import { CodebuffClient } from '@codebuff/sdk'

const client = new CodebuffClient({
  // Note: You need to pass in your own API key here.
  apiKey: process.env.CODEBUFF_API_KEY,
  cwd: process.cwd(),
  onError: (e) => console.error('Codebuff error:', e.message),
})

// First run
const run1 = await client.run({
  agent: 'base',
  prompt: 'Create a simple calculator class',
})

// Continue the same session with a follow-up
const run2 = await client.run({
  agent: 'base',
  prompt: 'Add unit tests for the calculator',
  previousRun: run1,
  handleEvent: (event) => {
    // Log all events
    console.log('Progress:', event)
  },
})

client.closeConnection()

import { z } from 'zod'
import {
  CodebuffClient,
  generateInitialRunState,
  withAdditionalMessage,
  getCustomToolDefinition,
} from '@codebuff/sdk'

const client = new CodebuffClient({
  // Note: You need to pass in your own API key here.
  apiKey: process.env.CODEBUFF_API_KEY,
  cwd: process.cwd(),
  onError: (e) => console.error('Codebuff error:', e.message),
})

// Create a run with an image
const emptyRun = await generateInitialRunState({ cwd: process.cwd() })
const runWithImage = withAdditionalMessage({
  runState: emptyRun,
  message: {
    role: 'user',
    content: [
      {
        type: 'image',
        image: new URL(
          'https://upload.wikimedia.org/wikipedia/en/a/a9/Example.jpg',
        ),
      },
    ],
  },
})

const result = await client.run({
  agent: 'my-custom-agent',
  prompt: 'Analyze this image and create code based on what you see',
  previousRun: runWithImage,

  // Custom agent definitions
  agentDefinitions: [
    {
      id: 'my-custom-agent',
      model: 'openai/gpt-5',
      displayName: 'Image Analyzer',
      instructionsPrompt:
        '1. describe all the details in the image. 2. answer the user prompt',
      // ... other AgentDefinition properties
    },
  ],

  // Custom tool definitions
  customToolDefinitions: [
    getCustomToolDefinition({
      toolName: 'fetch_api_data',
      description: 'Fetch data from an API endpoint',
      inputSchema: z.object({
        url: z.string().url(),
        method: z.enum(['GET', 'POST']).default('GET'),
        headers: z.record(z.string()).optional(),
      }),
      exampleInputs: [
        { url: 'https://api.example.com/data', method: 'GET' },
        {
          url: 'https://api.example.com/submit',
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
        },
      ],
      handler: async ({ url, method, headers }) => {
        const response = await fetch(url, { method, headers })
        const data = await response.text()
        return {
          toolResultMessage: `API Response: ${data.slice(0, 5000)}...`,
        }
      },
    }),
  ],

  handleEvent: (event) => {
    console.log('Agent progress:', event)
  },
})

client.closeConnection()