reliable, real-time investment insights by combining LangGraph-powered orchestration, advanced LLM reasoning (GPT-OSS-120B via Groq), and RAG with ChromaDB + HuggingFace embeddings. It features Planner, Retriever, Generator, News, Web Search, and Memory agents with intelligent tool routing, retry logic, and multi-source knowledge fusion, ensuring professional-grade accuracy. The system supports financial PDF ingestion, live market data, and fallback web search, guaranteeing comprehensive coverage across diverse queries.
Engineered with a modular, scalable architecture, it includes a high-performance FastAPI backend for agent orchestration, a Vite + React responsive UI (Vite, React, Tailwind CSS) for a premium client experience, and full Dockerization for portability. Deployed with a CI/CD pipeline, it adheres to enterprise software practices.
Achievements: Benchmarked on 100+ financial queries, the system achieved 99% query coverage with <3s P95 latency reducing manual research effort by ~40% while ensuring enterprise-level reliability, scalability, and real-world deployment readiness.
demo.mp4
Try the real-time TrueWealth AI: TrueWealth AI – Click Here
TrueWealth-AI/
├── .github/
│ └── workflows/
│ ├── ci.yml # Backend & Frontend Testing Workflow
│ └── main.yml # Docker Build & Deployment Workflow
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├── backend/
│ ├── app/
│ │ ├── agents/ # Multi-agent Logic
│ │ │ ├── duckduckgo.py # Web Search Agent
│ │ │ ├── executor.py # Retry & Routing Logic
│ │ │ ├── generator.py # Answer Synthesis Agent
│ │ │ ├── llm.py # Direct LLM Query Agent
│ │ │ ├── memory.py # Conversational Memory Agent
│ │ │ ├── planner_agent.py # Planning & Initialization Agent
│ │ │ ├── rag.py # Document Retrieval Agent
│ │ │ └── yfinance.py # Market News Agent
│ │ ├── core/ # System Kernels
│ │ │ ├── config.py # Constants & Paths
│ │ │ ├── logger.py # Centralized Logging
│ │ │ ├── state.py # Agentic State Definitions
│ │ │ └── workflow.py # LangGraph StateGraph Definition
│ │ ├── tools/ # Retrieval & Data Tools
│ │ │ ├── document_loader.py # PDF Ingestion
│ │ │ ├── llm_client.py # Groq LLM Client
│ │ │ ├── search_tools.py # Search Tool Definitions
│ │ │ └── vector_store.py # ChromaDB Management
│ │ └── main.py # FastAPI Application Entry
│ ├── logs/ # Persistent runtime & startup logs
│ ├── tests/ # 100% Logic Coverage Test Suite
│ │ ├── conftest.py # Mocking Infrastructure
│ │ ├── test_app.py # API Endpoint Tests
│ │ ├── test_document_loader.py
│ │ ├── test_duckduckgo_agent.py
│ │ ├── test_executor.py
│ │ ├── test_generator_agent.py
│ │ ├── test_llm_agent.py
│ │ ├── test_memory.py
│ │ ├── test_planner.py
│ │ ├── test_rag.py
│ │ ├── test_state.py
│ │ ├── test_tool_getters.py
│ │ ├── test_vector_store.py
│ │ ├── test_workflow.py
│ │ └── test_yfinance_agent.py
│ ├── .env.example # Reference for required API keys
│ ├── Dockerfile # Python 3.12 Slim Environment
│ └── requirements.txt # Pegged Backend Dependencies
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├── frontend/
│ ├── src/
│ │ ├── api/ # Axios Client for FastAPI
│ │ │ └── client.js
│ │ ├── components/ # Glassmorphic UI Components
│ │ │ ├── ChatInterface.jsx
│ │ │ ├── Sidebar.jsx
│ │ │ ├── StatusBar.jsx
│ │ │ └── WelcomeCard.jsx
│ │ ├── test/ # Vitest Setup
│ │ │ └── setup.js
│ │ ├── App.jsx # Main Routing & Layout
│ │ ├── index.css # Tailwind & Glassmorphic Styles
│ │ └── main.jsx # React Entry Point
│ ├── public/ # Static Assets
│ ├── Dockerfile # Node.js 20 Build Environment
│ ├── package.json # Frontend Dependencies & ESLint
│ ├── tailwind.config.js # DaisyUI & Theme Config
│ └── vite.config.js # Vite & Proxy Configuration
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├── app.png # Demo picture
├── app-1.png # Demo picture
├── demo.mp4 # Demo video
├── docker-compose.yml # Unified Container Orchestration
├── LICENSE # MIT License
