GITNUXSOFTWARE ADVICE
Business FinanceTop 10 Best Agent Monitoring Software of 2026
Ranked roundup of agent monitoring software for teams, covering performance and compliance tracking, with Helicone, Galileo, Lunary, and others compared.
How we ranked these tools
Gitnux editors write the brief first: the topic, the inclusion rules, and the frame for what belongs.
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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Helicone is the best fit when you need API-driven agent step tracing with cost controls and alerts across multiple agents, while Galileo is the stronger pick for repeatable run evaluation and governed oversight, and if you’re cost-sensitive the entry route is Datadog LLM Observability when you already rely on Datadog for correlated telemetry.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Helicone
Step-level agent tracing that groups tool calls and intermediate events under one execution timeline.
Best for: Fits when teams need agent step tracing and API-driven observability across multiple agents.
Galileo
Editor pickExperiment-style run comparisons that highlight which agent changes drove metric shifts, using the same trace context.
Best for: Fits when teams need repeatable agent run evaluation, comparisons, and governed access for operational oversight.
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.
Comparison Table
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.
A tradeoff appears in data governance, because high-volume capture can increase storage and processing needs unless capture filters are configured early. Helicone fits teams that already route all agent calls through a single gateway or SDK wrapper and want consistent monitoring across multiple agents and environments.
- +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.
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.
A practical tradeoff is that meaningful monitoring quality depends on setting up evaluation logic and stable identifiers for agent steps. Galileo fits teams with multiple agent versions or environments who want consistent run comparisons and controlled rollout gates for agent changes.
- +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.
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.
- +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
Best for: Fits when teams need trace-level debugging and operational oversight for AI agents.
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.
- +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
Best for: Fits when teams need trace-linked evaluation and regression monitoring for LangChain-style agents.
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
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
Best for: Fits when teams need end-to-end monitoring of agent tool calls with trace-level debugging and external automation.
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.
Best for: Fits when teams running LLM agents need reviewable traces plus automated evaluation signals for QA and coaching.
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
Best for: Fits when teams need repeatable QA evaluation workflows tied to agent session visibility.
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.
Best for: Fits when teams need ongoing monitoring of AI agent executions with repeatable scoring for supervisor review.
Conclusion
After evaluating 10 agent monitoring software, Helicone 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.
How to Choose the Right Agent Monitoring Software
Agent monitoring software tracks tool-using agent behavior by capturing run timelines, linking intermediate steps to final outcomes, and exposing traces for debugging and governance. This guide covers Helicone, Galileo, Lunary, Traceloop, LangSmith, Datadog LLM Observability, AgentOps, Portkey, HoneyHive, and Maxim AI.
Across these tools, tracing fidelity, automation surface, and admin controls vary from step-level execution views like Helicone to trace correlation inside Datadog APM like Datadog LLM Observability. The buying sections that follow map those differences to operational monitoring needs for reliability work and compliance workflows.
Agent Monitoring Software for traceable, testable AI agent execution
Agent monitoring software captures agent activity monitoring signals such as tool-call sequences, intermediate step events, and evaluation results tied back to a specific run. Helicone emphasizes step-level agent tracing that groups tool calls and intermediate events under one execution timeline.
Some platforms shift from raw traces to repeatable comparisons, like Galileo run traces that support experiment-style evaluation and metric regression tracking. Other options focus on trace correlation with broader telemetry, like Datadog LLM Observability, which connects LLM request timelines to APM traces so agent failures can be root-caused from the same operational view.
Agent tracing, evaluation workflows, and governance controls
Agent monitoring software needs run timelines that connect intermediate tool steps to the final agent outcome so failures can be debugged without guesswork. Helicone’s step-level execution timeline groups tool calls and intermediate events under one run view, which is the trace structure many teams standardize on for reliability work.
Step-level trace fidelity for tool-using agents
Helicone groups tool calls and intermediate events under one execution timeline, which supports step-ordered debugging. Lunary preserves tool inputs and outputs across intermediate steps in one run view so automated checks can flag missing tool steps.
Run comparison and regression detection workflows
Galileo uses experiment-style run comparisons that keep the same trace context to identify which agent changes moved metrics. Traceloop adds run replay that reconstructs the tool-call sequence to compare a failed run against prior successful executions.
Trace-linked evaluation outputs and replay artifacts
LangSmith ties dataset-backed evaluation runs to specific traces and prompts so feedback attaches to the trace that produced it. Portkey produces reviewable conversation replay artifacts that link model messages with tool steps for QA, coaching, and supervisor review.
Supervised QA scoring and calibration readiness
HoneyHive runs scorecards with automated criteria flags and keeps session-level review history so supervisors can compare agents over time. Maxim AI adds configurable scoring signals and event timelines so supervisor review can audit behavior end to end.
Telemetry correlation inside an existing observability plane
Datadog LLM Observability correlates trace-level LLM requests with Datadog APM timelines so agent failures can be root-caused from the same operational view. AgentOps performs span-level correlation across agent steps and tool executions to diagnose failure points inside multi-step runs.
Choose by trace structure, comparison philosophy, and integration and governance depth
First, teams need to decide whether monitoring should center on step-level execution traces or on run-level comparisons. Helicone and Lunary optimize the trace view for intermediate tool events, while Galileo and Traceloop optimize the workflow for comparing runs and replaying sequences across changes.
