6 Best AI Observability Tools for Production Agents in 2026

AI observability tools compared

What we looked for

The best AI observability tool should make five recurring jobs straightforward:

1. Capture a useful trace without rebuilding the application. Instrumentation should preserve prompts, model calls, tool use, retrieval, handoffs, errors, latency, and cost.
2. Read the trace quickly. The interface should make a long agent run understandable as a hierarchy, timeline, session, or conversation rather than a wall of telemetry.
3. Evaluate real behavior. Teams should be able to apply code checks, model-based scorers, and human review to production outputs without creating a separate data pipeline.
4. Test and ship a fix. A production failure should become a dataset example, experiment, and CI/CD check with as little glue code as possible.
5. Let developers and coding agents work from their normal tools. APIs are the baseline. A useful CLI, MCP server, structured output, or other agent-friendly interface shortens the path from a failed trace to a verified code change.

We also considered deployment options, alerting, and how well each platform fits the systems a team already operates. The ranking favors products that cover the complete debugging and improvement loop rather than products that only make one step exceptionally deep.

Where Sim fits: Observability inside the agent builder

An AI agent builder should not make teams assemble a separate telemetry stack before they can understand a run. Sim records the workflow as it executes, so builders can open a run and inspect the inputs, outputs, duration, errors, token usage, and cost for each block. Because the log follows the workflow graph, the trace uses the same mental model as the system the team designed.

That is especially useful when a workflow combines deterministic blocks with AI decisions. A team can see whether the failure came from the model, a tool call, a branch, an API, or the data passed between blocks. Sim also preserves the workflow state associated with the run, which helps distinguish a model-quality problem from a workflow-version problem.

Sim can also orchestrate managed agents that run on another provider. For example, the Claude Managed Agents block can call an agent hosted on the Claude Platform, select its environment, attach files or credential vaults, connect memory, and tag the run with metadata. Sim records the workflow-level handoff and result alongside the rest of the run. The provider still owns the managed agent's internal loop, so teams that need deeper scoring across that agent and other applications can instrument that layer with a dedicated observability platform.

This creates a practical division of labor. Sim's agent observability helps builders understand and operate the workflow they deployed. A dedicated platform such as Braintrust becomes useful when the organization wants one quality system across multiple agents, applications, frameworks, or runtime providers. Teams comparing the wider platform layer can also review the best AI agent platforms for 2026.

1. Braintrust: Best overall AI observability platform

Braintrust is the strongest choice for product and engineering teams that want to do more than inspect traces. It connects observability to a complete evaluation workflow, so teams can identify a production problem, understand its cause, test a proposed fix, and check future releases against the same failure mode.

Braintrust captures nested traces across LLM calls, tool invocations, retrieval steps, and application logic. Each trace can include inputs, outputs, errors, duration, time to first token, token counts, model details, metadata, and estimated cost. Sessions and thread-level views help teams follow behavior across multi-turn or multi-step interactions rather than reviewing isolated model calls.

The differentiator is what happens after the trace arrives. Braintrust can apply online scoring to production traces, using deterministic rules, custom scorers, or model-based evaluation to monitor dimensions such as correctness, relevance, safety, and tool-use quality. Scoring runs asynchronously, so quality checks do not add latency to the user-facing request.

When a trace exposes a failure, a team can add the example to a dataset, test a prompt or model change in the Playground, compare results, and run the resulting evaluation in CI. This creates a direct path from production evidence to regression protection.

That workflow matters for agents because many failures are technically valid. A tool call can succeed while choosing the wrong tool. A retrieval step can return documents while returning the wrong documents. An answer can be fluent while contradicting the source material. Braintrust lets teams score those outcomes using criteria that match the product rather than relying only on latency and error rates.

Braintrust also gives teams several ways to start collecting data. SDK integrations provide application-level context, OpenTelemetry fits teams that already emit standard traces, and the Braintrust AI gateway can provide fast visibility into model traffic. Engineers can work in code while product managers and domain experts inspect traces, annotate examples, and compare changes in the interface.

