Top 14 Agent Observability Tools

Abstract

Agent observability tools capture traces, metrics, logs, and evaluations across AI agent workflows. They help teams reconstruct execution paths, inspect tool calls and handoffs, diagnose failures, and monitor latency, cost, and output quality. This guide also covers complementary security platforms that discover agents, enforce runtime policies, or control the privileges agents receive.

Category	Tools Covered
Agent Security, Access, and Action Control	Apono Agent Privilege Guard, Wing Security, Check Point AI Security
Debugging Agent Behavior	Laminar, HoneyHive, LangWatch, LangSmith
Agent Evaluation and Regression Testing	Opik by Comet, Braintrust
Open Source and Self-hosted Observability	Langfuse, OpenLIT
Application and Infrastructure Correlation	Pydantic Logfire, Honeycomb Agent Observability, Coralogix AI Observability

Agents don’t fail in straight lines.

Traditional application monitoring may show that a request failed, but it often can’t explain why an AI agent selected a particular tool, delegated work to another agent, or attempted to modify a production resource. As agents gain access to databases, code repositories, cloud infrastructure, Kubernetes clusters, and internal applications, teams need visibility across the entire execution path, not just the final model response.

That visibility is becoming increasingly important for AI governance and auditability. 78% of business executives lack strong confidence that their organization could pass an independent AI governance audit within 90 days.

The right agent observability tool depends on what you need to see or control. Some platforms specialize in traces and debugging, others in evaluations and regression testing, and others in application or infrastructure correlation. Security-focused platforms address another question: What should the agent be permitted to do before an action reaches a sensitive system?

What is agent observability?

Agent observability collects and correlates telemetry across an agent’s execution path, including model calls, retrieval, memory, tool use, handoffs, and downstream services. It helps teams reconstruct what happened, identify where an execution failed, and monitor production factors such as latency, cost, and output quality.

It differs from application performance monitoring, which focuses on software and infrastructure health, and from agent access control, which determines what an agent is permitted to do. Low-risk prototypes may not need a dedicated platform, but production agents that access sensitive systems usually require deeper tracing, evaluation, governance, or runtime controls.

A dedicated agent observability platform may be unnecessary for a low-risk prototype with a single agent, a small set of tools, and adequate OpenTelemetry coverage. Production agents, multi-agent systems, and workflows that handle sensitive data or infrastructure usually require deeper tracing, evaluation, governance, or runtime controls.

Top Picks at a Glance

• Best for agent security, access, and action control: Apono Agent Privilege Guard
• Best for debugging agent behavior: Laminar
• Best for agent evaluation and regression testing: Braintrust
• Best open-source and self-hosted option: Langfuse 
• Best for application and infrastructure correlation: Honeycomb

Comparison Table: Best Agent Observability Tools Compared

Tool	Best for	Tracing	Evaluations	Open source/self-hosted?	Infrastructure correlation	Agent access controls
Apono Agent Privilege Guard	Runtime privilege control	Privilege and action audit trail	No	No	Resource, identity, and privilege context	Task-scoped access, intent checks, approvals, and revocation
Wing Security	Shadow AI discovery	Behavior and activity monitoring	No	No	SaaS, identity, and permission context	Governance and remediation
Check Point AI Security	Inline agent protection	Runtime security telemetry	Security testing	No	Security-stack correlation	Runtime policies and action blocking
Laminar	Multi-agent debugging	Yes	Yes	Open source and self-hosted	OpenTelemetry application context	No
HoneyHive	Production evaluation	Yes	Yes	Enterprise self-hosting	OpenTelemetry workflow context	No
LangWatch	Scenario testing	Yes	Yes	Open source and self-hosted	OpenTelemetry workflow context	No resource-level controls
LangSmith	LangChain and LangGraph teams	Yes	Yes	Enterprise self-hosting	Agent and application context	No
Opik by Comet	Open-source evaluation	Yes	Yes	Open source and self-hosted	Agent and application context	No
Braintrust	Regression testing	Yes	Yes	BYOC and enterprise self-hosting	Application and OpenTelemetry context	No
Langfuse	Open-source observability	Yes	Yes	Open source and self-hosted	LLM and application context	No
OpenLIT	OpenTelemetry-native monitoring	Yes	Yes	Open source and self-hosted	AI, system, GPU, and vector database telemetry	No
Pydantic Logfire	Python application observability	Yes	Via Pydantic Evals	Enterprise self-hosting	APIs, databases, services, and LLMs	No
Honeycomb Agent Observability	Full-stack correlation	Yes	Production quality signals	No	Applications, services, and infrastructure	No
Coralogix AI Observability	Enterprise AI monitoring	Yes	Yes	No	AI, application, and infrastructure telemetry	AI guardrails, not privilege issuance

14 Top Agent Observability Tools

The following reviews examine each platform’s strongest use case, core capabilities, deployment model, limitations, and pricing. The categories reflect each tool’s primary buying reason, although several products support capabilities across multiple areas.

