🚀 AI Crowdfunding Platform

Autonomous AI agents that create and fund software startups in real time.

An end-to-end demonstration of agentic AI development: a Founder Agent imagines startups and an Investor Agent evaluates and funds them — all streamed live to a React dashboard. Built with CrewAI + Google Gemini, FastAPI, and the Model Context Protocol.

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Why I Built This

I wanted to build something that demonstrates autonomous multi-agent collaboration beyond "chatbot with tools." This project showcases:

• Agents with real autonomy — the Founder creates projects from scratch; the Investor makes independent funding decisions using a scoring heuristic.
• Tool-calling without regex — Gemini's native function-calling eliminates the fragile output-parsing common in agent frameworks.
• Full-stack observability — every agent thought, tool call, and result streams to an event bus for real-time visualization.
• Protocol-driven connectivity — the backend exposes its capabilities via MCP, allowing it to be controlled by any compliant LLM client (like Claude Desktop).

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Features

1. Autonomous Agent Loop

Agents operate in a continuous loop:

1. Founder invents a startup concept, name, and funding goal.
2. Investor analyzes the deal flow, scoring projects on innovation and market fit.
3. Execution: if a project meets the criteria, the Investor deploys capital instantly.

Live event stream (right) showing real-time agent reasoning and tool usage.

2. High-Fidelity Agent Output

Agents don't just "talk" — they execute complex workflows. Below is the terminal output showing a full cycle where an Investor Agent evaluates projects and commits funding.

3. Universal MCP Integration

The entire platform is an MCP server. This means you can connect Claude Desktop (or any MCP client) to your running backend and interact with the data naturally.

"Claude, find me all AI projects seeking under $50k" -> Claude calls the search_projects tool directly.

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Architecture

graph TD
    Client[React Dashboard] -->|HTTP/WebSocket| API[FastAPI Backend]

    subgraph "AI Agent Layer (CrewAI)"
        Founder[Founder Agent]
        Investor[Investor Agent]
    end

    subgraph "Capabilities"
        Tools[Tool Definitions]
        LLM[Gemini 2.5 Flash]
    end

    Founder -->|Uses| Tools
    Investor -->|Uses| Tools
    Tools -->|HTTP| API

    MCP[MCP Server] -->|stdio| API
    Claude[Claude Desktop] -->|MCP Protocol| MCP

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Key Design Decisions

Decision	Rationale
Gemini over local LLMs	Gemini 2.5 Flash reliably calls tools without needing regex fallbacks — reduced agent code by ~65%.
Heuristic + LLM hybrid scoring	The Investor uses a deterministic scoring formula first, then lets the LLM reason about the top candidates. This makes investment decisions explainable.
Event-driven architecture	Every agent thought is a structured event (agent_thinking, tool_call, tool_result), enabling real-time dashboards and audit trails.
MCP integration	The same backend that agents use is also exposed as MCP tools, so any LLM client can interact with the platform without custom glue code.
Shared LLM factory	Single build_llm() function replaces duplicated config across agents — robust configuration management.

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Quick Start

Prerequisites

• Python 3.11+
• Node.js 18+
• A Gemini API key

Setup

# Clone and enter the project
git clone https://github.com/<your-username>/ai-crowdfund-platform.git
cd ai-crowdfund-platform

# Create a virtual environment
python -m venv .venv
source .venv/bin/activate

# Install Python dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env and add your GEMINI_API_KEY

Run

# Terminal 1 — Start the backend
uvicorn backend.app:app --reload --port 8000

# Terminal 2 — Start the frontend
cd frontend && npm install && npm run dev

# Terminal 3 — Run the autonomous agents
python -m agents.runner

Then open http://localhost:5173 to see the dashboard.

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Connecting context-aware AI (Claude)

To let Claude Desktop control the platform:

1. Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "crowdfund": {
      "command": "/path/to/project/.venv/bin/python",
      "args": ["/path/to/project/mcp_server/run.py"],
      "env": {
        "BACKEND_URL": "http://localhost:8000"
      }
    }
  }
}

2. Restart Claude. You can now ask: "Check the crowdfunding platform for new sports startups."

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Tech Stack

Layer	Technology
AI Agents	CrewAI, Google Gemini 2.5 Flash
Backend	FastAPI, Pydantic, WebSocket
MCP Server	Model Context Protocol (stdio)
Frontend	React 18, Vite, Tailwind CSS
Testing	pytest, FastAPI TestClient

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What I'd Add Next

• Due Diligence Agent — A third agent that researches market data before the Investor commits capital.
• Pydantic structured output — Replace free-text reflection with typed InvestmentDecision models.
• Agent memory — Persistent memory so the Investor recalls past decisions and avoids over-concentration.
• Docker Compose — One-command launch for backend + frontend + agents.

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License

MIT