OpenAI Agents SDK

from dotenv import load_dotenv
load_dotenv()  # loads XPANDER_API_KEY, OPENAI_API_KEY, etc. before any sdk import

from xpander_sdk import on_task, Task, Agents
from agents import Agent as OpenAIAgent, Runner

@on_task
async def handler(task: Task) -> Task:
    # 1. Load the xpander agent (instructions, skills, model are all on it).
    xpander_agent = await Agents(configuration=task.configuration).aget(
        agent_id=task.agent_id,
    )

    # 2. Build the OpenAI Agents SDK's own Agent from three xpander fields.
    native = OpenAIAgent(
        name=xpander_agent.name,
        instructions=xpander_agent.instructions.full,
        tools=xpander_agent.openai_agents_sdk_tools,
        model=xpander_agent.model_name,
    )

    # 3. Run the LLM loop with the task's user message.
    result = await Runner.run(native, input=task.to_message())

    # 4. Write the result back so xpander can store and display it.
    task.result = result.final_output
    return task

from agents import Agent as OpenAIAgent, Runner, function_tool

@function_tool
async def search_knowledge_base(query: str, num_documents: int = 5) -> list[dict]:
    """Search the agent's linked knowledge bases. Returns top-k matching documents."""
    # knowledge_bases_retriever() returns a callable: (query, agent=None, num_documents=5, **kwargs)
    retriever = xpander_agent.knowledge_bases_retriever()
    return retriever(query=query, num_documents=num_documents)

native = OpenAIAgent(
    name=xpander_agent.name,
    instructions=xpander_agent.instructions.full,
    # Concatenate the auto-wired tools with the KB retriever.
    tools=[*xpander_agent.openai_agents_sdk_tools, search_knowledge_base],
    model=xpander_agent.model_name,
)

from datetime import datetime, timezone
from xpander_sdk import on_task, Task, Agents, TaskUpdateEvent, TaskUpdateEventType
from agents import Agent as OpenAIAgent, Runner

@on_task
async def handler(task: Task):
    xpander_agent = await Agents(configuration=task.configuration).aget(agent_id=task.agent_id)
    native = OpenAIAgent(
        name=xpander_agent.name,
        instructions=xpander_agent.instructions.full,
        tools=xpander_agent.openai_agents_sdk_tools,
        model=xpander_agent.model_name,
    )

    final_output = ""
    # Runner.run_streamed yields events as the LLM produces them.
    streaming = Runner.run_streamed(native, input=task.to_message())
    async for event in streaming.stream_events():
        # Surface each text delta to xpander's SSE stream.
        if event.type == "raw_response_event" and getattr(event.data, "delta", None):
            chunk = event.data.delta
            final_output += chunk
            yield TaskUpdateEvent(
                type=TaskUpdateEventType.Chunk,
                task_id=task.id,
                organization_id=task.organization_id,
                time=datetime.now(timezone.utc),
                data=chunk,
            )

    task.result = final_output
    yield TaskUpdateEvent(
        type=TaskUpdateEventType.TaskFinished,
        task_id=task.id,
        organization_id=task.organization_id,
        time=datetime.now(timezone.utc),
        data=task,
    )