OpenAI Agents SDK: Complete Platform Profile

The OpenAI Agents SDK is OpenAI's official Python framework for building production-ready AI agents. Released in March 2025 as a successor to the experimental Swarm library, it brings first-party support for the most critical patterns in modern agentic systems: structured tool use, agent-to-agent handoffs, input and output guardrails, and end-to-end tracing. For teams already invested in the OpenAI ecosystem, the SDK offers the most direct path from prototype to production.

Explore the AI agent tools directory to see how the OpenAI Agents SDK fits into the broader landscape of agent development platforms and frameworks.

Overview#

OpenAI announced the Agents SDK in early 2025 following months of internal experimentation. The project evolved from Swarm, an open-source demonstration library that showed how lightweight primitives — agents, tools, and handoffs — could enable sophisticated multi-agent coordination without heavy framework overhead. The Agents SDK retains Swarm's philosophical minimalism while adding the production features that real deployments require: tracing, guardrails, type safety, and a richer tool ecosystem.

The SDK reached 20,000 GitHub stars within its first month of release, reflecting both the strength of OpenAI's brand and genuine developer hunger for an official, well-supported agentic framework. Unlike community-driven projects, the SDK benefits from tight integration with the broader OpenAI platform, including the Responses API, the OpenAI Dashboard's built-in trace viewer, and structured output capabilities introduced throughout 2024 and 2025.

As of early 2026, the SDK has become one of the most widely referenced starting points for teams building customer-facing agents, internal automation tools, and multi-step research pipelines.

Core Features#

Agents with Instructions and Tools#

At its core, the SDK models an agent as a Python object with a name, a natural-language system prompt (instructions), and a list of callable tools. Tools are defined as standard Python functions decorated with @tool, and the SDK automatically generates the JSON Schema that the model needs to invoke them correctly. This removes the error-prone boilerplate of manually constructing tool definitions.

Agents can be configured with specific models, temperature settings, and output formats. The SDK supports OpenAI's structured output mode, allowing agents to return Pydantic models directly rather than raw text. This is particularly valuable for downstream processing steps that depend on type-safe data rather than string parsing.

The agent loop — the cycle of calling the model, interpreting tool calls, executing tools, and feeding results back — is handled automatically by the SDK's Runner. Developers invoke Runner.run() or Runner.run_sync() and receive a typed result object containing the final output, all intermediate messages, and metadata about the run.

Multi-Agent Handoffs#

One of the SDK's signature capabilities is the handoff mechanism, borrowed directly from Swarm. A handoff is a special tool call that transfers control from one agent to another, along with the accumulated conversation context. This enables architectures where a triage agent routes requests to specialist agents without the user experiencing any discontinuity.

Handoffs are declared as part of an agent's tool list using the handoff() helper. When the model decides to transfer to another agent, the SDK manages the context window, carries over relevant history, and begins the new agent's loop. Teams use this pattern to build layered systems: a general-purpose agent handles most queries, while specialized agents take over for billing questions, technical support, or regulated domains that require stricter guardrails.

The handoff system is designed to be transparent. The SDK's tracing layer records each handoff event, making it straightforward to audit which agent handled which portion of a conversation and why.

Guardrails#

The SDK introduces input and output guardrails as first-class primitives. An input guardrail is a lightweight validation step that runs before the main agent begins processing. It can reject requests that fall outside policy, redact sensitive information, or modify the prompt before it reaches the model. Output guardrails similarly intercept the agent's response before it is returned to the caller.

Guardrails can be synchronous functions or asynchronous coroutines. The SDK runs them in parallel with the main agent loop where possible, minimizing latency overhead. This design is particularly useful for customer service applications where safety and compliance requirements must be enforced consistently across thousands of concurrent conversations.

Tracing and Observability#

Every run executed through the SDK is traced by default. The trace captures the full sequence of model calls, tool invocations, input/output content, token counts, and latency at each step. Traces are viewable in the OpenAI Dashboard without any additional configuration.

The tracing system is also extensible. Developers can attach custom span attributes, export traces to third-party observability platforms like Langfuse or LangSmith, and integrate with OpenTelemetry pipelines. For teams operating in regulated industries, the ability to maintain an immutable audit log of every agent decision is not optional — it is a compliance requirement.

Pricing and Plans#

The OpenAI Agents SDK itself is free and open source under the MIT license. Running agents in production costs money only at the API level: you pay for OpenAI model tokens consumed during agent loops. Tool calls that invoke external services incur whatever costs those services charge separately.

OpenAI's API pricing as of early 2026 is tiered by model. GPT-4o remains the workhorse model for most agent deployments, with a price per million input and output tokens that makes it viable for high-volume automation. o3-mini and o1 variants offer different cost/capability tradeoffs suitable for reasoning-heavy tasks. OpenAI offers volume discounts and committed-use agreements for enterprise customers processing very high token volumes.

There is no additional charge for using the SDK's tracing features within the OpenAI Dashboard. Teams that require self-hosted or third-party observability will incur whatever costs their chosen platform charges.

Strengths#

First-party support and long-term commitment. Because the SDK is maintained by OpenAI itself, it is the first to receive support for new model capabilities — structured outputs, new tool types, context window expansions — often on the day of launch.

