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Langfuse

14 articles · Page 1 of 1
Tech Freedom Circle
Tech Freedom Circle
Nov 6, 2026 · Artificial Intelligence

Langfuse Three-Level Agent Evaluation: Outcome, Trajectory & Step Framework Principles & Implementation

This article details Langfuse's three-level evaluation framework for AI agents—Outcome, Trajectory, and Step—explaining how its Trace, Evaluator, Dataset, and Experiment components enable decoupled observability and evaluation, with concrete implementation patterns for code-based rules, LLM-as-a-Judge, and human annotation to build reliable CI/CD gates and production monitoring loops.

AI TestingAgent EvaluationCI/CD Gates
0 likes · 33 min read
Langfuse Three-Level Agent Evaluation: Outcome, Trajectory & Step Framework Principles & Implementation
DeepHub IMBA
DeepHub IMBA
Nov 4, 2026 · Artificial Intelligence

15 Open-Source Tools for Production AI Agents: Frameworks, Workflows & Execution

This article reviews 15 open-source projects for building production-ready AI agents, covering agent frameworks like LangGraph and Pydantic AI, persistent workflows with Temporal, tool execution via Playwright and E2B, observability with Langfuse, model routing via LiteLLM, and reference applications for security monitoring and research.

AI AgentsE2BLangGraph
0 likes · 24 min read
15 Open-Source Tools for Production AI Agents: Frameworks, Workflows & Execution
DataFunTalk
DataFunTalk
Oct 5, 2026 · Artificial Intelligence

WeChat's Agentic OLAP Architecture: Observability, Memory & Access Control

WeChat's technical architecture team shares their exploration of adapting OLAP infrastructure for Agentic AI, covering distributed observability with Langfuse and ClickHouse, remote memory services using vector search and FUSE, and access protection mechanisms treating agents as authenticated identities.

Agentic AIClickHouseDiskANN
0 likes · 16 min read
WeChat's Agentic OLAP Architecture: Observability, Memory & Access Control
Qborfy AI
Qborfy AI
Aug 31, 2026 · Artificial Intelligence

Build an Enterprise‑Level AI Research Report Generator from Scratch with LangGraph

This article provides a step‑by‑step walkthrough for constructing a daily industry research‑report automation system using LangGraph, covering fan‑out parallel search, fan‑in aggregation, an evaluator‑optimizer feedback loop, human‑in‑the‑loop approval, checkpointing for resumability, and full observability with Langfuse, complete with runnable code.

AI automationHITLLangGraph
0 likes · 24 min read
Build an Enterprise‑Level AI Research Report Generator from Scratch with LangGraph
Qborfy AI
Qborfy AI
Aug 30, 2026 · Artificial Intelligence

Human-in-the-Loop and Time-Travel Debugging: Making AI Graphs Production-Ready

The article explains why autonomous agents need human supervision in critical steps, introduces three HITL scenarios, shows how LangGraph’s interrupt_before/after and update_state enable pause‑and‑review workflows, demonstrates time‑travel debugging and observability with Langfuse, and provides practical design principles and a production‑grade configuration.

AgentHuman-in-the-LoopLangGraph
0 likes · 20 min read
Human-in-the-Loop and Time-Travel Debugging: Making AI Graphs Production-Ready
Tech Freedom Circle
Tech Freedom Circle
Jul 14, 2026 · Artificial Intelligence

Designing Production‑Grade Observability and Evaluation with Langfuse + RAGAS for LLM Applications

This article presents a comprehensive, production‑ready guide for building end‑to‑end observability and quantitative evaluation of LLM‑powered RAG/Agent systems using the open‑source Langfuse platform together with the RAGAS benchmark, covering architecture, installation, code instrumentation, dataset management, metric collection, and best‑practice recommendations.

LLM ObservabilityLangChainLangGraph
0 likes · 46 min read
Designing Production‑Grade Observability and Evaluation with Langfuse + RAGAS for LLM Applications
ThinkingAgent
ThinkingAgent
Jul 14, 2026 · Operations

Why AI Agents Need Observability: Tracing, Monitoring, and SRE in the L8 Layer

A recent fintech chatbot failure exposed how missing tracing, cost attribution, and proper alerting can turn a three‑day incident into a three‑day investigation, prompting a detailed guide on the L8 observability layer that defines three pillars—Tracing, Metrics, Logs—and outlines best‑practice tooling, standards, and implementation steps for AI production systems.

AI ObservabilityCost attributionLangfuse
0 likes · 36 min read
Why AI Agents Need Observability: Tracing, Monitoring, and SRE in the L8 Layer
ThinkingAgent
ThinkingAgent
Jun 22, 2026 · Artificial Intelligence

How to Achieve Full‑Stack AI Observability: Tracking Prompts, Tool Calls, Traces, and Tokens

The article explains why modern LLM‑based AI systems are opaque, defines AI observability as a four‑dimensional practice (Prompt, Tool Call, Trace, Token), and provides concrete architectures, code samples, best‑practice checklists, and real‑world case studies to turn black‑box AI into a transparent, monitorable service.

AI ObservabilityLangfusePrompt tracking
0 likes · 30 min read
How to Achieve Full‑Stack AI Observability: Tracking Prompts, Tool Calls, Traces, and Tokens
Coder Trainee
Coder Trainee
Jun 13, 2026 · Artificial Intelligence

AI Agent Observability and Debugging: Building a Transparent Agent System

This article explains why AI agents behave like black boxes, introduces a three‑pillar observability framework (tracing, metrics, logging), demonstrates practical tracing with LangSmith and LangFuse, shows how to instrument agents with custom metrics, evaluate performance, and share best‑practice guidelines for production‑ready debugging.

AI agentLangChainLangSmith
0 likes · 19 min read
AI Agent Observability and Debugging: Building a Transparent Agent System
Amazon Cloud Developers
Amazon Cloud Developers
Dec 24, 2025 · Artificial Intelligence

Evaluating Agent Observability: A Multi‑Dimensional Framework for Behavior, Quality, and Cost

The guide outlines a comprehensive, multi‑dimensional observability framework for AI agents—covering behavior insight, quality assessment, latency and token metrics, tool‑call tracking, error tracing, and cost monitoring—while demonstrating practical implementation with OpenTelemetry, Amazon CloudWatch, and open‑source tools such as MLflow and Langfuse.

Agentic AIAmazon CloudWatchLangfuse
0 likes · 27 min read
Evaluating Agent Observability: A Multi‑Dimensional Framework for Behavior, Quality, and Cost
dbaplus Community
dbaplus Community
Jun 18, 2024 · Artificial Intelligence

How to Effectively Evaluate RAG Systems: Metrics, Tools, and Best Practices

Evaluating Retrieval‑Augmented Generation (RAG) systems requires both component‑level and end‑to‑end metrics—such as context relevance, recall, answer relevance, and groundedness—and can be automated with tools like TruLens, RAGAS, LangSmith, and Langfuse, enabling systematic selection and optimization of LLM applications.

AI metricsLLMLangSmith
0 likes · 8 min read
How to Effectively Evaluate RAG Systems: Metrics, Tools, and Best Practices