Overview
- Braintrust, Arize AX, Galileo, Langfuse, LangSmith, Datadog LLM Observability, and Confident AI help you evaluate, trace, and monitor agent behavior.
- These platforms evaluate how an agent behaved. Most stop there. Galileo can block an unsafe action during inference, and Datadog's separate Bits AI SRE product can investigate root causes across Datadog telemetry, but neither documents the broad, cross-system incident investigation and remediation covered below.
- Mezmo AURA sits above your eval stack as an autonomous execution harness and first responder for production incidents.
- AURA uses open-source components and connects to your chosen models, infrastructure, and tools. It investigates incidents, carries out approved actions, and retains reviewed context for future responses.
- Use the comparison table to choose an evaluation and tracing platform. The later sections explain how Mezmo AURA can respond when live agents contribute to production incidents.
When the eval suite is green and production is still broken
A passing eval suite cannot predict every production failure. An agent might call the correct tool at turn three, misread its response, and continue reasoning from bad data until turn eight. The final model call can look normal even though the full session produced the wrong customer outcome.
Agent observability tools reconstruct those multi-turn paths. Traditional application monitoring might record a successful API response. An agent trace can reveal that the agent misinterpreted the returned data and made several incorrect decisions afterward. Individual model calls often hide agent failures because agents maintain state, use tools, and pursue goals across many steps.
Production behavior also creates failure modes that curated tests never covered. A Latitude developer guide argues that production failures can outpace the scenarios represented in an evaluation set. Real users provide unfamiliar inputs, connected systems return stale data, and workflows encounter states absent from pre-release datasets. A green evaluation therefore shows that an agent passed known tests. It cannot guarantee that the surrounding production system will operate correctly.
You need to assess agent behavior across traces, prompts, and tool calls. You also need a defined way to investigate the surrounding production system and execute an approved response when an agent contributes to an incident.
The comparison table and seven vendor reviews cover evaluation, tracing, monitoring, and runtime controls. The Mezmo AURA section then covers production investigation and remediation after an agent-driven system fails.
Comparing seven evaluation and tracing platforms with Mezmo AURA
The comparison separates tools that evaluate or trace agent behavior from software that investigates and remediates production incidents. Most entries help you detect failures, inspect traces, or block unsafe outputs. AURA serves as an autonomous execution harness and first responder after an incident occurs.
The vendor reviews explain each platform’s capabilities and operating model. In the embedded use-case table, the final column distinguishes runtime controls from production incident response.
Braintrust
Braintrust fits AI engineering groups that want evaluation and prompt experimentation in one platform, with trace analysis connected to both workflows. Engineers can convert production traces into datasets, compare prompt or model variants, and inspect regressions before release. Brainstore supports fast searches across large trace volumes, while Loop generates test cases, runs evaluations, and iterates on prompts when you provide clear scoring criteria. A TrueFoundry review of Braintrust identifies the connection between evaluation, datasets, and prompt iteration as a core strength.
Braintrust runs as a proprietary SaaS platform and stores trace data on Braintrust infrastructure. Its purpose-built Brainstore database supports analytical queries over production traces. The AI Gateway routes requests to providers such as OpenAI, Anthropic, and Google through a unified API with zero-code logging. Native SDKs cover more than 50 frameworks, including LangChain, LlamaIndex, CrewAI, the OpenAI Agents SDK, and the Vercel AI SDK. OpenTelemetry support is limited to ingestion.
The platform includes more than 25 built-in scorers. Loop can generate custom scorers from natural-language descriptions and build evaluation datasets from production logs. Braintrust can report a runaway agent loop or failed tool call, but it does not investigate or remediate the surrounding production system.
Braintrust works best for prompt optimization, release testing, and ongoing output-quality analysis. Its MIT-licensed SDKs provide some inspectability, but the core platform is proprietary and closed source. A Future AGI comparison of Braintrust reports that self-hosting is limited to the Enterprise tier.
