Getting Began with Langfuse [2026 Guide]

The creation and deployment of purposes that make the most of Giant Language Fashions (LLMs) comes with their very own set of issues. LLMs have non-deterministic nature, can generate believable however false info and tracing their actions in convoluted sequences could be very troublesome. On this information, we’ll see how Langfuse comes up as a necessary instrument for fixing these issues, by providing a robust basis for complete observability, evaluation, and immediate dealing with of LLM purposes.

What’s Langfuse?

Langfuse is a groundbreaking observability and evaluation platform that’s open supply and particularly created for LLM purposes. It’s the basis for tracing, viewing, and debugging all of the phases of an LLM interplay, ranging from the preliminary immediate and ending with the ultimate response, whether or not it’s a easy name or an advanced multi-turn dialog between brokers.

Langfuse isn’t solely a logging device but in addition a way of systematically evaluating LLM efficiency, A/B testing of prompts, and amassing consumer suggestions which in flip helps to shut the suggestions loop important for iterative enchancment. The principle level of its worth is the transparency that it brings to the LLMs world, thus letting the builders to: 

• Perceive LLM behaviour: Discover out the precise prompts that had been despatched, the responses that had been obtained, and the intermediate steps in a multi-stage utility. 
• Discover points: Find the supply of errors, low efficiency, or sudden outputs quickly. 
• High quality analysis: Effectiveness of LLM responses could be measured towards the pre-defined metrics with each guide and automatic measures. 
• Refine and enhance: Knowledge-driven insights can be utilized to excellent prompts, fashions, and utility logic.
• Deal with prompts: management the model of prompts and check them to get one of the best LLM.

Key Options and Ideas

There are numerous key options that Langfuse affords like: 

1. Tracing and Monitoring 

Langfuse helps us capturing the detailed traces of each interplay that LLM has. The ‘hint’ is mainly the illustration of an end-to-end consumer request or utility stream. Inside a hint, logical items of labor is denoted by “spans” and calls to an LLM refers to “generations”.

2. Analysis 

Langfuse permits analysis each manually and programmatically as effectively. Customized metrics could be outlined by the builders which might then be used to run evaluations for various datasets after which be built-in as LLM-based evaluators.

3. Immediate Administration 

Langfuse gives direct management over immediate administration together with storage and versioning capabilities. It’s potential to check numerous prompts by way of A/B testing and on the identical time preserve accuracy throughout numerous locations, which paves the best way for data-driven immediate optimization as effectively.  

4. Suggestions Assortment 

Langfuse absorbs the consumer recommendations and incorporates them proper into your traces. It is possible for you to to hyperlink specific remarks or consumer scores to the exact LLM interplay that resulted in an output, thus giving us the real-time suggestions for troubleshooting and enhancing.  

Why Langfuse? The Drawback It Solves

Conventional software program observability instruments have very totally different traits and don’t fulfill the LLM-powered purposes standards within the following elements: 

• Non-determinism: LLMs won’t all the time produce the identical consequence even for an equivalent enter which makes debugging fairly difficult. Langfuse, in flip, data every interplay’s enter and output giving a transparent image of the operation at that second. 
• Immediate Sensitivity: Any minor change in a immediate may alter LLM’s reply fully. Langfuse is there to assist conserving observe of immediate variations together with their efficiency metrics. 
• Advanced Chains: Nearly all of LLM purposes are characterised by a mixture of a number of LLM calls, totally different instruments, and retrieving knowledge (e.g., RAG architectures). The one approach to know the stream and to pinpoint the place the place the bottleneck or the error is the tracing. Langfuse presents a visible timeline for these interactions. 
• Subjective High quality: The time period “goodness” for an LLM’s reply is commonly synonymous with private opinion. Langfuse allows each goal (e.g., latency, token depend) and subjective (human suggestions, LLM-based analysis) high quality assessments. 
• Value Administration: Calling LLM APIs comes with a value. Understanding and optimizing your prices shall be simpler in case you have Langfuse monitoring your token utilization and name quantity. 
• Lack of Visibility: The developer isn’t in a position to see how their LLM purposes are performing in the marketplace and subsequently it’s arduous for them to make these purposes steadily higher due to the shortage of observability. 

