!export OPENGRADIENT_PRIVATE_KEY="your-api-key"

!pip install opengradient
!opengradient config init

%pip install-qU langchain-opengradient

from langchain_opengradientimport OpenGradientToolkit

toolkit= OpenGradientToolkit(
# Not required if you have already set the environment variable OPENGRADIENT_PRIVATE_KEY
    private_key="your-api-key"
)

import opengradientas og
from pydanticimport BaseModel, Field


# Example 1: Simple tool with no input schema
defprice_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],
]
}


defformat_volatility(inference_result):
"""Function that formats the model output."""
returnformat(float(inference_result.model_output["Y"].item()),".3%")


# Create the tool
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,
)


# Example 2: Tool with input schema from the agent
classTokenInputSchema(BaseModel):
    token:str= Field(description="Token name (ethereum or bitcoin)")


deftoken_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:# ethereum
return{"price_series":[2010.1,2012.3,2020.1,2019.2]}


# Create the tool with schema
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",
)

# Add tools to the toolkit
toolkit.add_tool(volatility_tool)
toolkit.add_tool(token_tool)

# Create a tool to read from a workflow
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%')}",
)

# Add the tool to the toolkit
toolkit.add_tool(forecast_tool)

tools= toolkit.get_tools()

# View tools
for toolin tools:
print(tool)

from langchain_openaiimport ChatOpenAI
from langgraph.prebuiltimport create_react_agent

# Initialize LLM
llm= ChatOpenAI(model="gpt-4o")

# Create tools from the toolkit
tools= toolkit.get_tools()

# Create agent
agent_executor= create_react_agent(llm, tools)

# Example query for the agent
example_query="What's the current volatility of ETH?"

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

================================ 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%.