Multi‑Agent Storyworld Simulator
Stateful, multi‑agent narrative simulation with resumable episodic workflows and per‑character memory.
Project Overview
A multi-agent AI system for persistent, character-driven narratives that automates content generation. It uses a sophisticated orchestration system where specialized AI agents (for initialization, environment orchestration, and character behavior) collaborate to create emergent, goal-driven stories. Built on LangGraph, SQLite, and Pydantic, it features state synchronization and persistent execution, allowing stories to be paused and resumed from an exact checkpoint.
Technologies Used
PythonPydanticLangGraphSQLite
Key Features
- Multi-agent orchestration: 6+ specialized agents working in coordinated workflows, organized into Initialization, Environment, and Character layers.
- Autonomous character agents: Each character has autonomous agents with individual memory, making independent decisions that influence story outcomes.
- Persistent narratives: Allows stories to be paused and resumed from the exact checkpoint with no data loss, using SQLite.
- Per-character memory: Agents maintain short/long-term memory (115-250 events) and reference past events.
- Goal-driven stories: Narratives progress toward defined objectives, with validation agents (Scene Validator, Final Goal Validator) to pace the flow.
- Emergent interactions: Complex character behaviors emerge from well-designed agent coordination.
- Technology stack: Built on proven technologies (LangGraph, SQLite, Pydantic) for production-ready error handling and state management.