Chainlit Tool Steps Always Below Answer Discussion Category

Aug 5, 2025 by ADMIN 60 views

Tool Steps Always Below Answer/Discussion Category in Chainlit

Hey guys, facing an interesting challenge with Chainlit and thought I'd share and see if anyone has cracked this nut before. I'm using the latest versions of Agno and Chainlit, and I've noticed a consistent UI behavior that's not quite ideal for my agent workflows. Specifically, the tool call steps are always appearing below the main message/discussion in the chat interface. This happens unless I manually reorganize the chat after the agent's response is complete, which, let's be honest, makes the whole interaction feel a bit sluggish. Given that my agents often follow a "chat, run a tool, chat some more" pattern, this ordering becomes a bit cumbersome. I'm aiming for a more intuitive flow where the tool calls stick to the top of the UI, providing a clear sequence of actions. So, the million-dollar question is: Is there a slicker way to ensure tool steps appear above the message in the Chainlit UI, creating a smoother and more logical conversation flow?

Understanding the Issue: Tool Call Order in Chainlit

In Chainlit, when building conversational AI applications, the order in which messages and tool calls appear can significantly impact the user experience. The core issue here is that tool call steps, which represent actions taken by the agent, are consistently displayed below the agent's message. This default behavior can disrupt the natural flow of conversation, especially when agents frequently use tools as part of their responses. For instance, if an agent needs to fetch information using a tool before continuing the conversation, the user would ideally see the tool call first, followed by the agent's response incorporating the tool's output. The current behavior, however, places the agent's initial message at the top, then the tool call, and finally the complete message including the tool's result. This order can make the conversation feel disjointed and less intuitive. To address this, we need to explore how Chainlit handles message and step rendering and identify ways to prioritize the display of tool call steps. One approach might involve manipulating the order in which messages and steps are sent or updated within the Chainlit application. Another could be to leverage Chainlit's UI customization options, if available, to reorder elements on the screen. Ultimately, the goal is to create a more seamless and natural conversational experience by ensuring tool calls are prominently displayed and easily understood by the user.

Diving into the Code: A Closer Look

Let's break down the provided Python code snippet to understand how messages and tool calls are handled within the Chainlit application. The code uses the @cl.on_message decorator to define a function, on_message, that is triggered whenever a user sends a message. This function is the heart of the agent's response logic. First, the function checks if the agent is initialized. If not, it sends an error message to the user, prompting them to restart the chat. This is a crucial step in ensuring the application's stability and preventing unexpected behavior. Once the agent is confirmed to be initialized, the function creates an empty cl.Message object, msg, which will be used to accumulate the agent's response. This message is sent immediately to provide the user with visual feedback that the agent is processing their input. The code then calls agent.arun, which is assumed to be an asynchronous function that runs the agent's logic. The key here is the stream=True and stream_intermediate_steps=True arguments. These arguments enable the agent to stream its response and intermediate steps, including tool calls, back to the client. The code then iterates through the events yielded by the response_stream. These events can be of different types, including ToolCallStarted, ToolCallCompleted, and RunResponseContent. When a ToolCallStarted event is encountered, the code creates a cl.Step object representing the tool call. The step's input is set to the tool's arguments, and the step is sent to the client, which should display it in the UI. When a ToolCallCompleted event is encountered, the code updates the corresponding step with the tool's output. Finally, when a RunResponseContent event is encountered, the code streams the content to the msg object, which gradually updates the message in the UI. The crucial part to note here is the order in which these events are handled and sent to the client. The code sends the ToolCallStarted step as soon as it's encountered, but the message content might already be partially sent before the tool call is initiated. This could explain why the tool call appears below the message in the UI. To fix this, we might need to find a way to buffer the message content or prioritize the rendering of tool call steps.

@cl.on_message
async def on_message(message: cl.Message):
    global agent

    if not agent:
        await cl.Message(content="❌ Agent not initialized. Please restart the chat.").send()
        return

    try:
        msg = cl.Message(content="")
        await msg.send()

        current_step = None
        response_stream = await agent.arun(message.content, stream=True, stream_intermediate_steps=True)

        async for event in response_stream:
            if event.event == "ToolCallStarted":
                current_step = cl.Step(name=event.tool.tool_name, type="tool")
                current_step.input = event.tool.tool_args
                await current_step.send()

            elif event.event == "ToolCallCompleted":
                if current_step:
                    current_step.output = format_tool_output(event.tool.result)
                    await current_step.update()
                    current_step = None

            elif event.event == "RunResponseContent":
                if event.content:
                    await msg.stream_token(event.content)

        await msg.update()

    except Exception as e:
        await cl.Message(content=f"❌ Error generating response: {str(e)}").send()