Braintrust ranks first because each part of that workflow is unusually easy to operate:

• Tracing is easy: Teams can start through an SDK integration, OpenTelemetry, or the AI gateway, then add application-level spans when they need more context.
• Viewing traces is easy: The trace viewer presents nested work as a hierarchy, timeline, or conversation, with inputs, outputs, timing, metadata, tokens, and scores attached to the relevant span.
• Running evals is easy: A production example can move into a dataset, run through the Playground or application code, and be compared as a versioned experiment without exporting it into a separate testing system.
• Adding CI/CD checks is easy: The bt eval command runs JavaScript or Python evaluation files in a pipeline. API-key authentication, non-interactive execution, JSON output, sampling for pull-request smoke tests, and custom pass/fail reporters make it practical to automate release checks.
• Giving coding agents access is easy: The bt CLI can browse traces, query logs with SQL, run evals, and sync data from the terminal.

Other platforms cover many of these capabilities. Braintrust's advantage is how little translation is required between them: a coding agent can query a weak production trace, edit the application, run the relevant evals, and inspect the result in the same terminal session.

Pros

• Connects production traces directly to datasets, experiments, online scoring, and release checks.
• Captures nested agent activity, including model calls, tool use, retrieval, latency, tokens, errors, and cost.
• Supports SDK instrumentation, OpenTelemetry, and an AI gateway, so teams can choose the level of integration they need.
• Gives developers and coding agents terminal access to logs, SQL queries, evals, and synced data through the bt CLI.
• Gives engineers, product managers, and domain experts shared tools for reviewing traces and evaluating changes.

Cons

• Teams still need to define what a good result means and design scorers that reflect their product requirements.
• Advanced evaluation workflows take more setup than a basic proxy or request logger.
• BYOC and self-hosted deployment options require an enterprise plan.

Best for: Teams shipping customer-facing AI products or production agents that need shared observability, evaluation, experimentation, and release validation.

For a deeper feature and deployment overview, read Braintrust's observability documentation.

2. Galileo: Best for continuous quality and safety checks

Galileo focuses on evaluating AI outputs and agent behavior at production scale. Its platform combines tracing with purpose-built evaluators that can assess dimensions such as correctness, hallucination risk, safety, and task completion.

This approach is useful for high-volume applications where manually reviewing traces cannot keep up with traffic. Automated evaluators can surface groups of weak outputs and help teams focus human review on the runs most likely to matter.

Galileo is strongest when continuous quality classification is the primary requirement. Teams should still test how easily its scores connect to their existing development process, datasets, and release gates. An evaluator is most valuable when the result leads to a clear debugging and prevention workflow.

Pros

• Purpose-built evaluators cover common agent, RAG, quality, safety, and security requirements.
• Galileo reports that its Luna models run production evaluations with lower latency and cost than repeatedly using large judge models.
• Connects offline evaluation work to production monitoring and guardrails.
• Its configurable production metrics help high-volume teams prioritize outputs for human review.

Cons

• The platform's strongest differentiation depends on Galileo's proprietary evaluator technology.
• Teams should validate evaluator performance against their own domain-specific labels and edge cases.
• Its quality and safety emphasis may be narrower than the cross-functional observability and experimentation workflow some product teams need.

Best for: Enterprises running large volumes of AI traffic that want automated quality and safety signals across production outputs.

3. Langfuse: Best for self-hosted LLM tracing and prompt management

Langfuse combines LLM observability with prompt management, datasets, experiments, and evaluation. Teams can link a prompt version to the traces it produced, then compare cost, latency, and quality metrics as that prompt changes.

That connection between prompts and traces is useful when prompts are managed outside the application deployment cycle. Product or domain teams can update a prompt, while engineers retain visibility into the exact version used for each generation. Langfuse also supports model-based and code-based evaluation in experiments.

