GITNUXSOFTWARE REVIEW
Business FinanceTop 10 Best Agent Monitoring Software of 2026
Review agent monitoring software options with a factual tool roundup for teams, covering how LangSmith, Lunary, and Traceloop fit workflows.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Agents extract product evidence with provenance, then collect written reviews and transcribe video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
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LangSmith is the best overall pick for teams building LangChain-style agents because dataset-backed evaluation runs attach feedback to specific traces and prompts, making regressions easy to spot, while Lunary is a strong alternative for SMBs that want timeline tracing with automated checks across tool-using agent steps.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
LangSmith
Dataset-backed evaluation runs that attach feedback to specific traces and prompts for regression analysis.
Best for: Fits when teams need trace-linked evaluation and regression monitoring for LangChain-style agents.
Lunary
Editor pickTimeline tracing that preserves tool inputs and outputs across intermediate agent steps in one run view.
Best for: Fits when teams need run-trace monitoring with automated checks for tool-using agents.
Traceloop
Editor pickRun replay that reconstructs the tool-call sequence to compare failures against prior successful executions.
Best for: Fits when teams need trace-level debugging and operational oversight for AI agents.
Comparison Table
LangSmith
enterpriseLangSmith traces, evaluates, and monitors production LLM and agent applications.
Dataset-backed evaluation runs that attach feedback to specific traces and prompts for regression analysis.
LangSmith instruments and evaluates agent runs from LangChain and compatible workflows, then links traces to datasets, prompts, and feedback. The product centers on end-to-end observability for LLM applications, including experiment tracking and replayable evaluation runs.
It also provides an automation surface for running evaluation sets and routing feedback into analysis views. For agent monitoring use cases, LangSmith acts more like an agent evaluation and trace intelligence layer than a contact-center analytics system.
- +Trace-to-feedback linking connects agent executions with evaluation outcomes
- +Dataset-driven evaluations support repeatable runs across prompt and model changes
- +Experiment tracking keeps prompt and tool changes auditable over time
- +Automation APIs enable scheduled evaluation runs and batch processing
- –Agent monitoring depends on instrumentation coverage and trace capture design
- –Built-in dashboards focus on LLM traces more than operational workforce metrics
- –Deep governance features like RBAC granularity may require careful configuration
- –Speech and screen capture workflows need external pipelines outside core LangSmith
Agent developers and AI platform engineers
Debug tool-calling agent failures
Faster root-cause identification
RAG and search application teams
Test retrieval changes against examples
Evidence for retrieval impact
Show 2 more scenarios
RAG developers and context engineering teams
Verify context lineage for answers
Reproducible context-dependent answers
Connect retrieved content and intermediate tool outputs to the final response for reproducible results.
AI platform teams and DevOps engineers
Enforce dataset evaluation quality gates
Measured release quality checks
Run dataset evaluations as automated checks tied to prompt, model, or chain changes before release.
Best for: Fits when teams need trace-linked evaluation and regression monitoring for LangChain-style agents.
Lunary
SMBLunary provides monitoring, prompt management, evaluations, and analytics for LLM applications and agents.
Timeline tracing that preserves tool inputs and outputs across intermediate agent steps in one run view.
Lunary records agent run traces that include prompts, tool inputs and outputs, intermediate steps, and final results in a single view for investigation. It adds configuration for automated checks that flag missing tool calls, policy violations, or output format mismatches during monitoring. Teams can connect signals to dashboards and routing so supervisors see which runs failed and why, not just that failures spiked. Integration breadth is strongest when the agent framework already emits structured run events that Lunary can ingest.
A tradeoff is that Lunary’s monitoring depth depends on how consistently the agent emits events for each tool step and model turn. It fits best when agent orchestration already has stable identifiers for runs, tools, and outcomes, which makes alert grouping and evaluation comparisons accurate. It can be less efficient when monitoring needs rely on scraping logs that do not preserve tool inputs and outputs.
