LangChain & LangGraph

The LangChain integration installs as a LangChain callback handler. Every chain invoke, every LangGraph node, every tool call inside an AgentExecutor or ReAct loop is captured automatically.

Install

pip install "decimalai[langchain]"

Use

There are two ways to attach the handler. Process-wide instrumentation is the shorter one; a per-call handler is the one to reach for when you need an isolated trace.

Instrument the process

Every LangChain call in the process is traced from this point on.

import decimalai
decimalai.init(api_key="dai_sk_...", langchain=True)

# Or attach it yourself:
from decimalai.langchain import instrument
instrument(agent_name="my-chain")

chain.invoke({"input": "Hello!"})

This entry point was called install() in 0.10.0 and earlier. It was renamed to instrument() in 0.10.2 — same arguments, same behaviour — because install had come to mean something else entirely: adding a skill to your workspace. See Vocabulary.install() still works and emits a DeprecationWarning, so existing code keeps running. decimalai.init(langchain=True) is unaffected on every version.

instrument() returns early if the process is already instrumented, so a later call with different arguments is a no-op rather than a reconfiguration. Setting DECIMAL_AUTO_TRACE or a bare DECIMAL_API_KEY instruments at import, which means an explicit instrument(...) after that point will not take effect.

Pass a handler per call

Construct the handler yourself and hand it to a single run. Use this when two runs share a process and their traces must stay separate.

import decimalai
from decimalai.langchain import CallbackHandler
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

decimalai.init(api_key="dai_sk_...")   # or set DECIMAL_API_KEY

handler = CallbackHandler(agent_name="joke-bot")

llm = ChatOpenAI(model="gpt-4o")
prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
chain = prompt | llm

response = chain.invoke({"topic": "cats"}, config={"callbacks": [handler]})

A trace is sent when a chain, agent, or graph run completes — that is, on the outermost on_chain_end. Calling a bare llm.invoke("Hello!") with no surrounding chain never reaches that boundary and sends nothing.

LangGraph

The same handler traces a compiled graph. Each node becomes a span, with LLM and tool calls nested underneath it.

from decimalai.langchain import CallbackHandler

handler = CallbackHandler(agent_name="support-graph")

graph = builder.compile()
result = graph.invoke(
    {"messages": [("user", "Where is my order?")]},
    config={"callbacks": [handler]},
)

Multi-agent runs

Link a sub-agent’s trace to its orchestrator to get delegation analytics and subagent-health surfaces.

parent = CallbackHandler(agent_name="orchestrator", subagents=[{"name": "researcher"}])
child = CallbackHandler(agent_name="researcher", parent_trace_id=parent.get_trace_id())

What gets captured

Chain / agent execution with all intermediate steps
LLM calls with prompt templates fully resolved (not raw {variable} placeholders)
Tool calls from AgentExecutor, ReAct loops, or LangGraph nodes
Auto-detected manifest from tool schemas + model config

Framework noise filtering

LangGraph and LangChain emit a lot of internal spans (call_model, should_continue, Prompt, RunnableSequence, etc.). The trace UI auto-collapses these by default; toggle “Show internals” in the waterfall header to reveal them.

Overriding dynamic prompts

If your prompts are built at runtime (not stored in the chain config), pass them to instrument():

from decimalai.langchain import instrument

instrument(
    agent_name="my-chain",
    prompts={"system": "You are an assistant for ACME Corp."},
)

Python SDK

CLI

LangChain & LangGraph

Install

Use

Instrument the process

Pass a handler per call

LangGraph

Multi-agent runs

What gets captured

Framework noise filtering

Overriding dynamic prompts

What’s next

Tracing guide

Manifests

​Install

​Use

​Instrument the process

​Pass a handler per call

​LangGraph

​Multi-agent runs

​What gets captured

​Framework noise filtering

​Overriding dynamic prompts

​What’s next

Tracing guide

Manifests

Install

Use

Instrument the process

Pass a handler per call

LangGraph

Multi-agent runs

What gets captured

Framework noise filtering

Overriding dynamic prompts

What’s next