Quickstart

import decimalai

decimalai.init(
    api_key="dai_sk_...",
    openai_agents=True,
)

result = await Runner.run(agent, "Hello!")

import decimalai

decimalai.init(
    api_key="dai_sk_...",
    llamaindex=True,
)

# Query engines, retrievers, and LLM calls are traced automatically
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex.from_documents(documents)
response = index.as_query_engine().query("What is the revenue?")

import decimalai

decimalai.init(
    api_key="dai_sk_...",
    crewai=True,
)

# CrewAI agent tasks, tool calls, and LLM interactions are traced
from crewai import Crew
crew = Crew(agents=[...], tasks=[...])
result = crew.kickoff()

import decimalai

decimalai.init(
    api_key="dai_sk_...",
    autogen=True,
)

# AutoGen agent conversations and LLM calls are traced
from autogen import AssistantAgent
agent = AssistantAgent("assistant", llm_config={...})
result = await agent.run(task="Analyze this data")

import decimalai

decimalai.init(api_key="dai_sk_...")

@decimalai.trace(agent_name="my-agent")
def run_agent(query: str):
    result = call_llm(query)
    return result

run_agent("Hello!")

# Zero code changes — just set env vars
export DECIMAL_API_KEY="dai_sk_..."

# Supported values: langchain, openai-agents, llamaindex, crewai, autogen
export DECIMAL_AUTO_TRACE=langchain

python my_agent.py