- +Session traces connect model turns, tool calls, and final outcomes
- +Automated checks flag format issues and missing tool steps
- +Run-level artifacts make failure triage faster than metric-only views
- +Dashboards support team review around specific run patterns
- –Trace quality depends on agent frameworks emitting consistent step events
- –Advanced monitoring requires more instrumentation than log-only approaches
- –Complex alert routing takes time to tune for low-noise grouping
Contact center QA leads
Audit agent-assisted call workflows
Faster root-cause for failures
Agent platform engineers
Regression monitoring for tool chains
Earlier detection of breakages
Show 1 more scenario
Operations managers
Triage alerts by run patterns
Reduced time-to-action
Supervisors group failing runs by trace context to prioritize the most impactful issues.
Best for: Fits when teams need run-trace monitoring with automated checks for tool-using agents.
Traceloop
API-firstTraceloop provides OpenLLMetry instrumentation and monitoring for LLM and agent applications.
Run replay that reconstructs the tool-call sequence to compare failures against prior successful executions.
Traceloop is an agent monitoring tool focused on end-to-end visibility for AI agent runs, with an emphasis on tracing, run replay, and issue grouping. It records the sequence of tool calls and intermediate steps so supervisors can compare successful and failed executions across sessions.
Automation support centers on alerting tied to run outcomes and configurable dashboards for operational review. Its monitoring workflow targets teams that need audit-friendly traces for performance and governance checks.
- +Trace timeline shows tool calls and intermediate agent steps per run
- +Run replay supports root-cause analysis across retries and variations
- +Dashboards group runs by outcome for faster supervisor review
- +Automation hooks connect run failures to alerting workflows
- –Deep governance requires consistent tagging and trace context setup
- –Coverage can narrow when agents do not use instrumented tool wrappers
Operations and reliability teams
Group failing runs for triage
Lower triage time
AI platform governance teams
Replay agent executions with audit traces
Audit-friendly trace evidence
Show 1 more scenario
ML engineering teams
Monitor regressions with dashboards
Earlier visibility into issues
Track run outcomes in configurable dashboards and alert on recurring performance failures.
Best for: Fits when teams need trace-level debugging and operational oversight for AI agents.
Galileo
enterpriseGalileo monitors generative AI and agent quality with evaluations, guardrails, and production analytics.
Experiment-style run comparisons that highlight which agent changes drove metric shifts, using the same trace context.
Galileo collects agent execution data per run and stores it in a queryable format for triage, root-cause analysis, and ongoing measurement. Teams can track model behavior over time with configurable evaluations and metric definitions that can be attached to agent workflows. An API surface supports pulling monitoring signals into internal dashboards and triggering automation when thresholds fail.
- +Run-level traces make it easier to diagnose failing agent steps
- +Configurable evaluations support metric-based regression tracking
- +API enables routing alerts and metrics into existing operations systems
- +RBAC and audit logs support regulated oversight and access control
- –Evaluation setup requires careful step identification to avoid noisy results
- –Monitoring coverage depends on instrumentation quality across agent workflows
Platform engineering teams
Compare agent versions after deployments
Faster rollback decisions
Customer support operations
Track compliance drift in agent replies
Lower policy violations
Show 1 more scenario
Security and governance
Audit agent access and monitoring actions
Stronger oversight
RBAC and audit logs document who viewed runs and changed monitoring configuration.
Best for: Fits when teams need repeatable agent run evaluation, comparisons, and governed access for operational oversight.
Helicone
API-firstHelicone offers gateway-based logging, tracing, analytics, cost controls, and alerts for AI applications.
Step-level agent tracing that groups tool calls and intermediate events under one execution timeline.
Helicone records LLM and agent events with enough structure to separate model outputs from tool calls and intermediate reasoning steps. The UI centers on searchable conversations and execution traces, so supervisors can correlate regressions with specific prompts, parameters, and tool outcomes. An API and webhooks enable external pipelines to transform captured events into metrics, dashboards, and alerting payloads.
