dspy-langfuse

name

dspy-langfuse

description

LLM observability for DSPy with Langfuse -- auto-trace every LM call, attach scores and evaluations, run annotation queues for human review, and track experiments across prompt versions. Use when you want to set up Langfuse, langfuse.com, openinference-instrumentation-dspy, trace DSPy calls, LLM observability with scores, annotation queues, or experiment tracking. Also used for langfuse setup, pip install langfuse, DSPy trace viewer, langfuse vs phoenix, langfuse vs langtrace, observe decorator with DSPy, self-hosted tracing with evaluation, production LLM monitoring with scoring.

Langfuse -- LLM Observability and Evaluation for DSPy

Step 1: Understand the setup

Before writing code, clarify:

1. Cloud or self-hosted? Langfuse Cloud (managed) or self-hosted (Docker)?
2. What do you need beyond tracing? Scores/evals, annotation queues, experiment tracking, or just traces?
3. Do you need custom metadata on traces? User IDs, session IDs, tags for filtering?
4. Short-lived or long-running? Scripts need langfuse.flush(); servers do not.

What is Langfuse

Langfuse is an open-source LLM observability platform with auto-instrumentation for DSPy via the OpenInference plugin. It traces every LM call, retrieval, and module execution, then adds evaluation, scoring, and annotation on top.

What gets traced

Component	Details captured
LM calls	Prompts, responses, token counts, latency, cost
Retrievals	Queries, passages, relevance
Module executions	Input/output per module step
Full pipeline	Nested spans showing the complete call tree

What Langfuse adds beyond tracing

Feature	What it does
Scores	Attach numeric, boolean, categorical, or text scores to any trace
Annotation queues	Structured human review workflows for building ground truth
Experiments	Compare prompt versions, measure score changes across runs
Sessions	Group multi-turn traces (chatbots, agents)
Environments	Separate dev/staging/prod traces

When to use Langfuse

Use Langfuse when:

• You want tracing plus built-in evaluation and scoring
• You need annotation queues for human review
• You want experiment tracking to compare prompt versions
• You need session grouping for multi-turn applications

Do NOT use Langfuse when:

• You want the easiest one-line setup with no evaluation needs -- see /dspy-langtrace
• You want a purely local trace viewer with no cloud -- see /dspy-phoenix
• Your team already uses W&B -- see /dspy-weave
• You need the full ML lifecycle (model registry, deployment) -- see /dspy-mlflow

Setup

Install

pip install langfuse dspy openinference-instrumentation-dspy -U

Environment variables

export LANGFUSE_PUBLIC_KEY="pk-lf-..."
export LANGFUSE_SECRET_KEY="sk-lf-..."
export LANGFUSE_BASE_URL="https://cloud.langfuse.com"  # US: us.cloud.langfuse.com | EU: cloud.langfuse.com | JP: jp.cloud.langfuse.com | HIPAA: hipaa.cloud.langfuse.com

Quickstart

import dspy
from langfuse import get_client
from openinference.instrumentation.dspy import DSPyInstrumentor

# 1. Verify Langfuse credentials
langfuse = get_client()
langfuse.auth_check()

# 2. Auto-instrument DSPy (one line)
DSPyInstrumentor().instrument()

# 3. Configure DSPy
dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))  # or "anthropic/claude-sonnet-4-5-20250929", etc.

# 4. Use DSPy normally -- all calls are traced
program = dspy.ChainOfThought("question -> answer")
result = program(question="What is DSPy?")
# View traces at https://cloud.langfuse.com

Adding metadata to traces

Use @observe() and propagate_attributes() to enrich auto-captured traces with user IDs, session IDs, tags, and custom metadata:

from langfuse import observe, propagate_attributes

@observe()
def answer_question(question: str):
    with propagate_attributes(
        user_id="user_123",
        session_id="session_abc",
        tags=["production", "qa-pipeline"],
        metadata={"pipeline_version": "2.1"},
        version="2.1",
    ):
        program = dspy.ChainOfThought("question -> answer")
        return program(question=question)

result = answer_question("How do refunds work?")

Context manager alternative

For non-decorator workflows (batch jobs, scripts):

from langfuse import get_client, propagate_attributes

langfuse = get_client()

with langfuse.start_as_current_observation(as_type="span", name="batch-qa"):
    with propagate_attributes(
        user_id="batch_user",
        session_id="batch_001",
        metadata={"batch_size": 100},
    ):
        program = dspy.ChainOfThought("question -> answer")
        result = program(question="What is DSPy?")

langfuse.flush()  # Required for short-lived scripts
# Use langfuse.shutdown() instead if the process is exiting (also terminates background threads)

Controlling I/O capture

DSPy prompts can contain sensitive data or be very large. Disable input/output capture on specific observations:

@observe(capture_input=False, capture_output=False)
def process_pii_data(user_data: str):
    # Traces timing and structure but not the actual data
    return program(question=user_data)

Or disable globally via environment variable:

export LANGFUSE_OBSERVE_DECORATOR_IO_CAPTURE_ENABLED=false

Scoring traces

Langfuse scores attach evaluation results to traces. Score types: NUMERIC, CATEGORICAL, BOOLEAN, TEXT.

from langfuse import get_client

langfuse = get_client()

