See exactly what your AI did on a specific request. Use when you need to debug a wrong answer, trace a specific AI request, profile slow AI pipelines, find which step failed, inspect LM calls, view token usage per request, build audit trails, or understand why a customer got a bad response. Covers DSPy inspection, per-step tracing, OpenTelemetry instrumentation, and trace viewer setup., debug slow AI response, why is my AI pipeline slow, trace LLM token usage, OpenTelemetry for AI, Langfuse tracing, AI observability per request, debug wrong AI answer for specific user, which LLM call failed, latency profiling for AI, audit trail for AI decisions, inspect what the AI actually saw, per-request AI debugging, production AI request logs, DSPy inspect_history, trace AI reasoning steps.
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Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
See exactly what your AI did on a specific request. Use when you need to debug a wrong answer, trace a specific AI request, profile slow AI pipelines, find which step failed, inspect LM calls, view token usage per request, build audit trails, or understand why a customer got a bad response. Covers DSPy inspection, per-step tracing, OpenTelemetry instrumentation, and trace viewer setup., debug slow AI response, why is my AI pipeline slow, trace LLM token usage, OpenTelemetry for AI, Langfuse tracing, AI observability per request, debug wrong AI answer for specific user, which LLM call failed, latency profiling for AI, audit trail for AI decisions, inspect what the AI actually saw, per-request AI debugging, production AI request logs, DSPy inspect_history, trace AI reasoning steps.
See What Your AI Did on a Specific Request
Guide the user through tracing and debugging individual AI requests. The goal: for any request, see every LM call, retrieval step, intermediate result, token count, and latency.
How tracing differs from monitoring
Monitoring (/ai-monitoring)
Tracing (this skill)
Scope
Aggregate health across all requests
Single request, full detail
Question answered
"Is accuracy dropping this week?"
"Why did customer #12345 get a wrong answer at 2:14pm?"
Output
Scores, trends, alerts
Call traces, intermediate results, latencies
Timing
Periodic batch evaluation
Per-request, real-time
Step 1: Understand the situation
Ask the user:
What happened? A specific wrong answer, slow response, or unexpected behavior?
What does your pipeline look like? Single module or multi-step pipeline? Which DSPy modules?
Where is this running? Local development, staging, or production?
Then decide the approach:
Situation
Approach
Debugging a specific wrong answer right now
Step 2: Quick debugging with dspy.inspect_history
Need structured tracing in a running app
Step 3: DSPy callback system
Need per-step timing in pipelines
Step 4: Per-step tracing
Need a visual trace viewer for your team
Step 5: Connect Langtrace, Phoenix, or MLflow
Need to find patterns across many traces
Step 6: Search and filter traces
Step 2: Quick debugging (no extra tools needed)
Inspect the last LM calls
The fastest way to see what happened:
import dspy
# Run your program
result = my_program(question="What is our refund policy?")
# See the last 5 LM calls — shows full prompts and responses
dspy.inspect_history(n=5)
# Save history to a file for later analysis (DSPy 3.2+)
dspy.inspect_history(n=10, file_path="debug_trace.txt")
This shows:
The full prompt sent to the LM (including system message, few-shot examples, input)
For persistent traces without any extra dependencies:
import json
import time
from datetime import datetime
classTracedProgram(dspy.Module):
"""Wraps any DSPy program to log per-step traces to JSONL."""def__init__(self, program, log_path="traces.jsonl"):
self.program = program
self.log_path = log_path
defforward(self, **kwargs):
trace_id = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
steps = []
start = time.time()
result = self.program(**kwargs)
total_time = time.time() - start
# Log the trace
entry = {
"trace_id": trace_id,
"timestamp": datetime.now().isoformat(),
"inputs": {k: str(v) for k, v in kwargs.items()},
"outputs": {k: str(getattr(result, k, "")) for k in result.keys()},
"total_latency_ms": round(total_time * 1000),
}
withopen(self.log_path, "a") as f:
f.write(json.dumps(entry) + "\n")
return result
# Use it
traced = TracedProgram(my_program)
result = traced(question="How do refunds work?")
Step 3: DSPy callback system (recommended for structured tracing)
DSPy has a built-in callback system that hooks into every module, LM call, tool call, and adapter operation. This is the official observability API — use it instead of manual wrappers when possible.
Available callback hooks: on_module_start/end, on_lm_start/end, on_adapter_format_start/end, on_adapter_parse_start/end, on_tool_start/end, on_evaluate_start/end.
Do not mutate input/output data inside callbacks — this can cause subtle bugs in the pipeline.
