Unlocking the Power of Autogen Code Executors: A Step-by-Step Guide

In the dynamic world of software development, tools that streamline the coding process and enhance interactivity between different codebases are crucial. Autogen, known for its innovative approach to automated coding, has introduced a component that is vital for developers wanting to harness the power of automation: the Autogen Code Executor. This guide will delve into what makes the Autogen Code Executor a necessary tool for modern developers, how to set it up, and explore its practical applications.

Introduction to Autogen Code Executors

The Autogen Code Executor is a fundamental element within the Autogen framework, designed to facilitate the automatic execution of code snippets generated by conversational agents or other automated systems within the Autogen ecosystem. This technology not only simplifies the execution process but also bridges the gap between code generation and real-time application, providing a seamless workflow for developers.

Whether you are working on integrating multiple agents to communicate with each other or need to perform tasks such as generating dynamic graphics based on numerical data, understanding how to implement and use the Autogen Code Executors is essential.

Setting Up Your Code Executor

Step 1: Understanding the Documentation

Before diving into code, visit Autogen’s official documentation. The documentation provides a comprehensive overview of the Code Executor’s capabilities, local execution processes, and example codes. Familiarizing yourself with these documents ensures you have a solid foundational knowledge before starting practical implementation.

Step 2: Configuring the Environment

Setting up your environment is straightforward:

• First, create a Python notebook as this environment supports easy execution and visualization of the code.
• Import the necessary modules as outlined in the Autogen example. This step is crucial as it prepares your environment for the subsequent code execution setups.

Step 3: Creating the Code Executor

The core of the setup involves initializing the Code Executor within your local environment:

• Instantiate a local command-line based Code Executor. This setup mirrors the structure used when agents within Autogen interact and generate code, making it a robust solution for local deployment.
• Pair your Executor with a Code Executor Agent. This agent acts as a conversational interface, capable of processing and running the code generated by the Executor.

The Autogen configuration for this setup does not require interaction with large language models (LLM), simplifying the process and focusing purely on code execution.

Implementation and Testing

Running Example Code

Follow the steps from the documentation to implement the example code. This example will guide you through executing a simple script designed to test the setup’s functionality:

• Execute the predefined Python code by running the script in your notebook.
• The system will prompt for input if necessary, but even without it, the code will continue to execute, demonstrating both versatility and robustness.

Exploring Code Execution

Upon running the code, the Executor will process data and potentially generate graphical outputs or other file types based on the script. Check the specified temporary directory to review these outputs, ensuring your code runs as expected.

Troubleshooting and Advanced Settings

As you become more familiar with the Autogen Code Executor, you might want to customize it:

• Adjust the timeout settings for code execution to fit more extensive scripts.
• Set up a custom temporary directory to streamline file management and access.

Further customization can include tweaking how agents interact or modifying the source of the code — be it manual input or integration with external repositories like GitHub.

Future Applications and Enhancements

Looking ahead, the possibilities with Autogen Code Executors extend beyond simple executions:

• Integrate more complex conversational agents that can generate, test, and debug code collaboratively.
• Enhance the system to handle more complex data visualizations or interactive applications.

Conclusion

The Autogen Code Executor stands out as a pivotal tool for developers aiming to leverage automation in coding. By understanding its setup, operation, and potential for customization, developers can significantly enhance their productivity and broaden their project capabilities. As automation continues to shape the technological landscape, tools like the Autogen Code Executor will become invaluable assets in the developer’s toolkit.

For further information, queries, or community discussions, engaging with the Autogen community through forums or comment sections on their official platforms can provide additional insights and support. Embrace the future of coding with Autogen and see your coding processes transformed by efficiency and innovation.

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


This is one of the building block of Autogen, where you can ask an agent to execute code. I would suggest the viewer to check the documentation and try it. In the future agentic conversations, it might be useful.

