# API Integeration

OpenLedger enables seamless interaction with custom-trained large language models through secure API endpoints and a flexible chat interface. This guide outlines how developers can access, authenticate, and manage their AI agents via the OpenLedger proxy infrastructure.

### Python Integration <a href="#r528vc5zhy5b" id="r528vc5zhy5b"></a>

<figure><img src="/files/exdJPWdXWINvuymiCJaz" alt=""><figcaption></figcaption></figure>

This example demonstrates how to connect to an OpenLedger-hosted model using the OpenAI Python client.

**Usage**

* Set the base\_url to your OpenLedger proxy endpoint.
* Provide your api\_key for authorization.
* Specify the full model path, including adapter and version.

This method is recommended for backend services or scripts using Python.

### Curl <a href="#jc4tq2lz3zv5" id="jc4tq2lz3zv5"></a>

<figure><img src="/files/9ukWK13kctZ9WR311IqK" alt=""><figcaption></figcaption></figure>

This is a raw HTTP example using curl for environments where SDKs are not preferred.

**Usage**

* Define the POST request with headers for Content-Type and Authorization.
* Include your model path and message payload directly in the request body.

This method is useful for testing, automation scripts, and CLI environments.

### JavaScript / Node.js Integration <a href="#id-98mv61vxh7z7" id="id-98mv61vxh7z7"></a>

<figure><img src="/files/2sqtaGyBrgWK0VcOeVIt" alt=""><figcaption></figcaption></figure>

Use the OpenAI client library for JavaScript to integrate OpenLedger-hosted models in frontend or Node.js environments.

**Usage**

* Initialize the client with your API key and baseURL.
* Call the chat.completions.create() method with the model path and user input.
* Fully async/await compatible.

Ideal for web applications, bots, or services requiring browser-compatible interaction.

### Completion <a href="#xatjf3a9bjzw" id="xatjf3a9bjzw"></a>

With **OpenLedger**, users can:

* Build and contribute to Datanets
* Train and deploy models
* Interact and earn through tokenized chat
* Guide ecosystem direction via governance

All actions are on-chain, ensuring verifiability, transparency, and community ownership across the AI-data lifecycle.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://openledger.gitbook.io/openledger/product-walkthrough/api-integeration.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.