Agency Swarm vs OpenAI Agents SDK

Organizes multiple AI agents into a hierarchical structure defined by an agency chart that specifies which agents can communicate with which other agents. The Agency class serves as the central orchestrator that creates and initializes agents, establishes dedicated communication threads between agents according to the chart topology, and routes messages through the defined hierarchy. This enables complex multi-agent workflows where agents delegate tasks up/down the chain of command rather than all agents communicating freely.

Unique: Uses explicit agency-chart topology (similar to organizational structures) to define agent communication patterns, rather than allowing free-form agent-to-agent communication. The Agency class maintains thread objects for each defined communication channel, enforcing structured message flows through the hierarchy.

vs alternatives: Provides more explicit control over agent communication patterns than frameworks like LangGraph or AutoGen that allow more dynamic agent discovery, making it better suited for systems where communication topology must be strictly enforced.

Enables agents to delegate tasks to other agents through a thread-based message passing system where each agent pair has a dedicated Thread object that manages the conversation history and tool execution. When an agent needs to delegate work, it sends a message through the thread to another agent, which processes the message, executes tools if needed, and returns results back through the same thread. The Thread class handles OpenAI Assistants API integration, tool call processing, and maintains full conversation context.

Unique: Implements agent-to-agent communication through dedicated Thread objects that wrap OpenAI Assistants API conversations, maintaining full message history and handling tool execution within each thread. This differs from frameworks that use shared message queues or event buses by tying threads to specific agent pairs.

vs alternatives: Provides cleaner separation of concerns than agent frameworks using shared message buses, as each agent pair has isolated conversation context, but at the cost of higher API call overhead compared to in-process agent communication patterns.

Manages agent state including instructions, tools, model configuration, and conversation history. Agents maintain their own state objects that persist across interactions, storing role definitions, tool assignments, and model parameters. The framework enables agents to be configured once and reused across multiple conversations without reconfiguration.

Unique: Agents maintain persistent state objects that store instructions, tools, and configuration, enabling agents to be instantiated once and reused across multiple conversations without reconfiguration.

vs alternatives: Simpler than frameworks requiring agents to be reconfigured for each conversation, but lacks built-in persistence mechanisms for saving state across process restarts.

Provides observability into agent execution through callback handlers that track agent actions, tool calls, and message flows. The framework includes LocalCallbackHandler for local logging and TrackingManager for centralized execution tracking. Callbacks are invoked at key points in the execution flow (agent initialization, message processing, tool execution) enabling monitoring and debugging of agent behavior.

Unique: Implements callback-based observability system with LocalCallbackHandler and TrackingManager that capture execution events at key points in agent lifecycle, enabling detailed execution tracking without modifying agent code.

vs alternatives: Provides framework-native observability without external dependencies, but lacks integration with external monitoring platforms that frameworks like LangChain offer through LangSmith.

Provides Genesis Agency as a pre-built template agency that can be used as a starting point for creating custom agencies. Genesis Agency comes with pre-configured agents and communication patterns that can be extended or modified. This enables developers to start with a working multi-agent system and customize it rather than building from scratch.

Unique: Provides Genesis Agency as a pre-built, working agency template with configured agents and communication patterns that developers can extend or customize, reducing time to first working multi-agent system.

vs alternatives: Faster to get started than building agencies from scratch, but less flexible than frameworks providing only building blocks without opinionated templates.

Enables agents to be defined with natural language instructions and role descriptions that guide their behavior. Agents are instantiated with a name, description, and detailed instructions that specify their responsibilities, decision-making criteria, and interaction patterns. These instructions are sent to the OpenAI Assistants API and influence how the agent responds to messages and uses tools.

Unique: Agents are defined through natural language instructions and role descriptions that are passed to OpenAI Assistants API, enabling behavior specification through prompting rather than code configuration.

vs alternatives: More flexible than code-based configuration for behavior specification, but instruction quality is harder to validate and optimize compared to frameworks using formal behavior specifications.

Provides a BaseTool class that agents can inherit from to define custom tools with Pydantic model-based input validation and automatic schema generation. Tools are defined as Python classes where the run() method contains the implementation, and Pydantic models define the input parameters with type hints and validation rules. The framework automatically converts these tool definitions into OpenAI function-calling schemas that agents can invoke, ensuring type safety and input validation before tool execution.

Unique: Uses Pydantic models as the single source of truth for tool input schemas, automatically generating OpenAI function-calling schemas from Python type hints and validation rules. This eliminates manual schema definition and keeps tool logic and validation colocated in Python code.

vs alternatives: More developer-friendly than manually defining JSON schemas for each tool, and provides runtime validation that catches type errors before tools execute, unlike frameworks that rely on agent-side schema interpretation.

Provides a ToolFactory class that dynamically discovers and instantiates tools from Python modules or class definitions, enabling agents to access tools without explicit registration. The factory introspects tool classes, validates they inherit from BaseTool, and creates instances with proper initialization. This allows tools to be discovered at runtime from directories or module paths, reducing boilerplate tool registration code and enabling plugin-like tool loading patterns.

