The code consists of imported modules, functions, and notes for an asynchronous Python script that uses custom modules browser_use and langchain_ollama to execute a search task on the r/LocalLLaMA subreddit. The script defines two asynchronous functions: run_search() and main().

Cell 6

import asyncio
from browser_use import Agent
from browser_use.agent.views import AgentHistoryList
from langchain_ollama import ChatOllama


async def run_search() -> AgentHistoryList:
    agent = Agent(
        task="Search for a 'browser use' post on the r/LocalLLaMA subreddit and open it.",
        llm=ChatOllama(
            # model="qwen2.5:32b-instruct-q4_K_M",
            # model="deepseek-r1:14b",
            model="qwen2.5:latest",
            num_ctx=32000,
        ),
    )

    result = await agent.run()
    return result


async def main():
    result = await run_search()
    print("\n\n", result)

__all__ = {
  "main": main,
  "run_search": run_search
}

# Import necessary libraries
import asyncio
from browser_use import Agent
from browser_use.agent.views import AgentHistoryList
from langchain_ollama import ChatOllama

# Define constants for LLama model and context
DEFAULT_LLM_MODEL = "qwen2.5:latest"
DEFAULT_NUM_CTX = 32000

class LlamaAgent:
    """An agent using LLama to perform tasks."""
    
    def __init__(self, model=DEFAULT_LLM_MODEL, num_ctx=DEFAULT_NUM_CTX):
        """Initialize the LLama agent.

        Args:
            model (str): The name of the LLama model to use. Defaults to DEFAULT_LLM_MODEL.
            num_ctx (int): The number of context to use. Defaults to DEFAULT_NUM_CTX.
        """
        self.agent = Agent(
            task="Search for a 'browser use' post on the r/LocalLLaMA subreddit and open it.",
            llm=ChatOllama(model=model, num_ctx=num_ctx),
        )

    async def run_search(self) -> AgentHistoryList:
        """Run the search task using the LLama agent.

        Returns:
            AgentHistoryList: The result of the search task.
        """
        result = await self.agent.run()
        return result


async def main():
    """The main entry point of the script."""
    # Create a new LLama agent
    agent = LlamaAgent()

    # Run the search task
    result = await agent.run_search()

    # Print the result
    print("\n\n", result)


if __name__ == "__main__":
    # Run the main function
    asyncio.run(main())

Code Breakdown

Imported Modules

• asyncio: a built-in Python module for writing single-threaded concurrent code using coroutines, multiplexing I/O access over sockets and other resources, and implementing network clients and servers.
• browser_use: a custom module (not a built-in Python module) containing the Agent class and its related views.
• langchain_ollama: a custom module (not a built-in Python module) containing the ChatOllama class.

Functions

run_search()

• An asynchronous function that returns an instance of AgentHistoryList.
• It creates an instance of the Agent class with a task to search for a 'browser use' post on the r/LocalLLaMA subreddit and open it.
• The agent uses a chat Ollama model with a specified model name (qwen2.5:latest) and a large context buffer (num_ctx=32000).
• It executes the agent using the run() method and returns the result.

main()

• An asynchronous function that calls run_search() and prints the result.

Exported Functions

• The __all__ variable is set to a dictionary containing the main and run_search functions, making them available for import from other modules.

Notes

• The code uses asynchronous programming with the asyncio module.
• The langchain_ollama and browser_use modules are not standard Python modules and are likely custom implementations.
• The code defines two functions: run_search() and main(), which are exported for use in other modules.