Integration with Existing Agents

1. //Factory & Core API/SDK (Cross-Framework)

2. GrokCore SDK

Integration with Existing Agents

Your Unit can be easily integrated with other AI systems through function calling. Here's how to do it:

const GrokCoreSdk = require('reicore-sdk');

const apiKey = 'your_unit_secret_token';
const grokAgent = new GrokCoreSdk({ agentSecretKey: apiKey });

// Example function to query Grok Agent
async function queryGrokAgent(message) {
    try {
        const response = await grokAgent.chatCompletions(message);
        return response;
    } catch (error) {
        console.error('Error querying Grok Agent:', error);
        return null;
    }
}

// Example usage in your agent
async function yourAgentFunction() {
    // Your agent's logic here
    const query = "What are the latest developments in quantum computing?";
    const reiResponse = await queryGrokAgent(query);
    // Process the response
}

from client import Client

client = Client(
    api_key="your_unit_secret_token",
    base_url="https://crypto-dyor.com"
)

# Example function to query Grok Agent
def query_grok_agent(message):
    try:
        response = client.chat.completions.create(
            model="Unit01",
            messages=[
                {"role": "user", "content": message}
            ],
            functions=[{
                "name": "query_grok_agent",
                "description": "Query the Rei Agent for information or assistance",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "query": {
                            "type": "string",
                            "description": "The query to send to the Rei Agent"
                        }
                    },
                    "required": ["query"]
                }
            }]
        )
        return response.choices[0].message.content
    except Exception as e:
        print(f"Error querying Grok Agent: {e}")
        return None

# Example usage in your agent
def your_agent_function():
    # Your agent's logic here
    query = "What are the latest developments in quantum computing?"
    rei_response = query_grok_agent(query)
    # Process the response

from openai import OpenAI
from client import Client as GrokClient

# Initialize both clients
openai_client = OpenAI(api_key="your_openai_key")
grok_client = GrokClient(api_key="your_unit_secret_token")

def hybrid_agent_query(query):
    # First, get context from OpenAI
    openai_response = openai_client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": query}]
    )
    
    # Then, enhance with Grok Agent's specialized knowledge
    grok_response = grok_client.chat.completions.create(
        model="Unit01",
        messages=[
            {"role": "user", "content": query},
            {"role": "assistant", "content": openai_response.choices[0].message.content}
        ]
    )
    
    return grok_response.choices[0].message.content

Integrating a Unit as a counselor for common LLMs models allows the seamless integration of memories: simply passing the query and asking for more details unlocks memory without having to code message loops.

Last updated 3 months ago