code source: https://microsoft.github.io/autogen/docs/tutorial/code-executors/
github: https://github.com/LearnByDoing2024/Youtube/tree/main/20240501%20autogen%20code%20executors

https://buymeacoffee.com/learnbydoing

[h3]Transcript[/h3]
[Music] uh hello everyone welcome to my channel again today I’m going to show you uh one of the important building block of autogen that is the code executors uh so basically uh in this video I’m going to show you how to run a code executors uh it’s very intuitive and very easy and you can also check this documentation here uh in autogen websites it describes the overview and uh local execution and also the example code so we are going to run this example code and see if it works the reason I’m making this video is that yesterday I tried a project to actually make multiple agents to talk with each other and to generate a code but eventually if the um agents the agents uh can provide uh like a numerical output for example I ask it to generator the 14th Fibonacci number it can generate and output I can see in the chart history but when I ask it to generate a graph for example now I want to understand if the stock price uh is going up or going down I want to see the trend this graph I cannot find it in the local environment so I guess there must be some problem in the code generation process so now I go to the more basic uh building block of this uh uh agents interaction uh using autogen so now I’m checking the code executor this will be a more uh simple module that we can try so uh basically my first suggestion uh is that you could check this website to read the document uh carefully uh I’m not going to go through everything I’m just going to directly to the code let’s see if it works uh so basically if you create a um python notebook you can start running this code okay so basically in this example it does not um Intrigue the local LM so we do not even need the LM Studio you can see also in the example code that the L LM configuration for the code executed agents is false okay so it will not interact with the RM I’m going to to enlarge this window to let you better Vis visualize the code so uh basically to the first step is to import the uh the relevant modules and we start to create create a local command line called executor this is Executor this is kind of a very interesting structure because uh when I Yesterday I when I build the agents to talk with each other and generate code the structure is the same basically in autogen it can it um I don’t know if it is mandatory but now I only see this solution so first of all you need this ex executor to actually uh work on the code okay because it is a local command line code executor and you also need a code executor agent this agent is a conversible agent which means that it can generate conversations and it will quote this executor that we created here so uh basically I think uh uh this is a uh most important uh uh part which you if you are going to try let the multiple agents interact with with each other and generate code and run it locally uh this part will not change much okay so in the later videos I’m going to show you because now I still have many bugs and need to fix so at this moment let’s focus on this one let’s see how the executors work okay uh in apart from the l l configuration uh code execution configuration uh here is a human input mode always so it will ask you to input anything uh but if you don’t input this uh prompt I think the information to the agent you can always cancel and it will keep on running the code uh about the executor I don’t think there’s much to mention uh it has a the timeout for the code execution in seconds after 10 seconds it will time out and it also uh link to this uh temporary uh directory at this moment I I have not yet set it oburity uh to a v to a voer I want but uh at this moment I’m going to um leave it like this okay uh so this temporary directory you can print later to check uh the next step is to install all the packages that’s needed okay like uh it is 100% the same as the autogen uh documentation so basically from my point of view I think here it actually uh build this string uh or this uh paragraph uh it’s called a message with code BL so actually uh the code is existing uh where we already provide uh it and to put it in a variable and we let the agent to act on it so the possible variation I think in the future for example this one the the code block could be generated during the conversation uh with multiple agents interaction uh and of course uh you can also manually input a code or even quote from GitHub and so on because I already see some example which can do such things but here for Simplicity I think autogen documentation uh displays this part as a fixed coding part see this code is a um is a complete python code and uh uh the next part is to generate a reply from a given code so actually it uh um is the code execute agent the conversible agent to start generate reply okay based on this message okay so it is uh defining it is like the LM Studio assistant uh message structure is to tell the assistant the role of the conversible agent uh uh sorry the role here is a user that utilizing a Content okay and to input uh to the system a prompt and eventually it will uh print the reply okay when I run this part I can uh I can I can show you okay when you click around because the previous part I already wrun I’m going to uh show you only this part so you see here the prompt uh uh input box you can input anything you want if you don’t want you just uh exit okay and no human input received and it finished it’s just uh finish the code and this one exec zero is the execution succeeded then you go to the code output scatter plot okay where is it saved I checked it is in this temporary file this folder so I go to my folder and this is the picture I saw so basically running this part of the code is intuitive easy and you can quickly try uh if it is successful so there are still several things we can improve uh first of all is to set the tempor temporary directory uh to the directory we want so we can have better control to see the result and of course the naming of the output file of course you can uh make more uh detail provide more detail so you can search later and utilize later um and uh the further Improvement is to use uh this small building block to the agents interaction because uh when you ask a different agents to talk with with each other in the future video I’m going going to show you you’re are going to meet many problems it’s not like they talk with each other the code will run successfully uh largely when you try it uh I think uh you will meet many problems so today we view the first building block and in the future we’re going to explore how first of all how uh we can ask the agents to write the code for us then ask the agents to interact with each other to check the code test the code see if the code is uh is doing well okay uh and there are also many many more potentials that we can explore I wish this video is clear to you uh and thank you for the support if you have any doubt any question feel free to leave a comment have a nice day bye-bye