CrewAI Code Interpreter - How I Made AI Agents to Generate Execute Code (Vs AutoGen)

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AI Summary

Summary of Video Transcript: Integrating Open Interpreter with Crew AI

Introduction

• The video demonstrates how to integrate Open Interpreter with Crew AI to enable code creation and execution.
• Crew AI is compared with Autogen and Task Viewer, highlighting that Crew AI is non-coding by default, while Task Viewer is code-first, and Autogen handles both coding and non-coding tasks.
• Open Interpreter is added to Crew AI to incorporate coding capabilities.
• A user interface is created using Gradio, and integration with O Lama in Crew AI is also discussed.

Steps for Integration

1. Setting Up the Environment
   • Install necessary packages using pip install.
   • Export the OpenAI API key.
2. Creating the Application (app.py)
   • Import necessary modules from Crew AI, Lang Chain, and OpenAI.
   • Set up configuration and tools, including the LLM model and interpreter with autorun enabled.
   • Create CLI tools to execute code using Open Interpreter.
3. Creating Agents and Assigning Tasks
   • Define an agent with the role of a software engineer capable of CLI operations.
   • Assign tasks to the agent, such as identifying the OS and emptying the recycle bin.
   • Create a crew with a sequential manager and the OpenAI CH GPT model.
4. Running the Crew
   • Execute the crew with crew.kickoff() and print the results.
   • The agent performs tasks like identifying the OS and clearing the trash using Open Interpreter.
5. Adding a User Interface with Gradio
   • Define a CLI interface function that modifies task descriptions and returns results.
   • Launch the user interface with Gradio, providing a text box for input and text output.
6. Running Commands and Troubleshooting
   • Execute the code in the terminal and interact with the user interface.
   • Modify prompts and retry commands if necessary.
   • Note the direct execution of commands on the computer and the option to use Docker for safety.
7. Integrating O Lama
   • Install Lang Chain Community packages if not already included.
   • Modify the configuration to use the O Lama model with offline settings and API details.
   • Run the code with the O Lama model and observe the performance.

Conclusion

• The video concludes with a demonstration of the integration process and its potential issues.
• Links to related topics on Crew AI, Autogen, and Task Viewer are promised in the description.
• The creator encourages viewers to like, share, subscribe, and stay tuned for similar content.

Detailed Instructions and URLs

• No specific CLI commands or URLs were provided in the summary.