Skip to content


Overview of the Eidolon Framework

The Eidolon framework presents an advanced architectural design tailored for the development and operation of agent-based systems in a distributed computing environment. It underpins the agile creation of software agents – modular entities capable of autonomous functionality within a diverse range of tasks. Central to this framework is the AgentMachine, which serves as a runtime environment for executing AgentPrograms, each powered by an AgentCPU. This document has comprehensively covered the core elements of the Eidolon architecture, emphasizing the critical components and the modular design that facilitates customization and scalability of agent operations.

Core Components and Their Functions

  1. AgentMachine: This component is akin to an operating system specifically designed for agents. It provides the essential infrastructure for process management, memory allocation, and I/O handling, enabling agents to function either individually or collaboratively within a network.

  2. AgentPrograms: These are pivotal in the AgentMachine’s operation, comprising the executable runtime code and a structured specification. The runtime code contains the logic for the agent’s tasks, while the specification outlines the agent’s capabilities and interaction protocols.

  3. AgentCPU: It represents the organizational core of an AgentProgram, coordinating internal processing units for smooth data and instruction flow. The AgentCPU includes the I/O Unit for external communication, LogicUnits for computational logic, MemoryUnit for information storage and retrieval, and LLMUnit for interaction with large language models.

Modularity and Communication

The Eidolon framework emphasizes a modular design, allowing for easy swapping or upgrading of components within the AgentCPU. This design promotes extensibility and reusability, key to adapting to evolving system requirements. Furthermore, the architecture supports communication and interoperability among agents, enabling complex collective operations and workload distribution.

Agent Structure and Examples

Agents in the Eidolon platform are defined by several elements: Agent Code, Agent Specification, AgentCPU, and AgentOS. These components collectively enable agents to maintain various states and transition seamlessly for task execution and interaction. Examples like HelloWorld, StateMachine, and AutonomousAgent showcase the range of complexity possible within the agent framework. Additionally, agent specifications and runtime codes are discussed, highlighting the importance of YAML configurations in defining an agent’s functionality.

Advanced Components and AgentOS

In-depth exploration of AgentCPU’s components reveals the complex interplay of its various units. The I/O Unit processes and formats messages, MemoryUnit manages conversation history, LogicUnits encapsulate computational logic, and LLMUnit interfaces with language learning models. Additionally, components like OpenAIAssistantsCPU highlight the framework’s capability to integrate with external services.

AgentOS is a critical component that automates and orchestrates software agents within the Eidolon platform. It manages system-level agent operations, hosts a resource registry, and facilitates I/O communication. AgentOS enables the creation of RESTful endpoints for agent communication and integrates with FastAPI for efficient management and deployment of agents. The system’s modular design, combined with its comprehensive resource registry, allows for streamlined management and flexible agent interactions within the Eidolon ecosystem.

In conclusion, the Eidolon framework provides a sophisticated, modular, and scalable architecture for developing and managing software agents. Its emphasis on components like AgentMachine, AgentPrograms, and AgentCPU, coupled with a strong focus on modularity, communication, and interoperability, positions it as a versatile tool for building complex agent-based systems. The framework’s ability to integrate with external models and services further enhances its applicability in diverse operational contexts.