OpenGradient Python SDK

A Python SDK for decentralized model management and inference services on the OpenGradient platform. The SDK enables programmatic access to our model repository and decentralized AI infrastructure.

Key Features

• Model management and versioning
• Decentralized model inference
• Support for LLM inference with various models
• Trusted Execution Environment (TEE) inference with cryptographic attestation
• End-to-end verified AI execution
• Command-line interface (CLI) for direct access

Model Hub

Browse and discover AI models on our Model Hub. The Hub provides:

• Registry of models and LLMs
• Easy model discovery and deployment
• Direct integration with the SDK

Installation

pip install opengradient

Note: Windows users should temporarily enable WSL when installing opengradient (fix in progress).

Getting Started

1. Account Setup

You'll need:

• Private key: An Ethereum-compatible wallet private key for OpenGradient transactions
• Model Hub account (optional): Only needed for uploading models. Create one at Hub Sign Up

The easiest way to set up your configuration is through our wizard:

opengradient config init

2. Initialize the Client

import os
import opengradient as og

og_client = og.new_client(
    email=None,  # Optional: only needed for model uploads
    password=None,
    private_key=os.environ.get("OG_PRIVATE_KEY"),
)

3. Basic Usage

LLM Chat

completion = og_client.llm_chat(
    model_cid=og.TEE_LLM.GPT_4O,
    messages=[{"role": "user", "content": "Hello!"}],
    inference_mode=og.LlmInferenceMode.TEE,
)
print(f"Response: {completion.chat_output['content']}")
print(f"Tx hash: {completion.transaction_hash}")

Custom Model Inference

Browse models on our Model Hub or upload your own:

result = og_client.infer(
    model_cid="your-model-cid",
    model_input={"input": [1.0, 2.0, 3.0]},
    inference_mode=og.InferenceMode.VANILLA,
)
print(f"Output: {result.model_output}")

4. TEE (Trusted Execution Environment) Inference

OpenGradient supports secure, verifiable inference through TEE for leading LLM providers. Access models from OpenAI, Anthropic, Google, and xAI with cryptographic attestation:

# Use TEE mode for verifiable AI execution
completion = og_client.llm_chat(
    model_cid=og.TEE_LLM.CLAUDE_3_7_SONNET,
    messages=[{"role": "user", "content": "Your message here"}],
    inference_mode=og.LlmInferenceMode.TEE,
)
print(f"Response: {completion.chat_output['content']}")

Available TEE Models: The SDK includes models from multiple providers accessible via the og.TEE_LLM enum:

• OpenAI: GPT-4.1, GPT-4o, o4-mini
• Anthropic: Claude 3.7 Sonnet, Claude 3.5 Haiku, Claude 4.0 Sonnet
• Google: Gemini 2.5 Flash, Gemini 2.5 Pro, Gemini 2.0 Flash
• xAI: Grok 3 Beta, Grok 3 Mini Beta, Grok 4.1 Fast

For the complete list, check the og.TEE_LLM enum in your IDE or see the API documentation.

5. Examples

See code examples under examples.

CLI Usage

The SDK includes a command-line interface for quick operations. First, verify your configuration:

opengradient config show

Run a test inference:

opengradient infer -m QmbUqS93oc4JTLMHwpVxsE39mhNxy6hpf6Py3r9oANr8aZ \
    --input '{"num_input1":[1.0, 2.0, 3.0], "num_input2":10}'

Use Cases

1. Off-chain Applications: Use OpenGradient as a decentralized alternative to centralized AI providers like HuggingFace and OpenAI.

2. Verifiable AI Execution: Leverage TEE inference for cryptographically attested AI outputs, enabling trustless AI applications.

3. Model Development: Manage models on the Model Hub and integrate directly into your development workflow.

Documentation

For comprehensive documentation, API reference, and examples, visit:

• OpenGradient Documentation
• API Reference

Claude Code Users

If you use Claude Code, copy docs/CLAUDE_SDK_USERS.md to your project's CLAUDE.md to help Claude assist you with OpenGradient SDK development.

Support

• Run opengradient --help for CLI command reference
• Visit our documentation for detailed guides
• Join our community for support