2026 最佳 AI 监控与可观测性工具推荐
AI 可观测性平台、LLM 评估工具、运行监控和 Agent 调试仪表盘。深入了解你的 AI 系统。
LangSmith — Prompt Debugging and LLM Observability
Debug, test, and monitor LLM applications in production. LangSmith provides trace visualization, prompt playground, dataset evaluation, and regression testing for AI.
Latitude — AI Agent Engineering Platform
Open-source platform for building, evaluating, and monitoring AI agents in production. Observability, prompt playground, LLM-as-judge evals, experiment comparison. LGPL-3.0, 4,000+ stars.
Opik — Debug, Evaluate & Monitor LLM Apps
Trace LLM calls, run automated evaluations, and monitor RAG and agent quality in production. By Comet. 18K+ GitHub stars.
Grafana — Open Source Data Visualization & Observability
Grafana is the leading open-source platform for monitoring and observability. Visualize metrics, logs, and traces from Prometheus, Loki, Elasticsearch, and 100+ data sources.
Arize Phoenix — Open Source AI Observability and Evaluation
Arize Phoenix is an open-source platform for monitoring, evaluating, and debugging AI applications, providing tracing, experiment tracking, and automated evaluation for LLM and ML pipelines.
Coze Loop — Agent Prompt, Eval, and Observability Hub
Coze Loop unifies prompt iteration, evaluation, and trace observability, helping agent teams debug workflows without jumping across separate tools.
Gemini CLI Extension: Observability — Monitoring & Logs
Gemini CLI extension for Google Cloud observability. Set up monitoring, analyze logs, create dashboards, and configure alerts.
Langfuse — Open Source LLM Observability
Langfuse is an open-source LLM engineering platform for tracing, prompt management, evaluation, and debugging AI apps. 24.1K+ GitHub stars. Self-hosted or cloud. MIT.
SigNoz — Open Source APM & Observability Platform
SigNoz is an open-source Datadog/New Relic alternative with logs, traces, and metrics in one platform. Native OpenTelemetry support, ClickHouse backend, and powerful dashboards.
TensorZero — Open-Source LLMOps Platform in Rust
TensorZero is an open-source LLMOps platform that unifies an LLM gateway, observability, evaluation, optimization, and experimentation into a single performant system written in Rust.
Phoenix — Open Source AI Observability
Phoenix is an AI observability platform for tracing, evaluating, and debugging LLM apps. 9.1K+ stars. OpenTelemetry, evals, prompt management.
AgentOps — Observability Dashboard for AI Agents
Python SDK for monitoring AI agent sessions with real-time dashboards, token tracking, cost analysis, and error replay. Two lines of code to instrument any framework. 4,500+ GitHub stars.
Sentry — Open Source Error Tracking & Performance Monitoring
Sentry is the developer-first error tracking and performance monitoring platform. Capture exceptions, trace performance issues, and debug production errors across all languages.
Sentry MCP — Error Monitoring Server for AI Agents
MCP server that connects AI agents to Sentry for real-time error monitoring. Query issues, analyze stack traces, track regressions, and resolve bugs with full crash context. 2,000+ stars.
Pixie — eBPF-Based Auto-Instrumentation for Kubernetes Observability
CNCF observability platform that uses eBPF to capture metrics, traces, and logs from every pod with zero code changes.
AgentOps — Observability for AI Agents
Python SDK for AI agent monitoring. LLM cost tracking, session replay, benchmarking, and error analysis. Integrates with CrewAI, LangChain, AutoGen, and more. 5.4K+ stars.
OpenLIT — OpenTelemetry LLM Observability
Monitor LLM costs, latency, and quality with OpenTelemetry-native tracing. GPU monitoring and guardrails built in. 2.3K+ stars.
Langtrace — Open Source AI Observability Platform
Open-source observability for LLM apps. Trace OpenAI, Anthropic, and LangChain calls with OpenTelemetry-native instrumentation and a real-time dashboard.
Netdata — Real-Time Infrastructure Monitoring & Observability
Netdata is an open-source monitoring agent that collects thousands of metrics per second with zero configuration. Beautiful dashboards, ML-powered alerts, and instant deployment.
