Exploring Autogen Command Line Executor: A Guide to Enhancing Local Code Execution

In today’s rapidly evolving tech landscape, the ability to efficiently execute code and troubleshoot through command-line tools is indispensable for developers. One such impressive utility is the Autogen Command Line Executor. This sophisticated tool simplifies the process of running code, particularly when integrated with large language models (LLMs) like ChatGPT. If you’re a developer looking for a streamlined way to enhance your coding and debugging efficiency, understanding the Autogen Command Line Executor could be a game changer. Let’s delve into the functionalities and practical applications of this tool.

Introduction to Autogen Command Line Executor

The Autogen Command Line Executor is a powerful interface designed to run code directly from the command line, providing a bridge between human inputs and machine execution. This utility is particularly useful when working in conjunction with LLMs to execute code in a local environment. Understanding its core functionalities and operational mechanics is crucial for developers looking to harness its full potential.

Installation and Setup

Getting Started with Installation

Before diving into code execution, it is imperative to properly install the necessary packages. Errors during this phase are common, but they can typically be resolved by revisiting the installation steps, ensuring that all packages are correctly implemented.

Setting Up Your Working Environment

Configuring your working path and defining essential parameters like the executor command (X exor) are crucial first steps. These settings lay the groundwork for smooth operation and effective communication between the code executor and the local development environment.

Working with Command Line Executor

Basic Execution: A "Hello World" Example

Starting with a simple "Hello World" print function, users can familiarize themselves with passing commands to the executor. This initial step is not just about simplicity but also about verifying that the setup is correctly configured to run Python scripts or other supported languages.

Advanced Configuration Using Docker

For users willing to encapsulate their environment, using Docker provides an isolated and consistent platform for code execution. Though not mandatory for all users, Docker can simplify the deployment by handling dependencies and environment-specific issues internally, thereby reducing configuration overhead on the primary machine.

Autogen’s Configuration and Model Linking

Integrating LLMs

Connecting the Autogen Executor with an LLM, such as OpenAI’s ChatGPT, requires precise configuration, such as setting up URL and API keys within the LM Studio configuration. This integration is pivotal for fostering a responsive coding environment where the executor handles code running, while the LLM assists in generating or modifying code snippets.

Building a Conversible Agent

Creating conversible agents like a ‘code executor agent’ that can independently execute commands without human intervention and a ‘code writer agent’ to generate executable code underscores the versatility of the Autogen Executor. This differentiation in roles highlights a more structured approach to managing coding tasks.

Debugging and Troubleshooting Code

Common Errors and Resolutions

Understanding common errors such as package mismatches, incorrect path settings, or API misconfigurations can significantly reduce downtime. The speaker discusses personal experiences with debugging issues, often referring to alternative information sources and emphasizing the importance of manual verification over reliance on automated suggestions.

Practical Tips for Effective Debugging

Employing a strategic approach to debugging, such as checking console logs within the LM Studio or verifying the output against expected results, can aid developers in quickly pinpointing and resolving issues. This process is critical, especially when dealing with complex code or integrating multiple models.

Final Thoughts and Best Practices

Ensuring Accurate Execution

Verification of output, especially in coding tasks like calculating Fibonacci sequences, is crucial. Discrepancies in output should prompt a review of the entire execution pipeline, from code generation by the LLM to execution by the Autogen tool.

Continual Learning and Documentation

Given the nascent nature of tools like Autogen, continuous learning and staying updated with documentation are paramount. Each update can bring new features or changes in operation, impacting the overall workflow.

Conclusion

The Autogen Command Line Executor offers a robust platform for developers aiming to enhance their code execution processes. Through effective setup, integration with large language models, and comprehensive debugging, developers can optimize their coding workflows, ensuring precise and efficient outcomes. Remember, the key to leveraging the full capabilities of such tools lies in thorough understanding and continual adaptation based on evolving functionalities and developer needs.