Step 4: Manual per-step tracing in pipelines
For multi-step pipelines, trace each stage separately to see exactly where things go wrong:
import json
import time
import uuid
from datetime import datetime
classStepTracer:
"""Collects per-step timing and intermediate results."""def__init__(self):
self.steps = []
self.trace_id = str(uuid.uuid4())[:8]
deftrace_step(self, name, func, **kwargs):
"""Run a step and record its inputs, outputs, and latency."""
start = time.time()
result = func(**kwargs)
latency = time.time() - start
self.steps.append({
"step": name,
"inputs": {k: str(v)[:200] for k, v in kwargs.items()},
"outputs": {k: str(getattr(result, k, ""))[:200] for k in result.keys()},
"latency_ms": round(latency * 1000),
})
return result
defsummary(self):
"""Print a summary of all traced steps."""print(f"Trace {self.trace_id}:")
total = sum(s["latency_ms"] for s inself.steps)
for step inself.steps:
pct = step["latency_ms"] / total * 100if total > 0else0print(f" {step['step']}: {step['latency_ms']}ms ({pct:.0f}%)")
print(f" Total: {total}ms")
defto_dict(self):
return {
"trace_id": self.trace_id,
"timestamp": datetime.now().isoformat(),
"steps": self.steps,
"total_latency_ms": sum(s["latency_ms"] for s inself.steps),
}
# Use in a pipelineclassTracedRAG(dspy.Module):
def__init__(self):
self.retrieve = dspy.Retrieve(k=3)
self.answer = dspy.ChainOfThought("context, question -> answer")
defforward(self, question):
tracer = StepTracer()
retrieval = tracer.trace_step("retrieve", self.retrieve, query=question)
answer = tracer.trace_step(
"answer", self.answer,
context=retrieval.passages, question=question,
)
tracer.summary()
# Trace a1b2c3d4:# retrieve: 120ms (15%)# answer: 680ms (85%)# Total: 800msreturn answer
Save traces for later analysis
defsave_trace(tracer, path="traces.jsonl"):
withopen(path, "a") as f:
f.write(json.dumps(tracer.to_dict()) + "\n")
# Load and analyze tracesdefload_traces(path="traces.jsonl"):
withopen(path) as f:
return [json.loads(line) for line in f]
deffind_slow_traces(traces, threshold_ms=2000):
return [t for t in traces if t["total_latency_ms"] > threshold_ms]
deffind_failed_steps(traces):
return [
t for t in traces
ifany("error"instr(s.get("outputs", "")).lower() for s in t["steps"])
]
Step 4b: OpenTelemetry instrumentation
For production tracing with any backend (Jaeger, Zipkin, Datadog, etc.):
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
# Setup — do this once at app startup
provider = TracerProvider()
trace.set_tracer_provider(provider)
tracer = trace.get_tracer("my-ai-app")
classOTelTracedProgram(dspy.Module):
"""Wraps a DSPy program with OpenTelemetry spans."""def__init__(self, program):
self.program = program
defforward(self, **kwargs):
with tracer.start_as_current_span("ai_request") as span:
span.set_attribute("ai.inputs", json.dumps({k: str(v) for k, v in kwargs.items()}))
start = time.time()
result = self.program(**kwargs)
latency = time.time() - start
span.set_attribute("ai.latency_ms", round(latency * 1000))
span.set_attribute("ai.outputs", json.dumps(
{k: str(getattr(result, k, "")) for k in result.keys()}
))
return result
Trace individual pipeline steps with OTel
classOTelTracedRAG(dspy.Module):
def__init__(self):
self.retrieve = dspy.Retrieve(k=3)
self.answer = dspy.ChainOfThought("context, question -> answer")
defforward(self, question):
with tracer.start_as_current_span("rag_pipeline") as parent:
parent.set_attribute("question", question)
with tracer.start_as_current_span("retrieve"):
retrieval = self.retrieve(query=question)
with tracer.start_as_current_span("generate_answer"):
answer = self.answer(
context=retrieval.passages, question=question
)
return answer
Step 5: Connect a trace viewer or MLflow
Option A: Langtrace (best DSPy integration)
First-class DSPy auto-instrumentation — one line to trace all LM calls:
pip install langtrace-python-sdk
from langtrace_python_sdk import langtrace
langtrace.init(api_key="your-key") # or use LANGTRACE_API_KEY env var# That's it — all DSPy calls are now traced automatically
result = my_program(question="test")
# View traces at app.langtrace.ai
import phoenix as px
from openinference.instrumentation.dspy import DSPyInstrumentor
# Launch local trace viewer
px.launch_app() # Opens at http://localhost:6006# Auto-instrument DSPy
DSPyInstrumentor().instrument()
# All DSPy calls are now traced
result = my_program(question="test")
Option C: Jaeger (open-source, Docker)
docker run -d -p 16686:16686 -p 4317:4317 jaegertracing/all-in-one:latest
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
# Export spans to Jaeger
exporter = OTLPSpanExporter(endpoint="http://localhost:4317", insecure=True)
provider.add_span_processor(BatchSpanProcessor(exporter))
# View traces at http://localhost:16686
pip install -U mlflow>=2.18.0
mlflow server --backend-store-uri sqlite:///mydb.sqlite
import mlflow
mlflow.set_tracking_uri("http://127.0.0.1:5000")
mlflow.set_experiment("DSPy")
# Auto-trace all DSPy calls (LMs, retrievers, tools, modules)
mlflow.dspy.autolog()
result = my_program(question="test")
# View traces at http://127.0.0.1:5000
MLflow captures the full call tree including LM calls, retrievers, tools, and custom modules — more comprehensive than inspect_history.
