Using AgentKit with E2B

Develop Coding Agents using E2B Sandboxes as tools

E2B is an open-source runtime for executing AI-generated code in secure cloud sandboxes. Made for agentic & AI use cases.

E2B is a perfect fit to build Coding Agents that can write code, fix bugs, and more.

Setup

1. Install AgentKit and E2B

   Within an existing project, Install AgentKit and E2B from npm:

   npm

   npm install @inngest/agent-kit inngest @e2b/code-interpreter
   

   
   

   Don't have an existing project?

   To create a new project, create a new directory then initialize using your package manager:

   npm

   mkdir my-agent-kit-project && npm init
   

2. Setup your Coding Agent

   Create a Agent and its associated Network:

   import {
     createAgent,
     createNetwork,
     anthropic
   } from "@inngest/agent-kit";
   
   const agent = createAgent({
     name: "Coding Agent",
     description: "An expert coding agent",
     system: `You are a coding agent help the user to achieve the described task.
   
     Once the task completed, you should return the following information:
     <task_summary>
     </task_summary>
   
     Think step-by-step before you start the task.
     `,
     model: anthropic({
       model: "claude-3-5-sonnet-latest",
       max_tokens: 4096,
     }),
   });
   
   const network = createNetwork({
     name: "Coding Network",
     agents: [agent],
     defaultModel: anthropic({
       model: "claude-3-5-sonnet-20240620",
       maxTokens: 1000,
     })
   });
   
   

3. Create the E2B Tools

   To operate, our Coding Agent will need to create files and run commands.

   Below is an example of how to create the createOrUpdateFiles and terminal E2B tools:

   import {
     createAgent,
     createNetwork,
     anthropic,
     createTool
   } from "@inngest/agent-kit";
   
   const agent = createAgent({
     name: "Coding Agent",
     description: "An expert coding agent",
     system: `You are a coding agent help the user to achieve the described task.
   
     Once the task completed, you should return the following information:
     <task_summary>
     </task_summary>
   
     Think step-by-step before you start the task.
     `,
     model: anthropic({
       model: "claude-3-5-sonnet-latest",
       max_tokens: 4096,
     }),
     tools: [
       // terminal use
       createTool({
         name: "terminal",
         description: "Use the terminal to run commands",
         parameters: z.object({
           command: z.string(),
         }),
         handler: async ({ command }, { network }) => {
           const buffers = { stdout: "", stderr: "" };
   
           try {
             const sandbox = await getSandbox(network);
             const result = await sandbox.commands.run(command, {
               onStdout: (data: string) => {
                 buffers.stdout += data;
               },
               onStderr: (data: string) => {
                 buffers.stderr += data;
               },
             });
             return result.stdout;
           } catch (e) {
             console.error(
               `Command failed: ${e} \nstdout: ${buffers.stdout}\nstderr: ${buffers.stderr}`
             );
             return `Command failed: ${e} \nstdout: ${buffers.stdout}\nstderr: ${buffers.stderr}`;
           }
         },
       }),
       // create or update file
       createTool({
         name: "createOrUpdateFiles",
         description: "Create or update files in the sandbox",
         parameters: z.object({
           files: z.array(
             z.object({
               path: z.string(),
               content: z.string(),
             })
           ),
         }),
         handler: async ({ files }, { network }) => {
           try {
             const sandbox = await getSandbox(network);
             for (const file of files) {
               await sandbox.files.write(file.path, file.content);
             }
             return `Files created or updated: ${files
               .map((f) => f.path)
               .join(", ")}`;
           } catch (e) {
             return "Error: " + e;
           }
         },
       }),
     ]
   });
   
   const network = createNetwork({
     name: "Coding Network",
     agents: [agent],
     defaultModel: anthropic({
       model: "claude-3-5-sonnet-20240620",
       maxTokens: 1000,
     })
   });
   
   

   You will find the complete example in the E2B Coding Agent example.

   Designing useful tools

   As covered in Anthropic’s “Tips for Building AI Agents”, the best Agents Tools are the ones that you will need to accomplish the task by yourself.

   Do not map tools directly to the underlying API, but rather design tools that are useful for the Agent to accomplish the task.

Examples

Replicate Cursor's Agent mode

This examples shows how to use E2B sandboxes to build a coding agent that can write code and run commands to generate complete project, complete refactoring and fix bugs.

AgentsToolsNetworkIntegrations
Code-based Router

AI-powered CSV contacts importer

Let’s reinvent the CSV upload UX with an AgentKit network leveraging E2B sandboxes.

AgentsToolsNetworkIntegrations
Code-based Router