Braintrust offers a free tier with 1 GB of processed data, 10,000 scores, and unlimited users. Pro costs $249 per month, while Enterprise uses custom pricing. Reported retention ranges from 14 days on the starter tier to 30 days on Pro. Braintrust describes self-hosted or hybrid deployment as an Enterprise option, although MLflow's comparison lists self-hosting as unavailable. Buyers should confirm current deployment options directly with Braintrust.
Teams managing incidents caused or complicated by live agents need a separate response layer that can investigate the wider production environment and execute remediation.
Best for: AI engineering teams that want fast evaluation, prompt experimentation, and production-trace analysis in a managed platform.
Arize AX
Arize AX suits teams that need continuous evaluation and monitoring across high-volume production LLM workloads. It builds on Phoenix, Arize’s self-managed product for tracing and evaluation. Phoenix uses OpenInference instrumentation built on OpenTelemetry and supports more than 40 framework integrations. It provides more than 50 evaluation metrics for areas such as faithfulness, relevance, safety, toxicity, and hallucination detection. Phoenix also supports multi-step trajectory analysis, trace clustering, anomaly detection, and retrieval relevancy visualization for RAG. The self-managed Phoenix layer runs on a single node. Teams that need greater scale must use the managed Arize AX platform (Arize).
Signal reviews production traces on a schedule and groups related failures into prioritized issues with supporting evidence. Alyx investigates trace-level problems and proposes evaluators that could detect similar behavior. A person must confirm those evaluators before Arize applies them, which keeps evaluation changes under human control.
Arize AX’s documented workflow covers trace investigation, evaluation, and notification. Its monitors can route alerts to PagerDuty or Slack, but Alyx does not remediate the affected service or coordinate a response across the surrounding infrastructure. Arize AX therefore fits users who need production-scale LLM observability and broader evaluation coverage.
Phoenix uses the Elastic License 2.0, which restricts offering the software as a managed service. Arize AX uses tiered managed-cloud pricing, but the cited research does not provide current prices. Reported AX retention is seven days on the free tier and 15 days on Pro. Alyx Copilot, online evaluations, and monitoring require paid plans. Phoenix does not include prompt optimization, an AI gateway, or a governance layer.
Teams seeking automated incident diagnosis and remediation will need an execution layer above it.
Best for: Research-oriented teams that want extensive evaluation metrics and can begin with a free, single-node deployment.
Galileo
Galileo turns evaluation criteria into runtime guardrails for agent behavior. Its evaluation tools assess retrieval quality, instruction adherence, tool use, safety, and other aspects of an agent trace. Proprietary Luna-2 evaluators run those checks at lower cost than LLM-as-judge approaches. Galileo also provides automatic failure detection with root-cause analysis of agent behavior. Visual workflows expose complete execution traces for review.
Galileo Runtime Protection uses evaluation scores during inference. Input guardrails screen prompts for attacks or malformed requests, while output guardrails filter hallucinations, policy violations, and sensitive data. Action policies can also stop unauthorized tool calls and preserve decision rationales for audits.
Galileo fits users who need evaluations and active enforcement in the same platform. Trace and graph views help you inspect agent decisions. Its deployment options include SaaS, private cloud, and on-premises environments.
Runtime Protection focuses on unsafe model outputs and agent actions. Galileo’s cited product materials do not describe automated root-cause investigation across application and infrastructure telemetry or broader SRE remediation workflows. A blocked tool call may prevent immediate harm, but Galileo’s cited product materials do not document service-level diagnosis or restoration after an agent-driven production incident.
Galileo offers a free tier with 5,000 traces per month. Pro costs $100 per month for 50,000 traces, while Enterprise uses custom pricing. Deployment options include SaaS, private cloud, and on-premises environments. The supplied research does not identify an open-source license for the platform.
Best for: Teams that need production evaluations and runtime guardrails to block unsafe model outputs or agent actions.
Langfuse
Langfuse provides an open-source platform for tracing LLM applications and managing prompts. Its documented feature set in the Langfuse repository captures model calls, retrieval steps, and agent actions. Evaluations support model-based judging, code evaluators, user feedback, and custom pipelines.
