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

---

---

AI Summary

Summary: Integrating Open Interpreter with Crew AI

• Introduction
  • Open Interpreter can be added to Crew AI, similar to a code interpreter for chat GPT.
  • Crew AI Autogen and Task Viewer differ; Task Viewer is code-first, while Autogen handles both coding and non-coding tasks.
  • Open Interpreter enables coding tasks in Crew AI.
• Setup
  • Install necessary packages: open interpreter, Crew AI, Gradio, L chain, Lang chain, OpenAI.
  • Export OpenAI API key.
  • Create app.py and set up configuration and tools.
  • Configure LLM (Language Learning Model) for the interpreter.
  • Create CLI tool for code creation and execution.
• Creating Agents and Tasks
  • Define an agent with a software engineer role to perform CLI operations and code execution.
  • Assign tasks to the agent, such as identifying the OS and emptying the recycle bin.
  • Set up the crew with sequential task processing using OpenAI’s CH GPT model.
  • Run the crew to integrate Open Interpreter, allowing code writing and execution.
• User Interface
  • Import Gradio and define a CLI interface.
  • Modify task descriptions and launch the user interface.
  • Demonstrate code execution through the interface, with a focus on error handling and prompt optimization.
• Security Note
  • Be cautious with direct command execution, which can alter or delete files.
  • Docker can be used for safer command execution, but is not covered in this tutorial.
• Adding Olama
  • Install Lang chain community package.
  • Modify configuration to use Olama, a local language model.
  • Note the limitations of open-source models in executing tasks.
• Conclusion
  • The tutorial covers the integration of Open Interpreter with Crew AI and user interface setup.
  • Links to related tutorials on Crew AI Autogen and Task Viewer will be provided.
  • Encourages viewers to like, share, subscribe, and watch more related videos.