graph LR
Agent_Orchestrator["Agent Orchestrator"]
Agent_Core_Action_Executor["Agent Core / Action Executor"]
Tool_Management["Tool Management"]
Guardrail_System["Guardrail System"]
Session_Memory["Session & Memory"]
LLM_Integration["LLM Integration"]
Handoff_Mechanism["Handoff Mechanism"]
Multi_Agent_Communication_Protocol_MCP_["Multi-Agent Communication Protocol (MCP)"]
Agent_Orchestrator -- "Directs Execution Flow, Initiates Turn Processing" --> Agent_Core_Action_Executor
Agent_Core_Action_Executor -- "Requests Model Responses, Sends Prompts" --> LLM_Integration
LLM_Integration -- "Provides Model Responses" --> Agent_Core_Action_Executor
Agent_Core_Action_Executor -- "Invokes Tools, Requests Tool Schemas" --> Tool_Management
Agent_Orchestrator -- "Applies Input Guardrails, Applies Output Guardrails" --> Guardrail_System
Agent_Orchestrator -- "Manages Context, Stores/Retrieves Session Data" --> Session_Memory
Agent_Core_Action_Executor -- "Initiates Handoffs, Delegates Control" --> Handoff_Mechanism
Agent_Core_Action_Executor -- "Invokes Remote Tools/Services, Requests Tool Discovery" --> Multi_Agent_Communication_Protocol_MCP_
Tool_Management -- "Registers/Discovers Tools" --> Multi_Agent_Communication_Protocol_MCP_
click Agent_Orchestrator href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/openai-agents-python/Agent_Orchestrator.md" "Details"
click Agent_Core_Action_Executor href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/openai-agents-python/Agent_Core_Action_Executor.md" "Details"
click Tool_Management href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/openai-agents-python/Tool_Management.md" "Details"
click Guardrail_System href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/openai-agents-python/Guardrail_System.md" "Details"
click LLM_Integration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/openai-agents-python/LLM_Integration.md" "Details"
click Handoff_Mechanism href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/openai-agents-python/Handoff_Mechanism.md" "Details"
click Multi_Agent_Communication_Protocol_MCP_ href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/openai-agents-python/Multi_Agent_Communication_Protocol_MCP_.md" "Details"
The openai-agents-python architecture is designed as a modular SDK for orchestrating AI agents, emphasizing a clear separation of concerns to facilitate extensibility and maintainability. At its core, the Agent Orchestrator directs the flow, managing the agent's turn-based execution and interacting with the Agent Core / Action Executor for decision-making. This core component leverages LLM Integration for model interactions, Tool Management for external capabilities, and the Handoff Mechanism for inter-agent communication. The system is fortified by a Guardrail System for safety and a Session & Memory component for statefulness. The Multi-Agent Communication Protocol (MCP) extends capabilities to distributed multi-agent environments. This structure promotes a pipeline-like flow, where inputs are processed, actions are decided and executed, and outputs are generated, all while maintaining context and adhering to defined policies.
Agent Orchestrator [Expand]
The central control plane managing the lifecycle and execution flow of agents, including turn-based processing, guardrail application, and overall agent execution, adaptable for both standard and real-time interactions.
Related Classes/Methods:
Agent Core / Action Executor [Expand]
Encapsulates the agent's internal decision-making and execution logic, interpreting model responses and translating them into executable actions (tool calls, handoffs, computer actions).
Related Classes/Methods:
Tool Management [Expand]
Manages the definition, registration, and invocation of external functionalities (tools) available to agents, providing a standardized interface for interaction with external systems.
Related Classes/Methods:
Guardrail System [Expand]
Enforces predefined constraints and policies on agent inputs and outputs to ensure safe, compliant, and desired operational behavior throughout the agent's execution.
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Manages the conversational context and state across multiple turns or interactions, ensuring continuity and coherence by storing and retrieving relevant information for agents.
Related Classes/Methods:
LLM Integration [Expand]
Provides an abstraction layer for interacting with various Large Language Models (LLMs), handling provider-specific details and ensuring consistent model access and response processing.
Related Classes/Methods:
Handoff Mechanism [Expand]
Facilitates the transfer of control or context between different agents or external systems, enabling complex multi-agent workflows and task delegation.
Related Classes/Methods:
Multi-Agent Communication Protocol (MCP) [Expand]
Provides a framework for agents to discover, list, and invoke tools managed by a central server or other agents, enabling collaborative multi-agent systems and distributed tool access.
Related Classes/Methods: