A new framework for building multi-agent AI workflows with built-in tool use, handoffs, guardrails, and tracing.
Imagine you’re working on a complex conversational AI project. You don’t just want a single large language model (LLM) giving answers. You need multiple specialized “workers” that can route tasks among themselves, invoke various helper functions, and ensure that everything happening behind the scenes meets safety and policy standards. This is where the OpenAI Agents SDK steps in.
Think of it like an all-in-one production line for sophisticated AI workflows: multiple specialized agents, each with distinct goals, can chat with one another, call the right tools, and pass the baton when needed — all while maintaining guardrails for safety and logging everything for easy debugging.
This concept has been percolating in the AI community for a while, but the Agents SDK takes it a step further. It offers out-of-the-box concurrency for guardrails, built-in tracing, powerful function-calling, and a robust orchestration system (the Runner) that ties it all together. In other words, if you want a single harness to manage the “big picture” of your multi-step AI logic — from verifying user inputs to final outputs and everything in between — you’re in the right place.
In a nutshell, the OpenAI Agents SDK is a framework that helps you build apps powered by multiple language-model “agents.” Each agent is specialized, aware of its own domain expertise, and capable of either answering a user request, calling functions (tools), or handing the entire conversation off to another agent. This approach mimics how real teams operate: you have multiple team members (agents), each with their own skill sets, who might consult reference materials (tools) or pass the question to a colleague if they realize someone else is better qualified.
In traditional setups, you’d often have just one large language model. While that works for simpler tasks, it can get unwieldy when you want specialized behavior — for example, an agent that strictly handles math, one that is great at writing code, another that does corporate knowledge lookups, and so on. The Agents SDK makes it straightforward to spin up these specialized mini-bots under the same roof, orchestrate them, and keep track of who is doing what, when, and why.
The real engine powering this architecture is the Runner, which loops until the conversation yields a final output. The Runner interacts with your collection of agents and decides how to proceed based on each agent’s responses (whether to call a function, switch agents, or finalize a result). During that loop, the SDK automatically enforces guardrails (safety checks), and it writes detailed logs to a tracing system so you can see a step-by-step record of how the entire conversation flow took shape.
The net effect? You get a robust environment that can handle complex, multi-step user requests with real-time concurrency checks, flexible tool use, dynamic agent-switching, and detailed logging.
Below are the core features that distinguish the OpenAI Agents SDK from simpler single-LLM solutions. Let’s expand on each to illustrate typical use cases and benefits.
Multiple Agents
A single application can host many specialized (or general-purpose) agents. Each agent can be given a unique system prompt or domain knowledge — for instance, “Financial Agent” for stock analysis, “Math Tutor” for arithmetic and algebra, “Support Agent” for customer queries, and so on. This is especially useful in real-world scenarios where different requests might require significantly different skill sets. By dividing responsibilities, you improve accuracy and reduce the likelihood that a single overtaxed agent tries to do everything (and inevitably makes more mistakes).
Tool Use
Agents can call Python functions (or “hosted tools” on OpenAI’s servers) to perform concrete actions or retrieve external data. For example, your “Math Tutor” agent might call a matrix-multiplication function if the user asks about linear algebra. Alternatively, a “Booking Agent” might call an external flight-search API to get real flight data. This feature is crucial because it roots the agent’s output in real logic and data, preventing hallucinations that purely text-based models sometimes generate.
Handoffs
Handoffs allow an agent to say, “I’m not the right one to handle this request” and pass it off to a more suitable agent. Think of it as a built-in referral system. A “Frontline Agent” could be in charge of triaging user requests: if it detects a math question, it does a handoff to “Math Agent”; if it’s a coding request, it hands off to “DevAgent.” This structure feels natural, mirroring how large organizations direct questions to the relevant department.
Guardrails
Automatic checks on user input or final output help maintain compliance, safety, or consistency. For example, if your enterprise policy forbids certain discussion topics, you can implement an input guardrail that instantly halts the conversation for disallowed content. Output guardrails might ensure that final answers adhere to a specific format (like JSON with certain fields) or do not disclose proprietary data. These guardrails run concurrently in many cases, so they won’t slow your system down while providing critical protection.
Tracing
The entire process is logged, step by step, into a rich trace. You can see which agent was selected, which tools they called, why certain decisions were made, and how partial results were combined into a final answer. This is extremely valuable for debugging and auditing. If a user complains about an odd or incorrect answer, you can quickly look at the trace to see exactly which step went awry.
Why these features matter: In real-world deployments — especially in enterprise or production-grade apps — you often need specialized logic, strong compliance, and the ability to debug complicated workflows. The Agents SDK addresses these needs head-on.
One of the first things you’ll notice in the SDK is the modular design: each part is intended to do one job well, and the Runner is the conductor orchestrating the entire ensemble.
Below is a diagram that shows how your user input flows through the system, how agents are orchestrated, and how final results are returned:
flowchart LR
A[User Input] --> B[Runner.run]
B -->|checks input guardrails| GR1[Input Guardrails]
GR1 --fail--> STOP[Stop: tripwire]
GR1 --ok--> AS[Active Agent]
subgraph AgentsOrchestration
direction LR
AS --> D{LLM Calls and Reasoning}
D -->|Tool usage| T
D -->|Handoff| H
D -->|Final Output| O[Answer]
T --> X[Execute Tool]
X --> AS
H --> Y[Switch to Sub-agent]
Y --> C2[(New Agent)]
C2 --> D
end
O -->|checks output guardrails| GR2[Output Guardrails]
GR2 --fail--> HALT[Halt or Raise Exception]
GR2 --ok--> R[Final response to user]
B --> TR[(Tracing system)]
R --> TR
In this section, we’ll take a closer look at each major piece of the puzzle.
Agents are the heart of the system:
Tools let an agent do actions in the real world. Examples:
Agents produce JSON indicating which tool they want to call. The Runner sees that, executes the function, and returns the result to the agent’s context. This loop continues until the agent has the info it needs to finalize an answer.
Handoffs are context switches:
One important difference between a handoff and treating an agent as a tool is that a handoff permanently transfers conversation control to the new agent, whereas “Agent as Tool” is merely a temporary sub-call.
Guardrails ensure safety and validity:
If a guardrail triggers, the process ends or raises an exception. As an example, you might enforce that any agent response about medical advice must be disclaimers or references to official guidelines. If the agent tries to output unverified medical claims, the output guardrail blocks it.
Tracing provides a step-by-step log:
You can think of it like a “flight recorder” for your conversation. For multi-agent or multi-step tasks, this is indispensable for debugging or performance tuning.
The Runner orchestrates everything:
Because the Runner loops in a controlled manner, you can set boundaries like max turn count (preventing infinite loops) or max tokens. It also handles partial streaming if your application wants real-time token-by-token replies.
Below are some higher-level principles that drive how the Agents SDK operates, along with more examples on why these strategies matter and how they might manifest in a real deployment.
LLM-Driven Planning
Instead of writing elaborate if-else statements in your Python code, the agent’s LLM is the decision-maker. Given the user’s question, the agent’s system prompt, and any relevant context, the LLM decides if a direct answer is enough or if it should call a tool or pass the conversation to another agent.
lookup_order tool and see what their latest purchase was.” The LLM itself instructs the Runner to fetch the data, rather than you having to code explicit logic for every step.Divide & Conquer
Rather than asking one giant agent to handle everything from math to flight searches to coding tasks, you separate out responsibilities. A top-level “Triage Agent” might simply decide if the question is about math, coding, or customer support, then do a handoff to the relevant agent. Each specialized agent can be smaller, cheaper to run, and more accurate in its domain.
Parallel Guardrails
The system can run guardrails in parallel with the LLM call, so if any disallowed content or anomaly is found early, the LLM request is canceled. This concurrency saves tokens (and money) by stopping requests before the LLM can generate a full response. It also reduces latency because you don’t have to wait for the LLM to finish first if you already know you’re going to reject the user’s input.
Traces & Observability
With multi-agent conversation, debugging can otherwise become a nightmare. The built-in tracing ensures every step is recorded, from agent choices to tool calls to partial LLM outputs. This not only helps find and fix errors but also aids in optimizing your system’s design over time. You might discover, for instance, that your “Support Agent” frequently calls the same tool unnecessarily, hinting at a need for optimization.
Agent as Tool
If you want to keep conversation control but still temporarily enlist the help of another agent, you treat that agent like a tool. This means your original agent calls the second agent, obtains a concise result, and then continues. The conversation context remains with the original agent.
To round out this understanding, let’s look at a few sequence diagrams illustrating different aspects of the system.
We revisit the top-level orchestration, showing the main flow from user request to final answer, including guardrail checks and potential halts.
flowchart LR
subgraph Input
U((User Request)) --> I
I --fail--> X[Stop - tripwire exception]
I --ok--> A[Active Agent]
end
subgraph AgentLogic
A -->|LLM output| Router
Router -->|Tool call| T[Execute Tool]
Router -->|Handoff| Swap[Switch Agent]
Router -->|Final Answer| GA((Check Output Guardrails))
T --> A
Swap --> A2[New Active Agent]
A2 --> Router
end
I --> R(Runner)
R --> Router
GA --fail--> Y[Halt / Raise Exception]
GA --ok--> F([Return Final])
R --> TRACE[[Tracing system]]
F --> TRACE
Explanation:
This sequence diagram shows how the Runner can check user input in parallel while calling the LLM. If a violation is found first, the LLM call is canceled, saving resources.
sequenceDiagram
autonumber
participant Runner
participant InputGuardrailFunction as Input Guardrail
participant AgentLLM as LLM Call
Runner->>+InputGuardrailFunction: Start parallel check
Runner->>+AgentLLM: Send user input
alt guardrail triggers first
InputGuardrailFunction->>Runner: tripwire_triggered=True
Runner-->>AgentLLM: Cancel LLM request
Runner->>Runner: Raise exception
else LLM finishes first
AgentLLM->>Runner: LLM Output
InputGuardrailFunction->>Runner: guardrail result = OK
end
Explanation:
In this example, an agent decides it’s not the correct one to solve a query and hands the conversation to the “MathAgent.” The Runner updates which agent is active and proceeds with the math steps.
sequenceDiagram
autonumber
participant TriageAgent
participant Runner
participant MathAgent
TriageAgent->>Runner: LLM Output: transfer_to_MathAgent(...)
note over TriageAgent,Runner: Triage Agent decides to hand off
Runner->>Runner: Switch <current_agent> = MathAgent
Runner->>MathAgent: Provide question "2+2?"
MathAgent->>Runner: LLM final answer: "4"
note over Runner: Done, deliver "4" to user
Explanation:
This sequence diagram demonstrates how an agent can call a tool (a Python function or a hosted service), then use the result to proceed.
sequenceDiagram
autonumber
participant Agent
participant Runner
participant LLM
participant MyTool as MyPythonFunction
Agent->>LLM: Prompt with system + user question
LLM-->>Agent: Output: function_call = {"name":"MyTool","arguments":{...}}
Agent->>Runner: "I want to call MyTool"
Runner->>MyTool: Execute <MyTool(args)>
MyTool-->>Runner: returns result
Runner->>Agent: "Tool returned: X"
Agent->>LLM: Provide "X" as new info
LLM-->>Agent: Possibly final answer or more tool calls
Explanation:
So why care about all this? Couldn’t you just manually stitch together a few LLM calls and call it a day? The short answer: you could, but you’d be missing out on powerful built-in features that make multi-agent workflows simpler, safer, and easier to debug.
Seamless Handoffs
A major differentiator is how easy it becomes to have multiple specialized “minds” in one system. You don’t need complicated custom state machines or endless if-else logic. Each agent has its domain, and the system handles passing control around automatically.
Tool Ecosystem
Full function-calling support plus a user-friendly approach to turning Python functions into LLM “tools” means it’s easy to integrate your existing codebase. The SDK also includes built-in hosted tools like ephemeral code execution or web search. This fosters a “plug-and-play” approach for adding or swapping tools.
Parallel Guardrails
Safety checks happen in tandem, stopping runs early if they break constraints. This is a big deal for enterprise or regulated settings where you can’t afford to let certain queries proceed. For compliance teams, parallel guardrails reduce the risk of accidental data exposure or policy violation.
Rich Tracing
A “step-by-step flight recorder” for debugging multi-step or multi-agent flows. You can see each sub-step in the OpenAI Trace Viewer, including partial outputs. This is more than just logging; it’s a structured, interactive record of the conversation’s internal states and decisions.
Agent as Tool
You can nest an entire agent inside a single “function call,” so a high-level orchestrator can use sub-agents in ephemeral tasks and still keep overall control. This approach is especially helpful if you want to keep your conversation context in one place but occasionally consult a domain expert.
Easy Configuration
By centralizing orchestration in the Runner and letting each agent remain mostly “dumb” outside of the LLM logic, you reduce code complexity. Configuring your system is basically a matter of: “Here are the agents, here are the tools they can call, here are the guardrails,” and the Runner does the rest.
If you’re building a complex assistant or workflow that needs sub-tasks, specialized logic, or thorough compliance checks, OpenAI Agents SDK gives you a one-stop shop for advanced multi-agent orchestration. The result is a smoother user experience, fewer engineering headaches, and a robust safety net of guardrails and traces.
We hope this expanded explanation highlights both the conceptual value and the practical mechanics of the OpenAI Agents SDK. Happy building — and if you end up crafting unique multi-agent workflows, let us know how they turn out! Check the Agents SDK docs or our GitHub repo for deeper implementation details.