Agent Security, Access, and Action Control

These tools complement conventional observability platforms. Rather than focusing primarily on prompt and execution traces, they help security teams discover agents, inspect risky activity, enforce policies, and govern access to sensitive systems, capabilities that are also central to a broader AI agent security strategy.

1. Apono Agent Privilege Guard

Apono is a cloud-native privileged access management platform built on the principles of Zero Standing Privilege. Agent Privilege Guard complements tracing and evaluation tools by addressing a question they generally leave open: Should this agent be allowed to perform this action?

Apono creates just-in-time and just-enough privileges dynamically at runtime, scoped to the agent’s task. Its Intent-Based Access Control compares declared intent with requested actions and considers business context, resource sensitivity, and risk. Sensitive operations may require human approval, and privileges are automatically revoked when the task ends.

This approach limits the risks associated with stale tokens, inherited human credentials, excessive permissions, agent chaining, unauthorized data access, and destructive write actions, while reducing reliance on manual access workflows. These controls are part of a broader set of AI agent guardrails that cover identity, permissions, tool use, approvals, monitoring, and auditability.

Main features

• Just-in-time and just-enough privileges are scoped to the specific task
• Intent validation before agents access sensitive systems
• Human approval for high-risk or destructive operations
• Unified records of requests, approvals, privileges, and agent actions
• Direct integrations across cloud platforms, databases, Kubernetes, and SaaS applications

Best for: Cloud-native and regulated organizations that need runtime privilege guardrails for production agents.

Skip it if: You only need prompt tracing, output evaluation, or model-quality testing without access enforcement.

Deployment: Cloud-native platform with direct integrations across more than 200 systems.

Price: Contact Apono for customized pricing.

2. Wing Security

Wing Security analyzes how agents interact with applications, including their linked identities, permissions, and activity. Security teams can investigate cross-application activity and respond by revoking tokens, managing API keys, or remediating risky configurations.

Main features

• Discovery of sanctioned and unsanctioned AI tools and agents
• Mapping of OAuth grants, API keys, identities, and permissions
• Behavioral analysis across connected SaaS applications
• Automated remediation and token revocation

Best for: Security teams that need to uncover shadow AI and understand agents operating across their SaaS estate.

Skip it if: Your priority is developer-facing trace analysis, prompt experimentation, or regression testing.

Deployment: SaaS security platform.

Price: Sales-led pricing; a free assessment is available.

3. Check Point AI Security

Check Point AI Security provides discovery, governance, and inline protection for AI applications and agents. Its runtime controls inspect prompts, responses, data movement, and tool activity to assess whether agent actions are safe before execution.

Main features

• AI application and agent discovery
• Prompt, response, and sensitive-data inspection
• Runtime controls for tool use and agent actions
• Centralized AI security posture and policy management

Best for: Large enterprises that already use Check Point or need broad AI runtime security across an established security architecture.

Skip it if: You need an open-source developer tool for detailed traces, evaluations, and prompt iteration.

Deployment: Enterprise cloud and security platform integrations.

Price: By inquiry. 

Debugging Agent Behavior

4. Laminar

Laminar is an open-source, agent-first observability platform designed to reconstruct complex execution paths. It captures model calls, tool use, sub-agent activity, and other steps as OpenTelemetry-compatible traces.

Main features

• Detailed traces for tools, models, and sub-agents
• Timeline, transcript, and interactive debugging views
• Production signals, evaluations, and failure clustering
• OpenTelemetry-native instrumentation

Best for: Engineering teams that need deep, visual debugging of multi-step or multi-agent workflows.

Skip it if: You already have sufficient agent tracing in an existing application observability platform and don’t need a specialized debugger.

Deployment: Managed cloud or self-hosted through Docker, Helm, AWS, or Google Cloud.

Price: Free plan available. Paid cloud plans start at $30 per month.

5. HoneyHive

HoneyHive combines production observability with experimentation and continuous evaluation. Teams can inspect distributed traces, follow complete agent trajectories, detect drift, and compare production behavior with offline test results.

Main features

• Distributed tracing and agent trajectory analysis
• Online evaluations, dashboards, and alerts
• Versioned datasets, experiments, and human annotations
• Regression testing and CI/CD integration

Best for: Enterprise AI teams building a continuous evaluation and improvement process around production agents.

Skip it if: You prefer a small, fully open-source deployment with minimal platform overhead.