- +Agent execution traces show tool calls in order with call-level metadata
- +API and export paths fit metric pipelines and alerting workflows
- +Search and filtering connect failures to specific prompts and parameters
- +Project configuration supports environment separation for monitoring
- –High-volume capture needs careful filters to avoid unnecessary noise
- –Advanced governance controls require more upfront setup work
- –Deep analytics depend on how agent calls are instrumented
- –Complex multi-system correlation may require external log stitching
Agent engineering teams
Debug tool-call failures
Faster regression root-cause
Quality and evaluation teams
Run model behavior reviews
More consistent scorecards
Show 2 more scenarios
Platform and observability teams
Automate alerting from agent traces
Lower time to detect
Use the API and webhooks to turn monitoring events into alerts and dashboards.
Compliance-minded operations
Maintain monitoring records
Traceable incident evidence
Centralize captured prompts and responses for review workflows and incident investigations.
Best for: Fits when teams need agent step tracing and API-driven observability across multiple agents.
HoneyHive
enterpriseHoneyHive provides observability, evaluation, and testing for AI agents and LLM applications.
Scorecard-driven QA scoring with automated criteria flags for out-of-spec sessions.
HoneyHive monitors agent work by capturing activity signals and turning them into per-agent, per-session visibility for supervisors. The product centers on evaluation workflows with configurable scorecards and calibration-style review so teams can spot drift across interactions.
- +Configurable scorecards support consistent QA scoring across reviewers
- +Session-level review history helps supervisors compare agents over time
- +Automated criteria checks reduce manual QA triage work
- +Role-based access limits who can view and edit evaluation outputs
- –Agent monitoring scope depends on integration coverage for your channels
- –Calibration workflows need governance discipline to stay consistent
Agent supervision teams
Review per-session agent scorecards
More consistent reviews
Quality assurance reviewers
Flag sessions missing agreed criteria
Reduced missed QA issues
Show 1 more scenario
AI program administrators
Control reviewer access to evaluations
Controlled access to evidence
Restrict who can view supervisors and review evaluation outputs to align monitoring with governance.
Best for: Fits when teams need repeatable QA evaluation workflows tied to agent session visibility.
Portkey
API-firstPortkey provides an AI gateway with observability, routing, guardrails, and reliability controls.
Tool-call aware conversation replay that ties evaluation outputs to the exact model and tool steps.
Portkey targets agent activity monitoring for teams that need trace-level visibility across LLM-driven customer interactions, not just post-hoc dashboards. It centralizes conversation context, tool calls, and evaluation signals into a reviewable timeline that supervisors can use for coaching and QA workflows.
- +Conversation timeline links model messages with tool calls for faster root-cause review.
- +Evaluation runs produce review artifacts that supervisors can score and compare.
- +API and webhooks support pushing monitoring events into existing operations tooling.
- +Role-based team separation keeps review access scoped to the right groups.
- –Requires careful configuration to ensure consistent capture across agent workflows.
- –Some contact-center specific metrics need external data stitching for full coverage.
- –Large-scale usage can add latency during evaluation and review artifact generation.
- –Advanced governance relies on disciplined setup of evaluation rules and review templates.
Agent and tool-calling application developers
Standardize multi-model tool request paths
Consistent request routing
QA and evaluation teams
Run prompt regressions on recorded outputs
Clear regression signals
Show 2 more scenarios
AI platform and application engineering teams
Switch model providers without code changes
Faster provider switching
Use a gateway interface to route chat, completion, and embedding requests across providers.
Prompt engineers and AI product teams
Track prompt edits through deployment cycles
Traceable prompt history
Store prompt definitions and track revisions so prompt changes remain traceable from drafting to production use.
Best for: Fits when teams running LLM agents need reviewable traces plus automated evaluation signals for QA and coaching.
Maxim AI
enterpriseMaxim AI provides simulation, evaluation, observability, and quality management for AI agents.
Event-timeline run evidence tied to configurable scoring signals for agent run review and regression detection.
Maxim AI is agent monitoring software built for tracking what AI agents do in production and turning those behaviors into reviewable evidence. It focuses on workflow visibility, evaluation capture, and automated alerting around agent runs so supervisors can spot regressions and compliance gaps.
- +Run-level event timelines make it easier to audit agent behavior end to end.
- +Configurable scoring signals support repeatable evaluation of agent outputs.
