copyfrom typing import TypedDict, Annotated
from langchain_core.messages import BaseMessage
import operator

class AgentState(TypedDict):
    # operator.add is the reducer —
    # messages are appended, not overwritten
    messages: Annotated[
        list[BaseMessage],
        operator.add
    ]
    
    # Simple fields just get overwritten
    iteration_count: int
    final_answer: str | None
    tool_results: list[dict]

copyfrom pydantic import BaseModel, Field
from langgraph.graph import add_messages

class ResearchState(BaseModel):
    messages: Annotated[list, add_messages]
    query: str = ""
    sources: list[str] = Field(default_factory=list)
    draft: str = ""
    approved: bool = False
    revision_count: int = 0

# Nodes receive and return partial state
def draft_node(state: ResearchState):
    return {"draft": llm.invoke(state.query)}

copyfrom langgraph.prebuilt import tools_condition

# Check if LLM requested tool calls
def should_continue(state: AgentState) -> str:
    last_msg = state["messages"][-1]
    
    # Route to tools if tool_calls present
    if hasattr(last_msg, "tool_calls") \
            and last_msg.tool_calls:
        return "tools"
    
    # Check iteration limit
    if state["iteration_count"] >= 10:
        return "force_end"
    
    return "end"

copydef classify_task(state: ResearchState) -> str:
    if "math" in state.query.lower():
        return "calculator_agent"
    elif "code" in state.query.lower():
        return "code_agent"
    else:
        return "research_agent"

# Wire up the multi-path router
graph.add_conditional_edges(
    "classifier",
    classify_task,
    {
        "calculator_agent": "calculator",
        "code_agent":       "coder",
        "research_agent":   "researcher",
    }
)

copyfrom langgraph.graph import StateGraph
from langgraph.prebuilt import create_react_agent

# Create specialist sub-agents
researcher = create_react_agent(
    llm, tools=[search, fetch_url]
)
writer = create_react_agent(
    llm, tools=[draft_doc, format_md]
)

# Supervisor decides who acts next
def supervisor_node(state):
    decision = supervisor_llm.invoke(
        state["messages"]
    )
    return {"next_agent": decision}

copyfrom langgraph.types import Command

# Agents return Command to route execution
def researcher_agent(state):
    result = researcher.invoke(state)
    
    # Hand off to writer when done
    return Command(
        update={"research": result["output"]},
        goto="writer"
    )

# Writer signals completion
def writer_agent(state):
    draft = writer.invoke(state)
    return Command(
        update={"draft": draft},
        goto=END
    )