- ✅ 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)
npx palico init <project-name>
Checkout our quickstart guide.
Product.Demo.-.V2.copy.mp4
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).
Feature | Description |
---|---|
Streaming | Stream messages, data, and intermediate steps to your client-app |
Memory Management | Store conversation states between request without managing any storage infrastructure |
Tool Executions | Build Agents that can execute tools on client-side and server-side |
Feature Flags | Easily swap models, prompts, RAG, and custom logic at runtime |
Monitoring | Debug issues with faster with comprehensive logs and traces |
Since you own the implementation details, you can use Palico with most other external tools and libraries
Tools or Libraries | Supported | |
---|---|---|
Langchain | ✅ | |
LlamaIndex | ✅ | |
Portkey | ✅ | |
OpenAI | ✅ | |
Anthropic | ✅ | |
Cohere | ✅ | |
Azure | ✅ | |
AWS Bedrock | ✅ | |
GCP Vertex | ✅ | |
Pinecone | ✅ | |
PG Vector | ✅ | |
Chroma | ✅ |
Learn more from docs.
Make a code change and instantly preview it locally on our playground UI
Palico helps you create an iterative loop to systematically improve the performance of your application.
Define test-cases that models the expected behavior of your application
const testCases: TestDatasetFN = async () => {
return [
{
input: { // input to your LLM Application
userMessage: "What is the capital of France?",
},
tags: { // tags to help you categorize your test-cases
intent: "question",
},
metrics: [
// example metrics
containsEvalMetric({
substring: "Paris",
}),
levensteinEvalMetric({
expected: "Paris",
}),
rougeSkipBigramSimilarityEvalMetric({
expected: "Paris",
}),
],
},
];
};
Learn more about experiments
You can deploy your Palico app to any cloud provider using Docker.
Setup CI/CD and Pull-Request preview with Coolify and Palico. Learn more about deployment.
const response = await client.agent.chat({
agentName: "chat",
stream: true,
userMessage: "What is the weather today?",
payload: {
location: "San Francisco",
},
appConfig: {
model: "gpt-3.5-turbo",
provider: "openai",
},
});
import { useChat } from "@palico-ai/react";
const { messages, sendMessage } = useChat({
agentName: "assistant",
apiURL: "/api/palico",
});
Learn more about Client SDKs
The easiest way to contribute is to pick an issue with the good first issue
tag 💪. Read the contribution guidelines here.
Bug Report? File here | Feature Request? File here