Give it a business idea. Watch 7 AI agents debate, analyze, and build your startup blueprint — live.
Features · Architecture · Quick Start · Tech Stack · Demo
AgentForge is a production-grade multi-agent AI system that transforms a raw business idea into a comprehensive startup blueprint. It orchestrates 7 specialized AI agents through a sophisticated LangGraph state-machine workflow, featuring parallel execution, multi-step reasoning, and a unique Board Meeting debate mechanism for cross-validation.
💡 Example: You type "AI-powered fitness coaching app" → AgentForge deploys 7 agents simultaneously → Within minutes, you get a 30-page PDF blueprint covering market research, competitive analysis, technical architecture, financial projections, go-to-market strategy, operations plan, and risk assessment.
| Feature | Description |
|---|---|
| 🧠 7 Autonomous Agents | CEO, Research, Marketing, Developer, Finance, Analytics, Operations — each with domain-specific prompts and tools |
| 💎 Diamond Topology | Fan-out/fan-in parallel execution via LangGraph state machine (4 agents run simultaneously) |
| 🔄 Multi-Step Reasoning | Each agent runs a 3-step pipeline: Generate → Self-Critique → Refine with confidence scoring |
| ⚡ Real-Time Streaming | WebSocket-powered live visibility into agent thinking, reasoning steps, and inter-agent messages |
| 🏛️ Board Meeting Debate | Unique 4-round cross-functional debate where agents challenge each other's assumptions |
| 📄 Auto-Generated Reports | Beautiful PDF blueprints compiled from all agent outputs with charts and data |
| 🔐 JWT Authentication | Secure user sessions with protected routes and role-based access |
| 🐳 Docker Compose | One-command deployment with PostgreSQL, Redis, and all services containerized |
┌─────────────────────────────────────────────────────────────┐
│ PRESENTATION TIER │
│ React + Vite + WebSocket Client │
│ ┌──────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │Dashboard │ │ Live Stream │ │Report Viewer │ │
│ └──────────┘ └──────────────┘ └──────────────┘ │
└─────────────────────────┬───────────────────────────────────┘
│ REST + WebSocket
┌─────────────────────────┴───────────────────────────────────┐
│ APPLICATION TIER │
│ FastAPI + LangGraph + Claude/Gemini │
│ │
│ ┌─────────┐ ┌───────────────┐ ┌──────────────┐ │
│ │REST API │───▶│ Orchestrator │───▶│ AI Service │ │
│ └─────────┘ └───────┬───────┘ └──────────────┘ │
│ │ │
│ ┌───────────┼───────────┐ │
│ ▼ ▼ ▼ │
│ ┌────────┐ ┌────────┐ ┌────────┐ │
│ │Research│ │ Dev │ │Finance │ ... (7 agents) │
│ └────────┘ └────────┘ └────────┘ │
│ │ │
│ ┌───────────┴───────────┐ │
│ ▼ ▼ │
│ ┌──────────┐ ┌────────────┐ │
│ │Board Meet│ │Report Gen │ │
│ └──────────┘ └────────────┘ │
└─────────────────────────┬───────────────────────────────────┘
│
┌─────────────────────────┴───────────────────────────────────┐
│ DATA TIER │
│ PostgreSQL + Redis + SQLAlchemy ORM │
└─────────────────────────────────────────────────────────────┘
START → CEO Agent → ┬─ Research Agent ─┐
├─ Marketing Agent ─┤
├─ Developer Agent ─┤─→ Analytics → Operations → Board Meeting → Report → END
└─ Finance Agent ─┘
(parallel fan-out) (fan-in) (sequential) (debate)
┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Step 1: │────▶│ Step 2: │────▶│ Step 3: │────▶│ Validate: │
│ Generate │ │ Self-Critique│ │ Refine │ │ Pydantic │
│ (Claude API)│ │ (3 flaws) │ │ (Fix flaws) │ │ (JSON safe) │
└──────────────┘ └──────────────┘ └──────────────┘ └──────────────┘
│ │ │
🔴 stream: 🔴 stream: 🔴 stream:
agent_thinking reasoning_step agent_completed
sequenceDiagram
autonumber
actor User as Startup Founder
participant UI as React UI (WebSocket Client)
participant API as FastAPI Gateway
participant LG as LangGraph Orchestrator
participant AG as 7-Agent Cluster
participant DB as PostgreSQL Database
User->>UI: Submits raw startup idea
UI->>API: POST /api/blueprint (Initialize Session)
API->>DB: Create project record & audit trail
API->>LG: Trigger graph orchestration (background task)
LG->>UI: Establish WebSocket connection
rect rgb(30, 41, 59)
note over LG, AG: Parallel Agent Execution (Diamond Topology)