Vector — High-Performance Observability Data Pipeline
Vector collects, transforms, and routes logs, metrics, and traces from any source to any destination. Written in Rust, it handles 100x more throughput than Logstash/Fluentd on the same hardware with a unified config language.
OpenObserve — Rust-Based Petabyte-Scale Observability Platform
All-in-one Rust observability platform that ingests logs, metrics, traces and RUM into Parquet on object storage for 140x cheaper retention.
Evidently — ML & LLM Monitoring with 100+ Metrics
Evaluate, test, and monitor AI systems with 100+ built-in metrics for data drift, model quality, and LLM output. 7.3K+ stars.
Coroot — Open Source Observability with AI Root Cause Analysis
Coroot is a self-hosted observability and APM tool that combines metrics, logs, traces, and continuous profiling with eBPF-based auto-instrumentation and AI-powered root cause analysis in predefined dashboards.
PostHog LLM Observability — Track AI Agents in Production
PostHog LLM Observability traces every LLM call from your app — model, latency, cost, errors. Auto-detects via SDK wrapper. Free up to 100K events/month.
HyperDX — Open Source Full-Stack Observability Platform
A self-hosted observability platform that unifies logs, metrics, traces, and session replays in one interface powered by ClickHouse and OpenTelemetry.
SigNoz MCP Server — Query Traces, Logs & Alerts
SigNoz MCP Server connects MCP clients to your SigNoz instance: query traces/logs, inspect alerts, and automate observability workflows using an API key.
Judgeval — Tracing + Evaluation for Agent Apps
Judgeval adds tracing and evaluation to agent apps, helping teams score behavior and monitor live traffic with a small SDK and dashboard workflow.
Datadog LLM Observability — Trace Cost, Latency, Drift
Datadog LLM Observability traces OpenAI / Anthropic / Bedrock calls, tracks per-user cost, surfaces drift. Dashboards and span-level prompt view.
langfuse-mcp — Query Langfuse Traces via MCP
Connect Langfuse observability to Claude Code/Codex via MCP: fetch traces, prompts, and datasets (37 tools). Works with Langfuse Cloud or self-hosted.
DeepFlow — eBPF Observability for Cloud & AI
DeepFlow offers zero-code eBPF observability for Kubernetes/VMs—flows, metrics, traces, profiling—with OpenTelemetry support and a Docker Compose deploy.
AI 可观测性
AI Observability
As AI moves from prototypes to production, observability becomes critical. You need to know what your AI is doing, why it made a decision, how much it costs, and when it fails. LLM Observability — Opik, Langfuse, and AgentOps provide tracing, logging, and analytics for LLM applications. See every prompt, completion, tool call, and token cost in a unified dashboard.
Agent Debugging — Multi-step AI agents are hard to debug. Observability tools capture the full execution trace — every reasoning step, tool invocation, and decision point — so you can replay and diagnose failures. Evaluation Frameworks — DeepEval, Ragas, and custom eval pipelines measure AI quality systematically. Track accuracy, hallucination rates, latency, and cost across model versions.
Infrastructure Monitoring — Uptime Kuma and Grafana integrations monitor your AI endpoints, alert on degradation, and track SLAs. Essential for production AI services where downtime or quality drops directly impact users.
You can't improve what you can't measure — and AI systems are notoriously hard to measure.
常见问题
What is AI observability?+
AI observability is the practice of monitoring, tracing, and analyzing AI system behavior in production. It goes beyond traditional monitoring (is the server up?) to answer AI-specific questions: Is the model hallucinating? Are responses getting slower? Which prompts produce the best results? How much does each query cost? Tools like Opik and AgentOps provide dashboards that answer these questions in real-time.
How do I debug AI agent failures?+
Use tracing tools that capture the full agent execution: every LLM call, tool invocation, memory access, and decision point. AgentOps and Langfuse visualize these traces as timelines, letting you pinpoint exactly where an agent went wrong. For intermittent failures, set up automated evaluation that flags quality drops before users report them.
What metrics should I track for LLM applications?+
Essential metrics: latency (time to first token, total response time), cost (tokens per request, cost per user), quality (eval scores, hallucination rate, user feedback), and reliability (error rate, timeout rate, retry rate). Advanced: track these metrics per prompt template, per model version, and per user segment to identify regressions quickly.