14 best AI agent observability tools in 2026: A practical comparison

An AI agent can return a polished answer and still fail. It may call the wrong tool, use stale context, repeat an action, ignore an approval step, or complete a workflow that does not satisfy the user’s goal. Standard application monitoring can show a successful request and acceptable latency while missing the failure that matters.

AI agent observability tools help teams inspect the full execution path behind an agent run: model calls, retrieval, tool use, memory, state changes, handoffs, latency, cost, evaluations, and final outcomes. The strongest platforms also connect production traces to datasets and regression tests so teams can improve an agent without reintroducing known failures.

Before comparing vendors, read what an agent observability platform is. For procurement criteria that connect agent traces to business outcomes, see the agent analytics buyer’s guide.

Best AI agent observability tools compared

There is no universal winner because the products solve different parts of the agent engineering workflow. Some prioritize open-source tracing, some lead with evaluations, some extend a broader APM platform, and some sit in the model request path as an AI gateway. The table below summarizes the best fit for each platform.

*Prices and included usage were verified against public vendor pages on July 30, 2026. Amounts are in U.S. dollars and may exclude usage overages, model calls, seats, storage, longer retention, or enterprise deployment.

How we evaluated these agent observability platforms

This guide evaluates products against the workflow required to debug and improve production agents. We did not assign an arbitrary score because the importance of each criterion depends on the application. A regulated support agent, a coding agent, and an internal research agent need different deployment controls, retention periods, evaluators, and failure-response workflows.

We reviewed each platform across nine dimensions:

1. Trace completeness: Can the platform capture model calls, retrieval, reranking, tools, memory, state transitions, handoffs, errors, and final outputs in one nested trace?
2. Session and trajectory analysis: Can teams group multiple runs into a conversation, task, or long-running job and evaluate whether the overall goal was achieved?
3. Agent evaluations: Does the product support deterministic checks, code evaluators, LLM-as-a-Judge, human review, and evaluations at the span, trace, session, or trajectory level?
4. Production feedback loop: Can teams turn failures into datasets, run experiments, compare versions, and add regression coverage?
5. Search, dashboards, and alerts: Can engineers find recurring failure modes and monitor quality, cost, latency, and tool behavior over time?
6. Instrumentation portability: Does the platform support OpenTelemetry, OpenInference, standard SDKs, or trace export?
7. Deployment and data controls: Are SaaS, self-hosted, VPC, hybrid, regional, redaction, retention, and access-control options available?
8. Runtime architecture: Does telemetry run out of band, or does a gateway sit in the model request path? What are the failure and latency implications?
9. Pricing model: Is usage measured by spans, traces, events, requests, data volume, seats, evaluator calls, or a combination?

For the implementation details behind these criteria, see the complete guide to AI agent tracing and evaluation.

What to look for in an AI agent observability tool

Agent observability should reconstruct the execution that your application can actually observe. That includes the inputs and outputs of model calls, retrieval results, tool arguments and responses, state changes, handoffs, errors, timing, token usage, and application metadata. It should not claim to expose a model’s private hidden reasoning.

The surrounding runtime also matters. An agent harness determines how models, tools, memory, control flow, permissions, and telemetry work together. Observability is most useful when the harness emits consistent identifiers and structured spans from the start.

OpenTelemetry and OpenInference are the instrumentation layer

OpenTelemetry is a vendor-neutral standard for collecting and exporting traces, metrics, and logs. Its generative AI semantic conventions help applications describe model and agent operations consistently, although convention maturity and backend support still vary.

OpenInference extends OpenTelemetry with AI-specific semantics for LLMs, retrieval, tools, agents, and related application components. It is designed to make the same instrumentation useful across compatible backends.

Neither project is a complete observability platform. You still need a backend for storage, trace visualization, search, dashboards, alerts, evaluations, datasets, experiments, and access controls. For that reason, OpenTelemetry belongs in the architecture discussion rather than the ranked vendor list.