Langfuse is available as a cloud product and as a self-hosted open-source deployment. It is a practical fit for teams that value infrastructure control and want one system for tracing plus prompt operations.

As with any self-hosted platform, the apparent software savings should be weighed against the engineering cost of running a production data system. Teams should evaluate ingestion volume, retention, upgrades, and permissions before choosing deployment based on license alone.

Pros

• Available as both a managed cloud service and a self-hosted open-source platform.
• Links prompt versions to traces, metrics, and evaluation results.
• Combines tracing, prompt management, datasets, and experiments in one LLM engineering stack.
• Supports model-based and code-based evaluators for comparing prompt or model changes.

Cons

• Self-hosted deployments require ongoing database, storage, upgrade, and access-control work.
• The path from a production trace to a governed regression suite may require more assembly than Braintrust's trace-to-dataset workflow.
• Organizations should compare cloud and self-hosted feature availability before choosing a deployment model.

Best for: Teams that want open-source LLM observability with prompt versioning and are comfortable operating the stack themselves.

4. Arize AX: Best for enterprise production monitoring

Arize AX is a managed AI engineering platform that combines production observability, evaluation, experimentation, and monitoring. It uses OpenTelemetry and OpenInference to capture traces across model calls, retrieval, tool use, and the surrounding application workflow.

AX is strongest in environments where teams need to monitor a large stream of production behavior rather than inspect traces one at a time. Dashboards show traffic, latency, token use, cost, and evaluation results. Monitors can alert teams when an operational or quality metric crosses a threshold, including a latency spike, token increase, hallucination-rate change, or evaluation-score drop.

Online evaluation tasks continuously apply code-based or LLM-as-a-judge evaluators to incoming production data. Teams can target specific spans using filters and sampling, or evaluate complete traces and multi-turn sessions. Results attach to the relevant trace, so reviewers can move from a quality signal to the underlying execution context.

Arize AX also supports offline evaluations, datasets, and experiments, allowing teams to compare proposed changes before deployment and then check whether the improvement holds up in production. This gives enterprise teams one managed environment for investigating live behavior and measuring proposed changes.

Pros

• Combines production tracing, dashboards, monitors, alerts, experiments, and evaluations.
• Runs continuous online evaluations at span, trace, or session scope.
• Supports code-based and LLM-as-a-judge evaluators with filtering and sampling controls.
• Uses OpenTelemetry and OpenInference for detailed, vendor-neutral instrumentation.

Cons

• Arize AX is a commercial managed platform rather than a self-hosted open-source product.
• Its broad enterprise feature set may be more platform than a small team needs for basic tracing.
• Continuous LLM-based evaluations still require careful evaluator design, sampling decisions, and cost management.

Best for: Enterprise AI and data science teams that need scalable production tracing, continuous evaluations, quality monitoring, and alerting across many models or applications.

5. Datadog Agent Observability: Best for existing Datadog teams

Datadog Agent Observability extends Datadog's monitoring stack to agent and LLM workloads. It can represent agents, workflows, model calls, tasks, tool use, embeddings, and retrieval as nested spans, then connect those traces to familiar dashboards, monitors, and operational telemetry.

The main advantage is consolidation. If engineering and operations teams already use Datadog for services, logs, and incidents, agent telemetry can live beside the rest of the production system. That makes it easier to correlate a slow agent run with a failing dependency or infrastructure event.

Datadog also supports managed and custom evaluations attached to spans, traces, or sessions. Its metrics cover latency, errors, token use, and cost, while trace search helps teams isolate specific failure patterns.

The tradeoff is workflow focus. Datadog approaches the problem from production operations and APM. Teams that want evaluation datasets, prompt experiments, and direct trace-to-regression workflows should compare that experience closely with an AI-native platform such as Braintrust.

