Core Components of MythOS

MythOS is built around three foundational pillars: self-evolving AI agents, MCP-powered actions, and modular MCP servers. Together, these components create a robust framework for simulating complex systems.

Self-Evolving AI Agents

At the heart of MythOS are its AI agents—autonomous entities that go beyond scripted responses to exhibit adaptive, human-like behavior. Key features include:

Lightweight Initialization: Agents are created from simple prompts that define their role, personality, and goals. For example:
```
"Optimistic engineer, risk-tolerant, focused on rapid prototyping"
```
Recursive Reasoning: Agents use iterative thinking to process information, make decisions, and adapt their strategies based on new data.
Dynamic Evolution: Through interactions and reflection, agents can modify their internal states, goals, or even personality traits over time.
Memory-Driven Behavior: Agents leverage a long-term memory system to recall past events, relationships, and lessons, ensuring contextually relevant actions.

Example: An agent initialized as a cautious CFO might become more risk-tolerant after repeated successful collaborations with a bold CEO, reflecting real-world learning.

MCP-Powered Actions

Every MythOS agent is equipped with a suite of tools defined by the Model-Context-Protocol (MCP) standard. These tools allow agents to interact with their environment and other agents in structured, traceable ways. Core MCP actions include:

observe_environment(): Collects data about the simulation world, such as nearby agents, objects, or events.
speak_to(agent): Facilitates structured communication with other agents, adhering to social protocols.
search_memory(): Retrieves relevant information from the agent’s personal or shared memory (stored in a vector database).
move(location): Enables location-based actions within the simulation’s spatial environment.
modify_strategy(): Allows the agent to adapt its goals, priorities, or behavior based on new insights.

Example: In a simulated startup, an agent (Product Manager) might use observe_environment() to detect a drop in team morale, then call speak_to(CEO) to propose a morale-boosting initiative.

Modular MCP Servers

MythOS environments are powered by a collection of MCP servers, each handling a specific aspect of the simulation. These servers ensure modularity, scalability, and flexibility. The core servers are:

Environment Server: Manages the physical and spatial aspects of the simulation, such as locations, objects, and environmental changes.
Memory Server: Stores and retrieves long-term memory for agents using a vector database, supporting semantic search and context-aware recall.
Comms Server: Routes structured communications between agents, ensuring messages adhere to defined protocols.
Protocol Server: Defines and enforces social norms, hierarchies, and rules that govern agent interactions (e.g., who can speak to whom, under what conditions).
Knowledge Server (optional): Provides background information, domain-specific rules, or "lore" to enrich the simulation.

Example: In a crisis simulation, the Protocol Server might enforce a rule where only senior agents can make binding decisions, while the Environment Server simulates a declining resource pool.

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Self-Evolving AI Agents

At the heart of MythOS are its AI agents—autonomous entities that go beyond scripted responses to exhibit adaptive, human-like behavior. Key features include:

Lightweight Initialization: Agents are created from simple prompts that define their role, personality, and goals. For example:

"Optimistic engineer, risk-tolerant, focused on rapid prototyping"

Recursive Reasoning: Agents use iterative thinking to process information, make decisions, and adapt their strategies based on new data.

Dynamic Evolution: Through interactions and reflection, agents can modify their internal states, goals, or even personality traits over time.

Memory-Driven Behavior: Agents leverage a long-term memory system to recall past events, relationships, and lessons, ensuring contextually relevant actions.

Example: An agent initialized as a cautious CFO might become more risk-tolerant after repeated successful collaborations with a bold CEO, reflecting real-world learning.

MCP-Powered Actions

observe_environment(): Collects data about the simulation world, such as nearby agents, objects, or events.

speak_to(agent): Facilitates structured communication with other agents, adhering to social protocols.

search_memory(): Retrieves relevant information from the agent’s personal or shared memory (stored in a vector database).

move(location): Enables location-based actions within the simulation’s spatial environment.

modify_strategy(): Allows the agent to adapt its goals, priorities, or behavior based on new insights.

Example: In a simulated startup, an agent (Product Manager) might use observe_environment() to detect a drop in team morale, then call speak_to(CEO) to propose a morale-boosting initiative.

Modular MCP Servers

MythOS environments are powered by a collection of MCP servers, each handling a specific aspect of the simulation. These servers ensure modularity, scalability, and flexibility. The core servers are:

Environment Server: Manages the physical and spatial aspects of the simulation, such as locations, objects, and environmental changes.

Memory Server: Stores and retrieves long-term memory for agents using a vector database, supporting semantic search and context-aware recall.

Comms Server: Routes structured communications between agents, ensuring messages adhere to defined protocols.

Protocol Server: Defines and enforces social norms, hierarchies, and rules that govern agent interactions (e.g., who can speak to whom, under what conditions).

Knowledge Server (optional): Provides background information, domain-specific rules, or "lore" to enrich the simulation.

Example: In a crisis simulation, the Protocol Server might enforce a rule where only senior agents can make binding decisions, while the Environment Server simulates a declining resource pool.