The market also cannot be reduced to a simple proxy-versus-SDK choice. Most products combine several patterns:

• Direct SDK or automatic instrumentation creates spans inside the application process.
• OpenTelemetry collectors receive, process, sample, redact, and route telemetry to one or more backends.
• AI gateways can add routing, retries, caching, key management, guardrails, and request logging in the model request path.
• Managed, hybrid, and self-hosted backends determine where trace data is stored and who operates the platform.

The 14 best AI agent observability tools in 2026

1. Arize AX and Phoenix: Best overall for agent tracing and evaluation

Best for: Teams that want framework-agnostic tracing, online and offline evaluations, experiments, production monitoring, and enterprise deployment in one workflow.

Arize AX is the managed AI engineering platform, while Phoenix is the open-source project for tracing and evaluation. Both build around OpenTelemetry and OpenInference, which lets teams instrument agents across model providers and frameworks without tying the trace schema to one orchestration library.

The platform is strongest when observability needs to feed an improvement loop. Engineers can inspect nested agent and multi-agent traces, group runs into sessions, run evaluations on production traffic, build datasets from failures, compare changes in experiments, and monitor quality, cost, and latency after deployment. AX also includes agent trajectory visualizations, dashboards, monitors, human annotation workflows, and Alyx for natural-language analysis.

Agent improvement loop: In 2026, Arize AX extended observability into a repeatable production feedback loop. Signal, a managed agent built into AX, continuously reviews production traces, groups related failures, and surfaces ranked issues with evidence and proposed fixes. With a repository connected, managed agents can carry those investigations into code, propose patches, and open pull requests for human review.

That loop also includes Agent-as-a-Judge, which helps teams generate adaptive evaluation signals from production behavior instead of relying only on predefined rubrics; agent experiments that compare full harness behavior across tool use, retrieval, latency, trajectories, and eval results; swarm observability for fleets of managed agents; and voice-agent support for audio sessions, transcripts, replay, and evaluation. Signal ships on AX Free and Pro today. Full managed-agent workflows, Agent Studio, presets, and repository access are available to Enterprise customers in beta. See building the AI factory for self-improving agents and from Signal to PR for product details.

Tradeoffs: Phoenix gives developers control and a free open-source starting point, but operating storage and upgrades remains the team’s responsibility. AX Free and Pro are SaaS products. Self-hosting, dedicated support, advanced governance, and the broadest data controls are enterprise capabilities.

Pricing: Phoenix is free and open source. AX Free includes 25,000 spans per month, 1 GB of ingestion, and 15-day retention. AX Pro is $50 per month and includes 50,000 spans, 10 GB, and 30-day retention. Enterprise pricing is custom. See Arize pricing.

2. LangSmith: Best for LangGraph and LangChain teams

Best for: Teams whose primary development workflow is built around LangGraph or LangChain and who want tracing, evaluation, datasets, and deployment tooling in the same ecosystem.

LangSmith traces model calls, tools, retrieval, and agent steps, then connects those runs to offline and online evaluation. Its tight integration with LangGraph makes setup and trace interpretation particularly smooth for teams using the LangChain ecosystem. It also supports other frameworks, custom applications, and OpenTelemetry, so it should not be treated as LangChain-only.

LangSmith supports human review, custom evaluators, LLM-based evaluators, datasets, experiments, production monitoring, and trajectory evaluation for multi-step agents. Teams can use failed production traces as inputs to regression suites, which is the right operational connection between observability and evaluation.

Tradeoffs: The strongest developer experience is still within the LangChain ecosystem. Pricing combines seats and usage, which can make costs sensitive to both team size and trace volume. Self-hosted and hybrid deployment options are enterprise features.

Pricing: The Developer plan includes one seat and 5,000 base traces per month. Plus costs $39 per seat per month and includes 10,000 base traces before usage charges. Enterprise pricing is custom. See LangSmith pricing.

