IVQ 101-150
Concurrency, PII, and Memory Tracking
101. How does LangFuse handle concurrent traces in asynchronous applications? LangFuse SDK is thread-safe and async-compatible. Each trace is context-bound, and you can spawn multiple spans in parallel tasks without conflict.
102. Can LangFuse automatically redact PII (Personally Identifiable Information)? No native automatic redaction yet. Redact PII at the app level before sending input/output to LangFuse.
103. How do you track agent memory usage over time using LangFuse? Log memory state as span metadata or observations. Over time, use tags to track memory size or summary evolution.
104. What is the best way to trace recursive agent calls in LangFuse?
Use nested spans with clear naming like recurse_level:1, recurse_level:2, etc., and group under a root trace.
105. How can LangFuse be extended with custom span types?
Use the span_type parameter (e.g., span_type="embedding", "db") to categorize spans beyond defaults.
Token, Prompt, and Model Behavior
106. Does LangFuse support visualizing token-level diff between prompt versions? Not built-in. Export prompts via API and use external diff tools to visualize token-level changes.
107. How do you use LangFuse to measure the impact of temperature or top_p changes?
Log temperature and top_p as metadata. Compare score trends across different values using filters.
108. Can LangFuse be used to track embeddings or vector search latency? Yes. Wrap vector DB calls (e.g., Qdrant, Pinecone) in spans and log input/output vectors and latency.
109. How do you differentiate spans for summarization vs. classification tasks?
Use span_type="summarization" or "classification", or tag spans with task-specific identifiers.
110. What observability gaps does LangFuse fill that traditional APMs don’t? LangFuse traces LLM-specific components: prompts, generations, scores, retries, hallucinations, memory evolution—which APMs don’t track.
Complex Workflows & Branching
111. How do you trace fallback chains in a multi-model orchestration setup?
Use conditional spans (e.g., primary_model, fallback_model) with fallback status tagged for comparison.
112. Can LangFuse group traces by geographical region or user segment?
Yes. Add geo_region or segment as metadata/tags during trace initialization.
113. What’s the best way to visualize tool call latency in LangFuse? Log latency per tool call span and use scatterplots or histograms in the dashboard.
114. How can LangFuse assist in debugging streaming vs. non-streaming responses?
Log streaming partials vs. final outputs, then compare output_mode=stream vs output_mode=batch spans.
115. Can you integrate LangFuse with LangGraph for real-time trace flow visualization? Not natively, but you can attach LangFuse spans to LangGraph nodes and reconstruct the execution tree.
116. How do you represent conditional branches in trace trees in LangFuse? Use parent span with child spans for each branch path and tag them with the condition that triggered execution.
Bulk Processing, Testing, Debugging
117. How does LangFuse handle bulk imports or batch trace creation? You can use the REST API or batch SDK calls to log multiple traces and spans efficiently.
118. What’s the recommended way to test LangFuse locally before production rollout? Run with a dev project in LangFuse Cloud or a local Docker setup. Use mock inputs and validate trace structure.
119. Can LangFuse help surface dead ends or infinite loops in agent workflows?
Yes. Repeated recursive spans or no end span indicates possible infinite loops.
120. How do you monitor reward-based fine-tuning evaluations with LangFuse?
Log reward score as a span.score() and compare across prompts or agent strategies.
Compliance, Reporting, and Contextual Tracing
121. How does LangFuse help with compliance reporting in regulated industries? Log trace metadata for audit trails, include redacted user data, and tag violations. Export logs for compliance reports.
122. Can LangFuse generate automatic reports on prompt performance? Not yet built-in. Use the API to extract score summaries and build custom reports.
123. How do you attach external context (e.g., database lookups) to a trace?
Log it as a separate span or include it in span metadata (e.g., db_result, lookup_key).
124. What is the best way to trace retrieval-augmented generation (RAG) in LangFuse? Use a root span with child spans for retrieval, re-ranking, context assembly, and generation.
