Convert LangGraph agents to Agent Spec

This usage example showcases how a LangGraph Agent can be converted into an Agent Spec configuration in JSON format.

# Create a LangGraph Agent
from typing_extensions import Any, TypedDict
from langchain_openai.chat_models import ChatOpenAI
from langgraph.graph import END, START, StateGraph
from pydantic import SecretStr

class InputSchema(TypedDict):
    city: str

class OutputSchema(TypedDict):
    response: Any

class InternalState(TypedDict):
    weather_data: str

def get_weather(state: InputSchema) -> InternalState:
    """Returns the weather in a specific city.
    Args
    ----
        city: The city to check the weather for

    Returns
    -------
        weather: The weather in that city
    """
    return {"weather_data": f"The weather in {state['city']} is sunny."}

def llm_node(state: InternalState) -> OutputSchema:
    model = ChatOpenAI(
        base_url="your.url.to.llm/v1",
        model="/storage/models/Llama-3.1-70B-Instruct",
        api_key=SecretStr("t"),
    )
    result = model.invoke(
        f"Reformulate the following sentence to the user: {state['weather_data']}"
    )
    return {"response": result.content}

graph = StateGraph(InternalState, input_schema=InputSchema, output_schema=OutputSchema)
graph.add_node("get_weather", get_weather)
graph.add_node("llm_node", llm_node)
graph.add_edge(START, "get_weather")
graph.add_edge("get_weather", "llm_node")
graph.add_edge("llm_node", END)
assistant_name = "Weather Flow"
langgraph_agent = graph.compile(name=assistant_name)

# Convert to Agent Spec
from pyagentspec.adapters.langgraph import AgentSpecExporter

agentspec_config = AgentSpecExporter().to_json(langgraph_agent)