3. Langfuse: Best open-source platform with a managed cloud

Best for: Teams that value an open-source codebase, self-hosting, prompt management, trace analytics, and a relatively low-cost cloud entry point.

Langfuse provides traces, observations, sessions, prompt versioning, cost tracking, datasets, experiments, and evaluations. It supports SDKs and OpenTelemetry, and teams can use Langfuse Cloud or operate the open-source deployment themselves.

The product is approachable for application teams that need core LLM and agent observability without adopting a broader APM suite. Its unit-based pricing is transparent, but teams should model a realistic agent trace because one run can contain a trace, many observations, and multiple scores. Those objects are billed as separate units in Langfuse Cloud.

Tradeoffs: Self-hosting provides control over infrastructure and data, but production deployments require teams to operate and scale the supporting database, cache, object storage, and application services. Cloud customers should compare retention and governance requirements across plans.

Pricing: The cloud Hobby plan includes 50,000 units per month and 30-day data access. Core starts at $29 per month with 100,000 included units and 90-day data access. The open-source edition is free to self-host. See Langfuse pricing.

Teams evaluating a migration path can also read the detailed guide to when teams choose a Langfuse alternative.

4. Braintrust: Best for evaluation-first engineering

Best for: Teams that organize development around datasets, scorers, experiments, prompt iteration, and regression testing.

Braintrust combines tracing with an evaluation-centered development workflow. Teams can log production interactions, curate datasets, build deterministic or model-based scorers, compare experiments, and use the Loop agent to analyze data or assist with evaluation work.

Braintrust supports its own SDKs, OpenTelemetry, and an optional AI gateway. This gives teams more than one integration path and avoids the false choice between SDK-based tracing and gateway-based logging. Its pricing model is based on processed data and scores rather than a simple trace count.

Tradeoffs: Pro begins at $249 per month, which is a meaningful jump from the free tier. Self-hosted or on-premises deployment is an enterprise capability. Teams should also estimate the cost of evaluator scores separately from trace ingestion.

Pricing: Starter has no platform fee and includes 1 GB of processed data, 10,000 scores, unlimited users, and 14-day retention. Pro is $249 per month and includes 5 GB, 50,000 scores, and 30-day retention. See Braintrust pricing.

5. Datadog Agent Observability: Best for full-stack correlation

Best for: Organizations already using Datadog that want to connect agent failures with application traces, infrastructure, logs, security signals, incidents, and service ownership.

Datadog Agent Observability captures dynamic agent traces, including model calls, tools, workflows, retrieval, inputs, outputs, metadata, cost, and latency. Its evaluation workflow includes datasets, experiments, online and offline evaluations, human review, and production monitoring for quality, privacy, and safety.

The differentiator is correlation. An agent timeout can be investigated alongside the service trace, database latency, container state, deployment, error logs, and incident workflow that surrounded it. Teams already standardized on Datadog may avoid adding another operational backend.

Tradeoffs: Datadog is a hosted platform rather than an open-source or self-hosted agent observability product. The default 15-day trace retention may be short for teams that build evaluation datasets from older production failures, and retention extensions add cost.

Pricing: The Free plan includes 40,000 LLM spans per month, 15-day retention, and full feature access. Pro starts at $160 per month with annual billing for 100,000 LLM spans. Datadog bills LLM spans, while tool, workflow, agent, embedding, and retrieval spans are included. See Datadog pricing.

6. Comet Opik: Best open-source evaluation and observability combination

Best for: Teams that want open-source agent tracing, test suites, assertions, evaluation, and a managed cloud option with a low paid entry point.

Opik captures LLM calls, tools, agent steps, memory, context, and delegations across common frameworks. It adds datasets, experiments, LLM-as-a-Judge, custom metrics, online evaluation, production dashboards, cost tracking, and agent testing.

Comet describes the open-source edition as the same codebase used for hosted versions, which makes Opik relevant to teams that want a practical self-hosted path without starting from an instrumentation library alone. Its cloud plans provide a simpler operational route.

