javascript-sdk

npm install @inferencesh/sdk

import { inference } from '@inferencesh/sdk';
 
const client = inference({ apiKey: 'inf_your_key' });
 
// Run an AI app
const result = await client.run({
  app: 'infsh/flux-schnell',
  input: { prompt: 'A sunset over mountains' }
});
console.log(result.output);

npm install @inferencesh/sdk
# or
yarn add @inferencesh/sdk
# or
pnpm add @inferencesh/sdk

import { inference } from '@inferencesh/sdk';
 
// Direct API key
const client = inference({ apiKey: 'inf_your_key' });
 
// From environment variable (recommended)
const client = inference({ apiKey: process.env.INFERENCE_API_KEY });
 
// For frontend apps (use proxy)
const client = inference({ proxyUrl: '/api/inference/proxy' });

const result = await client.run({
  app: 'infsh/flux-schnell',
  input: { prompt: 'A cat astronaut' }
});
 
console.log(result.status);  // "completed"
console.log(result.output);  // Output data

const task = await client.run({
  app: 'google/veo-3-1-fast',
  input: { prompt: 'Drone flying over mountains' }
}, { wait: false });
 
console.log(`Task ID: ${task.id}`);
// Check later with client.getTask(task.id)

const stream = await client.run({
  app: 'google/veo-3-1-fast',
  input: { prompt: 'Ocean waves at sunset' }
}, { stream: true });
 
for await (const update of stream) {
  console.log(`Status: ${update.status}`);
  if (update.logs?.length) {
    console.log(update.logs.at(-1));
  }
}

const result = await client.run({
  app: 'image-processor',
  input: {
    image: '/path/to/image.png'  // Auto-uploaded
  }
});

// Basic upload
const file = await client.uploadFile('/path/to/image.png');
 
// With options
const file = await client.uploadFile('/path/to/image.png', {
  filename: 'custom_name.png',
  contentType: 'image/png',
  public: true
});
 
const result = await client.run({
  app: 'image-processor',
  input: { image: file.uri }
});

const input = document.querySelector('input[type="file"]');
const file = await client.uploadFile(input.files[0]);

// Start new session
const result = await client.run({
  app: 'my-app',
  input: { action: 'init' },
  session: 'new',
  session_timeout: 300  // 5 minutes
});
const sessionId = result.session_id;
 
// Continue in same session
const result2 = await client.run({
  app: 'my-app',
  input: { action: 'process' },
  session: sessionId
});

const agent = client.agent('my-team/support-agent@latest');
 
// Send message
const response = await agent.sendMessage('Hello!');
console.log(response.text);
 
// Multi-turn conversation
const response2 = await agent.sendMessage('Tell me more');
 
// Reset conversation
agent.reset();
 
// Get chat history
const chat = await agent.getChat();

import { tool, string, number, appTool } from '@inferencesh/sdk';
 
// Define tools
const calculator = tool('calculate')
  .describe('Perform a calculation')
  .param('expression', string('Math expression'))
  .build();
 
const imageGen = appTool('generate_image', 'infsh/flux-schnell@latest')
  .describe('Generate an image')
  .param('prompt', string('Image description'))
  .build();
 
// Create agent
const agent = client.agent({
  core_app: { ref: 'infsh/claude-sonnet-4@latest' },
  system_prompt: 'You are a helpful assistant.',
  tools: [calculator, imageGen],
  temperature: 0.7,
  max_tokens: 4096
});
 
const response = await agent.sendMessage('What is 25 * 4?');

import {
  string, number, integer, boolean,
  enumOf, array, obj, optional
} from '@inferencesh/sdk';
 
const name = string('User\'s name');
const age = integer('Age in years');
const score = number('Score 0-1');
const active = boolean('Is active');
const priority = enumOf(['low', 'medium', 'high'], 'Priority');
const tags = array(string('Tag'), 'List of tags');
const address = obj({
  street: string('Street'),
  city: string('City'),
  zip: optional(string('ZIP'))
}, 'Address');

const greet = tool('greet')
  .display('Greet User')
  .describe('Greets a user by name')
  .param('name', string('Name to greet'))
  .requireApproval()
  .build();

const generate = appTool('generate_image', 'infsh/flux-schnell@latest')
  .describe('Generate an image from text')
  .param('prompt', string('Image description'))
  .setup({ model: 'schnell' })
  .input({ steps: 20 })
  .requireApproval()
  .build();

import { agentTool } from '@inferencesh/sdk';
 