- +Alerting tied to monitored run events helps catch failure patterns quickly.
- +Evidence capture supports supervisor review without replaying every run.
- –Integration depth with external tools is limited compared with broader observability suites.
- –Governance controls like RBAC and audit logs may require extra operational discipline.
- –Actionable dashboards can feel thin without strong internal tagging conventions.
- –Setup depends on clean instrumentation of agent steps and outcomes.
Support QA leaders and supervisors
Score recorded agent conversations
Targeted follow-up actions
AI platform owners and engineering managers
Monitor usage and recurring behavior issues
Operational visibility into behaviors
Show 2 more scenarios
AI product managers and engineering teams
Validate AI feature behavior before release
Measured release confidence
Run prompt and application tests on representative inputs to assess expected behavior before deployment.
ML engineers and AI platform teams
Compare model and prompt variants consistently
More structured change decisions
Evaluate outputs across models, prompts, settings, or repeated runs using consistent criteria.
Best for: Fits when teams need ongoing monitoring of AI agent executions with repeatable scoring for supervisor review.
Datadog LLM Observability
enterpriseDatadog LLM Observability tracks AI application traces, agent workflows, latency, errors, and costs.
Trace-level correlation of LLM requests within Datadog APM so agent failures can be root-caused from the same timeline.
Datadog LLM Observability extends Datadog APM and tracing concepts to agent and LLM workloads by capturing prompts, model calls, latency, and errors as correlated telemetry. It connects directly to existing Datadog pipelines so agent monitoring can be reported alongside service metrics and deployments.
- +Correlates LLM calls with APM traces for end-to-end agent debugging
- +Centralizes telemetry and alerting for agent quality signals and failures
- +Uses Datadog monitors and alert workflows for automated response
- +Relies on Datadog’s existing ingestion and integrations for broad coverage
- –Quality evaluation and scorecard workflows require extra instrumentation effort
- –LLM-specific insights depend on correct tagging and consistent context propagation
- –Agent activity monitoring breadth can be limited without custom spans and events
- –RBAC granularity and audit trails can feel complex for smaller admin teams
SRE and platform reliability teams
Correlate LLM errors with service traces
Faster incident root-cause analysis
Customer support automation owners
Monitor chatbot response latency by model
Reduced turnaround time regressions
Show 2 more scenarios
LLM platform engineers
Tune prompts using automated alert signals
Higher agent task success
Use monitors and API configuration to trigger prompt or workflow changes when reliability drops.
Security and compliance teams
Audit agent calls across environments
Improved operational governance
Use correlated telemetry to review LLM request patterns tied to services and releases.
Best for: Fits when teams already run Datadog and need correlated agent and LLM telemetry for reliability work.
AgentOps
vertical specialistAgentOps records, evaluates, and monitors AI agent sessions with traces and developer analytics.
Span-level correlation across agent steps and tool executions to diagnose failure points inside multi-step runs.
AgentOps focuses on agent activity monitoring for teams that need trace-level visibility into multi-step tool use and decision loops. It centers on ingesting runtime signals from agent workflows, correlating them to sessions and spans, and turning those logs into operational views for performance and reliability checks.
- +Trace and span correlation ties agent steps to runtime outcomes
- +API exports telemetry for custom dashboards and internal governance
- +Configurable capture reduces noise while preserving troubleshooting context
- +Automation hooks support repeatable evaluations across releases
- –Requires disciplined instrumentation to avoid partial or misleading traces
- –Advanced filtering and alert tuning takes time for complex workflows
- –Deep workflow analytics can be limited for teams without consistent session IDs
- –Some admin workflows need operational familiarity with API-driven setup
Platform reliability engineers
Diagnose agent failures across tool calls
Faster incident root-cause analysis
ML operations teams
Audit model-driven reasoning outcomes
Lower evaluation drift risk
Show 2 more scenarios
AI application engineers
Monitor autonomous agents in production
Improved agent runtime reliability
Ingests workflow traces and exposes spans for performance checks and alerting on anomalies.
Security and governance teams
Enforce telemetry capture controls
Better compliance-ready audit trails
Applies configuration controls to define captured events and export them for governance pipelines.
