OpenAI Agents SDK

Operating contract

Inputs

Outputs

• Produce: agent topology, tool schemas, guardrails, handoff rules, tracing configuration, and runnable validation plan.

Capability and permission boundaries

Default to read-only analysis. Read only scoped records; redact secrets and regulated data. Writes, execution, network calls, production configuration, customer communication, billing changes, and delegation require explicit authority and an identified owner. Never widen tenant, time-window, or system scope implicitly.

Degraded mode

When required telemetry, evidence, execution, network access, or write authority is unavailable, return a partial result with each unassessed item labelled, preserve the safest existing state, and state the evidence or approval needed to continue. Never convert missing evidence into a pass.

Decision rules

Anti-Patterns

• Treating absent evidence as success. Fix: mark the check unassessed and name the missing source.
• Expanding one tenant or workflow to all tenants. Fix: enforce supplied scope at every query and action.
• Performing a production write during analysis. Fix: emit a reviewed change plan until authority is explicit.
• Reporting a metric without population, window, or source. Fix: attach all three.
• Hiding a failed threshold inside an average. Fix: report failure slices and the remediation owner.

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use When

• Build production AI agents with the OpenAI Agents SDK (Python) — 6 core primitives (Agent, Runner, Tools, Handoff, Guardrails, Tracing), multi-agent patterns (Centralized, Hierarchical, Decentralized, Swarm), dynamic/deterministic orchestration...

Evidence Produced

References

• Use the links and companion skills already referenced in this file when deeper context is needed.

Minimal Python SDK for building AI agents. Six primitives: Agent, Runner, Tools, Handoff, Guardrails, Tracing.

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

1. Agent — The Core Primitive

A configurable wrapper around an LLM that can take actions.

from agents import Agent

customer_service_agent = Agent(
    name="Customer Service Agent",
    model="gpt-4o",                    # or any OpenAI-compatible model
    instructions="""
        You are a helpful customer service agent.
        Handle returns, orders, and billing queries.
        Escalate complex issues to a human.
    """,
    tools=[get_order_status, process_refund, track_shipment],
    handoffs=[billing_agent, technical_agent],    # agents to delegate to
)

Agent parameters: name, instructions (system prompt), model, tools, handoffs, guardrails, output_type

2. Runner — The Agent Loop

Runs the agent's reasoning loop (think → act → observe → repeat).

from agents import Agent, Runner

agent = Agent(name="Assistant", instructions="You are a helpful assistant.", model="gpt-4o")

# Synchronous (simple scripts, testing)
result = Runner.run_sync(agent, "What is the capital of France?")
print(result.final_output)

# Asynchronous (web apps, FastAPI)
import asyncio
result = await Runner.run(agent, "Summarise this document.", max_turns=10)
print(result.final_output)

Key parameter: max_turns — safety valve against infinite loops. Always set it.

result = Runner.run_sync(agent, user_message, max_turns=15)

3. Tools — Extending Agent Capabilities

Custom Tools (Python Functions)

Any Python function becomes a tool with @function_tool. The SDK reads the name, docstring, and type hints automatically.

from agents import function_tool

@function_tool
def get_order_status(order_id: str) -> str:
    """Gets the current status of a customer order.
    
    Args:
        order_id: The unique order identifier (e.g. ORD-12345)
    """
    order = db.query("SELECT status FROM orders WHERE id = ?", [order_id])
    return f"Order {order_id} is: {order['status']}"

@function_tool
def calculate_refund(order_id: str, reason: str) -> dict:
    """Calculate and process a refund for an order.
    
    Args:
        order_id: The order to refund
        reason: Customer-provided reason for return
    """
    amount = get_order_total(order_id)
    return {"refund_amount": amount, "processing_days": 3}

Hosted Tools (Built-in OpenAI)

from agents import Agent
from agents.tools import WebSearchTool, FileSearchTool, CodeInterpreterTool

research_agent = Agent(
    name="Research Agent",
    model="gpt-4o",
    instructions="Research topics thoroughly and provide cited summaries.",
    tools=[
        WebSearchTool(),          # search the web
        FileSearchTool(            # search vector store
            vector_store_ids=["vs_abc123"]
        ),
        CodeInterpreterTool(),    # run Python code
    ],
)

Agent as Tool

# When you need a sub-agent but the calling agent retains control
analysis_tool = analysis_agent.as_tool(
    tool_name="RunAnalysis",
    tool_description="Run deep financial analysis on the provided data."
)

orchestrator = Agent(
    name="Orchestrator",
    tools=[analysis_tool, data_fetcher],
)

Handoff vs as_tool:

• handoff — complete transfer of control, caller stops
• as_tool — caller delegates subtask, caller resumes after

4. Handoffs — Multi-Agent Delegation

from agents import Agent, Runner

# Specialized agents
billing_agent = Agent(
    name="Billing Agent",
    instructions="Handle all billing, payment, and invoice queries.",
    tools=[lookup_invoice, process_payment],
)
technical_agent = Agent(
    name="Technical Agent",
    instructions="Resolve technical issues, bugs, and connectivity problems.",
    tools=[check_system_status, reset_connection],
)

# Triage agent with handoffs
triage_agent = Agent(
    name="Triage Agent",
    instructions="""
        Triage the user's request. If billing-related, route to Billing Agent.
        If technical, route to Technical Agent. Otherwise, answer directly.
    """,
    handoffs=[billing_agent, technical_agent],
)

result = Runner.run_sync(triage_agent, "My invoice shows the wrong amount")
print(result.final_output)

Handoff prompt best practices:

• Explicitly name which conditions trigger a handoff in each agent's instructions
• Each specialist agent must clearly state its purpose and domain
• Use result.last_agent to track which agent handled the final response

5. Multi-Agent Patterns

Centralized (Most Common)

One triage/orchestrator routes to specialists. Best for: customer support, internal assistants, helpdesks.