# Score a trace after evaluation
langfuse.create_score(
    trace_id="trace-id-from-dashboard",
    name="accuracy",
    value=0.92,
    data_type="NUMERIC",
    comment="Verified against ground truth",
)

Connecting DSPy metrics to Langfuse scores

After running dspy.Evaluate, push results to Langfuse for tracking:

from dspy.evaluate import Evaluate

evaluator = Evaluate(devset=devset, metric=metric, num_threads=4)
score = evaluator(my_program)

# Log the aggregate score
langfuse = get_client()
langfuse.create_score(
    trace_id=langfuse.get_current_trace_id(),
    name="dspy_eval_accuracy",
    value=score,
    data_type="NUMERIC",
)
langfuse.flush()

Tracing a RAG pipeline

from langfuse import observe, propagate_attributes
import dspy

DSPyInstrumentor().instrument()
dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))  # or "anthropic/claude-sonnet-4-5-20250929", etc.

class RAGPipeline(dspy.Module):
    def __init__(self):
        self.retrieve = dspy.Retrieve(k=3)
        self.answer = dspy.ChainOfThought("context, question -> answer")

    def forward(self, question):
        context = self.retrieve(question).passages
        return self.answer(context=context, question=question)

@observe()
def search_docs(question: str):
    with propagate_attributes(user_id="user_456", tags=["rag"]):
        pipeline = RAGPipeline()
        return pipeline(question=question)

result = search_docs("How do refunds work?")
# Trace shows: RAGPipeline -> Retrieve -> ChainOfThought (nested spans)

Langfuse vs other observability tools

Feature	Langfuse	Arize Phoenix	Langtrace	W&B Weave
DSPy auto-instrumentation	Yes (OpenInference plugin)	Yes (same plugin)	Yes (built-in)	Manual (@weave.op())
Setup effort	3 lines + env vars	2 lines + launch_app()	1 line	Decorator per function
Local mode (no cloud)	Yes (self-hosted Docker)	Yes (px.launch_app())	Yes (Docker)	No
Cloud option	Yes (managed, multi-region)	Yes (Arize platform)	Yes (app.langtrace.ai)	Yes (wandb.ai)
Built-in scoring/evals	Yes (4 score types)	Yes (evals module)	Basic	Yes (feedback)
Annotation queues	Yes	No	No	No
Experiment tracking	Yes	Basic	No	Yes
Session grouping	Yes	No	No	No
Best for	Tracing + evaluation + human review	Local trace viewer + evals	Easiest DSPy-first setup	Teams already on W&B

Decision guide

Want DSPy observability?
|
+- Need scoring + annotation queues + experiments? -> Langfuse (/dspy-langfuse)
+- Want local-first open-source trace viewer?      -> Phoenix (/dspy-phoenix)
+- Want easiest one-line auto-instrumentation?      -> Langtrace (/dspy-langtrace)
+- Team already uses W&B?                           -> Weave (/dspy-weave)
+- Need full ML lifecycle (registry, deploy)?       -> MLflow (/dspy-mlflow)

Gotchas

• Claude forgets langfuse.flush() in scripts and notebooks. Langfuse sends traces asynchronously in the background. In long-running servers this is fine, but in scripts, notebooks, and batch jobs the process exits before traces are sent. Always call langfuse.flush() (or langfuse.shutdown()) at the end of short-lived processes.
• Claude installs langfuse but forgets openinference-instrumentation-dspy. The DSPy auto-instrumentation lives in a separate package. Without it, DSPyInstrumentor is not available and no DSPy spans are captured. Install both: pip install langfuse openinference-instrumentation-dspy.
• Claude calls DSPyInstrumentor().instrument() after dspy.configure() and DSPy calls. The instrumentor must be activated before any DSPy module runs. Calls made before instrumentation are not captured. Always instrument first, then configure DSPy, then run modules.
• Claude hardcodes LANGFUSE_BASE_URL to US cloud for all users. Langfuse has region-specific endpoints: US (us.cloud.langfuse.com), EU (cloud.langfuse.com), Japan (jp.cloud.langfuse.com), HIPAA (hipaa.cloud.langfuse.com), and self-hosted URLs. Always ask the user which region or instance they use, or read it from environment variables rather than hardcoding.
• Claude creates a new get_client() instance in every function. get_client() returns a singleton -- calling it multiple times is safe but unnecessary clutter. Call it once at module level or in setup, then reuse the reference.
• Traces not appearing? Enable debug mode. Set export LANGFUSE_DEBUG="True" to get verbose logging that shows whether traces are being sent and any API errors. This is the fastest way to diagnose missing traces.

Cross-references

Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>

• Langtrace (easiest DSPy auto-instrumentation) -- /dspy-langtrace
• Arize Phoenix (local trace viewer + evals) -- /dspy-phoenix
• W&B Weave (team dashboards, experiment tracking) -- /dspy-weave
• MLflow (full ML lifecycle) -- /dspy-mlflow
• LangWatch (real-time optimizer progress) -- /dspy-langwatch
• Aggregate monitoring (not per-request) -- /ai-monitoring
• Per-request debugging (inspect_history, JSONL traces) -- /ai-tracing-requests
• Install /ai-do if you do not have it — it routes any AI problem to the right skill and is the fastest way to work: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do

Additional resources

• Langfuse DSPy integration docs
• Langfuse Python SDK docs
• For API details, see reference.md
• For worked examples, see examples.md