langchain/docs/extras/integrations/providers/bittensor.mdx

# NIBittensor

This page covers how to use the BittensorLLM inference runtime within LangChain.
It is broken into two parts: installation and setup, and then examples of NIBittensorLLM usage.

## Installation and Setup

- Install the Python package with `pip install langchain`

## Wrappers

### LLM

There exists a NIBittensor LLM wrapper, which you can access with:

```python
from langchain.llms import NIBittensorLLM
```

It provides a unified interface for all models:

```python
llm = NIBittensorLLM(system_prompt="Your task is to provide concise and accurate response based on user prompt")

print(llm('Write a fibonacci function in python with golder ratio'))
```

Multiple responses from top miners can be accessible using the `top_responses` parameter:

```python
multi_response_llm = NIBittensorLLM(top_responses=10)
multi_resp = multi_response_llm("What is Neural Network Feeding Mechanism?")
json_multi_resp = json.loads(multi_resp)

print(json_multi_resp)
```