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This library traces LangChain and LangGraph applications. It hooks into LangChain’s callback manager to emit spans that mirror the structure of your application:

  • Workflow spans for a graph or chain run — for example an invocation of a LangGraph StateGraph — capturing the overall input and output of the run.

  • Agent spans for agent invocations nested inside a workflow, including the agent name, id, description, and conversation/session id when available.

  • Tool spans for tool calls made during a run.

The spans nest to reflect the graph, so a single graph invocation produces a workflow span with the agent, tool, and model calls it triggered as children.

Installation

pip install opentelemetry-instrumentation-genai-langchain

See the examples directory for runnable workflow, agent, tools, and zero-code scenarios.

Usage

Call LangChainInstrumentor().instrument() once during startup, then build and invoke your graph as usual. The example below traces a simple two-node LangGraph StateGraph (START → researcher → summariser → END); the graph.invoke(...) call is recorded as a workflow span with the node model calls nested underneath.

from typing import Annotated, TypedDict

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import add_messages

from opentelemetry.instrumentation.genai.langchain import LangChainInstrumentor

LangChainInstrumentor().instrument()

llm = ChatOpenAI(model="<your-model>", temperature=0)


class State(TypedDict):
    messages: Annotated[list, add_messages]
    research: str


def researcher(state: State) -> dict:
    response = llm.invoke(
        [
            SystemMessage(content="Provide 2-3 factual sentences."),
            HumanMessage(content=state["messages"][-1].content),
        ]
    )
    return {"research": response.content, "messages": [response]}


def summariser(state: State) -> dict:
    response = llm.invoke(
        [
            SystemMessage(content="Condense the text into one sentence."),
            HumanMessage(content=state["research"]),
        ]
    )
    return {"messages": [response]}


builder = StateGraph(State)
builder.add_node("researcher", researcher)
builder.add_node("summariser", summariser)
builder.add_edge(START, "researcher")
builder.add_edge("researcher", "summariser")
builder.add_edge("summariser", END)
graph = builder.compile()

# Recorded as a workflow span with the two node LLM calls nested underneath.
graph.invoke(
    {
        "messages": [HumanMessage(content="What is the capital of France?")],
        "research": "",
    }
)

Configuration

By default, prompts and completions are not captured. To capture message content, set the environment variable OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT to one of NO_CONTENT, SPAN_ONLY, EVENT_ONLY, or SPAN_AND_EVENT.

Prompts and completions can instead be uploaded to external storage via a completion hook: set OTEL_INSTRUMENTATION_GENAI_COMPLETION_HOOK=upload with OTEL_INSTRUMENTATION_GENAI_UPLOAD_BASE_PATH (install the upload extra: pip install opentelemetry-util-genai[upload]), or pass a custom CompletionHook programmatically, which takes precedence over the environment variable:

LangChainInstrumentor().instrument(completion_hook=my_hook)

References

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Credits

Author: OpenTelemetry Authors

GitHub Statistics

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License expression

Apache-2.0
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Requires

Python >=3.10

Provides Extra

instruments

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