API Integration

OpenLedger provides a streamlined infrastructure for accessing custom-trained AI models through secure API endpoints and an intuitive chat interface. Developers can seamlessly connect, authenticate, and manage their AI agents using OpenLedger’s proxy architecture.

Python Integration

Integrate OpenLedger-hosted models using the OpenAI Python SDK.

How It Works

  • Configure the base_url to point to your OpenLedger proxy endpoint.

  • Authenticate requests using your API key.

  • Specify the complete model identifier, including adapter and version details.

  • Send prompts and receive responses through standard chat completion methods.

This approach is ideal for backend applications, automated workflows, and Python-based services.

CURL Integration

For environments where SDKs are unnecessary, OpenLedger also supports direct HTTP requests through cURL.

How It Works

  • Create a POST request targeting the OpenLedger endpoint.

  • Include Content-Type and Authorization headers.

  • Pass the model identifier and message payload within the request body.

  • Receive responses in a standard JSON format.

This method is particularly useful for testing, automation pipelines, and command-line workflows.

JavaScript / Node.js Integration

Developers can leverage the OpenAI JavaScript SDK to interact with OpenLedger-hosted models across web and server environments.

How It Works

  • Initialize the client with your API key and OpenLedger base URL.

  • Use the chat.completions.create() method to submit prompts.

  • Specify the target model path and input message.

  • Handle responses using modern async/await patterns.

This integration is well suited for web applications, AI assistants, bots, and browser-compatible services.

Completion

OpenLedger enables users to participate across the entire AI value chain through a unified ecosystem.

Users can:

  • Create, manage, and contribute to Datanets.

  • Train, fine-tune, and deploy AI models.

  • Engage with tokenized AI applications and earn rewards.

  • Participate in governance and influence ecosystem development.

Every interaction is recorded on-chain, providing transparency, verifiability, and community ownership throughout the AI, data, and model lifecycle.

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