├── README.md # Project Documentation
├── render.yaml # Render Deployment Config
├── run.py # Unified Local Startup Script
└── setup.py # Backend Package Metadata
| Step | Feature | Tech Stack / Tool Used |
|---|---|---|
| 1 | LLM-based Financial Query Understanding | Groq + GPT-OSS-120B |
| 2 | Professional Tone Personalization | Prompt Engineering + Advisor Persona Templates |
| 3 | RAG-based Financial Answering | LangChain + ChromaDB + Sentence Transformers |
| 4 | Financial Document Retriever Agent | RetrieverAgent + Vector Store Search |
| 5 | Answer Generator Agent | GeneratorAgent (LLM-based factual + professional financial style) |
| 6 | Financial News Retrieval Agent | YahooFinanceNewsTool |
| 7 | Web Search Agent (Fallback) | DuckDuckGo Search Tool |
| 8 | Planner Agent | LangGraph Planner Node |
| 9 | Intelligent Tool Routing & Fallback | Retry Logic + Conditional Branching + Multi-step Tool Selection |
| 10 | Short-Term Conversational Memory | LangGraph Memory Integration (Buffer-based) |
| 11 | PDF Knowledge Ingestion | PyPDFLoader + RecursiveCharacterTextSplitter |
| 12 | Vector Embedding & Storage | HuggingFaceEmbeddings + ChromaDB |
| 13 | State-based Multi-Agent Orchestration | LangGraph StateGraph + Conditional Edges + Dynamic State Updates |
| 14 | Multi-source Knowledge Fusion | LLM + RAG + Yahoo Finance + DuckDuckGo Combined Answer Synthesis |
| 15 | API Development & Hosting | FastAPI (High-performance asynchronous endpoints) |
| 16 | Modular Code Architecture | Separation of Concerns + Service/Agent Modules |
| 17 | Responsive Premium UI | Vite + React + Tailwind CSS + DaisyUI |
| 18 | Cloud Deployment | Render (Production hosting) |
| 19 | CI/CD Pipeline | GitHub Actions (Automated Testing & Docker Deployment) |
| 20 | Containerization for Portability | Docker (Multi-stage builds for Backend & Frontend) |
| Metric | Value |
|---|---|
| Mean Latency | 2.00s |
| P95 Latency | 3.05s |
| Query Coverage | 99% |
flowchart TD
A[User Query] --> B[Recall Memory]
B --> C[Planner Agent]
C --> D[LLM Analysis Attempt]
D -->|Failure / Incomplete| E[Executor Routing]
E --> F[RAG: PDF Retrieval]
E --> G[Yahoo Finance Search]
E --> H[DuckDuckGo Web Search]
F --> I[Synthesize Knowledge]
G --> I
H --> I
I --> J[Generator Agent]
D -->|Success| J
J --> K[Store Memory]
K --> L[Advisor Response]
The project implements a strict testing regime to ensure 100% logic coverage and UI stability.
Located in backend/tests/, these tests cover all agents, tools, and the LangGraph workflow.
- Run all tests:
cd backend pytest - Run with coverage:
pytest --cov=app tests/
Component-level and integration tests using Vitest and React Testing Library.
- Run tests:
cd frontend npm test
- Run linter:
npm run lint
TrueWealth AI is fully containerized for consistent deployment across environments.
- Unified Launch: Build and start both backend and frontend services using Docker Compose.
docker-compose up --build
- Persistent Logs: Application logs are automatically mapped to the host's
backend/logs/directory for persistence across container restarts.
- Backend Dockerfile: Optimized Python 3.12 slim image.
- Frontend Dockerfile: Multi-stage build that compiles React via Node.js and serves the production build.
GitHub Actions handles the full lifecycle:
- CI: Linting and tests are run on every pull request.
- CD: On successful merge to
main, Docker images are rebuilt and deployed to Render.
- Python 3.12+
- Node.js 20+
- Groq API Key
- Clone & Explore:
git clone https://github.com/Md-Emon-Hasan/TrueWealth-AI.git cd TrueWealth-AI - Environment:
Copy
backend/.env.exampletobackend/.envand set yourGROQ_API_KEY. - Launch:
python run.py
- UI:
http://localhost:3000 - API Docs:
http://localhost:5001/docs
- UI:
Md Emon Hasan
- Email: emon.mlengineer@gmail.com
- WhatsApp: +8801834363533
- GitHub: Md-Emon-Hasan
- LinkedIn: Md Emon Hasan
- Facebook: Md Emon Hasan