Self-hosting gives you direct control over deployment and application data. Langfuse uses an MIT license for its core repository, while some enterprise features carry a commercial license. A self-hosted deployment requires ClickHouse, PostgreSQL, Redis, and the Langfuse application services. Langfuse requires ClickHouse and does not support replacing it with another database. You can deploy the stack through Docker Compose, Kubernetes, or cloud infrastructure templates. Langfuse Cloud provides a managed alternative for teams that do not want to operate these services themselves. OpenTelemetry support is limited to ingestion.
Langfuse works best when developers need to inspect traces, test prompts, and evaluate changes before or after release. A developer can open a poor trace in the playground, adjust the prompt or model settings, and compare the new output against a dataset.
Langfuse does not document a workflow for production incident response. An Agenta comparison of LLM observability platforms describes an application-focused scope that does not include on-call coordination, infrastructure-level root-cause investigation, or automated remediation. You can use Langfuse to determine how an agent behaved, but you need a separate execution layer to investigate and resolve incidents across the production environment.
Enterprise features such as SSO and advanced role-based access controls live in separate folders under a commercial license. Reported retention ranges from 30 days on the free tier to three years on Pro. The cited research does not provide current prices. Langfuse includes a prompt playground, basic model-based scoring, and cost analytics. It does not include an AI gateway.
Best for: Teams that already operate ClickHouse and mainly need self-hosted tracing, cost analysis, and a prompt playground.
LangSmith
LangSmith provides debugging, testing, evaluation, and monitoring for LLM applications. Its traces expose model calls, tool use, intermediate agent steps, latency, and errors. You can also run dataset-based tests and automated evaluators to compare prompts or application versions before release.
LangSmith fits teams building primarily with LangChain or LangGraph. Its instrumentation can accept traces from custom code, including OpenTelemetry spans, but its debugging model follows LangChain concepts such as chains and agent steps. Applications that use other orchestration frameworks or direct model calls may require additional instrumentation. The cited material should document any claim that these applications provide less consistent trace detail. LangSmith provides native agent graph visualization and annotation queues for LangChain and LangGraph applications. Other frameworks can send data through an OpenTelemetry ingestion path or a traceable wrapper. Polly AI Assistant, topic clustering, and the Insights Agent are available on selected paid tiers.
LangSmith helps you determine where an agent behaved incorrectly and alert an operator when production metrics cross a threshold. Its monitoring does not investigate infrastructure-wide incidents or execute remediation. If a faulty agent action affects a database, service, or deployment, LangSmith can provide relevant trace evidence, but your incident-response tools and on-call staff must diagnose and fix the broader failure.
LangSmith is proprietary and closed source. Free and Plus plans run as SaaS with data on LangChain infrastructure. Self-hosting is available only through Enterprise contracts. The free tier includes 5,000 traces per month and 14-day retention. Plus costs $39 per seat each month. Retention of up to 400 days is available as a paid add-on, while Enterprise uses custom pricing.
Best for: Teams building primarily with LangChain or LangGraph that want native graph tracing, evaluation, and annotation workflows.
Datadog LLM Observability
Datadog LLM Observability suits teams that already use Datadog for application and infrastructure monitoring. The module correlates LLM traces with APM, infrastructure, and RUM data, which helps engineers connect a poor agent response to service errors, latency, or resource constraints elsewhere in the stack. Datadog delivers the module as multi-tenant SaaS, so teams seeking self-hosted or open-source workflows may prefer another option.
Datadog instruments LLM applications through an SDK that records prompts, responses, tool activity, latency, and token usage. The SDK sends trace data after or alongside each call rather than sitting between the application and model. A DeepInspect comparison of Datadog LLM Observability alternatives reports that most evaluations detect problems after the model has responded. AI Guard provides a narrower exception by blocking security threats such as prompt injection and unauthorized tool calls during runtime.
