Tags

Tags allow you to categorize and filter observations and traces in Langfuse.

Tags are strings (max 200 characters each) and an observation may have multiple tags. The full set of tags applied across all observations in a trace are automatically aggregated and added to the trace object in Langfuse. If a tag exceeds 200 characters, it will be dropped.

Propagating Tags to Observations

Use propagate_attributes() to apply tags to a group of observations within a context.

When using the @observe() decorator:

from langfuse import observe, propagate_attributes

@observe()
def my_function():
    # Apply tags to all child observations
    with propagate_attributes(
        tags=["tag-1", "tag-2"]
    ):
        # All nested observations automatically have these tags
        result = process_data()
        return result

When creating observations directly:

from langfuse import get_client, propagate_attributes

langfuse = get_client()

with langfuse.start_as_current_observation(as_type="span", name="my-operation") as root_span:
    # Apply tags to all child observations
    with propagate_attributes(tags=["tag-1", "tag-2"]):
        # All observations created here automatically have these tags
        with root_span.start_as_current_observation(
            as_type="generation",
            name="llm-call",
            model="gpt-4o"
        ) as gen:
            # This generation automatically has the tags
            pass

When using the context manager:

import { startActiveObservation, propagateAttributes } from "@langfuse/tracing";

await startActiveObservation("context-manager", async (span) => {
  span.update({
    input: { query: "What is the capital of France?" },
  });

  // Apply tags to all child observations
  await propagateAttributes(
    {
      tags: ["tag-1", "tag-2"],
    },
    async () => {
      // All observations created here automatically have these tags
      // ... your logic ...
    }
  );
});

When using the observe wrapper:

import { observe, propagateAttributes } from "@langfuse/tracing";

const processData = observe(
  async (data: string) => {
    // Apply tags to all child observations
    return await propagateAttributes(
      { tags: ["tag-1", "tag-2"] },
      async () => {
        // All nested observations automatically have these tags
        const result = await performProcessing(data);
        return result;
      }
    );
  },
  { name: "process-data" }
);

const result = await processData("input");

See JS/TS SDK docs for more details.

from langfuse import get_client, propagate_attributes
from langfuse.openai import openai

langfuse = get_client()

with langfuse.start_as_current_observation(as_type="span", name="openai-call"):
    # Apply tags to all observations including OpenAI generation
    with propagate_attributes(
        tags=["tag-1", "tag-2"]
    ):
        completion = openai.chat.completions.create(
            name="test-chat",
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "You are a calculator."},
                {"role": "user", "content": "1 + 1 = "}
            ],
            temperature=0,
        )

Alternatively, when using OpenAI without an enclosing span:

from langfuse.openai import openai

completion = openai.chat.completions.create(
  name="test-chat",
  model="gpt-3.5-turbo",
  messages=[
    {"role": "system", "content": "You are a calculator."},
    {"role": "user", "content": "1 + 1 = "}],
  temperature=0,
  metadata={"langfuse_tags": ["tag-1", "tag-2"]}
)

import OpenAI from "openai";
import { observeOpenAI } from "@langfuse/openai";
import { startActiveObservation, propagateAttributes } from "@langfuse/tracing";

await startActiveObservation("openai-call", async () => {
  // Apply tags to all observations
  await propagateAttributes(
    {
      tags: ["tag-1", "tag-2"],
    },
    async () => {
      const res = await observeOpenAI(new OpenAI()).chat.completions.create({
        messages: [{ role: "system", content: "Tell me a story about a dog." }],
        model: "gpt-3.5-turbo",
        max_tokens: 300,
      });
    }
  );
});

from langfuse import get_client, propagate_attributes
from langfuse.langchain import CallbackHandler

langfuse = get_client()
langfuse_handler = CallbackHandler()

with langfuse.start_as_current_observation(as_type="span", name="langchain-call"):
    # Apply tags to all child observations
    with propagate_attributes(
        tags=["tag-1", "tag-2"]
    ):
        response = chain.invoke(
            {"topic": "cats"},
            config={"callbacks": [langfuse_handler]}
        )

Alternatively, use metadata in chain invocation:

from langfuse.langchain import CallbackHandler

handler = CallbackHandler()

chain.invoke(
    {"animal": "dog"},
    config={
        "callbacks": [handler],
        "metadata": {"langfuse_tags": ["tag-1", "tag-2"]},
    },
)

import { startActiveObservation, propagateAttributes } from "@langfuse/tracing";
import { CallbackHandler } from "langfuse-langchain";

const langfuseHandler = new CallbackHandler();

// Apply tags to all child observations
await propagateAttributes(
  {
    tags: ["tag-1", "tag-2"],
  },
  async () => {
    await chain.invoke(
      { input: "<user_input>" },
      { callbacks: [langfuseHandler] }
    );
  }
);

Alternatively, when using the CallbackHandler, you can pass tags to the constructor:

const handler = new CallbackHandler({
  tags: ["tag-1", "tag-2"],
});

Or set tags dynamically via the runnable configuration in the chain invocation:

const langfuseHandler = new CallbackHandler()
const tags = ["tag-1", "tag-2"];

// Pass config to the chain invocation to be parsed as Langfuse trace attributes
await chain.invoke({ input: "<user_input>" }, { callbacks: [langfuseHandler], tags: tags });

When using the integration with the JS SDK (see interop docs), set tags via langfuse.trace():

import { CallbackHandler, Langfuse } from "langfuse-langchain";
const langfuse = new Langfuse();

const trace = langfuse.trace({
  tags: ["tag-1", "tag-2"],
});
const langfuseHandler = new CallbackHandler({ root: trace });

// Add Langfuse handler as callback to your langchain chain/agent
await chain.invoke({ input: "<user_input>" }, { callbacks: [langfuseHandler] });

Note on Attribute Propagation

We use Attribute Propagation to propagate `tags` across all observations of a trace. We will use all observations with `tags` to create `tags`-level metrics. Please consider the following when using Attribute Propagation:

Values must be strings ≤200 characters
Call early in your trace to ensure all observations are covered. This way you make sure that all Metrics in Langfuse are accurate.
Invalid values are dropped with a warning

Learn more: Python SDK | TypeScript SDK

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