import xpander_utils

# Use with various frameworks
from xpander_utils.integrations import langgraph_connector
graph = langgraph_connector.create_xpander_graph("my-agent-id")

from xpander_utils.integrations import langgraph_connector

# Create a graph that uses a xpander.ai agent
graph = langgraph_connector.create_xpander_graph("my-agent-id")

# Set up nodes and edges
graph.add_node("start", lambda x: x)
graph.add_node("xpander_agent", langgraph_connector.call_agent)
graph.add_node("process_result", lambda x: {"result": x.get("output")})

# Add edges
graph.add_edge("start", "xpander_agent")
graph.add_edge("xpander_agent", "process_result")

# Run the graph
result = graph.invoke({"input": "Hello, world!"})

from xpander_utils.integrations import crewai_connector
from crewai import Crew, Agent, Task

# Create an agent that uses xpander.ai
xpander_agent = crewai_connector.create_xpander_agent(
    "my-agent-id",
    name="Data Analyst",
    goal="Analyze the provided data"
)

# Create a task
task = Task(
    description="Analyze the customer data and provide insights",
    agent=xpander_agent
)

# Create and run the crew
crew = Crew(agents=[xpander_agent], tasks=[task])
result = crew.run("Here is the customer data: ...")