Langfuse Reviews and Details

Social recommendations and mentions

• How to change an LLM prompt in production without a code deploy

  Langfuse is the other serious option in this category if you also want observability, evals and traces bundled with prompt management. Different scope, more setup, worth comparing honestly. - Source: dev.to / 4 days ago

• Should Your Prompt Store Pick Your Model

  Langfuse with Microsoft.Extensions.AI has an appealing story: update prompts without redeploying. A prompt fetches its config blob—model, tokens, temperature—which the code passes straight to the LLM. - Source: dev.to / 8 days ago

• The Observability Crisis: Why OTel Alone Fails for AI and How to Build a Resilient Pipeline

  Langfuse is not a replacement for OpenTelemetry; it is a specialization layer built on top of it. Langfuse was engineered specifically for the unique telemetry needs of LLM applications. It acts as the semantic layer that OTel lacks. - Source: dev.to / 13 days ago

• Your AI Agent Works in Dev. It Will Fail in Production. Here's the Math.

  Langfuse and LangSmith exist for this. Use them. The 30 minutes you spend setting up observability saves you the 87 hours you'd spend debugging blind. - Source: dev.to / 25 days ago

• Strands Agents + Langfuse Evaluations

  In this project we will build a Python banking assistant agent using Strands Agents and make it observable and continuously evaluated using Langfuse — step by step. - Source: dev.to / 2 months ago

• Best AI Monitoring Tools in 2026: LLM, Agent, and MCP Observability Compared

  Langfuse is the open-source standard for LLM observability. It traces every LLM interaction — prompts, completions, latency, token usage, cost — and provides the tooling to debug, evaluate, and optimize LLM applications in production. Think of it as "Datadog for LLM calls" with a focus on prompt engineering workflows. - Source: dev.to / 3 months ago

• What is an LLM evaluation harness? A deep dive into lm-eval-harness

  You're monitoring production traffic. You need Langfuse / Phoenix / Helicone / Braintrust for that. Online eval is a different problem class: implicit feedback, drift detection, hallucination rates on your data, not on HellaSwag. - Source: dev.to / 3 months ago

• How to track LLM costs per customer in production

  Gateway or proxy attribution. A reverse proxy in front of the model-provider API records the request, computes the cost, and exposes per-customer breakdowns. Open-source options include Helicone, LiteLLM, Langfuse, and OpenLLMetry. Hosted equivalents serve as the AI cost observability layer for teams that want centralized visibility: LangSmith, Datadog LLM Observability, Arize Phoenix. Adds a network hop.... - Source: dev.to / 3 months ago

• Per-user cost attribution for your AI APP

  Same approach works with Langfuse, Phoenix, Braintrust, or your existing OTel pipeline — the metadata.userId pattern is the universal part. - Source: dev.to / 4 months ago

• Security in the Age of Coding Agents

  Harness-level logging and traces. If you're running agents through an orchestration layer - LangChain, LangGraph, CrewAI, or similar - ship traces to an observability tool. Langfuse is a solid open-source option for LLM tracing: every tool call, every input/output, timestamped. That's your audit trail. You really appreciate when the investigation "what did the agent do and when?" takes less than a minute. - Source: dev.to / 4 months ago

• 5 Things That Go Horribly Wrong When You Run AI Agents Without a Gateway (And How to Stop the Bleeding)

  Langfuse — Similar story. Great traces, helpful for the post-mortem. Not built to enforce budget ceilings. - Source: dev.to / 4 months ago

• Mitigating Risk, Unlocking Potential: Observability and Agentic Systems

  We’ve built strong processes and abstractions to manage complex distributed systems, but the sentiment is that these tools are not applicable when running agents at scale. This is false. The common thread around managing any complex system is understanding the internal state of that system. The practice around this is Observability. And the current standard, OpenTelemetry, is already widely supported in agentic... - Source: dev.to / 5 months ago

• Top 7 LLM Observability Tools in 2026: Which One Actually Fits Your Stack?

  TL;DR: Go with Langfuse if you want open-source and self-hosted. Pick Helicone if you want the fastest setup (2 minutes, no SDK). Stick with LangSmith if your stack already runs on LangChain. And if your org already pays for Datadog, their LLM module slots right in. - Source: dev.to / 6 months ago

• Top 5 LiteLLM Alternatives in 2026

  Langfuse isn’t a direct gateway replacement  it’s more of an observability and analytics layer for LLM applications. - Source: dev.to / 7 months ago

• Getting Started with AP2

  Also, from an LLMOps perspective, Langfuse has been introduced. This made building LangGraph graphs for SA and MA much easier. (Thanks, Langfuse!). - Source: dev.to / 8 months ago

• Building Self-Refining AI Agents with Ollama & Langfuse

  Today, I'm going to show you how to build a Self-Recursive Agent using Node.js, local LLMs (Ollama), and Langfuse. This agent doesn't just write; it crtiques its own work, fixes mistakes, and improves recursively until it meets a high quality standard. - Source: dev.to / 8 months ago

• Java's Agentic Framework Boom is a Code Smell

  Yes, Java SDKs are critical. But you don't need to rebuild entire orchestration engines just to write agents in Java. The ecosystem already has platforms solving the hard problems: memory (Zep, Mem0, LangMem), tools (specialized platforms), vectors (Pinecone, Weaviate, Qdrant), observability (LangSmith, Helicone, Langfuse). Integrate, don't rebuild. - Source: dev.to / 10 months ago

• Building Strands Agents with a few lines of code: Evaluating Performance with RAGAs

  In part 3, we implemented comprehensive observability for our restaurant agent using LangFuse. Now we're taking it further by adding automated evaluation that not only measures performance but also sends evaluation scores back to LangFuse for centralized monitoring. - Source: dev.to / about 1 year ago

• What Features Should I Look for in an AI Agent Observability Platform?

  Selecting the right observability platform is critical for ensuring your AI agents perform reliably, efficiently, and safely in production. The following features are essential for modern AI agent observability platforms, as demonstrated by industry leaders like Maxim AI, Langfuse, Arize AI, and others. - Source: dev.to / about 1 year ago

• AI: Introduction to Ollama for local LLM launch

  For monitoring, there are separate full-fledged monitoring solutions like Opik, PostHog, Langfuse or OpenLLMetry, maybe will try some next time. - Source: dev.to / over 1 year ago

• LLM Observability Explained (feat. Langfuse, LangSmith, and LangWatch)

  Langfuse has emerged as a favorite in the open-source community, and for good reason. It is incredibly powerful, offering deep, detailed tracing and extensive features for monitoring, debugging, and analytics. It requires a few more environment variables for its public key, secret key, and host, but the setup is still minimal. - Source: dev.to / about 1 year ago