How to Build an LLM Agent That Can Write and Run Code
A guide to creating an AI agent capable of writing and executing code using OpenAI Agents SDK and Docker.
1The Power of Code-Executing Agents
AI agents that can write and execute code are revolutionizing fields from data analysis to automation. With this capability, agents can inspect files, write data processing logic, and generate reports. OpenAI Agents SDK now makes it possible to develop such agents.
2Core Components
A code-executing agent has three core components: the model, the workspace, and the execution environment. The model is powered by an LLM, the workspace manages input and output files, and the execution environment safely runs the generated code.
3The Role of the Model
The LLM acts as the brain of the agent, inspecting available files, deciding on the type of analysis needed, and writing the necessary code. It can also read results and iterate to improve the process.
4Workspace
The workspace is where users can upload data files (CSV, PDF, etc.) and the agent can work on them to produce outputs. This space simplifies the management of inputs and outputs.
5Execution Environment
The LLM agent only writes code; it doesn't execute it. A secure execution environment, often Docker, is required to run the generated code safely.
6Control Mechanisms
Safety controls are crucial for the agent's operation. At the model level, system instructions are defined, while the workspace level determines file usage, and the execution environment manages dependencies.
7OpenAI Agents SDK
The OpenAI Agents SDK offers code execution capabilities through SandboxAgent. This agent configures the workspace using a Manifest to specify which files to use and integrates seamlessly with Docker.
8Docker Integration
Docker provides an ideal environment for safely executing code. With OpenAI Agents SDK, you can create a custom workspace using Docker and pre-install necessary dependencies.
9Case Study: CSV Analysis
As a case study, an agent can analyze a CSV file to detect anomalies in energy consumption. The agent inspects the data and generates reports and charts.
10Conclusion and Recommendations
LLM agents capable of executing code hold great potential in data analysis and automation. Tools like OpenAI Agents SDK and Docker offer effective methods to develop such agents and optimize workflows.
Original article: Towards Data Science — Kod Yazıp Çalıştırabilen Bir LLM Ajanı Nasıl Geliştirilir?