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.
LangFuse — Open Source LLM Observability & Tracing
Trace, evaluate, and monitor LLM applications in production. Open-source alternative to LangSmith with prompt management, cost tracking, and evaluation pipelines.
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.
Agenta — Open-Source LLMOps Platform
Prompt playground, evaluation, and observability in one platform. Compare prompts, run evals, trace production calls. 4K+ 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.
Gemini CLI Extension: Observability — Monitoring & Logs
Gemini CLI extension for Google Cloud observability. Set up monitoring, analyze logs, create dashboards, and configure alerts.
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.
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.
Axiom MCP — Log Search and Analytics for AI Agents
MCP server that gives AI agents access to Axiom log analytics. Query logs, traces, and metrics through natural language for AI-powered observability and incident response.
OpenLIT — OpenTelemetry LLM Observability
Monitor LLM costs, latency, and quality with OpenTelemetry-native tracing. GPU monitoring and guardrails built in. 2.3K+ stars.
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.
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.
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.
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.
OpenAI Agents SDK — Build Multi-Agent Systems in Python
Official OpenAI Python SDK for building multi-agent systems with handoffs, guardrails, and tracing. Agents delegate to specialists, enforce safety rules, and produce observable traces. 8,000+ stars.
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.
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.
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.
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.
VoltAgent — TypeScript AI Agent Framework
Open-source TypeScript framework for building AI agents with built-in Memory, RAG, Guardrails, MCP, Voice, and Workflow support. Includes LLM observability console for debugging.
Claude Code Agent: K8s Specialist — Kubernetes Operations
Claude Code agent for Kubernetes. Deployment configs, helm charts, troubleshooting, scaling, monitoring, and cluster management.
Uptime Kuma — Self-Hosted Uptime Monitoring
Monitor HTTP, TCP, DNS, Docker services with notifications to 90+ channels. Beautiful dashboard. 84K+ GitHub stars.
Gemini CLI Extension: Vertex AI — Model Management
Gemini CLI extension for Vertex AI. Deploy models, manage endpoints, run predictions, and monitor ML pipelines.
DeepEval — LLM Testing Framework with 30+ Metrics
DeepEval is a pytest-like testing framework for LLM apps with 30+ metrics. 14.4K+ GitHub stars. RAG, agent, multimodal evaluation. Runs locally. MIT.
Phidata — Build & Deploy AI Agents at Scale
Framework for building, running, and managing AI agents at scale. Memory, knowledge, tools, reasoning, and team workflows. Monitoring dashboard included. 39K+ stars.
mcp-agent — Build AI Agents with MCP Patterns
mcp-agent is a Python framework for building AI agents using the Model Context Protocol. 8.2K+ GitHub stars. Implements composable workflow patterns (orchestrator, map-reduce, evaluator-optimizer, rou
Google ADK — Official AI Agent Dev Kit
Google's open-source Agent Development Kit for building, evaluating, and deploying AI agents in Python. 18.7K+ stars. Multi-agent, tool use, eval. Apache 2.0.
Ragas — Evaluate RAG & LLM Applications
Ragas evaluates LLM applications with objective metrics, test data generation, and data-driven insights. 13.2K+ GitHub stars. RAG evaluation, auto test generation. Apache 2.0.
Glama — MCP Server Discovery and Management
Directory and management platform for MCP servers. Discover, install, and monitor Model Context Protocol servers for Claude Code, Cline, and other AI coding tools.
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.