> ## Documentation Index
> Fetch the complete documentation index at: https://interlocklabsinc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Run your DAG

## Tutorial Overview

The final step in our tutorial is to run our DAG. We'll need to set one argument for our dag's `execute` function, and move everything into an async function.

### Setting the execute args

If you will remember, we didn't set the arguments for our `CatFactTool`, so we will now do so with `set_execute_args`. The function takes a dictionary of the format: `{"args": list, "kwargs": dict}`. In our case, we have `max_length = 100` and `limit = 1`.

```python example_dag.py theme={null}
init_dict = {cat_fact_tool.get_id(): {"kwargs": {"max_length": 100}}}
result = await dag.execute(init_dict)
```

### Running with asyncio

The last step is to move all the code into an async function called main, and run it with asyncio.

```python example_dag.py theme={null}
from trellis_dag import LLM, DAG
from cat_fact_tool import CatFactsAPITool

import asyncio


async def main():
    # Step 2
    generate_cat_fact_llm_msgs = [
        {
            "role": "user",
            "content": "Tell me a random cat fact, as a sentence.",
        }
    ]
    generate_cat_fact_llm = LLM(
        "generate_cat_fact_llm", messages=generate_cat_fact_llm_msgs
    )

    distinguish_cat_fact_llm_msgs = [
        {
            "role": "user",
            "content": "Which of these was generated by an LLM? 1. {cat_fact_1} 2. {cat_fact_2} Give your answer as 1 or 2.",
        }
    ]
    distinguish_cat_fact_llm = LLM("distinguish_cat_fact_llm")
    distinguish_cat_fact_llm.set_messages(distinguish_cat_fact_llm_msgs)

    ## Step 3
    cat_fact_tool = CatFactsAPITool("cat_fact_tool")

    dag = DAG()
    dag.add_node(cat_fact_tool)
    dag.add_node(generate_cat_fact_llm)
    dag.add_node(distinguish_cat_fact_llm)

    dag.add_edge(cat_fact_tool, distinguish_cat_fact_llm)
    dag.add_edge(
        generate_cat_fact_llm,
        distinguish_cat_fact_llm,
        fn=lambda x: {"cat_fact_2": x["choices"][0]["message"]["content"]},
    )

    init_dict = {cat_fact_tool.get_id(): {"kwargs": {"max_length": 100}}}

    ## Step 4
    result = await dag.execute(init_dict)
    print(result)


if __name__ == "__main__":
    asyncio.run(main())
```

And with that... this tutorial is finished! We're excited to see what you build with Trellis :)