FuseBase vs Langfuse

Langfuse CLI 1.0 and new evaluator features

Langfuse CLI 1.0 released, along with new evaluator template gallery and reusable evaluators for production evaluations.

Deploying Langfuse Open-Source LLM Observability

This technical guide explains how to deploy Langfuse, an open-source observability platform for LLM applications, using Docker Compose. The deployment uses PostgreSQL for metadata, ClickHouse for trace and metrics analytics, Redis for cache/queueing, and S3-compatible object storage for media/exports, with Traefik and Let's Encrypt providing TLS. The article includes required prerequisites (Linux server 4 vCPU / 16GB RAM, Docker + Docker Compose, domain A record), step-by-step environment and docker-compose configuration, first-run setup (create organization/project and API keys), and a test-trace example using the Langfuse SDK and an OpenAI-compatible client. Publication date: 2026-08-12.

• Langfuse is an open-source observability platform for LLM applications that traces prompts/responses, tracks token usage and cost, and provides debugging analytics.
• The guide deploys Langfuse via Docker Compose using Traefik (TLS), PostgreSQL (metadata), ClickHouse (trace/metrics analytics), Redis (cache/queue), and S3-compatible object storage.
• Container images and versions shown include traefik:v3.7.0, postgres:17, clickhouse/clickhouse-server:26.5.1-alpine, and redis:7-alpine; Langfuse images used are langfuse/langfuse:3 and langfuse/langfuse-worker:3.

Reliable AI Agents: FSMs and Hidden Costs

This technical article argues that building production-grade AI agents requires engineering discipline rather than relying solely on LLM capability. It identifies common failure modes in naive agentic workflows—hallucination loops, infinite recursion, and context-window exhaustion—and recommends embedding LLMs inside deterministic Finite State Machines (FSMs) using an Orchestrator pattern to enforce valid transitions and step limits. The piece also highlights operational "hidden costs" (token complexity/latency, cost of failure, and observability/debugging overhead) and lists production best practices including human-in-the-loop approvals, structured output/schema validation, idempotent tool design, and fallback mechanisms.

• Agentic workflows are systems that perceive, plan, act, and observe to achieve multi-step goals and differ from simple prompt-response chatbots.
• Common failure modes in naive agents include: hallucination loops, infinite recursion (unbounded tool-call loops), and context window exhaustion.
• Finite State Machines (FSMs) and the Orchestrator pattern are recommended to govern LLM-driven agents, enforce valid state transitions, and limit steps.