Tradeoffs: As with any self-hosted platform, the engineering team owns deployment, upgrades, scale, backups, and availability. Organizations should compare enterprise governance, support, and deployment requirements directly with Comet.

Pricing: Open source is free. Free Cloud includes 25,000 spans per month, up to 10 users, and 60-day retention. Pro Cloud costs $19 per month and includes 100,000 spans. See Comet pricing.

7. Weights & Biases Weave: Best for existing W&B teams

Best for: Teams already using Weights & Biases for model experiments, assets, lineage, or ML workflows that want agent tracing and evaluation in the same platform.

W&B Weave traces model and function calls, captures inputs, outputs, metadata, latency, and token usage, and supports evaluations, scorers, production monitoring, and LLM-as-a-Judge. The platform is useful when teams want to compare agent behavior with model, dataset, and experiment history already managed in W&B.

Weave’s data-ingestion model measures stored trace metadata and logged inputs and outputs. This can align well with teams that prefer paying by payload volume rather than trace count, but it also means large prompts, retrieved documents, or tool outputs can materially affect cost.

Tradeoffs: Buyers should model representative payloads rather than estimating from request volume alone. Teams that do not use the broader W&B platform may find a more focused agent observability product simpler.

Pricing: The Free plan includes 1 GB of Weave data ingestion per month. Pro starts at $60 per month and includes 1.5 GB, with additional ingestion listed at $0.10 per MB. Enterprise private deployment is custom. See W&B pricing.

8. Galileo: Best for prebuilt evaluators and runtime guardrails

Best for: Teams that want packaged quality metrics, automated evaluation, failure analysis, and enterprise runtime controls.

Galileo combines tracing and evaluation with prebuilt and custom metrics for LLM and agent applications. Its focus on evaluator models can reduce the work required to stand up common checks for retrieval quality, hallucination, safety, and task performance.

Enterprise plans add real-time guardrails and hosted, VPC, or on-premises deployment options. This makes Galileo relevant when quality checks need to participate in runtime policy, although teams should separately measure the latency, cost, and false-positive rate of any inline evaluator.

Tradeoffs: Proprietary evaluation models can accelerate implementation, but they still need validation against domain-specific examples and human labels. A generic evaluator that performs well on a vendor benchmark may not match the failure costs of a particular application.

Pricing: Free includes 5,000 traces per month, unlimited users, and unlimited custom evaluations. Pro starts at $100 per month when billed annually and includes 50,000 traces. Enterprise pricing is custom. See Galileo pricing.

9. HoneyHive: Best for flexible data boundaries

Best for: Enterprises that need agent observability and evaluation with SaaS, single-tenant, hybrid, or fully self-hosted deployment choices.

HoneyHive supports distributed traces, dashboards, alerts, dataset curation, annotation queues, online evaluation, experiments, regression tracking, prompt management, and data export. It provides Python and TypeScript SDKs, OpenTelemetry support, and automatic instrumentation for common agent frameworks.

The deployment model is the main differentiator. Enterprise customers can use a HoneyHive-managed control plane with a self-hosted data plane or operate both control and data planes themselves. This can help security-sensitive teams keep raw trace and evaluation data within their environment.

Tradeoffs: The free Developer plan is generous enough to test the workflow, but HoneyHive does not publish a self-serve paid tier between free and enterprise. Buyers should ask how events are counted because an event can be a trace span or metric-label combination.

Pricing: Developer is free for 10,000 events per month, five users, and 30-day retention. Enterprise pricing is custom and includes hybrid or self-hosted options. See HoneyHive pricing.

10. Laminar: Best for long-running agent debugging

Best for: Teams that need full-text trace search, an agent debugger, SQL analysis, browser session recording, and pricing based on ingested data.

Laminar receives traces over OTLP and adds full-text search, dashboards, a SQL editor, datasets, evaluations, labeling queues, alerts, and an agent debugger. Its integrations include coding, browser, and general-purpose agent frameworks, which makes it relevant to long-running tasks with many tools and steps.

