エージェントのカスタマイズ¶

@property
def agent_planner(self) -> Agent:
    return Agent(
        role="Content Planner",
        goal="Plan engaging and factually accurate content on {topic}",
        backstory="You're working on planning a blog article about the topic: {topic}. You collect "
        "information that helps the audience learn something and make informed decisions. Your work is "
        "the basis for the Content Writer to write an article on this topic.",
        allow_delegation=False,
        verbose=self.verbose,
        llm=self.llm,
    ) 

@property
def task_plan(self) -> Task:
    return Task(
        description=(
            "1. Prioritize the latest trends, key players, and noteworthy news on {topic}.\n"
            "2. Identify the target audience, considering their interests and pain points.\n"
            "3. Develop a detailed content outline including an introduction, key points, and a call to action.\n"
            "4. Include SEO keywords and relevant data or sources."
        ),
        expected_output="A comprehensive content plan document with an outline, audience analysis, SEO keywords, "
        "and resources.",
        agent=self.agent_planner,
    ) 

from datarobot_genai.core.agents import make_system_prompt
from langgraph.prebuilt import create_react_agent

@property
def agent_planner(self) -> Any:
    return create_react_agent(
        self.llm(preferred_model="datarobot/azure/gpt-5-mini-2025-08-07"),
        tools=self.mcp_tools,
        prompt=make_system_prompt(
            "You are a content planner. You are working with a content writer colleague.\n"
            "You're working on planning a blog article about the topic. You collect information that helps the "
            "audience learn something and make informed decisions. Your work is the basis for the Content Writer "
            "to write an article on this topic.\n"
            "1. Prioritize the latest trends, key players, and noteworthy news on the topic.\n"
            "2. Identify the target audience, considering their interests and pain points.\n"
            "3. Develop a detailed content outline including an introduction, key points, and a call to action.\n"
            "4. Include SEO keywords and relevant data or sources."
        ),
        name="Planner Agent",
    ) 

@property
def research_agent(self) -> FunctionAgent:
    return FunctionAgent(
        name="ResearchAgent",
        description="Useful for finding information on a given topic and recording notes on the topic.",
        system_prompt=(
            "You are the ResearchAgent that can find information on a given topic and record notes on the topic. "
            "Once notes are recorded and you are satisfied, you should hand off control to the "
            "WriteAgent to write a report on the topic. You should have at least some notes on a topic "
            "before handing off control to the WriteAgent."
        ),
        llm=self.llm,
        tools=[self.record_notes],
        can_handoff_to=["WriteAgent"],
    ) 

functions:
  planner:
    _type: chat_completion
    llm_name: datarobot_llm
    system_prompt: |
      You are a content planner. You are working with a content writer colleague.
      You're working on planning a blog article about the topic.
      You collect information that helps the audience learn something and make informed decisions.
      Your work is the basis for the Content Writer to write an article on this topic.
      1. Prioritize the latest trends, key players, and noteworthy news on the topic.
      2. Identify the target audience, considering their interests and pain points.
      3. Develop a detailed content outline including an introduction, key points, and a call to action.
      4. Include SEO keywords and relevant data or sources. 

task agent:cli -- execute --user_prompt 'Write a document about the history of AI.' --stream 

task agent:cli -- execute --user_prompt '{"topic":"Generative AI"}' --stream 

from openai import OpenAI

client = OpenAI(
    base_url=CHAT_API_URL,
    api_key=API_KEY,
)

completion = client.chat.completions.create(
    model="datarobot-deployed-llm",
    messages=[
        {"role": "user", "content": "What would it take to colonize Mars?"},
    ],
    stream=True,  # Enable streaming
)

# Process streaming response
for chunk in completion:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True) 

task agent:dev 

task agent:cli -- execute --user_prompt 'Write a document about the history of AI.' 

task agent:cli -- execute --user_prompt '{"topic":"Generative AI"}' 

task agent:cli START_DEV=1 -- execute --user_prompt 'Write a document about the history of AI.' 

task agent:cli START_DEV=1 -- execute --user_prompt '{"topic":"Generative AI"}'