IVQ 51-100

Visualizations & Monitoring

51. How do you visualize multi-agent workflows using LangFuse? LangFuse supports hierarchical span visualization. Each agent or sub-agent can be logged as a span, forming a tree of nested spans visualized in the dashboard.

52. Can LangFuse be used to monitor external API calls made by the LLM? Yes. Wrap each external API call in a span with input/output data logged, allowing for latency tracking and error monitoring.

53. What’s the difference between input and output fields in a span?

• Input: The prompt, function call, or user input that triggered the span.

• Output: The response from an LLM, tool, or external API.

54. How can LangFuse be used to analyze failure rates across agents? Tag failed spans, log exceptions, and use filters to aggregate failure count per agent type or step.

55. Is there a way to aggregate trace data by prompt template in LangFuse? Yes. Add prompt template name or hash as metadata or tags, then filter or group by this value in the dashboard.

56. How do you tag traces in LangFuse for filtering and analysis? When creating traces or spans, use the .update() method or pass tags and metadata directly.

57. What’s the impact of LangFuse on LLM response latency? Minimal. SDK is lightweight and uses async logging. Overhead is usually <1ms per span.

58. Can LangFuse automatically detect anomalous behaviors or outliers? Not yet. However, you can build custom anomaly detectors by querying LangFuse metrics or exporting scores.

59. How do you use LangFuse to track prompt versions over time? Log a prompt_version tag or metadata on each span, and compare performance, scores, or latency across versions.

60. How does LangFuse integrate with LangChain’s CallbackHandler? LangFuse provides a LangChainCallbackHandler that auto-logs all LLM calls, tools, and agent decisions to LangFuse.

61. How do you analyze prompt performance across different models in LangFuse? Tag spans with model name (e.g. model=gpt-4) and use dashboard filters to compare scores, latency, or output quality.

62. What query capabilities does the LangFuse dashboard support? You can filter by:

• tags

• score thresholds

• trace/span names

• input/output content

• model names

• user/session IDs

63. Can LangFuse be used to monitor streaming LLM outputs? Yes. Partial outputs can be logged as incremental observations under a span.

64. How can LangFuse help with model comparison experiments (e.g., GPT-4 vs Claude)? Tag spans with model name and compare aggregated metrics like score, latency, or token usage side by side.

65. What types of errors or exceptions are tracked in LangFuse? Any logged Python exceptions, HTTP/API errors, or agent tool failures can be recorded in span metadata or output.

66. How do you track cost and latency metrics in LangFuse per span? Log latency_ms and token_count in span metadata. You can also track estimated cost per model call.

67. Can LangFuse be integrated with CI/CD pipelines for regression testing? Yes. Run automated test traces and compare outputs or scores to detect regressions before deployment.

68. How does LangFuse handle distributed tracing across microservices? Use shared trace IDs and propagate them across services. Each service logs its own spans under the same trace.

69. What kind of dashboards or analytics panels can you create in LangFuse?

• Score distribution histograms

• Latency scatterplots

• Prompt version comparisons

• Agent tool usage charts

70. How does LangFuse help ensure LLM responses meet compliance or policy rules? Tag or score outputs violating rules (e.g., PII, bias) and create review pipelines using LangFuse traces.

71. Can LangFuse correlate user feedback with specific LLM outputs? Yes. Attach feedback scores or tags to spans or traces based on user input or UI feedback buttons.

72. How do you implement prompt evaluations using LangFuse APIs? Use .score() on spans with criteria like fluency, factuality, helpfulness, or human feedback.

73. What’s the typical overhead of using LangFuse in production environments? Lightweight—only microseconds per span. Async batching avoids blocking main threads.

74. Can LangFuse support multiple projects or tenants from the same backend? Yes. Use the "project ID" concept with API keys or set up isolated deployments per tenant.

75. How do you log structured JSON input/output with LangFuse? Input/output fields in spans accept JSON, which is viewable and searchable in the dashboard.

76. What SDKs are available for integrating LangFuse with Node.js or Go? No official SDKs yet, but REST APIs are accessible from any language.

77. How do you use LangFuse to debug latency bottlenecks in LLM chains? Compare latency_ms across spans; sort and filter spans by execution time.

78. Can LangFuse help identify token overflow or truncation issues? Yes. Monitor token_count, output length, or log truncation indicators in span metadata.

79. How does LangFuse handle role-based access control (RBAC)? LangFuse Cloud offers organization-level user roles (admin, viewer). Self-hosted supports custom auth integrations.

80. How do you integrate LangFuse traces with Slack or monitoring alerts? Export via webhook, or use API to push anomalies (e.g., low scores, long latencies) to Slack/Discord.

81. What CLI tools are available for LangFuse users? Currently, LangFuse doesn’t provide a CLI, but the REST API and SDK offer full programmatic control.

82. How can LangFuse assist with tracing hybrid pipelines (LLMs + traditional code)? Log traditional code steps as spans just like LLM calls. You get full visibility across ML + software stack.

83. What steps are involved in setting up LangFuse self-hosted?

• Clone GitHub repo

• Setup Docker + PostgreSQL + ClickHouse

• Configure ENV vars

• Deploy UI and SDK

84. Does LangFuse provide an API to query traces programmatically? Yes. You can list, filter, and retrieve traces, spans, and scores using RESTful APIs.

85. How do you track and evaluate retries or fallback models using LangFuse? Log retry counts, fallback types, and model versions as span metadata. Use scoring to assess retry effectiveness.

86. Can LangFuse record intermediate tool calls in a LangChain agent? Yes. The LangChain callback integration captures each tool call as a span automatically.

87. How do you monitor token usage per user or session in LangFuse? Log token_count and user_id in spans, then aggregate and analyze by user or session.

88. How does LangFuse help prevent prompt regressions? Version prompts, tag traces, and compare historical scores to identify drops in quality.

89. What are best practices for organizing projects and tags in LangFuse?

• One project per major app/environment

• Standardize tags like model, version, feature, user_group

90. How does LangFuse visualize branching logic or conditional workflows? Branching is visualized using nested spans and conditional execution paths inside the trace tree.

91. How do you implement custom scoring metrics in LangFuse? Call span.score(name, value) with custom criteria like coherence, toxicity, or business KPIs.

92. Can LangFuse be integrated with prompt versioning tools like PromptLayer or Git? Yes. Log version hashes or Git commit IDs as tags/metadata to trace prompt lineage.

93. How does LangFuse support evaluation of function-calling or tool-using agents? Each function/tool call is a span. You can attach tool name, inputs, outputs, and response time.

94. What’s the difference between span_type="generation" and span_type="tool" in LangFuse?

• generation: An LLM text generation

• tool: External call like calculator, web search, or DB query

95. How do you track hallucination rates over time using LangFuse? Attach a hallucination=true tag or score, then visualize frequency over days or prompt versions.

96. Can LangFuse support multilingual LLM trace evaluation? Yes. Log detected or target language as metadata and compare per-language performance.

97. How do you secure API keys and credentials in LangFuse setups? Use environment variables or secrets managers. For Cloud, rotate keys regularly via dashboard.

98. What options does LangFuse offer for long-term trace archival? For self-hosted: offload to S3, BigQuery, etc. For Cloud: use export API or set data retention policy.

99. How do you compare trace timelines across different user personas? Tag each trace with persona_type, then filter and visualize durations, scores, and outputs.

100. How does LangFuse contribute to Responsible AI practices (e.g., fairness, bias detection)? By enabling traceable evaluations, tracking bias scores, and correlating feedback with model behavior.