Monitor LangGraph production runs with three complementary views: execution traces to inspect what happened in an individual request, online evaluation to flag quality or behavior changes across live traffic, and runtime metrics to spot deployment capacity pressure. For Agent Server, LangSmith documents deployment-specific trace destinations, production-trace evaluation, and autoscaling signals; these are related but answer different operational questions.
How do I trace a LangGraph agent run?
Use run-level traces to inspect execution flow and component behavior when a request fails or produces an unexpected answer. A useful trace should let an operator follow the run through relevant graph steps, see tool use, and connect behavior to the outcome. LangSmith’s online evaluation supports filtering production traces by metadata and tool calls, making consistent, non-sensitive metadata useful for narrowing which runs matter to a particular check. See LangSmith tracing for Agent Server.
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When investigating an incident, follow the trace to determine whether the issue was an unexpected tool call, a slow or failing component, or an output-quality problem. A trace is evidence of execution, not by itself a verdict that the answer was good or that the deployment had enough capacity.
Check where Agent Server sends traces
Tracing destination depends on the Agent Server deployment model. LangChain documents these choices:
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- Adjustable Depth & 2 Set Rack Rails: Includes two sets of Rack Rails. Short Rack Rails: Fit 18.9"–23.6" (480-600mm) deep network racks (Note: check cable clearance for depths under 600mm). Long Rack Rails: Fit 23.6"–31.5" (600-800mm) deep standard racks. Measure your rack depth before purchase to ensure a perfect fit.
- External Monitor Support & Flexible Operation--Features an VGA console output for connecting an external monitor, allowing convenient server access without opening the rack. Supports front panel buttons, touchpad, hotkeys, and OSD menu control. Support password prodected: provides 2-level password security (administrator and user), up to 8 authorized users and an administrator view and control the computers.
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- Built for Professional Environments – Ideal for server rooms, data centers, industrial control systems, and security monitoring centers where multiple computers need centralized management or when technicians need direct access to connected systems without an external monitor.
| Deployment | Documented trace options |
|---|---|
| Cloud | Traces to LangSmith SaaS. |
| Hybrid | Tracing can be disabled or sent to LangSmith SaaS. |
| Self-Hosted | Tracing can be disabled, sent to LangSmith SaaS, or sent to Self-Hosted LangSmith. |
These are documented Agent Server options, not a guarantee that every deployment is configured identically. Confirm the current configuration and data-handling implications for your environment before enabling tracing.
How do I catch agent quality regressions in production?
Add online evaluators that check outcomes tied to your users, safety requirements, or known failure modes. LangSmith describes online evaluation as real-time feedback on production traces; evaluators can be scoped with filters, including metadata and tool-call conditions. This helps direct checks toward relevant production runs rather than treating every trace as interchangeable. See LangSmith online evaluation for production traces.
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- LCD MONITOR: The KVM console features 19" LCD display and supports video resolutions up to 1280 x 1024
- GREAT COMPATIBILITY: The B021-000-19 is compatible with most PS/2 and USB KVM switches, making it easy to integrate with an existing system.
- USB PASS-THROUGH: The unit features a USB 2. 0 pass-through port for connection of a USB peripheral, such as a flash drive, CAC card reader, etc. .
- TAA-Compliant for GSA Schedule Purchases and 1-Year
Use evaluator results as monitoring evidence: review poor outcomes and unusual behavior, then route cases that need judgment to a human. An evaluator can flag a concern, but the team still needs to decide what constitutes an actionable failure and how it should affect a release or incident response.
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Keep offline regression tests in the release loop
Online evaluation observes live behavior; offline evaluation checks a curated dataset before rollout. Maintain representative examples and reference outputs to compare application versions, and turn useful production findings into future test cases. The two practices complement rather than replace one another: offline checks help catch known regressions before deployment, while online checks reveal behavior in actual traffic. See LangSmith evaluation guidance.