Langfuse doesn’t solely supply a scientific methodology for LLM interplay, but it surely additionally transforms the event course of right into a data-driven, iterative, engineering self-discipline as a substitute of trial and error. 

Getting Began with Langfuse

Earlier than you can begin utilizing Langfuse, you will need to first set up the consumer library and set it as much as transmit knowledge to a Langfuse occasion, which may both be a cloud-hosted or a self-hosted one. 

Set up

Langfuse has consumer libraries out there for each Python and JavaScript/TypeScript. 

Python Shopper 

pip set up langfuse 

JavaScript/TypeScript Shopper 

npm set up langfuse 

Or 

yarn add langfuse 

Configuration 

After set up, bear in mind to arrange the consumer along with your mission keys and host. Yow will discover these in your Langfuse mission settings.   

• public_key: That is for the frontend purposes or for circumstances the place solely restricted and non-sensitive knowledge are getting despatched.
• secret_key: That is for backend purposes and situations the place the total observability, together with delicate inputs/outputs, is a requirement.   
• host: This refers back to the URL of your Langfuse occasion (e.g., https://cloud.langfuse.com).   
• surroundings: That is an optionally available string that can be utilized to tell apart between totally different environments (e.g., manufacturing, staging, improvement).   

For safety and adaptability causes, it’s thought of good observe to outline these as surroundings variables.

export LANGFUSE_PUBLIC_KEY="pk-lf-..." 
export LANGFUSE_SECRET_KEY="sk-lf-..." 
export LANGFUSE_HOST="https://cloud.langfuse.com" 
export LANGFUSE_ENVIRONMENT="improvement"

Then, initialize the Langfuse consumer in your utility: 

Python Instance 

from langfuse import Langfuse
import os

langfuse = Langfuse(public_key=os.environ.get("LANGFUSE_PUBLIC_KEY"),    secret_key=os.environ.get("LANGFUSE_SECRET_KEY"),    host=os.environ.get("LANGFUSE_HOST"))

JavaScript/TypeScript Instance 

import { Langfuse } from "langfuse";

const langfuse = new Langfuse({  publicKey: course of.env.LANGFUSE_PUBLIC_KEY,  secretKey: course of.env.LANGFUSE_SECRET_KEY,  host: course of.env.LANGFUSE_HOST});

Establishing Your First Hint

The basic unit of observability in Langfuse is the hint. A hint usually represents a single consumer interplay or a whole request lifecycle. Inside a hint, you log particular person LLM calls (era) and arbitrary computational steps (span). 

Let’s illustrate with a easy LLM name utilizing OpenAI’s API. 

Python Instance 

import os
from openai import OpenAI
from langfuse import Langfuse
from langfuse.mannequin import InitialGeneration

# Initialize Langfuse
langfuse = Langfuse(
    public_key=os.environ.get("LANGFUSE_PUBLIC_KEY"),
    secret_key=os.environ.get("LANGFUSE_SECRET_KEY"),
    host=os.environ.get("LANGFUSE_HOST"),
)

# Initialize OpenAI consumer
consumer = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

def simple_llm_call_with_trace(user_input: str):
    # Begin a brand new hint
    hint = langfuse.hint(
        title="simple-query",
        enter=user_input,
        metadata={"user_id": "user-123", "session_id": "sess-abc"},
    )

    attempt:
        # Create a era inside the hint
        era = hint.era(
            title="openai-generation",
            enter=user_input,
            mannequin="gpt-4o-mini",
            model_parameters={"temperature": 0.7, "max_tokens": 100},
            metadata={"prompt_type": "commonplace"},
        )

