“1. Langfuse Best overall starting point”
“- Captures LLM inputs and outputs, retrieval spans, tool calls and nested execution traces.”
“- Shows token usage, latency, costs, errors and session metadata.”
“- Includes prompt management, evaluations and trace filtering.”
“- Open source; can self-host.”
“Best for: framework-independent applications and production debugging.”
“1. Langfuse if you want the complete trace history, flexibility and self-hosting.”
“2. LangSmith Best for agent debugging”
“- Detailed execution trees for chains, agents and tool calls.”
“- Captures intermediate inputs and outputs.”
“- Supports multi-step and multi-turn execution inspection.”
“- Dataset-based testing and evaluation.”
“Best for: LangChain and LangGraph applications.”
“2. LangSmith if your agents are built with LangGraph or LangChain.”
“3. Arize Phoenix Best for RAG debugging”
“- Traces retrieval, embedding and LLM operations.”
“- Inspect retrieved documents, relevance and context.”
“- Supports agent traces and OpenInference instrumentation.”
“- Open source and self-hostable.”
“Best for: complex RAG pipelines and framework-independent tracing.”
“3. Arize Phoenix if your biggest debugging challenge is retrieval quality and RAG.”
“- Detailed spans for LLM and agent execution.”
“- Trace inspection alongside evaluation scores.”
“- Compare runs and identify regressions.”
“- Connect production traces to test datasets.”
“Best for: teams wanting debugging and systematic quality evaluation together.”
“- Connects LLM traces to infrastructure traces.”
“- Monitors prompts, retrieval, tool calls and model performance.”
“- Adds production monitoring, alerting and service-level visibility.”
“Best for: enterprise environments already using Datadog.”
“- Tracks nested operations and LLM calls.”
“- Inspects inputs, outputs and intermediate execution.”
“- Supports evaluation, scoring and experiment comparison.”
“Best for: ML engineering teams already using Weights & Biases.”