The OpenAI Agents SDK is an open-source framework that bridges the gap between raw API calls and heavy orchestration libraries like LangChain. It costs nothing to use the SDK itself; your bill depends entirely on the underlying model inference. For a standard workload processing 5,000 documents daily (approx. 2,000 tokens each) using GPT-4o-mini, you’re looking at roughly $45/month in API costs—significantly cheaper than the $750+/month you’d burn using the full GPT-4o model for the same task.
Functionally, this is the production-ready successor to OpenAI’s experimental "Swarm" project. It abandons the "magical" autonomous loops of early agent frameworks in favor of explicit, programmable "handoffs." Think of it like a corporate phone tree that actually works: you define exactly when the Triage Agent transfers a task to the Research Agent, rather than hoping a generic "Manager LLM" figures it out. This makes debugging trivial because the control flow is hard-coded in Python, not hallucinated in a prompt.
The SDK excels at speed and simplicity. You can spin up a multi-agent system with guardrails and tracing in under 100 lines of code. It integrates natively with the Model Context Protocol (MCP), allowing your agents to connect to local servers and tools without custom wrappers. The built-in tracing offers visibility into agent thoughts without needing a third-party observability platform immediately.
However, it is deliberately "dumb" regarding state. Unlike LangGraph, which has a built-in persistence layer for long-running threads, the Agents SDK runs primarily on the client side. If your script crashes, the agent's memory is gone unless you’ve wired up your own database or integrated it with a durable execution engine like Temporal. It also lacks the vast ecosystem of document loaders found in LlamaIndex, meaning you’ll have to write your own ingestion logic.
Use the Agents SDK if you are a Python developer who wants to build deterministic, reliable agent workflows and hates the "black box" feel of other frameworks. Skip it if you need a no-code solution or out-of-the-box state management for week-long autonomous tasks. It is the best choice for teams that want to ship production agents today without drowning in abstraction.
Pricing
The SDK is free and open-source (MIT License). The real cost is strictly usage-based via the OpenAI API (or other compatible endpoints). There is no free tier for the API itself, but the SDK imposes no overhead.
The hidden cost lies in "handoffs" and "guardrails." Every handoff involves function calling tokens, and every guardrail check allows for a secondary LLM call to validate inputs/outputs. A poorly optimized swarm with frequent handoffs can double your token consumption compared to a monolithic prompt. Using GPT-4o-mini is essential for keeping these routing costs negligible ($0.15/1M input tokens) compared to the main task execution.
Technical Verdict
This is the cleanest agent abstraction currently available. It feels like native Python rather than a domain-specific language. Documentation is concise, and the type hinting is excellent. Latency is minimal as it adds almost zero overhead over standard API calls.
Reliability is high because it forces you to handle state transitions explicitly. Integration friction is low if you are already in the OpenAI ecosystem, but connecting to non-OpenAI models requires compliant endpoints. You can go from pip install to a running multi-agent swarm in about 5 minutes.
Quick Start
# pip install openai-agents
from agents import Agent, Runner
# Define a simple agent
agent = Agent(name="Greeter", instructions="You are a helpful assistant.")
# Run the agent synchronously
result = Runner.run_sync(agent, "Write a haiku about code reviews.")
print(result.final_output)Watch Out
- State is not persisted automatically; if the process dies, the conversation history is lost.
- Handoffs are just tool calls; infinite loops between two polite agents thanking each other will drain your wallet.
- There is no built-in RAG pipeline; you must bring your own vector database and retrieval logic.
- Guardrails add latency; enabling input/output validation doubles the round-trip time for that step.