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Showing content from https://python.langchain.com/docs/integrations/tools/opengradient_toolkit below:

OpenGradientToolkit | 🦜️🔗 LangChain

This notebook shows how to build tools using the OpenGradient toolkit. This toolkit gives users the ability to create custom tools based on models and workflows on the OpenGradient network.

Ensure that you have an OpenGradient API key in order to access the OpenGradient network. If you already have an API key, simply set the environment variable:

!export OPENGRADIENT_PRIVATE_KEY="your-api-key"

If you need to set up a new API key, download the opengradient SDK and follow the instructions to initialize a new configuration.

!pip install opengradient
!opengradient config init

This toolkit lives in the langchain-opengradient package:

%pip install -qU langchain-opengradient

Now we can instantiate our toolkit with the API key from before.

from langchain_opengradient import OpenGradientToolkit

toolkit = OpenGradientToolkit(
    
    private_key="your-api-key"
)

The OpenGradientToolkit offers two main methods for creating custom tools:

You can create tools that leverage ML models deployed on the OpenGradient model hub. User-created models can be uploaded, inferenced, and shared to the model hub through the OpenGradient SDK.

import opengradient as og
from pydantic import BaseModel, Field



def price_data_provider():
    """Function that provides input data to the model."""
    return {
        "open_high_low_close": [
            [2535.79, 2535.79, 2505.37, 2515.36],
            [2515.37, 2516.37, 2497.27, 2506.94],
            [2506.94, 2515, 2506.35, 2508.77],
            [2508.77, 2519, 2507.55, 2518.79],
            [2518.79, 2522.1, 2513.79, 2517.92],
            [2517.92, 2521.4, 2514.65, 2518.13],
            [2518.13, 2525.4, 2517.2, 2522.6],
            [2522.59, 2528.81, 2519.49, 2526.12],
            [2526.12, 2530, 2524.11, 2529.99],
            [2529.99, 2530.66, 2525.29, 2526],
        ]
    }


def format_volatility(inference_result):
    """Function that formats the model output."""
    return format(float(inference_result.model_output["Y"].item()), ".3%")



volatility_tool = toolkit.create_run_model_tool(
    model_cid="QmRhcpDXfYCKsimTmJYrAVM4Bbvck59Zb2onj3MHv9Kw5N",
    tool_name="eth_volatility",
    model_input_provider=price_data_provider,
    model_output_formatter=format_volatility,
    tool_description="Generates volatility measurement for ETH/USDT trading pair",
    inference_mode=og.InferenceMode.VANILLA,
)



class TokenInputSchema(BaseModel):
    token: str = Field(description="Token name (ethereum or bitcoin)")


def token_data_provider(**inputs):
    """Dynamic function that changes behavior based on agent input."""
    token = inputs.get("token")
    if token == "bitcoin":
        return {"price_series": [100001.1, 100013.2, 100149.2, 99998.1]}
    else:  
        return {"price_series": [2010.1, 2012.3, 2020.1, 2019.2]}



token_tool = toolkit.create_run_model_tool(
    model_cid="QmZdSfHWGJyzBiB2K98egzu3MypPcv4R1ASypUxwZ1MFUG",
    tool_name="token_volatility",
    model_input_provider=token_data_provider,
    model_output_formatter=lambda x: format(float(x.model_output["std"].item()), ".3%"),
    tool_input_schema=TokenInputSchema,
    tool_description="Measures return volatility for a specified token",
)


toolkit.add_tool(volatility_tool)
toolkit.add_tool(token_tool)

Read workflows are scheduled inferences that regularly run models stored on smart-contracts with live oracle data. More information on these can be found here.

You can create tools that read results from workflow smart contracts:

forecast_tool = toolkit.create_read_workflow_tool(
    workflow_contract_address="0x58826c6dc9A608238d9d57a65bDd50EcaE27FE99",
    tool_name="ETH_Price_Forecast",
    tool_description="Reads latest forecast for ETH price from deployed workflow",
    output_formatter=lambda x: f"Price change forecast: {format(float(x.numbers['regression_output'].item()), '.2%')}",
)


toolkit.add_tool(forecast_tool)

Use the built in get_tools() method to view a list of the available tools within the OpenGradient toolkit.

tools = toolkit.get_tools()


for tool in tools:
    print(tool)

Here's how to use your OpenGradient tools with a LangChain agent:

from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent


llm = ChatOpenAI(model="gpt-4o")


tools = toolkit.get_tools()


agent_executor = create_react_agent(llm, tools)


example_query = "What's the current volatility of ETH?"


events = agent_executor.stream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

Here's a sample output of everything put together:

================================ Human Message =================================

What's the current volatility of ETH?
================================== Ai Message ==================================
Tool Calls:
  eth_volatility (chatcmpl-tool-d66ab9ee8f2c40e5a2634d90c7aeb17d)
 Call ID: chatcmpl-tool-d66ab9ee8f2c40e5a2634d90c7aeb17d
  Args:
================================= Tool Message =================================
Name: eth_volatility

0.038%
================================== Ai Message ==================================

The current volatility of the ETH/USDT trading pair is 0.038%.

See the Github page for more detail.

• Tool conceptual guide
• Tool how-to guides

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