AgentIndex icon
AgentIndex
langfuse-mcp logo
langfuse-mcp
★ 112
vs
dunetrace logo
dunetrace
★ 68

langfuse-mcp vs dunetrace

langfuse-mcp: Langfuse MCP Server is a Model Context Protocol server for Langfuse observability. It provides 37 tools across 9 categories including traces, observations, sessions, exceptions, prompts, datasets, annotation queues, scores, and schema. It supports selective tool loading and read-only mode, and can be used with various MCP clients like Claude Code, Cursor, and Codex CLI.; dunetrace: Dunetrace monitors every run of AI agents in real-time, detecting structural failures like tool loops, retry storms, and context bloat within 15 seconds of completion. It fires alerts via Slack or webhook, provides plain-English explanations, and offers one-click fixes through Langfuse or GitHub PRs.

01

TL;DR

langfuse-mcp logo
langfuse-mcpObservability · ★ 112

Debug production errors by querying traces and exceptions

Read langfuse-mcp full review →
dunetrace logo
dunetraceObservability · ★ 68

Monitor production AI agents for silent failures like tool loops and cost spikes

Read dunetrace full review →
02

Side-by-Side Comparison

Field
langfuse-mcp logolangfuse-mcp
dunetrace logodunetrace
Category
Observability
Observability
Stars
★ 112
★ 68
License
MIT
NOASSERTION
Install Command
pip install langfuse-mcp
git clone https://github.com/dunetrace/dunetrace && cd dunetrace && cp .env.example .env && docker compose build && docker compose up -d
Updated
1w ago
1mo ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
agent-skills, ai-agents, claude-code
agent-observability, ai, ai-agents
03

Features

langfuse-mcp logolangfuse-mcp
01Comprehensive observability tools (traces, observations, sessions, exceptions, prompts, datasets, annotation queues, scores, schema)
02Selective tool loading to reduce token overhead
03Read-only mode for safe access
04Dataset item upsert functionality
05Includes a skill with debugging playbooks
dunetrace logodunetrace
01Real-time monitoring with 15-second alert latency
0217 automatic structural detectors (no configuration needed)
03Diagnose with plain-English explanations and LLM-powered root-cause analysis (via Langfuse)
04One-click fix application (prompt version or GitHub PR)
05Policies for mid-run guardrails (cost cap, tool call limit)
04

Use Cases

langfuse-mcp logolangfuse-mcp
↳Debug production errors by querying traces and exceptions
↳Analyze user sessions for performance optimization
↳Manage prompts and datasets for LLM evaluation
dunetrace logodunetrace
↳Monitor production AI agents for silent failures like tool loops and cost spikes
↳Automatically detect and alert on agent behavioral anomalies
↳Quickly diagnose and fix agent issues with one-click actions
FAQ

FAQ

What is the difference between langfuse-mcp and dunetrace?
Both langfuse-mcp and dunetrace are in the Observability category. langfuse-mcp has 112 stars, while dunetrace has 68 stars.
Which is better, langfuse-mcp or dunetrace?
The best choice depends on your use case. Choose langfuse-mcp if Debug production errors by querying traces and exceptions, and dunetrace if Monitor production AI agents for silent failures like tool loops and cost spikes.
Is langfuse-mcp free or open source?
Yes, langfuse-mcp is open source on GitHub (MIT).
Is dunetrace free or open source?
Yes, dunetrace is open source on GitHub (NOASSERTION).
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Alternatives to langfuse-mcp →Alternatives to dunetrace →langfuse-mcp details →dunetrace details →