Rank #3
- Compatible to: This Mounting Bracket is designed for the TAA compliant Universal VESA LCD Monitor in 19-inch network cabinet or server rack.
- Sturdy Structure: The LCD mounting bracket is made of cold rolled steel and supports 100mm & 75mm VESA mounted LCD panels.
- Adjustable Depth: This adjustable depth design enables an LCD panel to be mounted into the AV rack cabinet at various depths; allowing the rack or cabinet door to be closed.
- Multi-use: Besides using in 19" network cabinet or server rack, the LCD monitor can be mounted onto wall by adding this bracket onto a wall mount bracket or rack.
What should I monitor for a LangGraph deployment?
Track application behavior and deployment capacity separately. For Agent Server Production autoscaling, LangChain documents CPU utilization, memory utilization, and pending runs as scaling signals. Its published targets are 75% CPU utilization, 75% memory utilization, and 10 pending runs per container; for scale-down, the system waits 30 minutes before recomputing metrics and acting. These are deployment autoscaling parameters in LangChain documentation, whose publication year is not stated—not universal application SLOs or independently measured benchmarks. Set alert thresholds and service objectives to fit your workload. Details are in the Agent Server autoscaling documentation.
Pair those runtime signals with the measures that describe user experience and application quality, such as latency and evaluator outcomes. Capacity metrics can indicate resource pressure; they do not tell you whether an agent’s reasoning, tool choice, or final response met your requirements.
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- Product Dimension (W×D×H):18.9×23.6×1.77 inches [480x600x45mm]. [Mount depth]: 23.6- 31.8" [60-81cm]，Mounts into 19”-wide rack. The monitor is adjustable, the max angle is 110°
- External Monitor Support & Flexible Operation--Features an HDMI console output for connecting an external monitor, allowing convenient server access without opening the rack. This Rack KVM Switch support Three Switching Ways: OSD menu + Keyboard Hotkey+ Button. There are 2 OSD menu: Screen OSD and KVM OSD, also supports external USB mouse.
- Come with Handle & Lock. [All-IN-ONE Design] You just need to place the KVM directly into 1U rackmount and tighten the screws. Designed for data centers, enterprise IT, government, and educational institutions, delivering a secure, scalable, and efficient server management solution.
- [Security & Durability] 2 Level Password Security, only authorised users can view and control computers; This KVM console is upgraded with alumium for less wight, and the draw shell is made by steel for sturdy. Compatible with Dos/Windows, Linux, Unix, Mac OS8.6/9/10, Unix and SUN Solaris 8/9.
A practical production monitoring workflow
- Choose useful trace context. Decide which graph runs, component details, and metadata operators need to identify a problem. Prefer stable, non-sensitive metadata that supports meaningful filtering.
- Verify the trace destination. Check your Cloud, Hybrid, or Self-Hosted configuration and confirm whether traces go to LangSmith SaaS, Self-Hosted LangSmith, or are disabled.
- Use traces to investigate individual failures. Follow the run’s execution, tool calls, and component behavior to locate where the outcome went wrong.
- Configure a focused set of online evaluators. Tie checks to user outcomes or known failure modes, and filter the runs each evaluator examines where that improves relevance.
- Route concerning outcomes for review. Treat evaluator anomalies and poor results as signals for investigation, with human review where needed.
- Run offline regression evaluation before rollout. Compare changes against curated examples and add appropriate lessons from production cases to the dataset.
- Watch runtime capacity independently. Monitor CPU, memory, and pending runs alongside application-level quality and latency indicators; define workload-specific alert thresholds and service objectives.
What are the observability options beyond LangSmith?
LangSmith is directly documented for Agent Server tracing and online evaluation. Teams already using MLflow can also consider its LangGraph integration: LangChain describes MLflow support for tracing, experiment tracking, model management, and evaluation. The available documentation does not establish a complete feature, deployment-control, or cost comparison, so evaluate fit against your existing stack and requirements rather than assuming the tools are interchangeable. See LangChain’s MLflow integration documentation.
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