Observability Solutions
Overview
A growing ecosystem of tools and platforms provides observability for AI agents and LLM-based applications. These range from development-focused platforms with integrated tracing to specialized enterprise monitoring solutions.
Development Platforms with Observability
AgentOps
Platform: AgentOps
A comprehensive development platform with built-in observability designed specifically for AI agents. Provides agent-specific monitoring, analytics, and real-time performance tracking with cost optimization insights. Integrates with major agent frameworks including LangChain, AutoGen, and CrewAI.
LangSmith
Platform: LangSmith
LangChain's observability and evaluation platform. Provides trace visualization, debugging tools, performance analytics, and dataset management. Tightly integrated with the LangChain/LangGraph ecosystem. Supports prompt versioning, A/B testing, and automated evaluation pipelines.
Galileo
Platform: Galileo
Agent Reliability Platform purpose-built for multi-agent systems. Key capabilities:
- Graph Engine: traces complex agent paths and maps multi-agent workflows — every branch, decision, and tool call at a glance
- Insights Engine: identifies failure modes and provides actionable recommendations tied to specific components
- Trace View: collapsible interface showing complete execution paths with nested agent calls and decision points
- Graph View: maps multi-agent workflows, showing coordination patterns and information flow across sessions
- Timeline View: temporal breakdown of time spent in each phase (agent communication, retrieval, generation) to identify bottlenecks
- Log Stream Insights: real-time error detection with stack traces, automatic correlation of related events across distributed agents, pattern matching for recurring coordination issues
- Custom Metrics: define business-specific metrics evaluated by an LLM judge (e.g., GPT-4o)
Galileo tracks three distinct levels: Session (did it complete the task?), Step (were individual decisions correct?), and System (what patterns exist across sessions?). Native connectors for LangGraph, CrewAI, and other frameworks. Integrates via GalileoAsyncCallback in LangGraph pipelines.
Specialized Observability Platforms
Langfuse
Platform: Langfuse
YC W23 open-source LLM observability platform. Provides comprehensive traces, evaluations, and prompt management. Supports metrics collection and analysis for debugging and optimization. Available as self-hosted or cloud-managed. Strong community adoption with native SDKs for Python and TypeScript.
Openlit
Platform: Openlit
Open-source platform for AI Engineering built on OpenTelemetry. Provides LLM observability, GPU monitoring, guardrails, evaluations, prompt management, and a built-in playground. OpenTelemetry-native design ensures compatibility with existing observability infrastructure.
Weights & Biases (W&B) Weave
Platform: W&B Weave
Framework for tracking, experimenting, evaluating, deploying, and improving LLM-based applications. Extends W&B's experiment tracking capabilities to production LLM systems. Provides model comparison, prompt iteration tracking, and collaborative development features.
Braintrust
Platform: Braintrust
AI observability platform focused on measuring, evaluating, and improving AI in production. Features model comparison, prompt iteration tracking, regression detection using real user data, and continuous improvement workflows. Designed for teams that need to iterate quickly on production AI systems.
Comet Opik
Platform: Comet Opik
Enterprise-grade ML observability platform with experiment tracking, model monitoring, performance analytics, and team collaboration features. Extends Comet's established ML tracking capabilities to LLM and agent workloads.
agenttrace
Platform: agenttrace
Open-source local TUI and report generator for AI coding-agent session history. It reads local logs from tools such as Claude Code, Codex CLI, Gemini CLI, Qwen Code, Cursor, Aider, OpenCode, and Copilot-style traces, then summarizes token usage, estimated cost, latency, tool failures, and session health without sending prompts or code to a hosted service.
Platform Comparison
| Platform | Open Source | Enterprise | Real-time Monitoring | Cost Tracking | Evaluation Tools |
|---|---|---|---|---|---|
| AgentOps | ❌ | ✅ | ✅ | ✅ | ✅ |
| LangSmith | ❌ | ✅ | ✅ | ✅ | ✅ |
| Galileo | ❌ | ✅ | ✅ | ✅ | ✅ |
| Langfuse | ✅ | ✅ | ✅ | ✅ | ✅ |
| Openlit | ✅ | ✅ | ✅ | ✅ | ✅ |
| W&B Weave | ❌ | ✅ | ✅ | ✅ | ✅ |
| Braintrust | ❌ | ✅ | ✅ | ✅ | ✅ |
| Comet Opik | ❌ | ✅ | ✅ | ✅ | ✅ |
| agenttrace | ✅ | ❌ | ❌ | ✅ | ✅ |
Infrastructure-Level Observability
OpenTelemetry
The OpenTelemetry standard provides vendor-neutral instrumentation for traces, metrics, and logs. Many AI observability platforms (Openlit, Langfuse) are built on OpenTelemetry, enabling integration with existing observability stacks (Jaeger, Zipkin, Prometheus, Grafana).
Traditional APM Integration
For enterprise deployments, AI observability often integrates with existing APM tools: - Datadog: LLM observability add-on with trace correlation - New Relic: AI monitoring capabilities for LLM applications - Dynatrace: AI-powered observability with LLM support - Prometheus + Grafana: Open-source metrics and dashboards
Selection Guidance
For Startups and Small Teams
Start with Langfuse (open-source, self-hostable) or LangSmith (if using LangChain). Focus on traces and cost tracking initially, then add evaluations as the system matures.
For Coding-Agent Development
Use agenttrace when traces already live in local coding-agent logs and the team needs terminal-first cost, latency, and regression reports without adopting a hosted observability backend.
For Enterprise Organizations
Consider Braintrust or W&B Weave for comprehensive coverage. Integrate with existing enterprise monitoring tools (Datadog, Splunk) for unified observability. Implement custom business metrics alongside technical metrics.
For Research and Development
W&B Weave or Comet Opik excel at experiment tracking and model comparison. Prioritize reproducibility and detailed logging over production-scale performance.
Best Practices
- Start with traces: Full request traces provide the most debugging value early on
- Add evaluations incrementally: Begin with automated metrics, then add human evaluation
- Set cost alerts immediately: Token costs can escalate quickly without guardrails
- Use sampling in production: 100% trace capture is expensive; sample intelligently
- Correlate with business metrics: Connect technical metrics to business outcomes