LG->>AG: Fan-out: Invoke CEO, Research, Marketing, Dev, & Finance agents
AG-->>UI: Live-stream thinking tokens & reasoning steps via WS
end
rect rgb(15, 23, 42)
note over LG, AG: Consensus Debate & Report Generation
LG->>AG: Initiate structured 4-Round Board Meeting Debate
AG-->>UI: Broadcast inter-agent debate statements
LG->>API: Compile final consolidated markdown report
API->>DB: Save generated PDF/Markdown blueprint files
end
API-->>UI: Emit workflow completion signal
UI->>User: Render 3D debate visualization & enable PDF download
- Python 3.11+
- Node.js 18+
- Docker & Docker Compose
- Claude API key or Gemini API key
git clone https://github.com/rajmodi262/agentforge.git
cd agentforge
cp .env.example .env
# Edit .env with your API keysdocker-compose up --build# Backend
cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8000
# Frontend (new terminal)
cd frontend
npm install
npm run dev| Service | URL |
|---|---|
| 🌐 Frontend | http://localhost:5173 |
| ⚡ Backend API | http://localhost:8000 |
| 📚 API Docs | http://localhost:8000/docs |
| Layer | Technology | Purpose |
| 🧠 AI Engine | LangGraph, Claude API, Gemini API | Agent orchestration, multi-step LLM reasoning |
| ⚡ Backend | FastAPI, SQLAlchemy, Pydantic | Async REST API, ORM, schema validation |
| 🔄 Real-Time | WebSocket, Redis | Live agent streaming, event broadcasting |
| 🎨 Frontend | React 18, Vite, GSAP | Interactive dashboard, 3D animations |
| 🗄️ Database | PostgreSQL, SQLite (dev) | Persistent storage, session management |
| 🐳 DevOps | Docker Compose, GitHub Actions | Containerized deployment, CI/CD |
agentforge/
├── backend/
│ ├── app/
│ │ ├── agents/ # 7 AI agents + orchestrator
│ │ │ ├── base_agent.py # Multi-step reasoning engine
│ │ │ ├── orchestrator.py # LangGraph workflow builder
│ │ │ ├── board_meeting.py # Multi-round debate engine
│ │ │ ├── ceo_agent.py
│ │ │ ├── research_agent.py
│ │ │ ├── marketing_agent.py
│ │ │ ├── developer_agent.py
│ │ │ ├── finance_agent.py
│ │ │ ├── analytics_agent.py
│ │ │ ├── operations_agent.py
│ │ │ └── report_compiler.py
│ │ ├── api/ # REST endpoints
│ │ ├── models/ # Pydantic schemas + DB models
│ │ ├── services/ # Claude/Gemini, WebSocket, RAG
│ │ └── main.py # FastAPI application entry
│ ├── tests/ # Pytest test suite
│ └── requirements.txt
├── frontend/ # React + Vite SPA
├── docker-compose.yml
└── README.md
What makes AgentForge unique is the Board Meeting Debate Engine. After all 7 agents complete their analysis, they enter a structured 4-round debate:
| Round | Matchup | Topic |
|---|---|---|
| 1 | Finance → CEO | Revenue model & unit economics viability |
| 2 | Developer → Marketing | Go-to-market timeline vs. technical feasibility |
| 3 | Research → Finance | TAM/SAM accuracy & market sizing assumptions |
| 4 | Operations → Developer | Infrastructure scaling readiness |
A Board Secretary then synthesizes all debates into a consensus score, identifies key risks, and produces the final strategic recommendation.
When you submit a business idea, AgentForge generates a comprehensive blueprint including:
- ✅ Vision & Problem Statement — Clear articulation of the opportunity
- ✅ Market Research — TAM/SAM/SOM analysis with competitor mapping
- ✅ Go-to-Market Strategy — Pricing, channels, customer acquisition cost
- ✅ Technical Architecture — Full stack recommendation with MVP roadmap
- ✅ Financial Projections — 3-year revenue model with break-even analysis
- ✅ KPI Dashboard — Key metrics and success criteria
- ✅ Operations Plan — Hiring roadmap, launch timeline, risk mitigation
- ✅ Board Consensus — Cross-validated insights from agent debates
# AI Provider (at least one required)
ANTHROPIC_API_KEY=sk-ant-... # Claude API
GOOGLE_API_KEY=AIza... # Gemini API (free tier)
# Database
DATABASE_URL=postgresql://user:pass@localhost/agentforge
# Security
JWT_SECRET_KEY=your-secret-key
# Optional
MOCK_MODE=true # Use dynamic mocks (no API cost)
LOG_LEVEL=INFOContributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License — see the LICENSE file for details.
Built with ❤️ by Raj Modi
If you found this useful, please ⭐ the repo!