MCP Container

octagon-vc-agents

An MCP server that runs AI-driven venture capitalist agents (Fred Wilson, Peter Thiel, etc.), whose thinking is continuously enriched by Octagon Private Markets' real-time deals, valuations, and deep research intelligence. Use it to spin up programmable "VC brains" for pitch feedback, diligence simulations, term sheet negotiations, and more.

GitHubWebsite

GitHub Stars

14

User Rating

Not Rated

Favorites

0

Views

36

Forks

2

Issues

1

Installation
Difficulty
Intermediate
Estimated Time
10-20 minutes
Requirements
Python 3.8以上

Installation

Installation

Prerequisites

Please specify required software and versions:
Python: 3.8 or higher

Installation Steps

1. Clone Repository

bash
git clone https://github.com/OctagonAI/octagon-vc-agents.git
cd octagon-vc-agents

2. Install Dependencies

bash
pip install -r requirements.txt

3. Start Server

bash
python main.py

Troubleshooting

Common Issues

Issue: Server won't start Solution: Check Python version and reinstall dependencies. Issue: Invalid API key Solution: Verify the API key and ensure proper configuration.

Configuration

Configuration

Basic Configuration

Server Setup

Edit config.json to make necessary configurations: 
json
{
  "api_key": "your-api-key",
  "server_port": 5000
}

Environment Variables

Set the following environment variables as needed: 
bash
export API_KEY="your-api-key"
export DEBUG="true"

Advanced Configuration

Security Settings

Store API keys in environment variables or secure configuration files
Set appropriate file access permissions
Adjust logging levels

Performance Tuning

Configure timeout values
Limit concurrent executions
Set up caching

Examples

Examples

Basic Usage

Programmatic Usage

python
import requests

def call_vc_agent(agent_name, params):
    response = requests.post(
        f'http://localhost:5000/api/{agent_name}',
        json=params
    )
    return response.json()


Usage example

result = call_vc_agent('octagon-marc-andreessen-agent', {'pitch': 'New startup idea'})
print(result)

Use Cases

Obtaining pitch feedback for a specific startup
Simulating diligence on investment opportunities
Testing negotiation strategies for term sheets
Simulating investment decisions based on real-time market data
Author Information
OctagonAI
OctagonAI
GitHub

5

Followers

14

Repositories

0

Gists

0

Total Contributions

Tags
AIMachine LearningVenture CapitalSimulationInvestmentFinanceChatGPTReal-time DataResearch IntelligenceDiligenceNegotiationPitch Feedbackagentagentic-aiai
Related MCPs
sample-agentic-ai-web logo
sample-agentic-ai-web
39

This project demonstrates how to use AWS Bedrock with Anthropic Claude and Amazon Nova models to create a web automation assistant with tool use, human-in-the-loop interaction, and vision capabilities.

Python
nexent logo
nexent
2638

Nexent is a zero-code platform for auto-generating agents — no orchestration, no complex drag-and-drop required. Nexent also offers powerful capabilities for agent running control, data processing and MCP tools.

Python
argo logo
argo
413

ARGO is an open-source AI Agent platform that brings Local Manus to your desktop. With one-click model downloads, seamless closed LLM integration, and offline-first RAG knowledge bases, ARGO becomes a DeepResearch powerhouse for autonomous thinking, task planning, and 100% of your data stays locally. Support Win/Mac/Docker.

Python