# One central agent → N specialized agents
triage_agent = Agent(
    name="Triage",
    handoffs=[billing_agent, technical_agent, sales_agent],
)

Hierarchical

Multi-tier routing: triage → managers → specialists. Best for: deep research, complex enterprise workflows.

# Triage → Domain Manager → Domain Specialists
science_manager = Agent(name="Science Manager",
    handoffs=[physics_agent, chemistry_agent])
history_manager = Agent(name="History Manager",
    handoffs=[politics_agent, warfare_agent])
triage = Agent(name="Research Triage",
    handoffs=[science_manager, history_manager])

Dynamic vs Deterministic Orchestration

# Deterministic — hardcoded routing (predictable, inflexible)
def orchestrate(message: str):
    if "complaint" in message.lower():
        return Runner.run_sync(complaints_agent, message)
    return Runner.run_sync(inquiry_agent, message)

# Dynamic — LLM decides routing (flexible, less predictable)
triage_agent = Agent(
    name="Triage",
    instructions="Route requests to the most appropriate specialized agent.",
    handoffs=[complaints_agent, inquiry_agent],  # LLM picks at runtime
)

Decentralized (Debate / Brainstorm)

No central agent; agents exchange turns. Best for: ideation, debate, negotiation.

agents = [Agent(name=f"{role}", instructions=f"You are a {role}...") for role in roles]
# Manual round-robin loop — SDK does not auto-manage decentralized flow
for i in range(rounds):
    result = Runner.run_sync(agents[i % len(agents)], history, session=session)

Swarm (Parallel Exploration)

Many simple agents in parallel, results aggregated. Best for: creative generation, optimization.

import concurrent.futures

def run_agent(agent, prompt):
    return Runner.run_sync(agent, prompt).final_output

with concurrent.futures.ThreadPoolExecutor() as executor:
    results = list(executor.map(lambda a: run_agent(a, prompt), specialist_agents))

summary = Runner.run_sync(aggregator_agent, "\n".join(results))

6. Memory Management

from agents import SQLiteSession

# Persistent conversation memory (survives restarts)
session = SQLiteSession("my_app_db")
last_agent = triage_agent

while True:
    user_input = input("You: ")
    result = Runner.run_sync(last_agent, user_input, session=session)
    print("Agent:", result.final_output)
    last_agent = result.last_agent  # continue with whoever last responded

Sliding window for long conversations:

# Summarize old messages when context grows too large
if len(messages) > 20:
    summary = Runner.run_sync(summarizer_agent, "\n".join(messages[-20:]))
    messages = [{"role": "system", "content": f"Conversation so far: {summary.final_output}"}]

7. Guardrails — Safety Validation

from agents import Agent, Runner, GuardrailFunctionOutput, RunContextWrapper
from agents import input_guardrail, output_guardrail, InputGuardrailTripwireTriggered
from agents.types import TResponseInputItem

@input_guardrail
async def scope_guardrail(
    ctx: RunContextWrapper[None],
    agent: Agent,
    input: str | list[TResponseInputItem]
) -> GuardrailFunctionOutput:
    """Only allow customer service queries."""
    is_valid = any(kw in str(input).lower()
                   for kw in ['order', 'refund', 'account', 'billing', 'payment', 'delivery'])
    return GuardrailFunctionOutput(
        output_info="valid" if is_valid else "out of scope",
        tripwire_triggered=not is_valid,
    )

agent = Agent(
    name="CS Agent",
    instructions="Handle customer service queries.",
    input_guardrails=[scope_guardrail],
)

try:
    result = Runner.run_sync(agent, "What's the meaning of life?")
except InputGuardrailTripwireTriggered:
    print("Sorry, I can only help with customer service queries.")

8. Third-Party Models (OpenAI-Compatible)

from agents.extensions.models.litellm_model import LitellmModel

# Use DeepSeek, Anthropic, Gemini, Llama — anything via LiteLLM
agent = Agent(
    name="Multi-Model Agent",
    model=LitellmModel(model="deepseek/deepseek-chat", api_key="..."),
    instructions="You are a helpful assistant.",
)

# Or set globally
import agents
agents.set_default_openai_api("chat_completions")
agents.set_default_openai_client(AsyncOpenAI(
    base_url="https://api.deepseek.com/v1",
    api_key=os.environ["DEEPSEEK_API_KEY"],
))

Anti-Patterns

Source: Habib — Building Agents with OpenAI Agents SDK (Packt, 2025)