Unique: Implements runtime tool discovery through module introspection and factory pattern, allowing tools to be loaded from directories without explicit registration code. This contrasts with frameworks requiring manual tool registration for each agent.

vs alternatives: Reduces boilerplate compared to frameworks requiring explicit tool registration for each agent, but adds runtime introspection overhead and requires tools to follow discoverable naming conventions.

openai/openai-agents-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki openai/openai-agents-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 7 May 2026 ( 3a11cf ) Overview Getting Started Core Concepts Agent Architecture Runner and Execution Flow RunResult and Output Management RunState and Resumption Context and Dependency Injection Run Configuration Tools and Capabilities Tool System Overview Function Tools Hosted Tools Local Runtime Tools Agent as Tool Tool Use Behavior Tool Approval and Human-in-the-Loop Multi-Agent Coordination Handoff System Manager Pattern vs Handoffs Handoff Configuration Handoff History Management Safety and Validation Guardrail Architecture Input and Output Guardrails Tool Guardrails Guardrail Execution Strategies Tripwire Mechanism Model Integration Model Abstraction Layer OpenAI Responses API OpenAI Chat Completions API LiteLLM Multi-Provider Support Model Settings and Configuration Retry Policies Streaming Responses Session and Memory Management Session Protocol Session Implementations Conversation Tracking Modes Server-Managed Conversations Realtime and Voice Agents Realtime System Overview RealtimeSession Orchestration OpenAI Realtime WebSocket Model Audio Pipeline and Voice Activity Detection Realtime Configuration Realtime Tool Execution and Guardrails Interruption Handling

Getting Started | openai/openai-agents-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki openai/openai-agents-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 7 May 2026 ( 3a11cf ) Overview Getting Started Core Concepts Agent Architecture Runner and Execution Flow RunResult and Output Management RunState and Resumption Context and Dependency Injection Run Configuration Tools and Capabilities Tool System Overview Function Tools Hosted Tools Local Runtime Tools Agent as Tool Tool Use Behavior Tool Approval and Human-in-the-Loop Multi-Agent Coordination Handoff System Manager Pattern vs Handoffs Handoff Configuration Handoff History Management Safety and Validation Guardrail Architecture Input and Output Guardrails Tool Guardrails Guardrail Execution Strategies Tripwire Mechanism Model Integration Model Abstraction Layer OpenAI Responses API OpenAI Chat Completions API LiteLLM Multi-Provider Support Model Settings and Configuration Retry Policies Streaming Responses Session and Memory Management Session Protocol Session Implementations Conversation Tracking Modes Server-Managed Conversations Realtime and Voice Agents Realtime System Overview RealtimeSession Orchestration OpenAI Realtime WebSocket Model Audio Pipeline and Voice Activity Detection Realtime Configuration Realtime Tool Execution and Guardrails Int

Core Concepts | openai/openai-agents-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki openai/openai-agents-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 7 May 2026 ( 3a11cf ) Overview Getting Started Core Concepts Agent Architecture Runner and Execution Flow RunResult and Output Management RunState and Resumption Context and Dependency Injection Run Configuration Tools and Capabilities Tool System Overview Function Tools Hosted Tools Local Runtime Tools Agent as Tool Tool Use Behavior Tool Approval and Human-in-the-Loop Multi-Agent Coordination Handoff System Manager Pattern vs Handoffs Handoff Configuration Handoff History Management Safety and Validation Guardrail Architecture Input and Output Guardrails Tool Guardrails Guardrail Execution Strategies Tripwire Mechanism Model Integration Model Abstraction Layer OpenAI Responses API OpenAI Chat Completions API LiteLLM Multi-Provider Support Model Settings and Configuration Retry Policies Streaming Responses Session and Memory Management Session Protocol Session Implementations Conversation Tracking Modes Server-Managed Conversations Realtime and Voice Agents Realtime System Overview RealtimeSession Orchestration OpenAI Realtime WebSocket Model Audio Pipeline and Voice Activity Detection Realtime Configuration Realtime Tool Execution and Guardrails Inter

openai/openai-agents-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki openai/openai-agents-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 7 May 2026 ( 3a11cf ) Overview Getting Started Core Concepts Agent Architecture Runner and Execution Flow RunResult and Output Management RunState and Resumption Context and Dependency Injection Run Configuration Tools and Capabilities Tool System Overview Function Tools Hosted Tools Local Runtime Tools Agent as Tool Tool Use Behavior Tool Approval and Human-in-the-Loop Multi-Agent Coordination Handoff System Manager Pattern vs Handoffs Handoff Configuration Handoff History Management Safety and Validation Guardrail Architecture Input and Output Guardrails Tool Guardrails Guardrail Execution Strategies Tripwire Mechanism Model Integration Model Abstraction Layer OpenAI Responses API OpenAI Chat Completions API LiteLLM Multi-Provider Support Model Settings and Configuration Retry Policies Streaming Responses Session and Memory Management Session Protocol Session Implementations Conversation Tr