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from fastapi import FastAPI
from langgraph.graph import StateGraph
from typing import TypedDict, Annotated, List
from langgraph.graph.message import add_messages
from pydantic import BaseModel
# Initialize FastAPI app
app = FastAPI(title="LangGraph Agent API")
class State(TypedDict):
messages: Annotated[list[str], add_messages]
current_step: str
class AgentInput(BaseModel):
messages: List[str]
def collect_info(state: State) -> dict:
print("\n--> In collect_info")
print(f"Messages before: {state['messages']}")
messages = state["messages"] + ["Information collected"]
print(f"Messages after: {messages}")
return {
"messages": messages,
"current_step": "process"
}
def process_info(state: State) -> dict:
print("\n--> In process_info")
print(f"Messages before: {state['messages']}")
messages = state["messages"] + ["Information processed"]
print(f"Messages after: {messages}")
return {
"messages": messages,
"current_step": "end"
}
# Create and setup graph
workflow = StateGraph(State)
# Add nodes
workflow.add_node("collect", collect_info)
workflow.add_node("process", process_info)
# Add edges
workflow.add_edge("collect", "process")
# Set entry and finish points
workflow.set_entry_point("collect")
workflow.set_finish_point("process")
# Compile the workflow
agent = workflow.compile()
@app.post("/run-agent")
async def run_agent(input_data: AgentInput):
"""
Run the agent with the provided input messages.
"""
initial_state = State(messages=input_data.messages, current_step="collect")
final_state = agent.invoke(initial_state)
return {"messages": final_state["messages"]}
@app.get("/")
async def root():
"""
Root endpoint that returns basic API information.
"""
return {"message": "LangGraph Agent API is running", "endpoints": ["Navigate to https://jstoppa-langgraph-basic-example-api.hf.space/docs#/default/run_agent_run_agent_post to run the example"]}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860) |