Integrations

Use Neosantara AI models through partner integrations.

Vercel AI SDK

The Vercel AI SDK is a powerful Typescript library designed to help developers build AI-powered applications.

Install both the Vercel AI SDK and OpenAI’s Vercel package.

Shell

npm i ai @ai-sdk/openai

Instantiate the Neosantara client and call the generateText function with GPT-5 Mini to generate some text.

TypeScript

import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
import { generateText } from 'ai';

const neosantara = createOpenAICompatible({
  name: 'neosantara_xyz',
  apiKey: process.env.NAI_API_KEY ?? "",
  baseURL: 'https://api.neosantara.xyz/v1',
});

const { text } = await generateText({
  model: neosantara('nusantara-base'),
  prompt: 'Write a story about cat want a whiskas',
});

console.log(text)

Learn more in our Neosantara AI - Vercel AI SDK Guide.

LlamaIndex

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs).

Install llama-index

Shell

pip install llama-index

Here’s sample code to get you started with Llama Index + Neosantara AI:

Python

from llama_index.llms.openai_like import OpenAILike

llm = OpenAILike(
    model="nusantara-base",
    api_base="https://api.neosantara.xyz/v1",
    api_key="NAI_API_KEY",
    is_chat_model=True,
    is_function_calling_model=True,
    temperature=0.1,
)

response = llm.complete("Write up to 500 words essay explaining Large Language Models")

print(response)

See this tutorial blog for the RAG implementation details using Neosantara and LlamaIndex.

CrewAI

CrewAI is an open source framework for orchestrating AI agent systems.

Install crewai

Shell

pip install crewai
export NAI_API_KEY=***

Build an multi-agent workflow:

Python

import os
from crewai import LLM, Task, Agent, Crew

llm = LLM(model="openai/nusantara-base",
          api_key=os.environ.get("NAI_API_KEY"),
          base_url="https://api.neosantara.xyz/v1"
        )

research_agent = Agent(
    llm = llm,
    role="Research Analyst",
    goal="Find and summarize information about specific topics",
    backstory="You are an experienced researcher with attention to detail",
    verbose=True  # Enable logging for debugging
)

research_task = Task(
    description="Conduct a thorough research about AI Agents.",
    expected_output="A list with 10 bullet points of the most relevant information about AI Agents",
    agent=research_agent
)

## Execute the crew
crew = Crew(
    agents=[research_agent],
    tasks=[research_task],
    verbose=True
)

result = crew.kickoff()

## Accessing the task output
task_output = research_task.output

print(task_output)

Learn more in our CrewAI guide.

PydanticAI

PydanticAI is an agent framework created by the Pydantic team to simplify building agent workflows.

Install pydantic-ai

Shell

pip install pydantic-ai
export NAI_API_KEY=***

Build PydanticAI agents using Neosantara AI models

Python

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider

## Connect PydanticAI to LLMs on Neosantara
model = OpenAIChatModel(
    'nusantara-base',
    provider=OpenAIProvider(
        base_url="https://api.neosantara.xyz/v1",
        api_key=os.environ.get("NAI_API_KEY"),
    ),
)

## Setup the agent
agent = Agent(
    model,
    system_prompt='Be concise, reply with one sentence.',
)

result = agent.run_sync('Where does "hello world" come from?')  
print(result.output)

Learn more in our PydanticAI Guide

Arcade.dev

Arcade is a platform that lets AI securely use tools like email, files, and APIs to take real action—not just chat. Build powerful assistants in minutes with ready-to-use integrations or a custom SDK.

Our guide demonstrates how to integrate Neosantara AI’s language models with Arcade’s tools to create an AI agent that can send emails.

Prerequisites:

• Neosantara AI API key - see here https://app.neosantara.xyz/
• Arcade API key - see here https://arcade.dev/
• Gmail account to connect via OAuth

Shell

## install the required packages
!pip install -qU openai arcadepy

Gmail Configuration:

Shell

import os
from arcadepy import Arcade
from openai import OpenAI

## Set environment variables
os.environ["NAI_API_KEY"] = "XXXXXXXXXXXXX"  # Replace with your actual Neosantara API key
os.environ["ARCADE_API_KEY"] = "arc_XXXXXXXXXXX"    # Replace with your actual Arcade API key

## Initialize clients
nai_client = OpenAI(api_key=os.getenv("NAI_API_KEY"), base_url="https://api.neosantara.xyz/v1")
arcade_client = Arcade()  # Automatically finds the ARCADE_API_KEY env variable

## Set up user ID (your email)
USER_ID = "[e-mail]"  # Change this to your email

## Authorize Gmail access
auth_response = arcade_client.tools.authorize(
    tool_name="Google.SendEmail",
    user_id=USER_ID,
)

if auth_response.status != "completed":
    print(f"Click this link to authorize: {auth_response.url}")
    # Wait for the authorization to complete
    arcade_client.auth.wait_for_completion(auth_response)

print("Authorization completed!")

