AI-powered game development workbench. Natural language -> automated code review, config modification, test generation, and more.
- Agent Loop + Skill: One agent loop handles all tasks, guided by Skill .md files
- Deterministic Modification: Config/code changes via LLM intent parsing + Pydantic validation + code execution
- MCP Multi-Server: Dynamic tool discovery across FileSystem, Git, and custom game dev tools
- Progressive Disclosure UI: Chat-first interface, smart recommendations after scan
- Engine Agnostic: Abstract scanner + tool mapping (Unity first)
git clone https://github.com/Firefly0237/GameDev.git
cd GameDev
python -m venv venv
source venv/bin/activate
cp .env.example .env # Fill in DEEPSEEK_API_KEY
pip install -r requirements.txt
npm install -g @modelcontextprotocol/server-filesystem
pip install mcp-server-git
python create_test_project.py
streamlit run app.pyUser Input -> Skill Match -> Intent Router (3-way): ├── Deterministic (modify_config / modify_code) │ -> LLM parse -> Pydantic validate -> code execute ├── Agent Loop (review / test / translate / analyze) │ -> Plan-Execute: LLM plans steps -> tool calls -> verify └── Supervisor (generate_system / summarize_requirement) -> Agent Loop with multi-step planning
Python · LangGraph (Checkpoint) · MCP · DeepSeek API · Pydantic · Streamlit · SQLite · Docker
app.py: Streamlit main entry with scan, chat, recommendations, and historypages/: Shared action helpers, skill execution page, and Git panelconfig/: Environment settings and loggerdatabase/: SQLite task logs and LangGraph checkpoint storagescanner/: Engine-agnostic base scanner and Unity scanner implementationagents/: LLM client factorymcp_tools/: Custom GameDev MCP server and multi-server client managercontext/: Skill loading, project schema loading, and built-in Skill .md filesschemas/: Pydantic output models for structured executiongraphs/: Router, agent loop, deterministic executor, and safety layerlogs/: Runtime logsoutput/: Generated output artifactstest_project/: Sample Unity project for validation
MIT