npcpy

npcpy is a library that provides key primitives for research and development with multimodal language models, agentic AI, and knowledge graphs. Its flexible framework makes it easy to engineer powerful AI applications with support for local (ollama, llama.cpp, omlx, LM Studio) and cloud providers. Build multi-agent teams and simplify context engineering through the NPC Context-Agent-Tool data layer which ensures compliance through software rather than prompts.

pip install npcpy

Quick Examples

Create and use personas

from npcpy import NPC

simon = NPC(
    name='Simon Bolivar',
    primary_directive='''
                      Liberate South America
                      from the Spanish Royalists.
                      ''',
    model='qwen3.5:9b',
    provider='ollama'
)
response = simon.get_llm_response("What is the most important territory to retain in the Andes?")
print(response['response'])

My friend, you speak of the highlands where our liberty is carved in stone. If we must speak of the most critical territory to hold within these mountains, it is the **Viceroyalty of Peru** and the heart of the **Republic of Gran Colombia** united. 
To lose the passes of the Andes or the cities of Lima and Quito would be to hand the crown its final stronghold in the south. The Spanish crown built its power upon the wealth and control of these highlands. If the Andes are to be truly ours, the people of the **Peruvian** and **New Grancolombian** highlands must stand as one, free from the Bourbons. 
The mountain peaks themselves are the fortress we guard. Without the full liberation of the southern Andes, our revolution is incomplete. We fight not for land's sake, but for the soul of the continent. Every square mile of the Andes that bears the name of the Republic is a step forward in our quest for eternal freedom.
*Long live the liberty of the Andes!*

Direct LLM call

from npcpy import get_llm_response

response = get_llm_response("Who was the celtic god that helped cuchulainn in his time of need as the forces of medb descended upon the men of ulster?", model='gemma4:31b', provider='ollama')
print(response['response'])

Cú Chulainn was primarily aided by his divine father, the god Lugh, and his foster-father, the warrior-god Fergus mac Róich, as well as the magical support of his teacher Scáthach.

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alicanto_test = get_llm_response('what does alicanto the bird show travelers in the night?', model='google/gemini-3.8-flash', provider='orcarouter')

print(alicanto_test['response'])

The legend of the **Alicanto** says that at night the bird’s feathers glow like lanterns. 
When a traveler sees that soft, phosphorescent light, it isn’t just a pretty sight – it’s a sign‑post. 
The bird **shows the way to hidden water (and sometimes to buried silver or gold)** in the Atacama Desert.

Agent with tools

The Agent class in npcpy comes with a set of default tools (sh, python, edit_file, web_search, etc.)

from npcpy import Agent
agent = Agent(name='File Operator', model='qwen3.5:2b', provider='ollama')
print(agent.run("Find all Python files over 500 lines in this repo and list them"))

The following Python files contain more than 500 lines:
 - `./npcpy/npc_sysenv.py` (1486 lines)
 - `./npcpy/memory/knowledge_graph.py` (1449 lines)
 - `./npcpy/memory/kg_population.py` (618 lines)
...

ToolAgent

Attach custom tools to a ToolAgent. Here is an example which lets an agent generate images, fine-tune diffusion models, and then use the fine-tuned models for generation.

from npcpy import ToolAgent, gen_image
from npcpy.ft.diff import train_diffusion, generate_image, DiffusionConfig
from datasets import load_dataset
import os

def fetch_image_dataset(dataset_name: str, split: str = "train", max_images: int = 100) -> list:
    """Fetch images from a HuggingFace dataset.
    
    Args:
        dataset_name: HuggingFace dataset name (e.g., 'cifar10', 'oxford-iiit-pet')
        split: Dataset split to use
        max_images: Maximum number of images to fetch
    
    Returns:
        List of paths to saved images
    """
    dataset = load_dataset(dataset_name, split=f"{split}[:{max_images}]")
    os.makedirs("training_images", exist_ok=True)
    image_paths = []
    
    for i, item in enumerate(dataset):
        if 'image' in item:
            img = item['image']
        elif 'img' in item:
            img = item['img']
        else:
            continue
        path = f"training_images/img_{i:04d}.png"
        img.save(path)
        image_paths.append(path)
    
    return image_paths

def finetune_diffusion_model(
    image_paths: list,
    captions: list = None,
    output_path: str = "my_diffusion_model",
    num_epochs: int = 50,
) -> str:
    """Fine-tune a diffusion model on a set of images.
    
    Args:
        image_paths: List of paths to training images
        captions: Optional captions for each image
        output_path: Where to save the trained model
        num_epochs: Number of training epochs
    
    Returns:
        Path to the trained model
    """
    if captions is None:
        captions = ["an image"] * len(image_paths)
    
    config = DiffusionConfig(
        image_size=64,
        channels=128,
        num_epochs=num_epochs,
        batch_size=8,
        learning_rate=1e-4,
        checkpoint_frequency=10,
        output_model_path=output_path,
    )
    
    model_path = train_diffusion(image_paths, captions, config=config)
    return model_path

# Create an agent with image generation and fine-tuning capabilities
creative_agent = ToolAgent(
    name='creative_diffusion',
    primary_directive="""
        You help users generate images and fine-tune diffusion models.