Langfuse Reviews and Details

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• 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 / 18 days 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 / about 1 month 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 / about 2 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 / about 2 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 / about 2 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 / 2 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 / 2 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 / 3 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 / 4 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 / 5 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 / 6 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 / 6 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 / 9 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 / 11 months 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 / 12 months 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 / about 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

• How to Learn AI from Scratch

  And then there’s evaluation and observability—two things you must consider when your AI app is live. You need to know if the model is doing its job, and why it failed when it didn’t. Tools like LangSmith and LangFuse can help with this, but you’ll need to spend time experimenting with what works best for your stack. - Source: dev.to / about 1 year ago

• Top Open Source Tools for LLM Observability in 2025

  Langfuse is another open-source platform for debugging, analyzing, and iterating on language model applications. It offers tracing, evaluation, and prompt management. While Langfuse offers many capabilities, some (like the Prompt Playground and automated evaluation) are only available in the paid tier for self-hosted users. - Source: dev.to / about 1 year ago

• A Curated List of shadcn/ui-like React Component Collections

  It is reportedly used on websites like Langfuse and Million.dev. - Source: dev.to / over 1 year ago

• 10 Ways AI Can Speed Up your Mobile App Development

  LangFuse is a monitoring and debugging platform for LLM-powered applications. It provides insights into token usage and costs. It can also analyze latency, and the performance of AI interactions. The platform allows debug prompts, and analyzes how they behave in production. - Source: dev.to / over 1 year ago