The Serverless Infrastructure for AI Agents.
Build, run, and scale production AI agents with a single SDK. Agentlify handles the infrastructure—routing, observability, rate limits, and tool execution—so you can focus on building features.
Think of us as "Netlify for LLMs":
- DevX First: Install one SDK, access any model (OpenAI, Anthropic, Google, etc.).
- Serverless Agents: Define tools with simple callbacks in your existing code (Next.js API routes, Node.js services). No need for a separate "agent server" or complex Python microservices.
- Smart Infrastructure: Automatic model routing (cost/speed/quality), retries, and fallbacks.
- Full Observability: Real-time logs, cost tracking, and tool execution traces out of the box.
- Unified Agent API: Build agents that run anywhere.
- Local Tool Callbacks: Execute tools in your code, secure and simple.
- Smart Model Routing: Automatically optimize for cost, speed, or quality.
- Multi-Provider Support: Switch models without changing code.
- Function Calling: First-class support for OpenAI-compatible tools.
- Streaming: Built-in support for real-time responses.
- TypeScript: Full type safety included.
npm install agentlify-js
# or
yarn add agentlify-jsCreate an agent that can interact with your local code or APIs using tools with callbacks.
const Agentlify = require('agentlify-js');
const client = new Agentlify({
apiKey: process.env.AGENTLIFY_API_KEY,
routerId: process.env.AGENTLIFY_ROUTER_ID,
});
// Run an agent with a local tool
const response = await client.agents.run({
agentId: 'my-agent', // Create in Agentlify dashboard
messages: [{ role: 'user', content: 'What is the stock price of AAPL?' }],
tools: [
{
type: 'function',
function: {
name: 'get_stock_price',
description: 'Get current stock price',
parameters: {
type: 'object',
properties: { symbol: { type: 'string' } },
required: ['symbol'],
},
},
// This callback executes in YOUR code
callback: async (args) => {
// Call your DB, external API, etc.
const price = await fetchStockAPI(args.symbol);
return { price, currency: 'USD' };
},
},
],
});
console.log(response.choices[0].message.content);Agents are the core of Agentlify. You can define them in the dashboard and run them via the SDK. Tools can be Webhooks (server-side) or Callbacks (local).
// Example: Database Query Tool
const response = await client.agents.run({
agentId: 'data-assistant',
messages: [{ role: 'user', content: 'Find users in New York' }],
tools: [
{
type: 'function',
function: {
name: 'query_db',
description: 'Query database',
parameters: {
type: 'object',
properties: { city: { type: 'string' } },
},
},
callback: async (args) => {
// Securely access your DB here
return await db.users.find({ city: args.city });
},
},
],
});Stop hardcoding models. Let Agentlify route to the best model for the task.
const completion = await client.chat.create({
messages: [{ role: 'user', content: 'Analyze this complex contract...' }],
// Agentlify selects GPT-4, Claude 3 Opus, or others based on your router config
});Stream responses to your frontend easily.
const stream = await client.chat.create({
messages: [{ role: 'user', content: 'Write a long story...' }],
stream: true,
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content || '');
}const mp = new Agentlify({
apiKey: 'your-api-key',
routerId: 'YOUR_ROUTER_ID',
timeout: 30000,
});For full documentation, visit docs.agentlify.co.
- 📧 Email: help@agentlify.co
- � Issues: GitHub Issues
MIT License - see LICENSE file for details.