Pros

• Places agent and LLM telemetry in the same operational platform as application and infrastructure telemetry.
• Supports nested spans for agents, workflows, model calls, tools, retrieval, embeddings, and tasks.
• Provides Datadog dashboards, monitors, and trace search.
• Supports managed and custom evaluations at span, trace, and session scope.

Cons

• Span-based ingestion and retention costs can grow with high-volume, deeply nested agent traces.
• Teams not already using Datadog take on a broad monitoring platform to solve an AI-specific problem.
• Trace-to-dataset experimentation and regression workflows are less central than in an AI-native evaluation platform.

Best for: Larger organizations already standardized on Datadog that want AI telemetry inside their existing monitoring and incident-response environment.

6. PostHog AI Observability: Best for connecting AI and product behavior

PostHog AI Observability connects LLM and agent traces to the product data PostHog already captures. It records prompts, responses, tool calls, tokens, cost, latency, errors, spans, and sessions, then associates those traces with the user who triggered them.

The differentiator is the surrounding context. A team can move from an AI trace to the same user's session replay, product events, feature usage, or exception data. That helps answer questions a model trace cannot resolve by itself: Did the user accept the answer? Did they retry? Did the failure prevent activation or conversion? Is the problem concentrated in one account or product cohort?

PostHog supports LLM-as-a-judge, code-based, and sentiment evaluations on live generations. Because AI observability events are standard PostHog events, teams can build dashboards and alerts for changes in quality, cost, latency, errors, or sentiment. Its AI observability data is also available through PostHog's API and MCP server, so developers and coding agents can investigate traces without staying in the web application.

PostHog is less centered on the controlled trace-to-dataset-to-CI regression loop than Braintrust. Its advantage is connecting model behavior to customer behavior and the rest of the product engineering stack.

Pros

• Connects AI traces to product analytics, session replay, error tracking, feature usage, and user context.
• Supports production evaluations and alerts for quality, sentiment, cost, latency, and errors.
• Makes trace data available through the web app and API and MCP server.
• Fits naturally when a product team already uses PostHog for analytics and experimentation.

Cons

• Teams that only need AI observability may take on a much broader product platform.
• Offline evaluation datasets and CI/CD regression tests are less central to the workflow than in Braintrust.
• The volume and retention model should be reviewed alongside the rest of a team's PostHog event usage.

Best for: Product teams that want to understand how model behavior affects real users and connect AI quality with the rest of their product data.

How to choose an AI observability tool

Choose based on the action your team needs to take after it finds a bad trace.

• Choose Braintrust when the answer is: add it to a dataset, test a fix, compare the result, and block future regressions.
• Choose Galileo when the answer is: run automated quality and safety checks across high-volume traffic.
• Choose Langfuse when the answer is: connect it to prompt versions in a self-hosted LLM engineering stack.
• Choose Arize AX when the answer is: monitor production quality continuously and alert an enterprise team when behavior changes.
• Choose Datadog when the answer is: correlate it with the rest of the application's operational telemetry.
• Choose PostHog when the answer is: connect the trace to the user's product journey, session replay, feature usage, and errors.

Braintrust is our top pick when ease across the complete workflow is the deciding factor. It makes tracing, trace review, evaluation, CI/CD checks, and coding-agent access feel like parts of one system. The other tools remain credible choices when deployment control, specialized evaluators, enterprise operations, or product context matters more.

Build and observe agents in the same workspace with Sim

The observability platform is only one layer of the production stack. Teams also need a place to build, deploy, and operate the agent itself. Sim brings those jobs into one workspace, with native logs that follow every workflow block, tool call, model response, and managed-agent handoff. Builders can inspect a run using the same visual structure they used to design it, without setting up a separate tracing system first.

As the quality program grows, Sim's workflow-level record can sit alongside a dedicated platform for cross-application scoring, datasets, experiments, and release checks. That lets teams start with useful observability on the first run and add specialized evaluation infrastructure when they need it. Explore how to create an AI agent and add observability in the same workspace.