from langchain_core.tools import tool
 
@tool
def web_search(query: str) -> str:
    """Search the web for information on a topic."""
    results = {
        "machine learning trends 2025": (
            "[Source: MIT Tech Review] Multimodal models dominate enterprise AI adoption. "
            "Fine-tuning smaller models outperforms prompting larger ones for specific tasks. "
            "Agent frameworks emerge as the primary way to build AI applications."
        ),
        "climate change solutions": (
            "[Source: Nature] Carbon capture technology scales to industrial levels. "
            "Solar energy costs drop below fossil fuels globally. "
            "Reforestation programs show measurable impact on CO2 levels."
        ),
    }
    for key, value in results.items():
        if any(word in query.lower() for word in key.split()):
            return value
    return f"Search results for '{query}': General information found. Consider refining your query."

from langchain.chat_models import init_chat_model
from langgraph.prebuilt import create_react_agent
 
model = init_chat_model("gpt-4o-mini", model_provider="openai")
 
web_researcher = create_react_agent(
    model=model,
    tools=[web_search],
    name="web_researcher",
    prompt=(
        "You are a web researcher. Use the web_search tool to find information. "
        "Return raw findings with source attributions. Do not summarize."
    ),
)
 
summarizer = create_react_agent(
    model=model,
    tools=[],
    name="summarizer",
    prompt=(
        "You are a summarizer. Take lengthy text and condense it into "
        "3-5 key bullet points. Preserve source citations."
    ),
)
 
report_writer = create_react_agent(
    model=model,
    tools=[],
    name="report_writer",
    prompt=(
        "You are a report writer. Take research findings and produce a structured report "
        "with sections: Overview, Key Findings, and Conclusion. Include citations."
    ),
)

router = create_react_agent(
    model=model,
    tools=[],
    name="router",
    prompt=(
        "You are a research coordinator. Analyze the user's request and route to the right specialist:\n"
        "- web_researcher: when the user needs new information gathered\n"
        "- summarizer: when the user has text that needs condensing\n"
        "- report_writer: when the user has findings ready for a final report\n"
        "For a full research request, start with web_researcher."
    ),
    handoffs=["web_researcher", "summarizer", "report_writer"],
)

from pydantic import BaseModel, Field
 
class ReportOutput(BaseModel):
    title: str = Field(description="Report title")
    overview: str = Field(description="Brief overview of the research topic")
    key_findings: list[str] = Field(description="List of key findings with citations")
    conclusion: str = Field(description="Summary conclusion")
    sources: list[str] = Field(description="List of sources cited")
 
structured_report_writer = create_react_agent(
    model=model,
    tools=[],
    name="report_writer",
    prompt=(
        "You are a report writer. Produce a structured research report. "
        "Include an overview, key findings with citations, conclusion, and sources list."
    ),
    response_format=ReportOutput,
)

from langchain_core.messages import HumanMessage
 
result = router.invoke({
    "messages": [HumanMessage(content="Research the latest trends in machine learning")]
})
print(result["messages"][-1].content)

async for event in router.astream_events(
    {"messages": [HumanMessage(content="Research climate change solutions")]},
    version="v2",
):
    if event["event"] == "on_chat_model_stream":
        chunk = event["data"]["chunk"]
        if chunk.content:
            print(chunk.content, end="", flush=True)
    elif event["event"] == "on_tool_start":
        print(f"\n[Using tool: {event['name']}]")
    elif event["event"] == "on_chain_start":
        if "agent" in event.get("name", ""):
            print(f"\n[Agent: {event['name']}]")

from langchain.chat_models import init_chat_model
from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent
from pydantic import BaseModel, Field
 
@tool
def web_search(query: str) -> str:
    """Search the web for information on a topic."""
    results = {
        "machine learning": "[MIT Tech Review] Multimodal models dominate. Fine-tuning outperforms prompting.",
        "climate change": "[Nature] Carbon capture scales. Solar costs drop below fossil fuels.",
    }
    for key, value in results.items():
        if key in query.lower():
            return value
    return f"Results for '{query}': General information found."
 
model = init_chat_model("gpt-4o-mini", model_provider="openai")
 
web_researcher = create_react_agent(
    model=model, tools=[web_search], name="web_researcher",
    prompt="You are a web researcher. Use web_search to find information. Return raw findings with sources.",
)
 
summarizer = create_react_agent(
    model=model, tools=[], name="summarizer",
    prompt="You are a summarizer. Condense text into 3-5 key bullet points with citations.",
)
 
class ReportOutput(BaseModel):
    title: str = Field(description="Report title")
    overview: str = Field(description="Brief overview")
    key_findings: list[str] = Field(description="Key findings with citations")
    conclusion: str = Field(description="Summary conclusion")
    sources: list[str] = Field(description="Sources cited")
 
report_writer = create_react_agent(
    model=model, tools=[], name="report_writer",
    prompt="You are a report writer. Produce structured reports with overview, findings, conclusion, and sources.",
    response_format=ReportOutput,
)
 
router = create_react_agent(
    model=model, tools=[], name="router",
    prompt="You are a research coordinator. Route to web_researcher, summarizer, or report_writer as needed.",
    handoffs=["web_researcher", "summarizer", "report_writer"],
)
 
result = router.invoke({
    "messages": [HumanMessage(content="Research the latest trends in machine learning")]
})
print(result["messages"][-1].content)