DeepAgents

from pandaprobe.integrations.deepagents import DeepAgentsCallbackHandler

handler = DeepAgentsCallbackHandler(
    session_id="conversation-123",
    user_id="user-abc",
    tags=["production"],
)

from deepagents import create_deep_agent

agent = create_deep_agent(
    model="openai:gpt-5.4-nano",
    tools=[...],
    system_prompt="...",
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "Hello!"}]},
    config={"callbacks": [handler]},
)

from deepagents import create_deep_agent
from langchain.tools import tool

import pandaprobe
from pandaprobe.integrations.deepagents import DeepAgentsCallbackHandler


@tool
def search_papers(topic: str) -> str:
    """Return a short list of recent papers on a topic."""
    return (
        f"Top 3 recent papers on {topic}:\n"
        "1. Smith et al., 2024 — A survey of advances\n"
        "2. Lee & Kumar, 2024 — Empirical comparisons\n"
        "3. Garcia, 2023 — Foundations and theory"
    )


researcher_subagent = {
    "name": "researcher",
    "description": (
        "Searches for and summarizes recent academic papers on a given topic. "
        "Use this when the user asks for a literature overview."
    ),
    "system_prompt": (
        "You are a research assistant. When given a topic, call search_papers, "
        "then return a concise 2-sentence summary of what you found."
    ),
    "tools": [search_papers],
    "model": "openai:gpt-5.4-nano",
}


agent = create_deep_agent(
    model="openai:gpt-5.4-nano",
    tools=[],
    system_prompt=(
        "You are a senior research lead. For any literature/research request, "
        "delegate to the 'researcher' sub-agent via the task tool, then "
        "synthesize the result into a final answer."
    ),
    subagents=[researcher_subagent],
)


handler = DeepAgentsCallbackHandler(tags=["research-agent", "subagents"])

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "Give me a brief summary of recent research on retrieval-augmented generation.",
            }
        ]
    },
    config={"callbacks": [handler]},
)

final_message = result["messages"][-1]
print(f"Agent: {final_message.content}")

pandaprobe.flush()
pandaprobe.shutdown()