Pick the trace unit of analysis: step timeline versus run evidence
If debugging requires strict ordering across tool calls and intermediate events, Helicone’s step-level agent tracing is built around tool-call sequences in one execution timeline. If monitoring depends on consistent step event emission for tool inputs and outputs, Lunary’s timeline tracing preserves intermediate evidence so automated checks can validate tool step presence.
Choose a regression workflow that matches how agent changes happen
When prompt and model changes must be compared with experiment-style run context, Galileo’s run-level traces support metric regression tracking. When failures must be compared against prior successful attempts across retries, Traceloop’s run replay reconstructs the tool-call sequence for root-cause analysis.
Select evaluation artifacts that fit supervisor and QA review
If evaluation teams manage datasets and need feedback attached to the originating trace and prompt, LangSmith links trace-to-feedback and supports dataset-driven evaluations. If QA teams want reviewable conversation replay that ties evaluation signals to exact model and tool steps, Portkey generates conversation timelines that supervisors can score.
Decide whether scoring is your primary governance output
If consistent QA scoring across reviewers is the main operational control, HoneyHive uses configurable scorecards and automated criteria flags and retains session review history. If run-level audit evidence is the governance goal with repeatable scoring signals, Maxim AI ties event timelines to configurable scoring for supervisor regression detection.
Align with your telemetry plane to reduce instrumentation rework
If reliability work already runs through Datadog APM, Datadog LLM Observability correlates LLM request timelines with APM traces so agent failures appear in the same operational view. If teams need span-level correlation for end-to-end tool execution debugging plus API exports for custom dashboards, AgentOps concentrates on trace and span correlation across agent steps.
Who should buy Agent Monitoring Software
Teams that run multi-step, tool-using agents need traceable monitoring so failures can be tied to the exact intermediate step that broke. Tools that preserve tool-call sequences and span correlation reduce time spent matching logs to user-impacting outcomes.
Reliability and incident response teams debugging multi-step failures
Helicone and AgentOps both focus on trace correlation across tool execution so failure points inside multi-step runs can be diagnosed from one timeline without manually stitching events.
Operational evaluation teams running prompt and agent change experiments
Galileo supports repeatable agent run evaluation with experiment-style run comparisons that highlight which changes drove metric shifts using the same trace context.
QA supervisors who score interactions for out-of-spec behavior
HoneyHive is designed around configurable scorecards with automated criteria flags and session-level review history so supervisors can compare agents over time using consistent rubric criteria.
Dataset-focused evaluation teams needing regression analysis across prompts and models
LangSmith ties dataset-backed evaluations to specific traces and prompts so feedback can be attached to the exact trace that produced it for regression monitoring.
Teams standardizing on an existing observability stack
Datadog LLM Observability integrates agent and LLM telemetry by correlating LLM requests with Datadog APM traces so reliability dashboards and alerting can use unified timelines.
Common pitfalls when buying Agent Monitoring Software
A frequent failure mode is choosing a monitoring tool that captures traces at a granularity that does not match the debugging workflow. If the agent framework emits inconsistent step events, trace-based monitoring becomes noisy or incomplete.
Assuming trace capture works equally well without instrumented tool wrappers
Traceloop’s coverage can narrow when agents do not use instrumented tool wrappers, so tool-call capture must be validated before relying on replay for debugging.
Building regression comparisons without strict step identification
Galileo notes that evaluation setup needs careful step identification to avoid noisy results, so step naming and trace context should be standardized before starting regression tracking.
Letting high-volume capture overwhelm alerting and QA review
Helicone’s high-volume capture needs careful filters to avoid unnecessary noise, so capture filters and sampling rules should be defined alongside monitoring goals.
Treating scorecards as a drop-in governance layer without calibration discipline
HoneyHive’s calibration workflows need governance discipline to stay consistent, so scoring rubrics and reviewer calibration should be scheduled as part of rollout.
Expecting full workforce metric coverage without external stitching
Portkey notes that some contact-center specific metrics need external data stitching for full coverage, so end-to-end dashboards may require CRM and telephony data integration beyond trace artifacts.
How We Selected and Ranked These Tools
We evaluated agent monitoring software using three weighted factors, features at 40%, ease and value at 30% each. Helicone earned the highest rank because step-level agent tracing groups tool calls and intermediate events under one execution timeline, and its API and export paths fit metric pipelines and alerting workflows. Galileo followed with run-level traces that support experiment-style run comparisons for metric regression tracking while keeping the same trace context.
Lunary and Traceloop placed high because timeline tracing and run replay reconstruct tool-call sequences that support debugging across intermediate steps and retries. Datadog LLM Observability and AgentOps were scored strongly for trace correlation with existing observability timelines, while LangSmith and Portkey were scored on trace-linked evaluation and review artifacts.
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.
- Business FinanceTop 10 Best Agent Management Software of 2026
- Communication MediaTop 10 Best Call Center Agent Monitoring Software of 2026
- Business FinanceTop 10 Best Good Computer Monitoring Software of 2026
- Customer Experience In IndustryTop 10 Best Sales Agent Software of 2026
- Business FinanceTop 10 Best Regulatory Compliance Monitoring Software of 2026
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