OpenGradient Docs

Appearance

opengradient

Package opengradient ​

Version: 0.6.0

OpenGradient Python SDK for decentralized AI inference with end-to-end verification.

Overview ​

The OpenGradient SDK provides programmatic access to decentralized AI infrastructure, including:

  • LLM Inference -- Chat and completion with major LLM providers (OpenAI, Anthropic, Google, xAI) through TEE-verified execution
  • On-chain Model Inference -- Run ONNX models via blockchain smart contracts with VANILLA, TEE, or ZKML verification
  • Model Hub -- Create, version, and upload ML models to the OpenGradient Model Hub

All LLM inference runs inside Trusted Execution Environments (TEEs) and settles on-chain via the x402 payment protocol, giving you cryptographic proof that inference was performed correctly.

Quick Start ​

python
import opengradient as og

# Initialize the client
client = og.init(private_key="0x...")

# Chat with an LLM (TEE-verified)
response = client.llm.chat(
    model=og.TEE_LLM.CLAUDE_3_5_HAIKU,
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=200,
)
print(response.chat_output)

# Stream a response
for chunk in client.llm.chat(
    model=og.TEE_LLM.GPT_4O,
    messages=[{"role": "user", "content": "Explain TEE in one paragraph."}],
    max_tokens=300,
    stream=True,
):
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

# Run on-chain ONNX model inference
result = client.alpha.infer(
    model_cid="your_model_cid",
    inference_mode=og.InferenceMode.VANILLA,
    model_input={"input": [1.0, 2.0, 3.0]},
)
print(result.model_output)

Client Namespaces ​

The Client object exposes three namespaces:

  • llm -- LLM chat and completion
  • alpha -- On-chain ONNX model inference, workflow deployment, and scheduled ML model execution (only available on the Alpha Testnet)
  • model_hub -- Model repository management

Model Hub (requires email auth) ​

python
client = og.init(
    private_key="0x...",
    email="you@example.com",
    password="...",
)

repo = client.model_hub.create_model("my-model", "A price prediction model")
client.model_hub.upload("model.onnx", repo.name, repo.initialVersion)

Framework Integrations ​

The SDK includes adapters for popular AI frameworks -- see the agents submodule for LangChain and OpenAI integration.

Submodules ​

  • agents: OpenGradient Agent Framework Adapters
  • alphasense: OpenGradient AlphaSense Tools
  • client: OpenGradient Client -- the central entry point to all SDK services.
  • types: OpenGradient Specific Types
  • workflow_models: OpenGradient Hardcoded Models

Functions ​

init() ​

python
def init(private_key: str, email: Optional[str] = None, password: Optional[str] = None, **kwargs) ‑> `Client`

Initialize the global OpenGradient client.

This is the recommended way to get started. It creates a Client instance and stores it as the global client for convenience.

Arguments

  • private_key: Private key for OpenGradient transactions.
  • email: Email for Model Hub authentication. Optional.
  • password: Password for Model Hub authentication. Optional. **kwargs: Additional arguments forwarded to Client.

Returns

The newly created Client instance.

Classes ​

Client ​

Main OpenGradient SDK client.

Provides unified access to all OpenGradient services including LLM inference, on-chain model inference, and the Model Hub. Handles authentication via blockchain private key and optional Model Hub credentials.

Constructor ​

python
def __init__(private_key: str, email: Optional[str] = None, password: Optional[str] = None, rpc_url: str = 'https://ogevmdevnet.opengradient.ai', api_url: str = 'https://sdk-devnet.opengradient.ai', contract_address: str = '0x8383C9bD7462F12Eb996DD02F78234C0421A6FaE', og_llm_server_url: Optional[str] = 'https://llmogevm.opengradient.ai', og_llm_streaming_server_url: Optional[str] = 'https://llmogevm.opengradient.ai')

Arguments

  • private_key: Private key for OpenGradient transactions.
  • email: Email for Model Hub authentication. Optional.
  • password: Password for Model Hub authentication. Optional.
  • rpc_url: RPC URL for the blockchain network.
  • api_url: API URL for the OpenGradient API.
  • contract_address: Inference contract address.
  • og_llm_server_url: OpenGradient LLM server URL.
  • og_llm_streaming_server_url: OpenGradient LLM streaming server URL.

Variables ​

  • alpha: Alpha Testnet features including on-chain inference, workflow management, and ML model execution.
  • llm: LLM chat and completion via TEE-verified execution.
  • model_hub: Model Hub for creating, versioning, and uploading ML models.

InferenceMode ​

Enum for the different inference modes available for inference (VANILLA, ZKML, TEE)

Variables ​

  • static TEE
  • static VANILLA
  • static ZKML

TEE_LLM ​

Enum for LLM models available for TEE (Trusted Execution Environment) execution.

TEE mode provides cryptographic verification that inference was performed correctly in a secure enclave. Use this for applications requiring auditability and tamper-proof AI inference.

Variables ​

  • static CLAUDE_3_5_HAIKU
  • static CLAUDE_3_7_SONNET
  • static CLAUDE_4_0_SONNET
  • static GEMINI_2_0_FLASH
  • static GEMINI_2_5_FLASH
  • static GEMINI_2_5_FLASH_LITE
  • static GEMINI_2_5_PRO
  • static GPT_4O
  • static GPT_4_1_2025_04_14
  • static GROK_2_1212
  • static GROK_2_VISION_LATEST
  • static GROK_3_BETA
  • static GROK_3_MINI_BETA
  • static GROK_4_1_FAST
  • static GROK_4_1_FAST_NON_REASONING
  • static O4_MINI