Comparison
Feature
Langtrace
Arize Phoenix
MLflow
Jaeger
DSPy auto-instrumentation
Yes (built-in)
Yes (plugin)
Yes (autolog)
Manual
Setup effort
One line
Two lines + Docker
pip + server
Docker + manual spans
Self-hosted option
Yes
Yes
Yes
Yes
Cloud option
Yes
Yes
Databricks
No
LM call details
Prompts, tokens, cost
Prompts, tokens
Full call tree
Custom attributes
Best for
DSPy-first teams
Open-source + local UI
ML teams, experiment tracking
Teams already using Jaeger
For in-depth guides: /dspy-langtrace, /dspy-phoenix, /dspy-mlflow.
Step 6: Use traces to improve your AI
Find patterns in wrong answers
# Load JSONL traces and find failuresimport json
defload_traces(path="traces.jsonl"):
withopen(path) as f:
return [json.loads(line) for line in f]
wrong_traces = [t for t in load_traces() if"error"in json.dumps(t).lower()]
# Check which step is most often the bottleneckfrom collections import Counter
slow_steps = Counter()
for t in wrong_traces:
if t.get("steps"):
slowest = max(t["steps"], key=lambda s: s["latency_ms"])
slow_steps[slowest["step"]] += 1print(slow_steps)
# Counter({"retrieve": 23, "answer": 7})# -> Retrieval is the problem, not the answer generation
Build training data from failures
failed_examples = []
for t in wrong_traces:
ex = dspy.Example(
question=t.get("inputs", {}).get("question", ""),
).with_inputs("question")
failed_examples.append(ex)
# Add to training set and re-optimize# See /ai-improving-accuracy
Gotchas
Building custom tracing wrappers instead of using DSPy callbacks. Claude defaults to writing manual time.time() wrappers around each step. DSPy has a built-in callback system (BaseCallback with on_module_start/end, on_lm_start/end, etc.) that hooks into every operation automatically. Use it instead of reinventing tracing infrastructure.
Using inspect_history in production.inspect_history prints to stdout and only logs LM calls — it misses retriever, tool, and module-level data. For production, use the callback system or an external trace viewer (Langtrace, Phoenix, MLflow). Reserve inspect_history for local debugging.
Tracing the whole request instead of individual steps. Claude wraps the entire pipeline in one timing block, which shows total latency but not which step is slow. Always trace at the step level — either with per-step callbacks or by wrapping individual modules in the forward() method.
Forgetting to save traces before they are needed. Claude often adds tracing after a bug is reported, but the problematic request is already gone. Add JSONL trace logging or connect a trace viewer before you need it — you cannot debug traces you did not log.
Mutating inputs or outputs inside callback hooks. The callback system passes live references to module inputs and outputs. Modifying them in place (e.g., truncating a field for logging) silently corrupts the pipeline data. Always copy before modifying: inputs_copy = dict(inputs).
Additional resources
Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>
Use /ai-monitoring for aggregate health checks across all requests
Use /ai-fixing-errors for code-level debugging (crashes, config issues)
Use /ai-building-pipelines to structure pipelines that are easy to trace
Use /ai-improving-accuracy to optimize based on patterns found in traces
Use /dspy-langtrace for in-depth Langtrace setup (auto-instrumentation, self-hosted)
Use /dspy-phoenix for in-depth Phoenix setup (local UI, evals)
Use /dspy-mlflow for MLflow tracing and experiment tracking
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
