sdk/javascript-sdk/SKILL.md
JavaScript/TypeScript SDK for inference.sh - run AI apps, build agents, integrate 250+ models. Package: @inferencesh/sdk (npm install). Full TypeScript support, streaming, file uploads. Build agents with template or ad-hoc patterns, tool builder API, skills, human approval. Use for: JavaScript integration, TypeScript, Node.js, React, Next.js, frontend apps. Triggers: javascript sdk, typescript sdk, npm install, node.js api, js client, react ai, next.js ai, frontend sdk, @inferencesh/sdk, typescript agent, browser sdk, js integration
npx skillsauth add inference-sh/agent-skills javascript-sdkInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Build AI applications with the inference.sh JavaScript/TypeScript 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
Requirements: Node.js 18.0.0+ (or modern browser with fetch)
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' });
Get your API key: Settings → API Keys → Create API Key
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));
}
}
| Parameter | Type | Description |
|-----------|------|-------------|
| app | string | App ID (namespace/name@version) |
| input | object | Input matching app schema |
| setup | object | Hidden setup configuration |
| infra | string | 'cloud' or 'private' |
| session | string | Session ID for stateful execution |
| session_timeout | number | Idle timeout (1-3600 seconds) |
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]);
Keep workers warm across multiple calls:
// 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
});
Use pre-built agents from your workspace:
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();
Create custom agents programmatically:
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?');
| Model | App Reference |
|-------|---------------|
| Claude Sonnet 4 | infsh/claude-sonnet-4@latest |
| Claude 3.5 Haiku | infsh/claude-haiku-35@latest |
| GPT-4o | infsh/gpt-4o@latest |
| GPT-4o Mini | infsh/gpt-4o-mini@latest |
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'
}
]
});
For browser apps, proxy through your backend to keep API keys secure:
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
}));
Full type definitions included:
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 250+ 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
development
Render videos from React/Remotion component code via inference.sh. Pass TSX code, get MP4. Supports all Remotion APIs: useCurrentFrame, useVideoConfig, spring, interpolate, AbsoluteFill, Sequence. Configurable resolution, FPS, duration, codec. Use for: programmatic video generation, animated graphics, motion design, data-driven videos, React animations to video. Triggers: remotion, render video from code, tsx to video, react video, programmatic video, remotion render, code to video, animated video, motion graphics code, react animation video
tools
Generate videos with Pruna P-Video and WAN models via inference.sh CLI. Models: P-Video, WAN-T2V, WAN-I2V. Capabilities: text-to-video, image-to-video, audio support, 720p/1080p, fast inference. Pruna optimizes models for speed without quality loss. Triggers: pruna video, p-video, pruna ai video, fast video generation, optimized video, wan t2v, wan i2v, economic video generation, cheap video generation, pruna text to video, pruna image to video
documentation
Still-to-video conversion guide: model selection, motion prompting, and camera movement. Covers Wan 2.5 i2v, Seedance, Fabric, Grok Video with when to use each. Use for: animating images, creating video from stills, adding motion, product animations. Triggers: image to video, i2v, animate image, still to video, add motion to image, image animation, photo to video, animate still, wan i2v, image2video, bring image to life, animate photo, motion from image
tools
Generate videos with Google Veo models via inference.sh CLI. Models: Veo 3.1, Veo 3.1 Fast, Veo 3, Veo 3 Fast, Veo 2. Capabilities: text-to-video, cinematic output, high quality video generation. Triggers: veo, google veo, veo 3, veo 2, veo 3.1, vertex ai video, google video generation, google video ai, veo model, veo video