Palico AI - Tech-Stack for Iterative Development

Documentation | Quickstart | Agents | Memory | Tracing | Evaluations | SDKs | Deployment

Developing an LLM application requires continuously trying different combinations of models, prompts, RAG datasets, call chaining, custom code, and more. Palico helps you build a tech-stack designed for the iterative nature of LLM Development.

With Palico you can

• ✅  Build any application with complete flexibility (docs)
• ✅  Preview changes locally with a Playground UI (docs)
  
• ✅  Improve performance with Experiments and Evals (docs)
  
• ✅  Debug issues with logs and traces (docs)
  
• ✅  Integrate with your frontend with ClientSDKs or REST API (docs)
  
• ✅  Setup Continous Integration and Pull-Request Previews (docs)
  
• ✅  Manage your application from a control panel (docs)
  

[!TIP] ⭐️ Star this repo to get release notifications for new features.

⚡ Get started in seconds ⚡

npx palico init <project-name>

Checkout our quickstart guide.

Overview of your Palico App

https://github.com/user-attachments/assets/8c8a1c62-f70d-45a5-82f7-21b2073ba9f0

🛠️ Building your Application

Build your application with complete flexibility

With Palico, you have complete control over the implementation details of your LLM application. Build any application by creating a Chat function.

import { Chat } from '@palico-ai/app';
import OpenAI from 'openai';

// 1. implement the Chat type
const handler: Chat = async ({ userMessage }) => {
  // 2. implement your application logic
  const response = await openai.chat.completions.create({
    model: 'gpt-3.5-turbo-0125',
    messages: [{ role: 'user', content: userMessage }],
  });
  return {
    message: response.choices[0].message.content,
  };
};

// 3. export the handler
export default handler;

Learn more about building your application with palico (docs).

Build complex interactions with powerful primitives