Engineering Resources

Step-by-step guide to migrate your prompt management and observability from Helicone to Langfuse, covering prompt templates, variables, versioning, and tracing.

Last updated Jul 22, 2026

Step-by-step guide to migrate a Promptfoo eval suite to Langfuse: turn tests into dataset items, port assertions to evaluators, and keep failing CI on regressions.

Last updated Jul 16, 2026

Migrate from Arize AX to Langfuse: keep OpenInference, switch the exporter, export datasets, and re-run experiments without importing old scores.

Last updated Sep 12, 2026

Step-by-step guide to migrating from Arize Phoenix to Langfuse: keep OpenInference instrumentation, replace phoenix.otel.register(), and recreate datasets, prompts, and experiments.

Last updated Sep 12, 2026

Step-by-step guide to migrate from Braintrust to Langfuse: swap SDK instrumentation, export datasets via API, keep autoevals scorers, and re-run experiments.

Last updated Sep 12, 2026

Migrate from LangSmith to Langfuse: fan out traces via OpenTelemetry, copy datasets and prompts, re-run experiments, and optionally reingest trace history.

Last updated Sep 12, 2026

What the 🪢 knot emoji means, its Unicode codepoint, how to copy and use it, and why an LLM engineering platform picked it as a logo.

Last updated Sep 7, 2026

Ten copy-paste code evaluators for LLM applications: output validation, PII screening, RAG citation checks, numeric tolerance, refusal detection, and more.

Last updated Jul 6, 2026

AI agent evaluation explained: how to measure trajectory, tool use, task completion, and multi-turn quality, with offline and online evaluation patterns.

Last updated Sep 14, 2026

Use Langfuse for AI safety research: trace agent behavior, review failures, evaluate safety monitors, and compare interventions with open-source infrastructure.

Last updated Sep 9, 2026

Answer relevance evaluation for LLM apps: what the metric measures, judge prompt design, a runnable Langfuse experiment, and how Ragas answer relevancy fits.

Last updated Jul 16, 2026

Four working patterns for chatbot analytics on LLM traces: offline intent classification, LLM-as-a-judge detection, score dashboards, and agent-driven analysis.

Last updated Aug 12, 2026

Langfuse is an open-source AI engineering platform for tracing, prompt management, evals, and experiments. September 2026 clarifications of what Langfuse includes.

Last updated Sep 12, 2026

Reuse Python or TypeScript application functions and deterministic graders, compare versions on a fixed dataset, and catch regressions in CI.

Last updated Sep 11, 2026

Use RAGAS to evaluate your RAG pipelines traced with Langfuse to measure the quality of your retrieval and sythesis.

Last updated Aug 12, 2026

Golden dataset evaluation for LLM apps: build test sets from production traces, keep them fresh, and compare prompt versions over time with experiments.

Last updated Aug 12, 2026

Hallucination detection for LLM apps: taxonomy, LLM-as-a-judge evaluators on production traces, offline experiments with references, and code pre-screens.

Last updated Jul 31, 2026

Why Langfuse stores agent traces in ClickHouse and how we operate it at scale: the data model, the write path, read isolation, and the trade-offs we engineer around.

Last updated Jul 24, 2026

How to build an LLM evaluation strategy: quality dimensions from failure modes, evaluator selection, CI release gates, production monitoring, and human review.

Last updated Sep 8, 2026

RAG faithfulness evaluation explained: what the faithfulness metric measures, how to design an LLM-as-a-judge for it, and runnable Langfuse Python SDK code.

Last updated Jul 16, 2026

How to evaluate entire sessions in Langfuse rather than individual traces: session scores, LLM-as-a-judge on conversations, annotation, and SDK patterns.

Last updated Aug 17, 2026

We manage hundreds of prompts across several LLM features. How should we organize, version, and test them? Folders, labels, prompt experiments, and UI edits.

Last updated Sep 4, 2026

Where Langfuse Cloud's EU region stores data and backups, what the DPA and DSAR process cover, and which controls and certifications apply to EU teams.

Last updated Sep 4, 2026

How product teams use Langfuse without writing code: session replay, annotation queues, user feedback, dashboards, and natural language questions over traces.

Last updated Sep 10, 2026

Benchmark of the latency impact of the Langfuse Python SDK and its OpenAI, Langchain, and LlamaIndex integrations.

Last updated Aug 10, 2026

LLM cost management: track token spend accurately, attribute it to users and features, and control it with alerts. Pitfalls and how to solve them in Langfuse.

Last updated Sep 7, 2026

Set up LLM regression testing that fails CI: golden datasets, experiment thresholds, RegressionError, and GitHub Actions via langfuse/experiment-action.

Last updated Sep 11, 2026

Where to mask PII in an LLM application: client-side SDK masking, server-side ingestion masking, gateway-level filtering, and detection with evaluators. Patterns and trade-offs.

Last updated Jul 6, 2026

Learn how to build a prompt CI/CD pipeline: version prompts with labels, validate on datasets, gate promotion in CI, roll out gradually, and roll back fast.

Last updated Aug 21, 2026

Performance benchmark on Langfuse Prompt Management measuring latency of retrieving and compiling prompts.

Last updated Aug 10, 2026

How to trace AI coding agents with Langfuse: Claude Code, OpenAI Codex, GitHub Copilot, Cursor, and five more. Setup patterns, cost tracking, and team governance.

Last updated Sep 11, 2026

How to collect user feedback on AI responses and turn it into evaluation datasets: capture ratings as scores, filter the failures, build dataset items, gate CI.

Last updated Sep 4, 2026

Langfuse works from any language with an OpenTelemetry SDK: point the OTLP exporter at the Langfuse endpoint. Setup for Go, Java, C#/.NET, and Ruby.

Last updated Jul 22, 2026

Use DeepEval with Langfuse to evaluate LLM applications: run DeepEval metrics in your Python pipeline and store results as scores on traces and experiments.

Last updated Aug 12, 2026

In this session, we talked about advanced techniques and best practices for implementing traceability and observability in multi-step LLM systems.

Last updated Sep 7, 2026

An LLM gateway is a proxy layer between your application and model providers: one API, failover, caching, cost controls. How gateways work and how to pick one.

Last updated Sep 7, 2026