The @palico-ai/client-js is a Javascript library that helps you communicate with your Palico application. This library should primarily be used in secured server-side environments, such as a NodeJS server or a NextJS server runtime.

​
Setup and Installation

To install the Palico Client SDK on your NodeJS project, run the following command:

npm install @palico-ai/client-js

​
Create a Palico Client

1

Generate a Service Key

You can generate a service key from your Palico App project directory

npm run palico generate apikey
2

Create a Palico Client

import { createClient } from "@palico-ai/client-js";

const client = createClient({
  serviceKey: "<your-service-key>",
  apiURL: "http://localhost:8000", // replace with your API URL
});

​
Calling an Agent

Here’s an example of how you can call an agent using the Palico Client SDK:

const response = await client.agent.chat({
  agentName: "chat",
  userMessage: "What is the weather today?",
  payload: {
    location: "San Francisco",
  },
  appConfig: {
    model: "gpt-3.5-turbo",
    provider: "openai",
  },
});

const { conversationId, requestId, message, data, toolCalls } = response;

Learn about the API reference for the chat method here.

​
Stream Response

You can also stream the response from the agent. Here’s an example of how you can stream the response from the agent:

  const response = await client.agent.chat({
    agentName: "chat_stream",
    userMessage: "Hello",
    appConfig: {},
    stream: true,
  });

for await (const chunk of response.readChunks()) {
  // Do something with the chunk
}

You can also get the merged chunks from the stream during or after the stream has completed:

const mergedChunks = await response.getMergedChunks();

​
API Reference

​
chat()

Input

The chat method accepts the following parameters:

​
agentName
string
required

The name of the agent you want to call. This should match the agent folder name.

​
userMessage
string
required

Message to send to the agent.

​
payload
json

Additional data to send to the agent.

​
appConfig
json

App Config value to send to your agent. Learn more about App Config.

​
stream
boolean

Should the response be streamed back to the client

​
toolCallResults
ToolCallResult[]

For client-side tool execution, the results of the tool call. The schema for a ToolCallResult is as follows:

interface ToolCallResult {
  name: string;
  result: any;
}

Output (Non-Streaming)

The chat method returns a promise that resolves to a ChatResponse object:

​
conversationId
string
required

Unique identifier for the conversation.

​
requestId
string
required

Unique identifier for the request.

​
message
string

The message to be sent back to the user.

​
data
json

Additional data to be sent back to the user.

​
toolCalls
ToolCall[]

If the agent requires a client-side tool execution, this field will contain the tool name and parameters. The schema for a ToolCall is as follows:

interface ToolCall {
  id: string;
  name: string;
  parameters?: JSON;
}

The @palico-ai/client-js is a Javascript library that helps you communicate with your Palico application. This library should primarily be used in secured server-side environments, such as a NodeJS server or a NextJS server runtime.

​
Setup and Installation

To install the Palico Client SDK on your NodeJS project, run the following command:

npm install @palico-ai/client-js

​
Create a Palico Client

1

Generate a Service Key

You can generate a service key from your Palico App project directory

npm run palico generate apikey
2

Create a Palico Client

import { createClient } from "@palico-ai/client-js";

const client = createClient({
  serviceKey: "<your-service-key>",
  apiURL: "http://localhost:8000", // replace with your API URL
});

​
Calling an Agent

Here’s an example of how you can call an agent using the Palico Client SDK:

const response = await client.agent.chat({
  agentName: "chat",
  userMessage: "What is the weather today?",
  payload: {
    location: "San Francisco",
  },
  appConfig: {
    model: "gpt-3.5-turbo",
    provider: "openai",
  },
});

const { conversationId, requestId, message, data, toolCalls } = response;

Learn about the API reference for the chat method here.

​
Stream Response

You can also stream the response from the agent. Here’s an example of how you can stream the response from the agent:

  const response = await client.agent.chat({
    agentName: "chat_stream",
    userMessage: "Hello",
    appConfig: {},
    stream: true,
  });

for await (const chunk of response.readChunks()) {
  // Do something with the chunk
}

You can also get the merged chunks from the stream during or after the stream has completed:

const mergedChunks = await response.getMergedChunks();

​
API Reference

​
chat()

Input

The chat method accepts the following parameters:

​
agentName
string
required

The name of the agent you want to call. This should match the agent folder name.

​
userMessage
string
required

Message to send to the agent.

​
payload
json

Additional data to send to the agent.

​
appConfig
json

App Config value to send to your agent. Learn more about App Config.

​
stream
boolean

Should the response be streamed back to the client

​
toolCallResults
ToolCallResult[]

For client-side tool execution, the results of the tool call. The schema for a ToolCallResult is as follows:

interface ToolCallResult {
  name: string;
  result: any;
}

Output (Non-Streaming)

The chat method returns a promise that resolves to a ChatResponse object:

​
conversationId
string
required

Unique identifier for the conversation.

​
requestId
string
required

Unique identifier for the request.

​
message
string

The message to be sent back to the user.

​
data
json

Additional data to be sent back to the user.

​
toolCalls
ToolCall[]

If the agent requires a client-side tool execution, this field will contain the tool name and parameters. The schema for a ToolCall is as follows:

interface ToolCall {
  id: string;
  name: string;
  parameters?: JSON;
}