Deployment: Managed cloud, hybrid, and enterprise self-hosted options.

Price: The developer plan is free for up to 10,000 events per month.

6. LangWatch

LangWatch combines open-source observability with scenario-based testing. In addition to tracing prompts, model calls, and tools, teams can simulate multi-turn conversations and test whether an agent completes a task under different conditions.

Main features

• Open-source traces and production monitoring
• Multi-turn scenario and simulation testing
• Evaluations, annotations, and visual debugging
• OpenTelemetry support and self-hosting

Best for: Teams that want to test complete agent interactions before deployment and monitor the same workflows in production.

Skip it if: Your use case requires only lightweight request tracing without simulation or evaluation workflows.

Deployment: Managed cloud or self-hosted with Docker Compose.

Price: A free developer plan is available. Growth pricing starts at €29 per core seat per month, with additional charges for event usage.

7. LangSmith

LangSmith provides tracing, evaluation, prompt management, and production monitoring for agent applications. It works particularly well with LangChain and LangGraph, although its SDKs and OpenTelemetry support allow teams to instrument other frameworks.

Main features

• Agent, tool, and model tracing
• Offline and online evaluations
• Cost, latency, and trajectory monitoring
• SDKs for Python, TypeScript, Go, and Java

Best for: Teams building with LangChain or LangGraph that want an integrated development-and-production workflow.

Skip it if: You need free self-hosting or want to avoid close alignment with a particular agent-development ecosystem.

Deployment: Managed cloud, bring-your-own-cloud, and enterprise self-hosted options.

Price: The developer plan includes up to 5,000 base traces per month. Plus costs $39 per seat per month, plus usage.

Agent Evaluation and Regression Testing

8. Opik by Comet

Opik is an open-source platform for tracing, evaluating, and testing LLM applications and agents. It represents agent runs as execution graphs and sessions, making it easier to analyze tool selection, multi-step behavior, token consumption, and cost.

Main features

• End-to-end execution graphs and session tracing
• More than 30 evaluation metrics
• Test suites, assertions, and LLM-as-a-judge scoring
• OpenTelemetry and more than 40 integrations

Best for: Evaluation-focused teams that want an open-source platform with an accessible managed-cloud option.

Skip it if: Your main requirement is infrastructure correlation or runtime privilege enforcement.

Deployment: Open-source self-hosting or Comet’s managed cloud.

Price: Open source is free. Cloud plans include a free tier, while Pro starts at $19 per month.

9. Braintrust

Braintrust is built around systematic evaluation. Teams can trace production agents, build versioned datasets, score outputs, and run repeatable experiments before approving model, prompt, or workflow changes.

Main features

• Tracing for prompts, tools, and agent workflows
• Versioned datasets and repeatable experiments
• Code, model-based, and human evaluation
• CI quality gates and production scoring

Best for: Mature AI engineering teams that treat evaluations as part of their software delivery lifecycle.

Skip it if: You need a low-cost open-source platform or primarily want infrastructure-level observability.

Deployment: Managed cloud, BYOC, or enterprise self-hosting.

Price: The Starter plan is free. Pro costs $249 per month, with customized enterprise pricing.

Open Source and Self-hosted Observability 

10. Langfuse

Langfuse is a mature, MIT-licensed platform for tracing, prompt management, evaluations, datasets, and human annotations. It captures agent graphs, sessions, model calls, tool use, token consumption, and cost while supporting both native framework integrations and OpenTelemetry.

Main features

• Open-source agent and LLM tracing
• Prompt management, datasets, and evaluations
• Session, token, latency, and cost analysis
• Extensive self-hosting and data-control options

Best for: Teams that want a broadly adopted open-source platform and are prepared to operate the underlying infrastructure.

Skip it if: You need a fully managed experience without maintaining components such as databases and analytics storage.

Deployment: Managed cloud or self-hosted with Docker, Kubernetes, AWS, Azure, and Google Cloud.

Price: The open-source core is free. Managed cloud plans are available, while enterprise self-hosting uses customized pricing.

11. OpenLIT

OpenLIT is an Apache 2.0-licensed observability platform built around OpenTelemetry. It collects AI traces, metrics, token usage, cost, GPU telemetry, and vector database performance, helping teams connect model behavior with the infrastructure supporting it.

Main features

• OpenTelemetry-native traces and metrics
• Model, GPU, vector database, token, and cost monitoring
• More than 60 AI and infrastructure integrations
• Export to existing observability backends

Best for: Teams seeking a free, self-hosted platform with strong AI infrastructure and cost visibility.

Skip it if: You need a mature managed-cloud service immediately.

Deployment: Self-hosted.

Price: Self-hosting is free without platform usage limits.