Laminar prices its core platform by data volume rather than by root trace or child span. This can be attractive for high-span-count agents, but teams should still benchmark real payload sizes and evaluator usage before comparing monthly costs.

Tradeoffs: Laminar is newer than several platforms on this list, so enterprise buyers should assess support, roadmap, migration, and operational maturity alongside the feature set. The free plan has one seat, one project, and seven-day retention.

Pricing: Free includes 1 GB of data. Starter costs $30 per month for 3 GB, 30-day retention, and unlimited seats. Pro costs $150 per month for 10 GB and six-month retention. Enterprise supports on-premises deployment. See Laminar pricing.

11. Maxim AI: Best for simulation and cross-functional evaluation

Best for: Product and engineering teams that want simulation, prompt experimentation, online evaluations, datasets, and production observability in one quality workflow.

Maxim AI supports tracing, online evaluation at session, trace, and span levels, human evaluation, datasets, alerts, prompt experimentation, and simulation. The combination is useful for teams that want to test agent behavior across generated scenarios before monitoring similar criteria in production.

Maxim also supports OpenTelemetry-based trace forwarding and enterprise in-VPC deployments. Its seat-based plans can work well for collaborative teams, although buyers should compare seat costs with log allowances and retention.

Tradeoffs: The Developer plan keeps data for three days, and Professional retains it for seven days. That is enough for early iteration but often too short for production failure analysis or durable evaluation dataset creation.

Pricing: Developer is free for up to three seats and 10,000 logs per month. Professional costs $29 per seat per month for 100,000 logs and online evaluations. Business costs $49 per seat per month and extends retention to 30 days. See Maxim pricing.

12. Portkey: Best for AI gateway and provider reliability

Best for: Teams that need model routing, retries, fallbacks, load balancing, caching, key management, guardrails, and observability at the gateway layer.

Portkey combines an AI gateway with request logs, traces, feedback, metadata, filters, alerts, prompt management, and guardrails. It is especially relevant when the operational problem includes provider reliability and policy enforcement in addition to agent debugging.

A gateway can reduce repeated integration work across model providers and centralize controls. It also becomes a runtime dependency when hosted in the request path. Teams should evaluate fail-open or fail-closed behavior, timeouts, regional routing, privacy mode, key handling, self-hosting, and what happens during a gateway outage.

Tradeoffs: Portkey’s observability is strongest around traffic flowing through the gateway. Deep visibility into arbitrary application state, internal orchestration, or tools may still require SDK or OpenTelemetry instrumentation. The hosted Production plan is not intended for organizations that require custom data residency controls; those needs move to Enterprise.

Pricing: Developer includes 10,000 recorded logs per month with three-day log retention. Production costs $49 per month for 100,000 logs and 30-day retention. The open-source gateway can be self-hosted, while enterprise deployments are custom. See Portkey pricing.

13. AgentOps: Best for fast agent framework instrumentation

Best for: Developers who want quick setup, visual session replay, cost tracking, and broad framework coverage.

AgentOps focuses specifically on agents. It captures LLM calls, tools, multi-agent interactions, token use, cost, errors, and security-relevant events. Time-travel debugging lets developers replay a session and inspect the sequence that led to a failure.

The product advertises integrations across hundreds of models and frameworks, making it a practical starting point for teams that want an agent-focused trace view without first designing a full observability schema.

Tradeoffs: AgentOps is strongest in tracing, replay, cost analysis, and developer debugging. Teams that need extensive experiment management, evaluation datasets, annotation queues, and sophisticated production quality monitoring should compare those workflows with broader platforms.

Pricing: Basic is free for 5,000 events. Pro starts at $40 per month and adds unlimited events, log retention, export, and role-based permissions. Enterprise offers on-premises deployment and self-hosting. See AgentOps pricing.

