Framework Integrations

from value import initialize_sync, auto_instrument

client = initialize_sync(agent_secret="your_agent_secret")
auto_instrument()  # Instruments all available libraries

pip install value-python[genai]

from value import initialize_sync, auto_instrument
from google import genai

client = initialize_sync(agent_secret="your_agent_secret")
auto_instrument(["gemini"])

# Calls are automatically traced
gemini_client = genai.Client(api_key="your-key")
response = gemini_client.models.generate_content(
    model="gemini-2.5-flash",
    contents=["Write a haiku"]
)

pip install value-python[langchain]

from value import initialize_sync, auto_instrument
from langchain_openai import ChatOpenAI

client = initialize_sync(agent_secret="your_agent_secret")
auto_instrument(["langchain"])

# LangChain calls are automatically traced
llm = ChatOpenAI(model="gpt-4")
response = llm.invoke("Hello")

from value import initialize_sync

client = initialize_sync(agent_secret="your_agent_secret")

def my_agent_function(user_input):
    with client.action_context(user_id="user_123", anonymous_id="session_abc") as ctx:
        # Your agent logic
        result = process(user_input)
        
        ctx.send(
            action_name="agent_execution",
            **{
                "value.action.description": "Processed user request",
                "input_length": len(user_input),
            }
        )
        return result

from value import get_supported_libraries, is_library_available

# List all supported libraries
print(get_supported_libraries())  # ['gemini', 'langchain']

# Check if a library's instrumentation is installed
print(is_library_available("gemini"))  # True/False