Agent.stream Vs Model.stream: Real-time Output Bug?

by SLV Team 52 views
Agent.stream vs Model.stream: Real-time Output Bug?

Hey guys! So, I've been wrestling with a bit of a head-scratcher in LangChain, and I figured I'd share it with you all. It's about the agent.stream function and how it behaves compared to model.stream. Basically, I've noticed that agent.stream doesn't seem to output in real-time, unlike model.stream. Let's dive in and see what's happening, shall we?

The Problem: Delayed Agent.stream Output

The core issue here is the difference in how agent.stream and model.stream handle the output. When you use model.stream, you get a fantastic, real-time stream of tokens. It's like watching the model write a sentence, word by word, as it generates the text. It's awesome for that sense of immediacy and engagement. Now, when I tried to do the same with agent.stream, I was disappointed. Instead of seeing the output in real-time, I only got the complete response after the agent had finished its entire process. It's like waiting for the whole essay to be written before you can read a single word.

This difference is more than just a cosmetic thing; it impacts how users perceive and interact with your application. Real-time streaming makes the application feel more responsive and dynamic. Imagine a chatbot that waits until the very end to give you an answer. It's not the best user experience. If you are developing something that depends on immediate feedback, this is going to be a huge problem.

Here's the code I'm using, which is pretty straightforward. You can easily copy and paste it.

for chunk in agent.stream({
    "messages": [{"role": "user", "content": "Search for AI news and summarize the findings"}]
}, stream_mode="values"):
    latest_message = chunk["messages"][-1]
    if latest_message.content:
        print(latest_message.content)

With this code, I'm expecting to see the output streaming in real-time. Instead, I only get the full summary once the agent has finished. The goal here is to get something similar to what model.stream provides. This could be due to how agents handle intermediary steps before arriving at the final answer.

Expected Behavior vs. Actual Behavior

The expectation is clear: we want agent.stream to output tokens in real-time, just like model.stream. The model.stream function works perfectly, providing a seamless stream of text as it's generated. But with agent.stream, it seems to wait until the agent has completed all its internal operations before sending the complete response. It is as if agent.stream bundles up everything before revealing the result.

To make this clearer, let's look at the contrast between the two:

  • model.stream: Outputs tokens in real-time, providing immediate feedback and a dynamic user experience.
  • agent.stream: Delays the output until the entire process is complete, which may not meet the requirements of all applications.

The real problem here is the delay. This difference is not immediately obvious, and it can catch you by surprise if you are used to the behavior of model.stream. If you're building a chatbot or any application requiring continuous updates, this is a major issue.

A Minimal Reproducible Example

I've also made a minimal, reproducible example to show you the problem. If you run this code, you'll see the complete output only after the agent is done, which confirms the issue.

for chunk in agent.stream({
    "messages": [{"role": "user", "content": "Search for AI news and summarize the findings"}]
}, stream_mode="values"):
    latest_message = chunk["messages"][-1]
    if latest_message.content:
        print(latest_message.content)

Is This a Bug or Expected Behavior?

So, the big question is whether this delayed output is a bug or just how agent.stream is designed to work. If it's a bug, we need to find a way to get that real-time streaming going. If it's expected behavior, then the documentation needs to make this crystal clear. Right now, it's not super obvious, and it's definitely not what I expected.

I'm leaning towards it being a bug since the user might expect the streaming behavior to be consistent across the LangChain interface. However, I can't say for certain. If this is intended, perhaps there needs to be an additional parameter to control this behavior, to offer some flexibility.

System Information

Here is my system information, in case that is relevant:

  • OS: Windows
  • OS Version: 10.0.19045
  • Python Version: 3.10.18 (packaged by conda-forge)
  • Langchain: 1.0.2

I'm hoping we can find a solution or a workaround to enable the real-time streaming, like the way that the model does.

Investigating the Root Cause

One of the first things to look at is how agents handle the response generation internally. Model streaming, in general, relies on the models producing text incrementally. The agent might work differently. Agents are designed to handle several tasks, and these tasks may involve more steps before generating the final response. It's possible that the agent completes all its internal tasks and then presents the result.

Here are a few factors that might be contributing to the delay:

  • Agent Architecture: The structure of the agent (e.g., using tools, chains, etc.) could be a key factor. Different agent types could have varying behaviors.
  • Intermediary Steps: Agents may perform several intermediary steps, like looking for information or using a tool. These steps could be completed first, before streaming the result.
  • Output Buffering: There might be some form of output buffering occurring within the agent.stream function, where the output is collected before it is sent.

To figure out what is going on, we might need to dig into the LangChain code itself. It will likely reveal how the streaming works for the model and the agent.

Potential Solutions and Workarounds

If this is a bug, here are some potential solutions that we could consider:

  • Fix the agent.stream function. The ideal solution is to modify the function to stream the tokens in real-time, mirroring the behavior of the model.stream function.
  • Add an option for real-time streaming. Another approach could be to introduce a parameter to agent.stream to control whether the output is streamed in real-time or delayed until the agent is complete.

Here are some possible workarounds that could help:

  • Modify the Agent Structure: You might be able to change the design of the agent to use intermediate steps to output in real-time. This might involve splitting the process into smaller steps that can stream independently.
  • Custom Streaming: It might be possible to build a custom solution by capturing the intermediary steps and streaming the outputs as they occur. It would be a manual way to provide real-time updates.

Conclusion: Seeking Clarification and a Solution

In a nutshell, the core issue is that agent.stream doesn't provide real-time token streaming like model.stream, which is problematic for creating responsive, user-friendly applications. I'm wondering if this is a bug or the intended behavior. I also think a clear solution or workaround would be highly useful. What do you all think?

If you've encountered this issue or have any insights, please chime in! Any feedback or suggestions would be amazing.