const researcher = agentTool('research', 'my-org/researcher@v1')
  .describe('Research a topic')
  .param('topic', string('Topic to research'))
  .build();

import { webhookTool } from '@inferencesh/sdk';
 
const notify = webhookTool('slack', 'https://hooks.slack.com/...')
  .describe('Send Slack notification')
  .secret('SLACK_SECRET')
  .param('channel', string('Channel'))
  .param('message', string('Message'))
  .build();

import { internalTools } from '@inferencesh/sdk';
 
const config = internalTools()
  .plan()
  .memory()
  .webSearch(true)
  .codeExecution(true)
  .imageGeneration({
    enabled: true,
    appRef: 'infsh/flux@latest'
  })
  .build();
 
const agent = client.agent({
  core_app: { ref: 'infsh/claude-sonnet-4@latest' },
  internal_tools: config
});

const response = await agent.sendMessage('Explain quantum computing', {
  onMessage: (msg) => {
    if (msg.content) {
      process.stdout.write(msg.content);
    }
  },
  onToolCall: async (call) => {
    console.log(`\n[Tool: ${call.name}]`);
    const result = await executeTool(call.name, call.args);
    agent.submitToolResult(call.id, result);
  }
});

// From file path (Node.js)
import { readFileSync } from 'fs';
const response = await agent.sendMessage('What\'s in this image?', {
  files: [readFileSync('image.png')]
});
 
// From base64
const response = await agent.sendMessage('Analyze this', {
  files: ['data:image/png;base64,iVBORw0KGgo...']
});
 
// From browser File object
const input = document.querySelector('input[type="file"]');
const response = await agent.sendMessage('Describe this', {
  files: [input.files[0]]
});

const agent = client.agent({
  core_app: { ref: 'infsh/claude-sonnet-4@latest' },
  skills: [
    {
      name: 'code-review',
      description: 'Code review guidelines',
      content: '# Code Review\n\n1. Check security\n2. Check performance...'
    },
    {
      name: 'api-docs',
      description: 'API documentation',
      url: 'https://example.com/skills/api-docs.md'
    }
  ]
});

const client = inference({
  proxyUrl: '/api/inference/proxy'
  // No apiKey needed on frontend
});

// app/api/inference/proxy/route.ts
import { createRouteHandler } from '@inferencesh/sdk/proxy/nextjs';
 
const route = createRouteHandler({
  apiKey: process.env.INFERENCE_API_KEY
});
 
export const POST = route.POST;

import express from 'express';
import { createProxyMiddleware } from '@inferencesh/sdk/proxy/express';
 
const app = express();
app.use('/api/inference/proxy', createProxyMiddleware({
  apiKey: process.env.INFERENCE_API_KEY
}));

import type {
  TaskDTO,
  ChatDTO,
  ChatMessageDTO,
  AgentTool,
  TaskStatusCompleted,
  TaskStatusFailed
} from '@inferencesh/sdk';
 
if (result.status === TaskStatusCompleted) {
  console.log('Done!');
} else if (result.status === TaskStatusFailed) {
  console.log('Failed:', result.error);
}

import { RequirementsNotMetException, InferenceError } from '@inferencesh/sdk';
 
try {
  const result = await client.run({ app: 'my-app', input: {...} });
} catch (e) {
  if (e instanceof RequirementsNotMetException) {
    console.log('Missing requirements:');
    for (const err of e.errors) {
      console.log(`  - ${err.type}: ${err.key}`);
    }
  } else if (e instanceof InferenceError) {
    console.log('API error:', e.message);
  }
}

const response = await agent.sendMessage('Delete all temp files', {
  onToolCall: async (call) => {
    if (call.requiresApproval) {
      const approved = await promptUser(`Allow ${call.name}?`);
      if (approved) {
        const result = await executeTool(call.name, call.args);
        agent.submitToolResult(call.id, result);
      } else {
        agent.submitToolResult(call.id, { error: 'Denied by user' });
      }
    }
  }
});

const { inference, tool, string } = require('@inferencesh/sdk');
 
const client = inference({ apiKey: 'inf_...' });
const result = await client.run({...});

# Python SDK
npx skills add inference-sh/skills@python-sdk
 
# Full platform skill (all apps via CLI)
npx skills add inference-sh/skills@infsh-cli
 
# LLM models
npx skills add inference-sh/skills@llm-models
 
# Image generation
npx skills add inference-sh/skills@ai-image-generation