🦜️🔗 LangGraph AI agent

1"""Module defines the main entry point for the Apify Actor.
2
3Feel free to modify this file to suit your specific needs.
4
5To build Apify Actors, utilize the Apify SDK toolkit, read more at the official documentation:
6https://docs.apify.com/sdk/python
7"""
8
9from __future__ import annotations
10
11import logging
12
13from apify import Actor
14from langchain_openai import ChatOpenAI
15from langgraph.prebuilt import create_react_agent
16
17from src.models import AgentStructuredOutput
18from src.tools import tool_calculator_sum, tool_scrape_instagram_profile_posts
19from src.utils import log_state
20
21
22async def main() -> None:
23    """Define a main entry point for the Apify Actor.
24
25    This coroutine is executed using `asyncio.run()`, so it must remain an asynchronous function for proper execution.
26    Asynchronous execution is required for communication with Apify platform, and it also enhances performance in
27    the field of web scraping significantly.
28
29    Raises:
30        ValueError: If the input is missing required attributes.
31    """
32    async with Actor:
33        # Charge for Actor start
34        await Actor.charge('actor-start')
35
36        # Handle input
37        actor_input = await Actor.get_input()
38
39        query = actor_input.get('query')
40        model_name = actor_input.get('modelName', 'gpt-4o-mini')
41        if actor_input.get('debug', False):
42            Actor.log.setLevel(logging.DEBUG)
43        if not query:
44            msg = 'Missing "query" attribute in input!'
45            raise ValueError(msg)
46
47        llm = ChatOpenAI(model=model_name)
48
49        # Create the ReAct agent graph
50        # see https://langchain-ai.github.io/langgraph/reference/prebuilt/?h=react#langgraph.prebuilt.chat_agent_executor.create_react_agent
51        tools = [tool_calculator_sum, tool_scrape_instagram_profile_posts]
52        graph = create_react_agent(llm, tools, response_format=AgentStructuredOutput)
53
54        inputs: dict = {'messages': [('user', query)]}
55        response: AgentStructuredOutput | None = None
56        last_message: str | None = None
57        async for state in graph.astream(inputs, stream_mode='values'):
58            log_state(state)
59            if 'structured_response' in state:
60                response = state['structured_response']
61                last_message = state['messages'][-1].content
62                break
63
64        if not response or not last_message:
65            Actor.log.error('Failed to get a response from the ReAct agent!')
66            await Actor.fail(status_message='Failed to get a response from the ReAct agent!')
67            return
68
69        # Charge for task completion
70        await Actor.charge('task-completed')
71
72        # Push results to the key-value store and dataset
73        store = await Actor.open_key_value_store()
74        await store.set_value('response.txt', last_message)
75        Actor.log.info('Saved the "response.txt" file into the key-value store!')
76
77        await Actor.push_data(
78            {
79                'response': last_message,
80                'structured_response': response.dict() if response else {},
81            }
82        )
83        Actor.log.info('Pushed the into the dataset!')