Step

LLM powered Assistants take multiple steps to process a user’s request, forming a chain of thought. Unlike a Message, a Step has a type, an input/output and a start/end.

Depending on the config.ui.cot setting, the full chain of thought can be displayed in full, hidden or only the tool calls.

In Literal AI, the full chain of thought is logged for debugging and replayability purposes.

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A Simple Tool Calling Example

Lets take a simple example of a Chain of Thought that takes a user’s message, process it and sends a response.

import chainlit as cl


@cl.step(type="tool")
async def tool():
    # Simulate a running task
    await cl.sleep(2)

    return "Response from the tool!"


@cl.on_message
async def main(message: cl.Message):
    # Call the tool
    tool_res = await tool()

    # Send the final answer.
    await cl.Message(content="This is the final answer").send()

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Step API

There are two ways to create steps, either by using the the @cl.step decorator or by using the cl.Step class.