> ## Documentation Index
> Fetch the complete documentation index at: https://composio-27-feat-docs-revamp.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Code Execution Agent

> The project generates and executes code based on user-defined problems. It utilizes the Composio and connects your AI Agent to E2B's Code Interpreter to facilitate code execution, allowing users to input a problem statement and receive executable code as output. The agent is designed to operate in a sandbox environment, ensuring safe execution and accurate results. Key functionalities include code generation, execution, and result interpretation, making it an invaluable resource for developers and data scientists alike.

<Tabs>
  <Tab title="JavaScript">
    <Steps>
      <Step title="Import Required Packages">
        Import necessary packages for the Code Execution Agent:

        <CodeGroup>
          ```javascript Import statements
          import dotenv from 'dotenv';
          import { ChatOpenAI } from "@langchain/openai";
          import { createOpenAIFunctionsAgent, AgentExecutor } from "langchain/agents";
          import { pull } from "langchain/hub";
          import { LangchainToolSet } from "composio-core";

          dotenv.config();
          ```
        </CodeGroup>
      </Step>

      <Step title="Initialize Composio Toolset">
        Set up the Composio toolset and get the required tools:

        <CodeGroup>
          ```javascript Connect to CodeInterpreter
          const toolset = new LangchainToolSet({ 
              apiKey: process.env.COMPOSIO_API_KEY
          });

          const tools = await toolset.getTools({ 
              actions: ["codeinterpreter_create_sandbox", "codeinterpreter_execute_code"] 
          });
          ```
        </CodeGroup>
      </Step>

      <Step title="Set up the AI Model">
        Initialize the OpenAI ChatGPT model:

        <CodeGroup>
          ```javascript Initialise Model
          const llm = new ChatOpenAI({ 
              model: "gpt-4o",
              apiKey: process.env.OPEN_AI_API_KEY
          });
          ```
        </CodeGroup>
      </Step>

      <Step title="Create the AI Agent">
        Set up the agent's prompt and create the OpenAI Functions Agent:

        <CodeGroup>
          ```javascript Setup Agent
          const prompt = await pull("hwchase17/openai-functions-agent");
          const agent = await createOpenAIFunctionsAgent({ llm, tools, prompt });
          ```
        </CodeGroup>
      </Step>

      <Step title="Set up the Agent Executor">
        Create the AgentExecutor to run the agent:

        <CodeGroup>
          ```javascript Creating Agent Executor
          const agentExecutor = new AgentExecutor({ 
              agent, 
              tools, 
              verbose: true,
          });
          ```
        </CodeGroup>
      </Step>

      <Step title="Define the Code Execution Function">
        Create the main function to generate and execute code:

        <CodeGroup>
          ```javascript Main Function
          async function executeCodeAgent(userProblem) {
              // Generate code
              console.log("Generating code for the problem...");
              const codeGenerationResult = await agentExecutor.invoke({ 
                  input: `Generate Python code to solve the following problem: ${userProblem}. 
                          Only provide the code, no explanations.`
              });
              const generatedCode = codeGenerationResult.output;
              console.log("Generated Code:", generatedCode);

              // Execute code
              console.log("\nExecuting the generated code...");
              const executionResult = await agentExecutor.invoke({ 
                  input: `Execute the following Python code:\n${generatedCode}`
              });
              console.log("\nExecution Result:", executionResult.output);
          }
          ```
        </CodeGroup>
      </Step>

      <Step title="Run the Code Execution Agent">
        Execute the agent with a sample problem:

        <CodeGroup>
          ```javascript Run the Agent
          const userProblem = "Create a list of prime numbers up to 50";
          executeCodeAgent(userProblem).catch(error => console.error("An error occurred:", error));
          ```
        </CodeGroup>
      </Step>
    </Steps>
  </Tab>
</Tabs>

## Complete Code

Here's the complete JavaScript code for the Code Execution Agent:

<CodeGroup>
  ```javascript JavaScript Final Code
  import dotenv from 'dotenv';
  import { ChatOpenAI } from "@langchain/openai";
  import { createOpenAIFunctionsAgent, AgentExecutor } from "langchain/agents";
  import { pull } from "langchain/hub";
  import { LangchainToolSet } from "composio-core";

  dotenv.config();

  async function executeCodeAgent(userProblem) {
      const toolset = new LangchainToolSet({ 
          apiKey: process.env.COMPOSIO_API_KEY
      });

      const tools = await toolset.getTools({ 
          actions: ["codeinterpreter_create_sandbox", "codeinterpreter_execute_code"] 
      });

      const llm = new ChatOpenAI({ 
          model: "gpt-4o",
          apiKey: process.env.OPEN_AI_API_KEY
      });

      const prompt = await pull("hwchase17/openai-functions-agent");
      const agent = await createOpenAIFunctionsAgent({ llm, tools, prompt });

      const agentExecutor = new AgentExecutor({ 
          agent, 
          tools, 
          verbose: true,
      });

      console.log("Generating code for the problem...");
      const codeGenerationResult = await agentExecutor.invoke({ 
          input: `Generate Python code to solve the following problem: ${userProblem}. 
                  Only provide the code, no explanations.`
      });
      const generatedCode = codeGenerationResult.output;
      console.log("Generated Code:", generatedCode);

      console.log("\nExecuting the generated code...");
      const executionResult = await agentExecutor.invoke({ 
          input: `Execute the following Python code:\n${generatedCode}`
      });
      console.log("\nExecution Result:", executionResult.output);
  }

  const userProblem = "Create a list of prime numbers up to 50";
  executeCodeAgent(userProblem).catch(error => console.error("An error occurred:", error));
  ```
</CodeGroup>