Documentation Index
Fetch the complete documentation index at: https://docs.localtrain.ai/llms.txt
Use this file to discover all available pages before exploring further.
Welcome to the localtrain.ai Typescript SDK!
A powerful TypeScript toolkit designed to help you seamlessly build AI-powered agents using any LLMs on runtimes like Node.js.
Explore the SDK to integrate AI into your applications effortlessly and scale your capabilities!
Before you dive in, make sure you have Node.js installed on your machine.
Installation
First, install the Core Package of localtrain.ai:
Using npm
npm install @localtrain-ai/core
Using yarn
yarn add @localtrain-ai/core
📚 Usage Guide
Example: Building an AI Agent with the SDK Core
Let’s walk through creating a Customer Feedback Analyzer Agent step by step:
Step 1: Initialize the SDK
Start by importing the required components and initializing the localtrain.ai SDK:
import { LocalTrain, Agent, ContextManager, IAgent } from '@localtrain-ai/core';
import { Openai } from '@localtrain-ai/llm/openai';
async function main() {
// Initialize the SDK
LocalTrain.initialize();
}
LocalTrain.initialize() sets up the SDK, making it ready to use.
Step 2: Attach Providers to the Registry
Add the necessary providers (e.g., OpenAI) for your agent to use:
import { LocalTrain, Agent, ContextManager, IAgent } from '@localtrain-ai/core';
import { Openai } from '@localtrain-ai/llm/openai';
async function main() {
// Initialize the SDK
LocalTrain.initialize();
// Attach providers to the provider registry
LocalTrain.useProviders(
// Add OpenAI as a provider
new Openai({
apiKey: process.env.OPENAI_API_KEY // Pass your OpenAI API Key
}),
// You can add more providers here, separated by commas
);
}
LocalTrain.useProviders() function registers the providers, allowing your agent to leverage their services (e.g., OpenAI for LLM functionalities).
Define an agent with its configuration and purpose:
// Create a new agent instance
const feedbackAgent = new Agent({
contextManager: new ContextManager().setValue("appId", "feedback-analyzer"),
});
// Define the agent details
const agentDetails: IAgent = {
name: "Customer Feedback Analyzer",
description: "Analyzes customer feedback and provides insights.",
appId: "feedback-agent-id",
inputs: [
{
name: 'feedback',
title: 'Customer Feedback Input',
type: 'Text',
options: [],
}
],
behaviours: [
{
behaviourId: 'sentiment-analysis',
name: 'AnalyzeFeedback',
provider: 'open-ai',
inputs: {
prompt: `You are a customer feedback analyzer. Your role is to process customer feedback and provide insights such as:
- Key themes (e.g., product issues, feature requests)
- Sentiment (positive, negative, neutral)
- Actionable recommendations
Feedback: @feedback`,
temperature: 0.7, // Adds variability for creative summarization
model: "gpt-4o-mini", // Specify the model
responseType: "text",
},
behaviours: [],
}
]
} as IAgent;
// Add the configuration to the agent
feedbackAgent.addConfig(agentDetails);
feedbackAgent and specifying its name, description, and behaviors. Each behavior defines how the agent interacts with the OpenAI provider.
Step 4: Run the Agent
Finally, run the agent with a custom prompt:
const feedback = "The app is great, but I wish the loading time was faster. Also, adding dark mode would be helpful.";
const result = feedbackAgent.run({ feedback });
console.log("Feedback Analysis Result:", result);
main();
run() method executes the agent with the given prompt and logs the results.
Ready to build your first AI-powered agent? 🚀 Dive into the Documentation and start exploring today!
Join our community to connect, share ideas, and get support:
We’d love to hear from you and help you build amazing projects!
📜 License
This project is licensed under the MIT License.
See the LICENSE file for details.
