> ## Documentation Index
> Fetch the complete documentation index at: https://www.traceloop.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Getting Started with Traceloop Hub

> Set up Hub as a smart proxy to all your LLM calls.

Hub is a next generation smart proxy for LLM applications. It centralizes control and tracing of all LLM calls and traces.
It's built in Rust so it's fast and efficient. It's completely open-source and free to use.

## Installation

### Local

1. Clone the repo:

```bash theme={null}
git clone https://github.com/traceloop/hub
```

2. Copy the `config-example.yaml` file to `config.yaml` and set the correct values (see below for more information).

3. Run the hub by running `cargo run` in the root directory.

### With Docker

Traceloop Hub is available as a docker image named `traceloop/hub`. Make sure to create a `config.yaml` file
following the [configuration](./configuration) instructions.

```bash theme={null}
docker run --rm -p 3000:3000 -v $(pwd)/config.yaml:/etc/hub/config.yaml:ro -e CONFIG_FILE_PATH='/etc/hub/config.yaml'  -t traceloop/hub
```

## Connecting to Hub

After running the hub and [configuring it](./configuration), you can start using it to invoke available LLM providers.
Its API is the standard OpenAI API, so you can use it as a drop-in replacement for your LLM calls.

You can invoke different pipelines by passing the `x-traceloop-pipeline` header. If none is specified, the default pipeline will be used.

```python theme={null}
import openai

client = OpenAI(
    base_url="http://localhost:3000/api/v1",
    # default_headers={"x-traceloop-pipeline": "optional-pipeline-name"},
)
```