14. Traceloop: Best for OpenLLMetry users

Best for: Teams that already use OpenLLMetry or want an OpenTelemetry-native instrumentation library that can send data to multiple observability backends.

Traceloop is built around OpenLLMetry, an Apache 2.0 instrumentation project for LLM applications. The hosted platform adds monitoring, evaluation, CI/CD integration, and prompt management, while enterprise customers can deploy on-premises.

The open instrumentation layer is the strongest reason to consider Traceloop. A team can use OpenLLMetry while retaining flexibility over the backend, including sending traces through an OpenTelemetry collector.

Tradeoffs: The hosted free plan retains data for only 24 hours. Traceloop also states that it is joining ServiceNow, so new buyers should confirm product roadmap, contracting, support, and migration expectations during evaluation.

Pricing: Free includes up to 50,000 spans per month with 24-hour retention. Enterprise pricing is custom and includes custom retention and an on-premises option. See Traceloop pricing.

Why Helicone is not in the greenfield shortlist

Helicone remains available and can still serve existing users. However, the company announced on March 3, 2026 that it had been acquired by Mintlify and that Helicone would remain live in maintenance mode, with security updates, model support, bug fixes, and performance work continuing. For a new long-term platform decision, teams should account for that product status and compare active-development alternatives before committing.

AI agent observability versus agent evaluation

Observability and evaluation solve related problems:

• Observability records and organizes what the system did.
• Evaluation applies criteria to decide whether the behavior or outcome was acceptable.
• Experiments compare candidate changes against datasets before release.
• Monitoring tracks those signals over production traffic and time.

A trace can reveal that an agent called a refund tool twice. A deterministic evaluator can flag the duplicate action. A session evaluator can decide whether the customer request was ultimately resolved. An experiment can test a routing change against prior failures. A monitor can alert the team if duplicate-tool rates rise after deployment.

This article owns the commercial comparison of observability products. For deeper coverage, use these focused resources:

• Compare dedicated LLM and agent evaluation platforms.
• Learn the methods, metrics, and workflows behind LLM evaluation.
• Understand why AI agents need evaluations beyond trace inspection.
• Choose when to use LLM-as-a-Judge and when deterministic or human evaluation is better.
• Estimate evaluator usage and infrastructure with the guide to LLM evaluation costs.
• Turn production failures into durable requirements with the guide to evaluating agents and building better specifications.

How to choose the right agent observability tool

Start with the constraints that would disqualify a platform, then compare workflow quality. A polished trace viewer is not useful if the product cannot meet data residency requirements, represent the agent’s execution model, or support the evaluation process your team needs.

During a proof of concept, instrument the same representative workflow in the finalists. Use a real agent with retrieval, at least two tools, one retry, one handoff or sub-agent, and a multi-turn session. Then compare:

• How much of the execution appears without manual spans
• Whether parent-child relationships and session identifiers remain correct
• How the platform handles large inputs, tool outputs, errors, and streaming
• How quickly an engineer can find a known failure pattern
• How a production trace becomes an evaluation example
• Whether the same evaluator works in experiments and production
• How redaction, sampling, export, retention, and access controls behave
• What 100,000 and 1 million representative agent runs would cost

Agent observability implementation checklist

1. Define the trace hierarchy. Decide what counts as a session, trace, agent span, tool span, retrieval span, model span, and workflow span.
2. Use stable identifiers. Propagate user, session, task, experiment, prompt version, model version, deployment, and environment metadata.
3. Capture observable state transitions. Record tool arguments, tool results, routing decisions, errors, retries, handoffs, and approvals without claiming access to hidden model reasoning.
4. Protect sensitive data. Redact or tokenize PII, credentials, retrieved documents, and tool outputs before export when possible.
5. Measure outcomes as well as operations. Track task completion, tool correctness, policy adherence, latency, token use, and cost.
6. Evaluate at the right level. Use deterministic checks for schemas and invariants, model-based judges for semantic criteria, and human review for ambiguous or high-risk behavior.
7. Keep failures. Sample routine traffic, but retain errors, low-scoring sessions, user corrections, escalations, and unusual trajectories.
8. Create a regression loop. Promote representative failures into versioned datasets and run experiments before deployment.
9. Monitor release impact. Compare quality, cost, latency, and failure distributions by prompt, model, tool, agent, and application version.
10. Review the economics quarterly. Agent traces become deeper as systems add tools and sub-agents, so pricing assumptions can age quickly.