        # Make the precise LLM name
        chat_completion = consumer.chat.completions.create(
            mannequin="gpt-4o-mini",
            messages=[{"role": "user", "content": user_input}],
            temperature=0.7,
            max_tokens=100,
        )

        response_content = chat_completion.selections[0].message.content material

        # Replace era with the output and utilization
        era.replace(
            output=response_content,
            completion_start_time=chat_completion.created,
            utilization={
                "prompt_tokens": chat_completion.utilization.prompt_tokens,
                "completion_tokens": chat_completion.utilization.completion_tokens,
                "total_tokens": chat_completion.utilization.total_tokens,
            },
        )

        print(f"LLM Response: {response_content}")
        return response_content

    besides Exception as e:
        # Document errors within the hint
        hint.replace(
            stage="ERROR",
            status_message=str(e)
        )
        print(f"An error occurred: {e}")
        increase

    lastly:
        # Guarantee all knowledge is shipped to Langfuse earlier than exit
        langfuse.flush()


# Instance name
simple_llm_call_with_trace("What's the capital of France?")

Finally, the next move after executing this code can be to go to the Langfuse interface. There shall be a brand new hint “simple-query” that consists of 1 era “openai-generation”. It’s potential so that you can click on it with a view to view the enter, output, mannequin used, and different metadata. 

Core Performance in Element

Studying to work with hint, span, and era objects is the principle requirement to make the most of Langfuse. 

Tracing LLM Calls

• langfuse.hint(): This command begins a brand new hint. The highest-level container for an entire operation. 
  • title: The hint’s very descriptive title.  
  • enter: The primary enter of the entire process.  
  • metadata: A dictionary of any key-value pairs for filtering and evaluation (e.g., user_id, session_id, AB_test_variant).  
  • session_id: (Elective) An identifier shared by all traces that come from the identical consumer session.  
  • user_id: (Elective) An identifier shared by all interactions of a selected consumer.  

• hint.span(): This can be a logical step or minor operation inside a hint that isn’t a direct input-output interplay with the LLM. Software calls, database lookups, or complicated calculations could be traced on this method. 
  • title: Title of the span (e.g. “retrieve-docs”, “parse-json”).  
  • enter: The enter related to this span.  
  • output: The output created by this span.  
  • metadata: The span metadata is formatted as extra.  
  • stage: The severity stage (INFO, WARNING, ERROR, DEBUG).  
  • status_message: A message that’s linked to the standing (e.g. error particulars).  
  • parent_observation_id: Connects this span to a mother or father span or hint for nested buildings. 

• hint.era(): Signifies a selected LLM invocation. 
  • title: The title of the era (as an illustration, “initial-response”, “refinement-step”).  
  • enter: The immediate or messages that had been communicated to the LLM.  
  • output: The reply obtained from the LLM.  
  • mannequin: The exact LLM mannequin that was employed (for instance, “gpt-4o-mini“, “claude-3-opus“).  
  • model_parameters: A dictionary of specific mannequin parameters (like temperature, max_tokens, top_p).  
  • utilization: A dictionary displaying the variety of tokens utilized (prompt_tokens, completion_tokens, total_tokens).  
  • metadata: Further metadata for the LLM invocation.  
  • parent_observation_id: Hyperlinks this era to a mother or father span or hint.  
  • immediate: (Elective) Can establish a selected immediate template that’s beneath administration in Langfuse. 

Conclusion

Langfuse makes the event and maintenance of LLM-powered purposes a much less strenuous endeavor by turning it right into a structured and data-driven course of. It does this by giving builders entry to the interactions with the LLM like by no means earlier than by way of intensive tracing, systematic analysis, and highly effective immediate administration.  

Furthermore, it encourages the builders to debug their work with certainty, pace up the iteration course of, and carry on enhancing their AI merchandise when it comes to high quality and efficiency. Therefore, Langfuse gives the required devices to guarantee that LLM purposes are reliable, cost-effective, and actually highly effective, regardless of if you’re growing a primary chatbot or a complicated autonomous agent. 

Often Requested Questions

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