OpenAI Agents SDK Architecture: Building Multi-Agent Systems (2026)

The OpenAI Agents SDK (originally released as Swarm, then relaunched as the Agents SDK in 2025) is OpenAI's framework for building multi-agent AI systems. Unlike raw API calls or simple chat wrappers, the Agents SDK introduces a structured model for agents, handoffs between agents, tool use, guardrails, and tracing — giving teams a coherent way to build complex AI workflows that would be unwieldy to implement from scratch.

Documenting an OpenAI Agents SDK architecture is essential for engineering reviews, onboarding, debugging, and communicating system capabilities to stakeholders. This guide covers the core components of the Agents SDK, the most common multi-agent patterns, and ready-to-use prompt templates for generating accurate architecture diagrams.

Core concepts of the OpenAI Agents SDK

Agents

An agent in the Agents SDK is a configured LLM instance with a system prompt, a set of tools, and optionally a set of handoffs. Unlike a raw API call, an agent encapsulates its role and capabilities as a reusable unit. Your architecture diagram should show each agent as a distinct node with its role (triage agent, research agent, writing agent) and its tool set clearly labeled.

Handoffs

Handoffs are the primary mechanism for routing between agents. An agent with handoffs can transfer control to another agent — for example, a triage agent receiving a customer query can hand off to a billing specialist agent or a technical support agent based on the query content. Handoffs are directional: the source agent stops generating responses when it decides to hand off, and the target agent picks up the conversation with its own system prompt and tool context. Architecture diagrams must make handoff directions explicit — show which agents can hand off to which others and under what conditions.

Tools

Tools are functions the agent can call — database lookups, API calls, code execution, file reads. The Agents SDK supports three categories: function tools (Python functions decorated with@function_tool), hosted tools (OpenAI-managed capabilities like web search and code interpreter), and agents-as-tools (another agent invoked as a tool call rather than via handoff, preserving the calling agent's context). Your diagram should show each tool, what external system it connects to, and whether it is a function tool, hosted tool, or agent-as-tool.

Guardrails

Guardrails are validation functions that run before or after an agent's response. Input guardrails can block or modify incoming messages (e.g., PII detection, prompt injection filtering, topic restriction). Output guardrails validate or transform the agent's response before it reaches the caller. In production systems, guardrails are a critical security and quality control layer — document them explicitly in your architecture diagram with what they check and what happens on failure.

Runner and tracing

The Runner is the execution engine that drives an agent loop — sending messages, handling tool calls, routing handoffs, and enforcing turn limits. The Agents SDK includes built-in tracing support that records every LLM call, tool invocation, and handoff decision. For production systems, tracing output feeds into observability platforms (OpenAI's trace dashboard, or exported to external systems like LangSmith or your own trace store).

Multi-agent patterns with the OpenAI Agents SDK

Triage + specialist pattern

The most common Agents SDK architecture. A triage agent receives all incoming requests and routes them to specialist agents based on intent classification. Each specialist has a focused system prompt and tool set appropriate to its domain. The triage agent acts as the single entry point, reducing context pollution and improving specialist performance by keeping each agent's scope narrow.

Sequential pipeline pattern

A linear chain of agents where each agent's output becomes the next agent's input. Useful for multi-step content generation, report construction, and multi-stage analysis workflows. In this pattern, agents don't use handoffs — the Runner is invoked sequentially for each stage, passing the prior output as context. The architecture diagram should show the pipeline stages with data flow between them.

Parallel fan-out pattern

Multiple agents run concurrently on the same task from different angles — a research agent, a fact-checking agent, and a perspective agent, for example. Results are aggregated by a synthesis agent. The OpenAI Agents SDK supports this via asyncio concurrent execution. Architecture diagrams for this pattern should clearly show the parallel branches and the aggregation point.

Agent-as-tool pattern

Instead of handing off, an orchestrator agent invokes specialist agents as tools — getting a structured response without relinquishing control of the conversation. This is useful when the orchestrator needs to call multiple specialists and synthesize their outputs in a single response. The architecture diagram should distinguish between handoff relationships (control transfer) and tool relationships (subroutine call).

Prompt templates for OpenAI Agents SDK diagrams

Customer support multi-agent system

Research and synthesis pipeline

Code review agent with handoffs

OpenAI Agents SDK vs. other multi-agent frameworks

Production architecture considerations

Moving an Agents SDK system from prototype to production requires several additional architectural components that should appear in your diagram:

• State persistence: By default, the Runner is stateless between invocations. For multi-turn conversations, you need an external state store (Postgres, Redis) to persist conversation history and tool call results between user requests.
• Rate limiting and cost control: Multi-agent systems can make many concurrent LLM calls. Your architecture should show rate limiting at the Runner level, per-user budget enforcement, and cost tracking — especially for systems with unpredictable fan-out.
• Async execution and queuing: Long-running agent loops should not block API responses. Use a task queue (Celery, Temporal, or a simple job table in Postgres) to run agent loops asynchronously and return results via webhook or polling.
• Observability: The SDK's built-in tracing covers the agent loop. For full production observability, supplement with request-level metrics (latency, cost, token counts), error rates per agent, and handoff frequency monitoring.
• Guardrail failure handling: Define explicit fallback behavior when a guardrail blocks a request — a structured error response, a fallback agent, or a human escalation path. The “happy path” diagram should always be accompanied by the failure path diagram.

Frequently asked questions about OpenAI Agents SDK architecture

What is the difference between a handoff and an agent-as-tool in the OpenAI Agents SDK?

A handoff transfers full control to another agent — the calling agent stops generating, and the target agent takes over the conversation from that point forward with its own system prompt and context. An agent-as-tool invokes another agent as a subroutine — the calling agent makes a tool call, receives the result, and continues generating its own response. Use handoffs when the called agent should own the rest of the interaction; use agent-as-tool when you need a specialist result but the calling agent should synthesize the final response.

How do I diagram an OpenAI Agents SDK system?

Describe your agent system in plain English — the agents, their roles, the handoff conditions, the tools each agent has, and the guardrails in place — and generate an architecture diagram from that description. The key elements to capture are: each agent as a named node with its LLM model, handoff arrows with conditions labeled, tool nodes connected to the agents that use them, guardrail annotations on the relevant agents, and the external systems each tool connects to. The result is a diagram that communicates the system's capabilities and routing logic to both engineers and non-technical stakeholders.

Related guides: multi-agent orchestration patterns, LangGraph architecture diagrams, AI agent architecture diagrams, and agentic AI security architecture.