[h3]Watch this video for the full details:[/h3]


Autogen Command Line Executor with LM Studio
Suggestions:
1. When content is empty and you have error message, change local LLM model
2. Always check output with caution.

reference source: https://microsoft.github.io/autogen/docs/topics/code-execution/cli-code-executor/
github: https://github.com/LearnByDoing2024/Youtube/tree/main/20240501%20autogen%20command%20line%20executors

https://buymeacoffee.com/learnbydoing

[h3]Transcript[/h3]
hello everyone welcome to my channel again so now I’m going to show you the other project uh I think of following the previous one the code executor uh of autogen now we are moving to the command line code executor this is actually a part where we are going to interact with the llm the code executor and uh to run the code uh in local environment uh as always I always suggest you to read carefully about the documentation because uh since autogen is uh very new uh if you ask the like a chat gbt or other llm to get a solution to write the code it does not always work so I would suggest you to read it by yourself understand structure of the code and try the sample code first I’m going to show you uh how I wrong the sample code in local environment what kind of result I get and what kind of error I get I wish it will be helpful for you because this is still another building block for you to build a more complicated uh complicated a agentic conversation uh so without further Ado I’m going to move to the coding environment uh before before you try to run this code my suggestion is that always prepare your LM Studio load uh model that you want and start the server later I’m going to also share with you uh why some of the models does not work so I also tried to debug some of the issues uh from stack flow and different information Source uh but uh you do not always get the right answer so I’m going to share you share with you how I solve the issue uh first of all uh let’s come here to the code uh first of all you need to install the package if not present usually I don’t think it’s a big problem because when you run this it will give you the aror code you are going to come back to reinstall the package uh the first step is actually set a working path uh and also uh to Define X exor okay and uh this one is actually a very simple function uh to um maybe not a print function to actually print the code result so the code we are running here is print hello word and the language is python so this is a very simple um example to let you know how you’re going to pass this executor the code block okay the code block have has these uh uh components this is very simple one when you try it you see here output is hello word okay and the file is saved here so basically this is a coding folder that I already created uh on the left side you can see here is coding folder this is very simple you can try so I’m going to move to the more complicated part first of all according to the documentation when you um uh assigning uh config configuring the agents um you have two ways one is to use with Docker because you see here is Docker command line Cod executor I have never used a Docker before but from my very uh in understanding I think it’s an environment which where you can execute the code and uh make sure it does not confuse you with local environment okay but I’m if uh in the future is necessary I’m going to dig into it at this moment I just want to share with you if you don’t want to use it just leave it and you can go to the uh later part okay so here uh we started the um the importing the packages like before and set up our local uh LM studio um link I would say because this configuration of this model mro the name is uh you you can set any name you like important is a URL and API key these are all in here the LM Studio you can see let me see here yeah we’re going to see how to set up uh follow up let’s come to next part okay so here uh actually I created first of all a conversible agent is called code executor agent okay so it does not interact with LM it actually will quote the executor to run the code and it does not require human input then we can see here there is a system message setting to tell the code writer um it’s it’s job uh is like a initial setting of the row and we’re going to set also the code writer agent it is also a conversible agent uh but more uniquely it has the system message setting of the above and we configure uh this uh model to it if you see the do documentation this configuration has many variations like if you actually uh link to the API of open Ai and so on this this has many uh variations for here I configure it in local environment is very simple I use the name I said before mro uh but you do not need to worry if you change the local server change it to another model uh you don’t need to change the code this is one of the convenient parts of local L studio uh then uh this code uh this writer agent only write the code but do not execute the code yeah I have not yet tried when we turn on this configuration uh because for now I think uh because I already get the results I would keep this setting unchanged so max consecutive auto reply is two okay I think running a very simple code do not need to have many conversations and the human input is never okay so basically now we have a code executor agent to quote this executor to run a code and we have the writer agent to understand a very complicated role not very complicated just a very comprehensive role to write a code but not around a code uh here I leave the original code block from the document of autogen uh you can see here uh yeah it is U configuring the larger language model to this gbt 4 and with API key and so on okay I’m running it locally so I don’t need it I leave it here so first of all uh we test the first code block this is I think is the same uh as the documentation uh so basically we initiate this chart from the code executor agent okay which is a a simple conversible agent like a start point and the start a chart with code writer agent so the code writer agent will write the code as we defined here and the message is we manually input here of course you can also choose to menual input okay different ways to give the prompt to the workflow to the pipeline and we are asking it to write python code to calculate the 14th Fibonacci number okay