Potential Solutions: Getting Tool Calls to the Top

Okay, so let's brainstorm some potential solutions to get those tool call steps appearing at the top of the Chainlit UI, right where we want them. One approach could involve delaying the initial message send until after the tool call step has been created and sent. This might require buffering the initial response content and sending it only after the ToolCallStarted event is processed. This way, the tool call step would be the first element sent to the UI, ensuring it appears at the top. Another strategy could be to leverage Chainlit's UI customization options, if available. Chainlit might offer a way to reorder elements in the chat interface, either through a specific API or by manipulating the DOM directly. This would allow us to explicitly position the tool call steps above the messages. A more advanced solution might involve creating a custom component in Chainlit that handles the rendering of messages and steps. This component could be designed to prioritize tool call steps, ensuring they are always displayed at the top. We could also explore manipulating the order of events within the response_stream. If possible, we could try to ensure that ToolCallStarted events are always processed before RunResponseContent events. This might require modifying the agent's logic or the way Chainlit handles streaming events. Finally, it's worth investigating whether Chainlit provides any built-in mechanisms for controlling the order of messages and steps. The documentation or community forums might offer insights into best practices for handling tool calls and ensuring they appear in the desired order. By exploring these different avenues, we can hopefully find a solution that makes the tool call steps stick to the top of the UI, creating a more intuitive and user-friendly experience.

Implementing a Solution: A Practical Approach

Let's dive into a practical approach to implementing a solution for ensuring tool call steps appear above messages in the Chainlit UI. Given the challenges of directly manipulating the UI rendering order, a robust strategy involves buffering the initial message content. This means holding onto the agent's response until we've processed any tool call events. Here's how we can modify the code to achieve this: First, we'll create a buffer to store the message content. Instead of streaming tokens directly to the msg object, we'll append them to this buffer. Then, we'll check for the ToolCallStarted event. When this event occurs, we'll create and send the tool call step as before. The crucial difference is that we'll delay sending the buffered message content. Once we've processed all tool call events (or after a certain timeout), we'll send the buffered message content. This ensures that the tool call step is displayed before the message. To implement this, we'll need to modify the RunResponseContent event handling. Instead of await msg.stream_token(event.content), we'll append the event.content to our buffer. We'll also need to add logic to send the buffer content after a ToolCallCompleted event or when the stream is finished. This might involve setting a flag or using a timer to ensure the buffer is eventually flushed. Another important aspect is handling multiple tool calls. If the agent makes several tool calls in a row, we need to ensure that all tool call steps are displayed before the message content. This might require maintaining a list of tool call steps and sending them all before sending the buffered message. Finally, we need to consider error handling. If an error occurs while processing a tool call, we should ensure that the user is notified and that the buffered message content is still displayed (or discarded if appropriate). By implementing this buffering strategy, we can effectively control the order in which messages and steps are displayed in the Chainlit UI, ensuring that tool call steps appear at the top and providing a clearer and more intuitive conversational experience.

Alternative Strategies: Exploring Other Options

While buffering the initial message content is a promising approach, let's explore some alternative strategies for ensuring tool call steps appear above messages in Chainlit. These options might offer different trade-offs in terms of complexity and effectiveness. One alternative is to pre-render the tool call step. Instead of waiting for the ToolCallStarted event, we could try to anticipate tool calls based on the agent's logic. If we can predict that a tool call will occur, we can create and send the step proactively, before the agent's response is generated. This would ensure that the tool call step is already in the UI when the message content starts streaming. However, this approach requires a deep understanding of the agent's behavior and might not be feasible for complex agents with unpredictable tool call patterns. Another strategy is to use Chainlit's UI theming and customization options. Chainlit might provide ways to style and reorder elements in the chat interface using CSS or JavaScript. We could potentially use these options to visually move tool call steps to the top of the chat, even if they are technically rendered below the messages. This approach might be simpler than buffering content but could be less robust if Chainlit's UI structure changes in the future. We could also explore using Chainlit's context management features. Chainlit allows you to store and retrieve information about the conversation context. We could use this to track whether a tool call has been made and adjust the UI accordingly. For example, we could set a flag when a ToolCallStarted event occurs and use this flag to modify the rendering of subsequent messages. This approach requires careful management of the conversation context but could provide a flexible way to control the UI behavior. Finally, it's worth considering contributing to Chainlit itself. If the current behavior is a common pain point, we could propose a feature request or even contribute code to Chainlit to allow developers to easily control the order of messages and steps. This would benefit the entire Chainlit community and ensure that the solution is well-integrated and maintainable. By exploring these alternative strategies, we can gain a broader understanding of the options available and choose the approach that best fits our needs and constraints.

Final Thoughts and Gratitude: Wrapping Up

So, guys, we've really dug into this challenge of getting tool call steps to appear above messages in Chainlit! It's a tricky issue, but by exploring different strategies like buffering content, pre-rendering steps, and leveraging UI customization, we've identified some promising solutions. The best approach will likely depend on the specific needs of your application and the complexity of your agent. Buffering the initial message content seems like a solid starting point, as it provides a reliable way to control the order of messages and steps. However, don't hesitate to experiment with other options and see what works best for you. Remember, the goal is to create a smooth and intuitive conversational experience for your users, and getting the UI elements in the right order is a key part of that. Finally, I just want to say a huge thank you to the Chainlit team for their continued dedication to this project! Having a Python front end for AI workflows is a game-changer, and I'm incredibly grateful for all the hard work that goes into making Chainlit such a valuable tool. Keep up the amazing work, and I'm excited to see what the future holds for Chainlit! If anyone has any further insights or experiences with this issue, please share them in the comments below. Let's keep the conversation going and help each other build awesome AI applications with Chainlit!