125. How does LangFuse handle logs from agent frameworks like AutoGen or CrewAI? Manually wrap tasks in spans or use callbacks to log tools, memory, retries, and planning decisions.
Evaluation & Human-in-the-Loop
126. Can LangFuse be used to compare human vs AI-generated responses?
Yes. Use a source=human or source=ai tag and compare evaluations.
127. How do you capture feedback from human evaluators in LangFuse?
Log feedback via span.score(name="human_rating", value=4.5) or attach comments in metadata.
128. Can LangFuse visualize nested function calls in OpenAI’s function calling? Yes. Log each function call as a child span under the parent generation span.
129. How do you identify bottlenecks in tool usage across spans?
Filter by span_type="tool" and sort by latency_ms. Visualize over time or group by tool name.
130. Can LangFuse integrate with time-series databases for long-term trend analysis? Yes. Export trace metrics via API and ingest into Prometheus, TimescaleDB, or similar tools.
Prompt Evolution, Sampling, and Frontend Behavior
131. How does LangFuse help identify degraded LLM performance after an update?
Compare score trends or latency pre/post model change using tags like model_version.
132. What are the implications of trace sampling in high-load systems with LangFuse? To reduce cost and volume, you can log only a subset of traces. Risk: rare edge-case failures may be missed.
133. Can LangFuse log interactions from chat-based interfaces like Slack or Discord? Yes. Capture the user input/output and log as a trace or span.
134. How do you trace multi-turn conversations with context windows in LangFuse? Use one trace per session, log each turn as a span, and include context tokens in input metadata.
135. Does LangFuse offer encryption-at-rest and in-transit for self-hosted setups? Yes. Self-hosting uses PostgreSQL and ClickHouse, which can be secured via TLS and volume encryption.
136. How do you use LangFuse to audit prompt evolution history? Log prompt hashes or Git commit IDs as tags; visualize or export trace timelines by version.
137. Can LangFuse alert you when a prompt exceeds token limits?
Yes. Manually log token_count and trigger alerts if > context_window.
138. How does LangFuse assist in A/B testing different agent strategies? Tag traces by strategy ID or config, then compare output quality, scores, latency, and fallback frequency.
139. Can LangFuse monitor model drift or output inconsistencies over time? Yes. Log outputs and use span scores to analyze variance, regressions, or outlier behavior.
140. How do you trace and optimize embeddings vs generation latency separately?
Log separate spans for embedding, retrieval, and generation. Compare latency across types.
141. How do you correlate LangFuse traces with frontend user behavior (e.g., clicks, input)?
Attach session_id, user_id, or ui_action metadata to each trace.
142. Can LangFuse be used to evaluate reasoning steps in CoT (Chain-of-Thought) prompts? Yes. Log each reasoning step as a span and evaluate correctness, coherence, or token path.
143. How do you track API rate limits and quota usage via LangFuse? Log rate-limit headers and remaining quotas as span metadata; use score alerts for nearing thresholds.
144. Does LangFuse support synthetic test trace generation for QA workflows? Yes. You can programmatically simulate traces with known outputs and evaluate scoring logic.
145. How do you structure traces for nested agents or hierarchical decision trees? Use a root span per decision cycle and nest sub-agents/tools as children.
146. Can LangFuse track performance differences between hosted and local LLMs?
Yes. Tag each span with deployment=local or cloud, and compare latency and score metrics.
147. How do you visualize agent retries and fallback logic in LangFuse? Log each retry/fallback as a new child span with retry index or fallback reason tagged.
148. What role does LangFuse play in a CI/CD pipeline for prompt testing? Run test prompts post-deploy, log outputs, and verify against baseline scores or conditions.
149. How do you integrate LangFuse logs with Grafana or Datadog dashboards? Export metrics via API and push to Grafana Loki, Prometheus, or Datadog via custom ingestion pipelines.
150. How does LangFuse help monitor grounding failures in RAG pipelines? Track retrieval relevance, attach feedback or score spans, and label hallucinations or grounding errors.
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