These practices form part of a broader AI engineering discipline that connects observability, evaluation, experimentation, and production improvement.

Frequently asked questions

What is AI agent observability?

AI agent observability is the practice of collecting and analyzing the observable execution path of an agent. A useful trace includes model calls, retrieval, tools, memory operations, workflow steps, handoffs, errors, latency, cost, and outcomes. It helps teams understand how an agent arrived at an action and where the application failed.

How is agent observability different from LLM observability?

LLM observability often focuses on prompts, completions, tokens, latency, cost, and response quality for model calls. Agent observability must also represent tools, retrieval, state, control flow, memory, retries, handoffs, permissions, and multi-step task outcomes. A platform can support both, but agent workloads require a richer trace hierarchy.

How is agent observability different from agent evaluation?

Observability provides the evidence of what happened. Evaluation applies criteria to that evidence. A trace may show the arguments passed to a tool, while an evaluator determines whether those arguments were correct. Production systems generally need both.

Is OpenTelemetry enough for AI agent observability?

OpenTelemetry provides a standard way to instrument and export telemetry. It does not provide the complete backend required for storage, trace visualization, search, dashboards, alerts, evaluations, datasets, experiments, or human review. Most teams use OpenTelemetry with one or more observability platforms.

Do I need a separate agent observability tool if I use Datadog?

Not necessarily. Datadog now provides agent tracing, evaluations, datasets, experiments, and production monitoring. It can be a strong choice for teams that value correlation with APM and infrastructure data. A specialist platform may still be preferable when open-source deployment, longer default retention, a particular evaluation workflow, or a different pricing model is more important.

What are the best open-source agent observability tools?

Phoenix, Langfuse, and Comet Opik are leading open-source platforms that combine tracing with evaluation workflows. OpenLLMetry is an open-source instrumentation project rather than a complete backend. The right choice depends on license requirements, storage architecture, self-hosting effort, framework support, and the desired path to managed or enterprise deployment.

What should an agent trace contain?

An agent trace should contain the user or system input, model operations, retrieved context, tool calls and results, memory reads and writes, routing and handoff events, retries, errors, timing, token usage, model and prompt versions, relevant application state, and the final outcome. Sensitive data should be redacted according to the application’s security policy.

How much do AI agent observability tools cost?

Entry plans range from free open-source software and free cloud tiers to paid plans beginning around $19 to $249 per month among the products in this guide. Total cost depends on the billing unit. Vendors may charge by span, trace, event, request, ingested data, seat, evaluator score, retention, or a combination, so teams should price a representative agent workload rather than compare base plans alone.

Can observability prevent an agent from taking a harmful action?

Observability alone records and analyzes behavior. Runtime guardrails, permissions, approval gates, policy checks, sandboxing, and application controls are needed to block or pause actions. Observability provides the evidence required to test and monitor whether those controls work.

Final recommendation

Choose the platform that can represent your real execution path and connect a production failure to a verified improvement. For many teams, the practical shortlist begins with Arize AX or Phoenix, LangSmith, Langfuse, Braintrust, Datadog, and Opik, then expands based on requirements for gateway routing, proprietary evaluators, simulation, private deployment, or specialized agent debugging.

The proof of concept should end with a concrete result: the team can find a failed session, explain the observable cause, turn it into an evaluation example, compare a fix, and monitor the new version in production. That workflow matters more than the number of integrations or dashboard screenshots on a pricing page.

Explore Arize AX or start with the open-source Phoenix project.