Bits AI SRE serves a separate purpose within Datadog. LLM Observability monitors applications that you build, while Bits AI SRE investigates alerts and proposes likely root causes across Datadog telemetry. The LLM Observability module can surface quality, cost, and performance failures, but it does not execute general remediation for production incidents.
Datadog provides LLM Observability as a proprietary, multi-tenant SaaS product. It does not offer a self-hosted or open-source edition in the cited material. Its architecture has a notable advantage for existing customers because it correlates LLM traces with APM, infrastructure, and RUM telemetry. The supplied research does not document pricing tiers, retention windows, or native RAG and vector database coverage.
Best for: Organizations already standardized on Datadog that need to correlate LLM behavior with application and infrastructure signals.
Confident AI / DeepEval
Confident AI pairs a managed enterprise platform with DeepEval, its Apache 2.0 framework for testing LLM applications and agents. DeepEval uses familiar Pytest patterns, so you can define expected behavior, run evaluations in CI/CD, and block regressions before deployment. Tests can score complete agent trajectories or individual steps such as tool calls and retrieval.
Confident AI adds shared datasets, experiment tracking, prompt versioning, human annotation, and production trace monitoring. The Confident AI managed platform fits AI engineering groups that want a consistent evaluation standard across products while keeping tests close to their existing Python and CI workflows.
DeepEval documents a narrow telemetry scope. Its OpenTelemetry collection records the names of evaluation metrics rather than prompts, inputs, outputs, or evaluation data. Confident AI can monitor live traces with online evaluations, but its published capabilities do not describe paging, on-call coordination, incident investigation across the wider production stack, or automated remediation. Confident AI fits code-native quality testing and regression control. When an agent failure affects production services, pair it with an incident-response layer that can diagnose the failure and carry out approved actions.
DeepEval is available under the Apache 2.0 license and can run with Python test suites in self-managed CI environments. Confident AI provides the proprietary managed layer for shared datasets, experiments, prompt versions, annotations, and production monitoring. The supplied research does not document pricing tiers, retention windows, or native vector database coverage.
Best for: Python teams that want pytest-style agent evaluations and CI/CD regression gates backed by a managed collaboration platform.
Where these tools stop and production incident response begins
The seven platforms primarily detect, trace, and evaluate agent behavior. They can expose weak outputs and problematic tool calls, while some can also block unsafe actions. Their documented workflows generally do not extend to investigation and remediation across the production environment.
A survey summarized by MarkTechPost reported that 89% of more than 1,300 respondents had implemented observability. It also reported that 52.4% ran offline evaluations, 37.3% ran online evaluations, and 29.5% ran no evaluations. Because the categories may overlap and the survey did not measure remediation, the results describe adoption of observability and evaluation rather than proving a gap in incident response.
Production incident response begins with triage after an alert and continues through diagnosis, an approved corrective action, and verification. Responders must connect an agent failure to the relevant infrastructure and data before determining the cause. Routing an alert to Slack or PagerDuty notifies a person but does not perform that work.
Use the tools compared above when you need evaluation and tracing. If your agents can affect production systems, add incident-response capabilities that investigate failures, carry out approved corrective actions, and retain useful context for future incidents.
Mezmo AURA: the layer that acts once agents are live in production
AURA’s Apache 2.0 code and MCP-based architecture let it work with existing evaluation, observability, and remediation tools. You can connect your preferred model, expose tools through MCP, and run AURA on infrastructure you control. Its framework-independent design allows it to connect with any of the seven platforms in this comparison without replacing their evaluation and tracing functions. AURA uses evaluation results and production telemetry as inputs for incident investigation and approved remediation. It uses their evidence alongside production telemetry to investigate incidents, identify likely root causes, and execute approved remediation.
Mezmo built AURA as an SRE agent for production AI, not as another eval tool. It gives live agents a governed path from detection into investigation and remediation, so a diagnosis does not stop at a dashboard. AURA carries out actions only within the permissions and approval rules you configure, which keeps the workflow safe enough to run against production.
