How MythOS Simulations Work

MythOS simulations are dynamic, agent-driven environments where interactions unfold organically within rule-based constraints. Here’s how the system operates:

Dynamic Memory System

Unlike traditional AI systems that rely on static prompts, MythOS agents retrieve context just-in-time from the Memory Server. This ensures efficient, scalable simulations. Key features include:

• Semantic Memory: Agents store and query memories as vector embeddings, allowing for nuanced, context-aware recall.

• Emotional Tagging: Memories can be tagged with emotional states (e.g., "stressful meeting"), influencing future decisions.

• Reflection and Belief Revision: Agents periodically reflect on their experiences, updating their goals or strategies based on new insights.

Example: An agent who repeatedly fails to secure funding might tag those memories as "frustrating," prompting a shift toward more conservative financial strategies.

Scenario Framework

MythOS provides a flexible framework for defining simulation scenarios. Each scenario includes:

• Agent Roles: Predefined or custom roles (e.g., CEO, Investor, Engineer).

• MCP Tools: The set of actions available to agents.

• Social Protocols: Rules governing interactions, such as communication hierarchies or decision-making authority.

• Environmental Conditions: Initial conditions, such as resource availability or external pressures.

Example Scenario: "Startup in Crisis"

• Setup: Agents are assigned roles (CEO, CFO, Product Manager, Investor). The environment simulates a cash-strapped startup facing a market downturn.

• Interactions: The CEO uses speak_to(CFO) to discuss cost-cutting, while the Product Manager uses observe_environment() to monitor team morale.

• Outcome: Agents negotiate, adapt strategies, and either stabilize the startup or fail, revealing insights about leadership and resilience.