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Documentation Index

Fetch the complete documentation index at: https://docs.lumiqtrace.com/llms.txt

Use this file to discover all available pages before exploring further.

The LumiqtraceCallbackHandler integrates with LangChain’s callback system to automatically capture every LLM call, tool invocation, chain step, and retrieval operation as linked spans in LumiqTrace. You pass it once and it instruments the entire execution graph — no changes to your chain or agent logic are needed.

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How it works

LangChain calls lifecycle methods on every registered BaseCallbackHandler at each stage of execution. The LumiqTrace handler subscribes to:
LangChain eventWhat LumiqTrace captures
handleLLMStart / handleLLMEndModel, provider, latency, tokens, cost, finish reason
handleChainStart / handleChainEndChain type, input/output, duration
handleToolStart / handleToolEndTool name, input args, output, duration
handleRetrieverStart / handleRetrieverEndQuery, retrieved document count, duration
handleAgentActionTool selected, reasoning, input
All events share the same trace_id so you see the entire chain as a single flame graph in the Traces view.

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TypeScript

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Installation

npm install @lumiqtrace/sdk @langchain/core @langchain/openai

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Basic usage

import { lumiqtrace, LumiqtraceCallbackHandler } from "@lumiqtrace/sdk";
import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage, SystemMessage } from "@langchain/core/messages";

lumiqtrace.init({ apiKey: process.env.LUMIQTRACE_API_KEY! });

const handler = new LumiqtraceCallbackHandler();

const llm = new ChatOpenAI({
  model: "gpt-4o",
  callbacks: [handler],
});

const response = await llm.invoke([
  new SystemMessage("You are a helpful assistant."),
  new HumanMessage("Summarize the history of the internet in three sentences."),
]);

console.log(response.content);

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Chains

Pass the handler to RunnableSequence or any chain using the config argument: 
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { RunnableSequence } from "@langchain/core/runnables";

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are an expert summarizer. Be concise."],
  ["human", "{topic}"],
]);

const chain = RunnableSequence.from([
  prompt,
  new ChatOpenAI({ model: "gpt-4o" }),
  new StringOutputParser(),
]);

const result = await chain.invoke(
  { topic: "quantum computing" },
  { callbacks: [new LumiqtraceCallbackHandler()] }
);

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Agents and tools

The handler captures the full agent loop: plan → tool call → observation → next plan. Each iteration appears as linked spans. 
import { createOpenAIFunctionsAgent, AgentExecutor } from "langchain/agents";
import { DynamicTool } from "@langchain/core/tools";
import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts";
import { lumiqtrace, LumiqtraceCallbackHandler } from "@lumiqtrace/sdk";

lumiqtrace.init({ apiKey: process.env.LUMIQTRACE_API_KEY! });

const tools = [
  new DynamicTool({
    name: "get-weather",
    description: "Get current weather for a city",
    func: async (city: string) => `Weather in ${city}: 22°C, sunny`,
  }),
];

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant with access to tools."],
  ["human", "{input}"],
  new MessagesPlaceholder("agent_scratchpad"),
]);

const agent = await createOpenAIFunctionsAgent({
  llm: new ChatOpenAI({ model: "gpt-4o" }),
  tools,
  prompt,
});

const executor = new AgentExecutor({
  agent,
  tools,
  callbacks: [new LumiqtraceCallbackHandler()],
});

const result = await executor.invoke({ input: "What's the weather in Tokyo?" });

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RAG with retriever tracing

import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "@langchain/openai";
import { createRetrievalChain } from "langchain/chains/retrieval";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { Document } from "@langchain/core/documents";

const vectorStore = await MemoryVectorStore.fromDocuments(
  [new Document({ pageContent: "LangChain is a framework for building LLM apps." })],
  new OpenAIEmbeddings()
);

const retriever = vectorStore.asRetriever();

const combineDocsChain = await createStuffDocumentsChain({
  llm: new ChatOpenAI({ model: "gpt-4o" }),
  prompt: ChatPromptTemplate.fromMessages([
    ["system", "Answer based on context: {context}"],
    ["human", "{input}"],
  ]),
});

const retrievalChain = await createRetrievalChain({
  retriever,
  combineDocsChain,
});

// All retrieval and LLM calls appear as linked spans
const result = await retrievalChain.invoke(
  { input: "What is LangChain?" },
  { callbacks: [new LumiqtraceCallbackHandler()] }
);

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Python

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Installation

pip install lumiqtrace langchain-core langchain-openai

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Basic usage

import lumiqtrace
from lumiqtrace.integrations.langchain_handler import LumiqtraceCallbackHandler
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage

lumiqtrace.init(api_key="lqt_your_api_key_here")

handler = LumiqtraceCallbackHandler()

llm = ChatOpenAI(model="gpt-4o", callbacks=[handler])

response = llm.invoke([
    SystemMessage(content="You are a helpful assistant."),
    HumanMessage(content="What is the Turing test?"),
])
print(response.content)

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Chains

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_messages([
    ("system", "Summarize the following topic in two sentences."),
    ("human", "{topic}"),
])

chain = prompt | ChatOpenAI(model="gpt-4o") | StrOutputParser()

result = chain.invoke(
    {"topic": "large language models"},
    config={"callbacks": [LumiqtraceCallbackHandler()]},
)

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Agents

from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_core.tools import Tool
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

def get_weather(city: str) -> str:
    return f"Weather in {city}: 22°C, sunny"

tools = [Tool(name="get-weather", func=get_weather, description="Get weather for a city")]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant with access to tools."),
    ("human", "{input}"),
    MessagesPlaceholder("agent_scratchpad"),
])

agent = create_openai_functions_agent(
    llm=ChatOpenAI(model="gpt-4o"),
    tools=tools,
    prompt=prompt,
)

executor = AgentExecutor(
    agent=agent,
    tools=tools,
    callbacks=[LumiqtraceCallbackHandler()],
)

result = executor.invoke({"input": "What's the weather in Paris?"})

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How traces look in the dashboard

A LangChain agent run produces a trace like this in the flame graph: 
AgentExecutor (chain)          ─────────────────────────────── 1.8s
  ├── ChatOpenAI (llm)         ──────────────── 820ms  gpt-4o
  │     → tool_calls: [get-weather]
  ├── get-weather (tool)       ──── 12ms
  └── ChatOpenAI (llm)         ──────── 640ms  gpt-4o
        → finish_reason: stop
Each row is a span. The total trace cost is the sum of both LLM spans. Tool call spans show the tool name and execution time but have no token cost.
To associate a LangChain trace with a user session, wrap the chain invocation in withLumiqtraceContext:
import { withLumiqtraceContext } from "@lumiqtrace/sdk";

return withLumiqtraceContext({ userId, sessionId }, () =>
  chain.invoke({ input: userMessage }, { callbacks: [handler] })
);

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Guardrails with LangChain

Pass guardrail options to LumiqtraceCallbackHandler to enable pre/post content checks on LLM calls made by the chain: 
const handler = new LumiqtraceCallbackHandler({
  guardrails: { pre: true, post: true, failClosed: false },
});

handler = LumiqtraceCallbackHandler(guardrails={"pre": True, "post": True})