Understand
AURA investigates incidents by gathering telemetry and operational context through connected MCP tools. Its reasoning loop plans an investigation, executes tool calls, synthesizes the evidence, and checks whether the evidence supports its diagnosis. If the evidence remains incomplete, AURA can revise the plan and continue investigating. This process connects agent traces and evaluation results with the wider production context needed for root-cause analysis.
Engineers can inspect which signals AURA used and why it favored one hypothesis over another. That visibility helps you review a diagnosis before approving any production action.
Act
AURA turns a diagnosis into a proposed response through the tools and runbooks you connect. You decide which actions AURA can attempt and which changes require review.
Approval rules determine which actions AURA can execute on its own and which actions require human review. You can require approval before every action, permit routine actions with later review, or let AURA handle established cases while escalating unfamiliar ones. Version-controlled configuration records those boundaries so reviewers can see what the agent may read and execute. This governed, human-in-the-loop model lets AURA act as a first responder in production without bypassing the controls a production environment requires.
Improve
AURA checks whether an approved change resolved the underlying condition before closing the investigation. It then compresses the useful evidence, reasoning, and remediation path into reusable memory for future incidents.
After review, the evidence from a confirmed fix can support a similar diagnosis or inform a rule in your configuration repository. After review, AURA can reuse that operational context instead of rebuilding the same understanding from raw telemetry.
AURA does not replace the evaluation, tracing, or prompt-testing functions of Braintrust, Arize AX, Galileo, Langfuse, LangSmith, Datadog LLM Observability, or Confident AI. If those functions meet your requirements, keep the specialized platform that fits your development workflow. Add Mezmo AURA when live agents require production investigation, controlled remediation, and reusable incident context.
Which tool fits your team
Choose an evaluation or tracing platform when your primary requirement is pre-release testing or behavior analysis. Braintrust supports experimentation, Arize AX supports production evaluation, and Galileo adds runtime guardrails. Langfuse fits self-hosted LLM engineering, while LangSmith fits applications built with LangChain or LangGraph. Datadog suits existing Datadog users, and Confident AI fits pytest-style CI workflows.
Agents that can change production state require controls beyond trace analysis. Tracing can reveal what happened after an agent calls an operational tool or affects a customer workflow, but it does not by itself investigate and remediate the surrounding incident. Production failures may also expose behavior that the original evaluation set did not test.
Keep your evaluation platform for regression testing and behavior analysis. Consider Mezmo AURA when you also need a first responder that investigates production incidents and carries out approved actions. AURA provides an open-source execution layer that connects to supported tools and models in your existing stack, while your evaluation platform continues to handle testing and behavior analysis.
FAQs
What is the difference between AI observability and AgentOps?
AI observability measures the quality and operational performance of model calls, while AgentOps follows an agent’s decisions and tool use across a task. Mezmo AURA connects that agent context with production telemetry after monitoring detects a problem. The combined context supports incident investigation and approved corrective action.
Is LLM observability the same as agent monitoring?
LLM observability examines individual model calls and outputs, while agent monitoring follows decisions and tool use across a complete task. Mezmo AURA combines agent context with connected production telemetry during incident investigation. That connection lets you assess the agent’s behavior alongside the condition of the surrounding services.
Do I need both an evaluation tool and an AI SRE agent?
Evaluation tools test whether an agent produces acceptable behavior under defined conditions. AURA acts as a first responder when live agent-driven systems cause or encounter production incidents. Use both when agents can affect production systems and your evaluation platform does not provide the investigation and remediation controls you require.
Does AURA replace Braintrust, Arize, or LangSmith?
AURA does not replace platforms used for tracing, evaluation, or prompt testing. Mezmo built AURA for production investigation and supervised remediation alongside those platforms. You can preserve your existing testing workflow while adding Mezmo AURA for controlled incident investigation and approved action.
Is AURA open source?
Mezmo AURA is open source under the Apache License 2.0. You can connect your models and tools, deploy AURA on your own infrastructure, and inspect its reasoning workflows. Source access lets you review the orchestration layer and maintain its configuration in version control.