Application and Infrastructure Correlation

12. Pydantic Logfire

Pydantic Logfire connects AI agent activity with the surrounding Python application. Teams can follow a request from a user interaction through model calls, tool selection, APIs, databases, vector stores, and application logic, all within a single distributed trace. 

Main features

• Full-stack traces for agents and Python applications
• Model, tool, API, database, and vector store correlation
• Logs, spans, metrics, and OpenTelemetry support
• Native integration with Pydantic AI

Best for: Python and Pydantic teams that want agent telemetry inside a broader application-observability workflow.

Skip it if: Your environment isn’t Python-centric, or you need an extensive dedicated evaluation suite.

Deployment: Managed cloud, dedicated environments, and enterprise self-hosting.

Price: A free personal plan is available. Team starts at $49 per month, and Growth costs $249 per month.

13. Honeycomb Agent Observability

Honeycomb brings agent telemetry into a mature distributed-observability platform. Its Agent Timeline connects model calls, tool use, handoffs, retries, and downstream services across multiple traces and agents.

Main features

• Multi-agent timeline and conversation reconstruction
• Correlation between AI activity and downstream services
• OpenTelemetry-based instrumentation
• Production SLO, latency, and reliability analysis

Best for: SRE and platform teams that need to connect agent behavior with application and infrastructure performance.

Skip it if: Your primary requirement is prompt experimentation, specialized evaluation workflows, or agent privilege governance.

Deployment: Managed observability platform with OpenTelemetry ingestion.

Price: A free plan is available. Pro starts at $150 per month, with customized enterprise pricing.

14. Coralogix AI Observability

Coralogix combines AI-specific monitoring with full-stack application and infrastructure observability. Its AI Center traces agents, tracks cost, discovers AI activity, and evaluates outputs for issues such as hallucinations, toxicity, sensitive-data exposure, and incorrect tool selection.

Main features

• Agent, tool, and model tracing
• Cost, latency, and quality monitoring
• Evaluators for hallucinations, PII, toxicity, and tool selection
• Correlation with application and infrastructure telemetry

Best for: Enterprises that want to consolidate AI, application, infrastructure, and security monitoring.

Skip it if: You need a lightweight open-source tool for one small agent application.

Deployment: Managed observability platform.

Price: Usage-based pricing with a free trial; contact Coralogix for an organization-specific estimate.

How We Compared These Agent Observability Tools

Agent observability is not a single product category. Some platforms focus on reconstructing execution paths, while others specialize in evaluation, infrastructure correlation, security monitoring, or runtime privilege control. Grouping all 14 tools into a single ranking would obscure these differences and make direct comparisons less useful.

We organized the list around each platform’s strongest buying reason:

• Agent security, access, and action control: Tools that discover agents, monitor risky activity, enforce runtime policies, or govern which systems and privileges an agent can use.
• Debugging agent behavior: Platforms that trace prompts, tool calls, handoffs, memory, and sub-agent activity to help developers investigate unexpected outcomes.
• Agent evaluation and regression testing: Tools that measure output quality, run repeatable tests, score production behavior, and detect regressions before release.
• Open-source and self-hosted observability: Platforms that give teams greater control over deployment, telemetry, customization, and data residency.
• Application and infrastructure correlation: Tools that connect agent behavior with APIs, databases, services, latency, and infrastructure health. Teams also need visibility into the data and context layer behind retrieval, tool calls, and downstream decisions.

These categories reflect each product’s primary strength, not the limits of its feature set. Several platforms support capabilities across multiple sections, so buyers should use the comparison table to assess secondary requirements such as evaluations, OpenTelemetry support, self-hosting, infrastructure context, and agent access controls.

Visibility Shows the Risk. Privilege Controls Stop it.

Development teams need traces and evaluations to understand unexpected agent behavior. Operations teams need to correlate cost, latency, reliability, and infrastructure. Security teams need to know which resources agents can access and whether a sensitive action should proceed.

Tracing, evaluation, and infrastructure monitoring can explain what happened. They don’t necessarily control what an agent is permitted to do. When agents can query sensitive data, change cloud configurations, deploy code, or write to production systems, teams also need an authorization layer that acts before execution.

Apono Agent Privilege Guard creates agent privileges dynamically for the task, evaluates intent before sensitive actions proceed, requires human authorization when risk warrants it, and revokes access automatically when the task ends. Every request, approval, privilege, and action is recorded with the context that security and audit teams need.

The same Zero Standing Privilege model can govern engineers, service identities, copilots, and autonomous agents without forcing development teams to rely on manual access workflows.

See how Apono Agent Privilege Guard helps teams deploy AI agents with task-scoped privileges, runtime guardrails, and zero standing access. Book a live demo.