DSPy

DSPy is a framework that enables you to build modular AI systems with code instead of hand-crafted prompting

Install dspy

Shell

pip install -U dspy
export NAI_API_KEY=***

Build a question answering agent

Python

import dspy

#Configure dspy with a LLM from Neosantara AI
lm = dspy.LM('neosantara_ai/archipelago-70b', 
             api_key=os.environ.get("NAI_API_KEY"), 
             api_base="https://api.neosantara.xyz/v1")

#Configure dspy to use the LLM
dspy.configure(lm=lm)

## Gives the agent access to a wikipedia search tool
def search_wikipedia(query: str):
    # Use a more general search or the specific page if known to be reliable
    # For a general search, you might use a different tool or API
    # For this specific case, we stick to the provided URL but simplify the output
    results = dspy.ColBERTv2(url='https://id.m.wikipedia.org/wiki/Prabowo_Subianto')(query, k=1) # Get top 1 result
    # Return the raw text content for the LLM to process
    return results[0]['text'] if results else "No relevant information found."


## setup ReAct module with question and string answer signature
react = dspy.ReAct("question -> answer: str", tools=[search_wikipedia])

pred = react(question="when is prabowo was born?")

print(pred.answer)

Learn more in our DSPy Guide.

AutoGen(AG2)

AG2 (formerly AutoGen) is an open-source framework for building and orchestrating AI agents.

Install autogen

Shell

pip install autogen
export NAI_API_KEY=***

Build a coding agent

Python

import os
from pathlib import Path
from autogen import AssistantAgent, UserProxyAgent
from autogen.coding import LocalCommandLineCodeExecutor

  config_list = [
      {
          # Let's choose the Nusantara-Base model
          "model": "nusantara-base",
          "base_url": "https://api.neosantara.xyz/v1",
          # Provide your Neosantara.xyz API key here or put it into the NAI_API_KEY environment variable.
        "api_key": os.environ.get("NAI_API_KEY"),
          # We specify the API Type as 'openai' so it uses the OpenAI client class
        "api_type": "openai",
        "stream": False,
        "price": [300/1000000*1000, 1500/1000000*1000] # Input: $300/M tokens = $0.3/k tokens, Output: $1500/M tokens = $1.5/k tokens
    }
]

## Setting up the code executor
workdir = Path("coding")
workdir.mkdir(exist_ok=True)
code_executor = LocalCommandLineCodeExecutor(work_dir=workdir)

## Setting up the agents

## The UserProxyAgent will execute the code that the AssistantAgent provides
user_proxy_agent = UserProxyAgent(
    name="User",
    code_execution_config={"executor": code_executor},
    is_termination_msg=lambda msg: "FINISH" in msg.get("content"),
)

system_message = """You are a helpful AI assistant who writes code and the user executes it.
Solve tasks using your coding and language skills.
"""

## The AssistantAgent, using Neosantara's nusantara-base model, will take the coding request and return code
assistant_agent = AssistantAgent(
    name="NAI Coding Assistant",
    system_message=system_message,
    llm_config={"config_list": config_list},
)

## Start the chat, with the UserProxyAgent asking the AssistantAgent the message
chat_result = user_proxy_agent.initiate_chat(
    assistant_agent,
    message="Provide code to count the number of prime numbers from 1 to 10000.",
)

Learn more in our Autogen Guide.

Agno

Agno is an open-source library for creating multimodal agents.

Install agno

Shell

pip install -U agno ddgs

Build a search and answer agent

Python

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from agno.tools.duckduckgo import DuckDuckGoTools

agent = Agent(
    model=OpenAILike(
        id="nusantara-base"
        api_key=os.environ.get("NAI_API_KEY")
        base_url="https://api.neosantara.xyz/v1"
    ),
    tools=[DuckDuckGoTools()],
    markdown=True
)
agent.print_response("What's happening in Indonesia?", stream=True)

Learn more in our Agno Guide.

Pinecone

Pinecone is a vector database that helps companies build RAG applications.

Here’s some sample code to get you started with Pinecone + Neosantara AI:

Python

from pinecone import Pinecone, ServerlessSpec
from openai import OpenAI

pc = Pinecone(
  api_key="PINECONE_API_KEY", 
  source_tag="NEOSANTARA_AI"
)
nai = OpenAI(
    api_key="NAI_API_KEY", 
    base_url="https://api.neosantara.xyz/v1"
)

## Create an index in pinecone
index = pc.create_index(
    name="serverless-index",
    dimension=1536,
    metric="cosine",
    spec=ServerlessSpec(cloud="aws", region="us-west-2"),
)

## Create an embedding on Neosantara AI
textToEmbed = "Our solar system orbits the Milky Way galaxy at about 515,000 mph"
embeddings = nai.embeddings.create(
    model="nusa-embedding-0001", 
  	input=textToEmbed
)

## Use index.upsert() to insert embeddings and index.query() to query for similar vectors