Best for: Fits when teams need end-to-end monitoring of agent tool calls with trace-level debugging and external automation.
Conclusion
After evaluating 10 agent monitoring software, LangSmith stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Frequently Asked Questions About Agent Monitoring Software
How does step-level tracing differ between Helicone and AgentOps for debugging tool calls?
Which tool supports run-level evaluation comparisons when agent behavior changes across deployments?
How do Traceloop and Lunary handle run replay or replay-like debugging for failed executions?
What breaks if monitoring relies only on aggregated metrics instead of trace timelines?
When should teams choose Datadog LLM Observability versus AgentOps for incident workflows tied to application telemetry?
How do integrations and APIs affect how agent monitoring plugs into existing automation pipelines?
How does Portkey connect evaluation outputs to the exact model and tool steps during a supervisor review?
What data migration path is usually required when adding agent monitoring to an existing LLM app?
Which security and admin controls matter most for governed access to agent monitoring data?
Where does extensibility differ between Maxim AI and Helicone for scaling monitoring across many agent executions?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
How to Choose the Right Agent Monitoring Software
We see agent monitoring software options mainly differ in how they turn agent activity into usable evidence: integration depth for your workflows, the data model behind what gets tracked, audit trail and access control, and how reliably events and recordings match real agent actions. Those differences determine what you can automate, what you can prove, and what you can act on during QA and coaching.
Event timeline accuracy
Why it matters
Agent monitoring depends on Event timeline accuracy so supervisors can trust that what they review reflects the actual sequence of calls, chats, or sessions. When timestamps drift, root-cause analysis for delays, escalations, and quality issues becomes guesswork.
How to check
Ask for a demo of a single agent session and compare the session timeline to the displayed monitoring events. Then run a simple test: trigger a known moment (for example, an escalation) and verify the exact ordering and timestamp shown in the monitoring view.
Automation rules and trigger options
Why it matters
Automation rules and trigger options separate tools that can only report from tools that can drive consistent review and follow-up. The right triggers reduce manual triage and keep QA aligned to your internal playbooks.
How to check
Request a walkthrough of one workflow that sends an alert or queues a review based on agent behavior. Check whether rules can be configured with thresholds, windows, and multiple conditions (for example, escalation plus short resolution time) and whether changes take effect immediately.
Retention and data scope controls
Why it matters
Retention and data scope controls matter because monitoring data grows quickly and may include sensitive interactions. Clear controls help you keep only what you need for QA and audits without breaking investigations or reporting.
How to check
Ask what data types are retained and for how long, then check whether retention can be set per data type or per tenant. Also ask for a way to export or delete monitoring artifacts tied to an agent or date range.
RBAC roles and audit log coverage
Why it matters
RBAC roles and audit log coverage determine who can view sensitive agent evidence and whether actions are traceable for internal governance. Without strong controls, QA processes lose credibility and compliance becomes harder to demonstrate.
How to check
In the demo, create at least two roles (reviewer vs supervisor) and verify which screens, recordings, and exports each role can access. Then check the audit log: ask what user actions appear there and whether exports and configuration changes are logged.
API access for events and records
Why it matters
API access for events and records separates monitoring tools that stay isolated from ones that integrate deeply with your operations. When events and review outcomes are accessible, teams can automate reporting, case creation, and external dashboards.
How to check
Ask the vendor for an API walkthrough focused on pulling monitoring events and fetching review outcomes. In the demo, request a sample request or example payload and confirm whether you can filter by agent, time range, and monitoring type.
Red flags
- Timeline looks right in the UI but event ordering changes between views.
- Rules support basic alerts only, with no configurable time windows or multi-condition triggers.
- Access control is unclear or audit logs don’t show exports and configuration changes.
Fit by situation
- If you’re investigating escalations and want supervisors to trust ordering, prioritize Event timeline accuracy.
- If you need QA follow-ups that happen automatically after specific agent behavior, prioritize Automation rules and trigger options.
- If your team needs to connect monitoring evidence to internal workflows, prioritize API access for events and records.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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