Minimal learning curve for existing OpenAI users. Teams already using the OpenAI Python library will find the Agents SDK immediately familiar. The mental model is simple: agents are objects, tools are functions, and the runner handles the loop.

Production-grade tracing out of the box. Unlike many frameworks that treat observability as an afterthought, the SDK ships with full tracing enabled by default, integrated directly into the platform teams are already using to manage their API usage.

Clean multi-agent architecture via handoffs. The handoff primitive makes it genuinely straightforward to build hierarchical and parallel agent systems without resorting to complex graph configurations or message-passing boilerplate.

Limitations#

Tied to OpenAI's API ecosystem. While the SDK can technically be configured to work with OpenAI-compatible endpoints, it is designed around OpenAI's specific APIs and feature set. Teams that need to run models on-premises or across multiple providers will find frameworks like Semantic Kernel or LlamaIndex more accommodating.

No built-in long-term memory. The SDK does not include native memory persistence. Conversation history is maintained within a single run, but cross-session memory requires developers to implement their own storage layer.

Limited visual tooling. Unlike platforms such as LangGraph Studio or Flowise, the SDK has no drag-and-drop agent builder or visual flow editor. All configuration is done in code, which suits experienced developers but may create friction for teams with less Python expertise.

Ideal Use Cases#

• Customer service automation: Deploy multi-agent systems where a triage agent classifies queries and hands off to specialized agents for billing, returns, or technical support.
• Internal knowledge assistants: Build agents that call internal APIs and databases, returning structured, verified answers to employee questions.
• Document processing pipelines: Orchestrate agents that extract, transform, and validate data from unstructured documents using tool calls and structured output.
• Code review and generation assistants: Use handoffs to route code analysis tasks between agents specialized in security review, performance optimization, and style enforcement.

Getting Started#

Install the SDK and set your API key:

pip install openai-agents
export OPENAI_API_KEY="sk-..."

Define a simple agent with a tool:

from agents import Agent, Runner, tool

@tool
def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"It is currently 22°C and sunny in {city}."

agent = Agent(
    name="WeatherAgent",
    instructions="You are a helpful weather assistant.",
    tools=[get_weather],
)

result = Runner.run_sync(agent, "What's the weather in Tokyo?")
print(result.final_output)

To add a handoff, define a second agent and include it in the first agent's handoffs list. The runner manages the rest automatically. Full documentation is available in the official OpenAI Agents SDK reference.

How It Compares#

OpenAI Agents SDK vs LangChain: LangChain offers a much broader ecosystem of integrations, memory modules, and retrieval components. The OpenAI Agents SDK is narrower in scope but simpler to learn and maintain. For teams starting fresh with OpenAI models, the SDK's lower complexity is a genuine advantage. See the OpenAI Agents SDK vs LangChain comparison for a detailed breakdown.

OpenAI Agents SDK vs CrewAI: CrewAI emphasizes a role-based crew metaphor with explicit task delegation between named agents. The OpenAI Agents SDK is more flexible in how agents relate to each other, using handoffs rather than predefined task assignments. See the OpenAI Agents SDK vs CrewAI comparison for specifics.

OpenAI Agents SDK vs PydanticAI: PydanticAI prioritizes type safety and model-agnosticism. If your team works across multiple LLM providers or values strict validation throughout the agent loop, PydanticAI's approach is worth examining.

Bottom Line#

The OpenAI Agents SDK occupies a distinct niche: it is the best-supported, most ergonomic framework for teams committed to OpenAI's model family. Its minimalist design philosophy prevents the kind of abstraction overhead that makes other frameworks difficult to debug and maintain at scale. The built-in tracing, guardrails, and handoff system address the three most common pain points teams encounter when moving agent prototypes into production.

The trade-offs are real. You are trading provider flexibility and ecosystem breadth for a tighter, more integrated experience. For startups building quickly on GPT-4o and teams inside enterprises where OpenAI is an approved vendor, those trade-offs will often be worth accepting.

Best for: Python developers building production agents on OpenAI models who value simplicity, first-party support, and integrated observability over maximum provider flexibility.

Frequently Asked Questions#

Is the OpenAI Agents SDK the same as Swarm? No. The Agents SDK is the production-ready successor to Swarm, which was an experimental research library. The SDK adds guardrails, integrated tracing, proper async support, and structured output integration that Swarm lacked. Swarm is no longer actively maintained.

Can I use the OpenAI Agents SDK with non-OpenAI models? The SDK is designed primarily for OpenAI's API, but it can be configured to work with any OpenAI-compatible endpoint. Support for non-OpenAI models is not a primary design goal, and some features like structured output depend on OpenAI-specific capabilities.

Does the SDK support async agent execution? Yes. Runner.run() is a native async coroutine, making it straightforward to integrate into FastAPI applications or other async Python services. Runner.run_sync() is provided as a convenience wrapper for synchronous contexts.

How does tracing work in production? All runs are traced automatically. Traces appear in the OpenAI Dashboard under the Traces section. You can also register custom trace processors to export spans to external systems like Langfuse or an OpenTelemetry collector.

What is the difference between a tool and a handoff? A tool is a Python function that the agent can call to retrieve information or perform an action. A handoff is a special mechanism that transfers control from one agent to another, passing along conversation history. Handoffs are used for multi-agent routing; tools are used for single-agent capabilities.