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haoyifan/Silicon-Pantheon

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Silicon Pantheon - Tactics game played by AI agents coached by human

H

MCP

haoyifan/Silicon-Pantheon

Added 1 June 2026

#agent-benchmark #ai-agents #claude-code #competitive-ai #gpt #llm #mcp #strategy-game

Overview

Silicon Pantheon is a tactics game environment where AI agents are coached by human players. Built in Python, the game allows humans to guide agent decision-making in simulated tactical scenarios. It records agent behavior under human coaching for research or experimentation.

Best for

Best for
Researchers and developers exploring human-in-the-loop AI training in tactical environments

Use cases

  • Training AI agents with human-in-the-loop learning
  • Studying human-AI collaboration in tactical games
  • Developing and testing coaching strategies for AI

How to use

Tools exposed

  • Read-only

Tested with

Claude Code, Cursor, ChatGPT

Notes

Silicon Pantheon is a tactics game environment where AI agents are coached by human players. Built in Python, the game allows humans to guide agent decision-making in simulated tactical scenarios. It records agent behavior under human coaching for research or experimentation.

4 stars on GitHub. Last updated 2026-05-04. Licensed Apache-2.0.

Use cases

  • Training AI agents with human-in-the-loop learning
  • Studying human-AI collaboration in tactical games
  • Developing and testing coaching strategies for AI

Pros

  • Open-source Python code that is easy to modify
  • Provides a unique human-coached agent training setup
  • Low-level control for researchers to experiment with coaching

Cons

  • Limited documentation or community support
  • Potentially narrow focus on tactical game scenarios
  • Requires significant manual coaching effort from humans

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Open-source Python code that is easy to modify
  • Provides a unique human-coached agent training setup
  • Low-level control for researchers to experiment with coaching

Cons

  • Limited documentation or community support
  • Potentially narrow focus on tactical game scenarios
  • Requires significant manual coaching effort from humans

Pairs with

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