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openai-agents
by Jawad-Chaudhary
The Evolution of Todo: A spec-driven, AI-native journey from a Python CLI to a Cloud-Native, Event-Driven AI Chatbot deployed on Kubernetes. Built with Claude Code, Spec-Kit Plus, Next.js, FastAPI, and Dapr.
⭐ 0🍴 0📅 2026年1月22日
SKILL.md
name: openai-agents description: Build AI agents with OpenAI Agents SDK, tool registration, conversation history, and stateless execution. Use when creating AI agents, registering tools, or handling conversations.
OpenAI Agents SDK Integration
Agent Initialization
from openai import OpenAI
from openai.agents import Agent, Runner
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
agent = Agent(
name="TaskAssistant",
instructions="""You are a helpful task management assistant.
Use the provided tools to help users manage their todo list.
Always confirm actions with friendly responses.
If a user's request is ambiguous, ask for clarification.""",
model="gpt-4o",
tools=[] # MCP tools registered here
)
Tool Registration (from MCP)
tools = [
{
"type": "function",
"function": {
"name": "add_task",
"description": "Create a new task",
"parameters": {
"type": "object",
"properties": {
"user_id": {"type": "string"},
"title": {"type": "string"},
"description": {"type": "string"}
},
"required": ["user_id", "title"]
}
}
}
]
agent.tools = tools
Running Agent with History
async def run_agent(messages: list[dict]) -> tuple[str, list[str]]:
"""Run agent with conversation history."""
runner = Runner(agent=agent)
result = await runner.run(messages=messages)
# Extract response
response = result.messages[-1].content[0].text.value
# Extract tool calls
tool_calls = [
call.function.name
for msg in result.messages
if hasattr(msg, 'tool_calls')
for call in msg.tool_calls
]
return response, tool_calls
Message Format
messages = [
{"role": "user", "content": "Add a task"},
{"role": "assistant", "content": "I've added the task"},
{"role": "user", "content": "Show my tasks"}
]
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