from typing import Annotated, TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
# 1. Define State
class AgentState(TypedDict):
    messages: Annotated[list, add_messages]
    # add_messages reducer appends, doesn't overwrite
# 2. Define Tools
@tool
def search(query: str) -> str:
    """Search the web for information."""
    # Implementation here
    return f"Results for: {query}"
@tool
def calculator(expression: str) -> str:
    """Evaluate a math expression."""
    return str(eval(expression))
tools = [search, calculator]
# 3. Create LLM with tools
llm = ChatOpenAI(model="gpt-4o").bind_tools(tools)
# 4. Define Nodes
def agent(state: AgentState) -> dict:
    """The agent node - calls LLM."""
    response = llm.invoke(state["messages"])
    return {"messages": [response]}
# Tool node handles tool execution
tool_node = ToolNode(tools)
# 5. Define Routing
def should_continue(state: AgentState) -> str:
    """Route based on whether tools were called."""
    last_message = state["messages"][-1]
    if last_message.tool_calls:
        return "tools"
    return END
# 6. Build Graph
graph = StateGraph(AgentState)
# Add nodes
graph.add_node("agent", agent)
graph.add_node("tools", tool_node)
# Add edges
graph.add_edge(START, "agent")
graph.add_conditional_edges("agent", should_continue, ["tools", END])
graph.add_edge("tools", "agent")  # Loop back
# Compile
app = graph.compile()
# 7. Run
result = app.invoke({
    "messages": [("user", "What is 25 * 4?")]
})

from typing import Annotated, TypedDict
from operator import add
from langgraph.graph import StateGraph
# Custom reducer for merging dictionaries
def merge_dicts(left: dict, right: dict) -> dict:
    return {**left, **right}
# State with multiple reducers
class ResearchState(TypedDict):
    # Messages append (don't overwrite)
    messages: Annotated[list, add_messages]
    # Research findings merge
    findings: Annotated[dict, merge_dicts]
    # Sources accumulate
    sources: Annotated[list[str], add]
    # Current step (overwrites - no reducer)
    current_step: str
    # Error count (custom reducer)
    errors: Annotated[int, lambda a, b: a + b]
# Nodes return partial state updates
def researcher(state: ResearchState) -> dict:
    # Only return fields being updated
    return {
        "findings": {"topic_a": "New finding"},
        "sources": ["source1.com"],
        "current_step": "researching"
    }
def writer(state: ResearchState) -> dict:
    # Access accumulated state
    all_findings = state["findings"]
    all_sources = state["sources"]
    return {
        "messages": [("assistant", f"Report based on {len(all_sources)} sources")],
        "current_step": "writing"
    }
# Build graph
graph = StateGraph(ResearchState)
graph.add_node("researcher", researcher)
graph.add_node("writer", writer)
# ... add edges

from langgraph.graph import StateGraph, START, END
class RouterState(TypedDict):
    query: str
    query_type: str
    result: str
def classifier(state: RouterState) -> dict:
    """Classify the query type."""
    query = state["query"].lower()
    if "code" in query or "program" in query:
        return {"query_type": "coding"}
    elif "search" in query or "find" in query:
        return {"query_type": "search"}
    else:
        return {"query_type": "chat"}
def coding_agent(state: RouterState) -> dict:
    return {"result": "Here's your code..."}
def search_agent(state: RouterState) -> dict:
    return {"result": "Search results..."}
def chat_agent(state: RouterState) -> dict:
    return {"result": "Let me help..."}
# Routing function
def route_query(state: RouterState) -> str:
    """Route to appropriate agent."""
    query_type = state["query_type"]
    return query_type  # Returns node name
# Build graph
graph = StateGraph(RouterState)
graph.add_node("classifier", classifier)
graph.add_node("coding", coding_agent)
graph.add_node("search", search_agent)
graph.add_node("chat", chat_agent)
graph.add_edge(START, "classifier")
# Conditional edges from classifier
graph.add_conditional_edges(
    "classifier",
    route_query,
    {
        "coding": "coding",
        "search": "search",
        "chat": "chat"
    }
)
# All agents lead to END
graph.add_edge("coding", END)
graph.add_edge("search", END)
graph.add_edge("chat", END)
app = graph.compile()