Quickstart

pip install "decimalai[langchain]"

import decimalai
from decimalai.langchain import instrument
from langchain_core.messages import HumanMessage

decimalai.init()  # reads DECIMAL_API_KEY, exported in step 2

# Traces every run under this agent's name, and turns on the skill
# loader, which routes the agent's skills into each chat-model call.
instrument(agent_name="support-agent", enable_skill_loader=True)

# Run your agent as usual
agent.invoke({"messages": [HumanMessage(content="Hello!")]})

pip install "decimalai[openai-agents]"

import decimalai
from decimalai.openai_agents import instrument
from agents import Agent, Runner

decimalai.init()  # reads DECIMAL_API_KEY, exported in step 2

# Installs the trace processor once and turns on the skill loader: the
# skills menu goes into the agent's instructions and a load_skill tool
# lets the model fetch a skill's full body.
instrument(agent_name="support-agent", enable_skill_loader=True)

# The Agent's name decides which skills it is offered and which agent
# its traces are filed under, so use your DecimalAI agent's name.
agent = Agent(name="support-agent", instructions="You are a support agent.")
result = await Runner.run(agent, "Hello!")

pip install "decimalai[claude-agent-sdk]"

import decimalai

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

# Every query() stream is traced automatically — model turns,
# tool calls, token usage, cost
from claude_agent_sdk import query, ClaudeAgentOptions
async for message in query(prompt="Hello!", options=ClaudeAgentOptions()):
    ...

pip install "decimalai[llamaindex]"

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?")

pip install "decimalai>=0.14" "crewai>=1.15.27" "openinference-instrumentation-crewai>=1.1.21" openinference-instrumentation-openai

import decimalai
from decimalai.crewai import instrument

decimalai.init()  # reads DECIMAL_API_KEY, exported in step 2

# Turns on CrewAI tracing and the skill loader, which puts the agent's
# skills into every agent turn. Call it before the first kickoff().
instrument(agent_name="support-agent", enable_skill_loader=True)

result = crew.kickoff()   # `crew` is your existing Crew
decimalai.flush()         # short-lived script: send the trace before exit

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

# Frameworks: langchain, openai-agents, adk, llamaindex, crewai, otel,
#             autogen   (an alias for otel; AutoGen/AG2 is no longer
#             an integration — instrument its agents yourself)
# Raw provider SDKs (no framework): openai, anthropic, google
export DECIMAL_AUTO_TRACE=langchain
# export DECIMALAI_SKILLS=0   # optional: trace only, deliver no skills

python my_agent.py