uh then it will use this PR print uh package to print out uh the chat results let’s see okay this is just a result I got like uh 30 minutes ago you can see first of all the code executor write this prompt to the code writer and code writer give back this code to the code executor agent and then code executor agent actually executed this code with x exor and give the feedback to code writer and we can see here this is output 233 uh I just checked this is correct Fibonacci number 14 is 377 uh is correct oh sorry uh 14 is three yeah sorry uh my mistake this one is wrong okay um I think is a problem about the sequence numbering because actually the 14 is 337 13 is 233 so basically the code wrong uh but we needed to do some adjustments okay so let’s continue uh we can see here uh it gives uh the the writer writes to the agent and the agent to the writer uh important thing is that this one this block has zero prompt or zero feedback message then we wrap up the chat results here okay so which is content uh and so on uh you can also see that uh for the different uh different I think a different conversation you can see the completion token how many tokens were generated to the Tok token and so on if you are interested you can check the I think efficiency of this conversation um yeah this is the result at 30 minutes ago uh the number I mean the sequence of the number of course we need to double check and to change I think there’s something wrong with the code okay then let’s see what I uh encountered these errors so basically yesterday and also uh just one hour ago I tried to run this code but the LM Studio configuration you see I have many models previously I used mistro and meta they have the same problem here because whenever uh you run this code until this step code executor to uh agent to code writer this part you see there is a zero message zero text string variable to pass to The Next Step the system will give you a error code it’s called a message array must only contain objects with a Content field that is not empty so for MST for llama we have this problem but if I change to stable code instruct this model I get a red result not a red result we I bypass this error message um so let’s see uh the example of the error message you can see this is the the conversation between the agents and the local uh lar language model server and when you go down you see here uh this part the content is empty the row is user and the stream is false so if you use the other two model you’ll get error here okay so the error message is just what I showed you okay so this is one of the Breakthrough I have done for just three days to actually uh find a solution for this error we just change the model and try and let’s come back here um this is if I remember correctly this was one of the solution I get from the previous uh uh previous trial stage you can see here I get a number is a 3,779 137 this is a quite strange number because when you check the table Fibonacci number 377 makes sense but uh 9137 does not make much sense so uh I guess there must be some issues here okay so let’s see you can check the code okay so to understand if it is a problem of the code or it is a problem of the uh of the L language model okay uh so here you can see all the statistics about the cost uh how many tokens actually in the uh prompt and how many total token yeah so uh I make a Mark here the power result is different from the online document when I say online documents I mean here okay so basically uh you see the output of the Auto website it’s just 377 it’s very clear does not have any issue and uh yes you can see if it is using GPT actually uh the total total token number is larger yeah but I guess uh the uh gbt 4 uh API works better than local uh uh L language model so let’s go on we try another instance to run a code okay so um the first time when I run it I see this strange number more than 3 million then I just reun the code and I’m going to show you first my previous exam uh uh previous uh result is 377 let’s see here yeah he’s right this time we got it right 377 and we can see the total token is more than a, uh so it worked okay so when I first try this uh code the first time wrong I get a 3 million very large number the second time I get 377 which is correct and uh yeah this is one part I want to mention to you uh always check the results that you desired or the real result because this uh whole workflow may have some uh errors uh so this is the previous example I want to show you let’s see 30 minutes go I Wrong this code again is 377 yeah it’s correct and there is no issue especially here there’s no the error of zero content does not trigger any issue it is fine and the token is more than a, uh when you debug this kind of issue I always recommend you to come back to the console of the LM Studio to actually check uh check the message here it is a conversational uh log and it is very useful for you to pinpoint the different issues okay okay um yeah so the first time where around the block two uh it is obvious that the more tokens has has uh uh better result okay at least it matches the document 377 this result so in the end uh in the document autogen document it asked the user to uh stop the executor uh I’m not sure if this one is related to the docker application but uh you can try at this moment it does not serve anything so I leave it here okay so just a quick wrap up for this command line Cod executor we already incorporate uh seral things here first of all we ask the agents to interact with each other to write the code and to give a feedback to print out the output okay it’s a very uh very simple numeric output but it is U uh it is successful uh the second thing is that whenever you have a bug you have a the code not running do go back to the log of the LM Studio to check if is there any issue and check carefully with autogen documentation the last thing is no matter which pipeline you are using to build the multi-agent environment to interact uh uh very simply with a local large language model be careful and verify your results because as you see uh I already got a error number result so I wish this video is useful for you uh if you have any question any issue just uh leave a comment and I’m going to see you next time so uh I expect that in the future we can explore together about the more complicated agents interaction