Compare agent-first AI evaluation tools vs LLM-only platforms. Learn how Latitude, Braintrust, LangSmith, and Langfuse handle multi-turn agents with trajectory evaluation.
César Miguelañez
By Latitude · Updated March 2026
Tools compared: Latitude, Braintrust, Langfuse, LangSmith, Arize AI, Maxim AI, Galileo
Key Takeaways
Agent evaluation and LLM evaluation are architecturally distinct problems — most platforms were built for the latter.
Agents evaluated only on final-output quality pass 20–40% more test cases than trajectory-level evaluation reveals (Wei et al., 2023).
The critical failure surface for agents is at the step level: tool call arguments, state propagation, and goal alignment drift — none of which single-turn scoring can detect.
Agent-native tools (Latitude) surface issue patterns automatically; LLM-first tools require manual trace correlation.
Tool selection should match your primary use case: LangSmith for LangChain stacks, Langfuse for self-hosted/open-source needs, Braintrust for systematic pre-deployment experiments, Latitude for production multi-turn agents.
Agent Evaluation Is Not LLM Evaluation
Most AI evaluation tools were built for single-turn LLM scoring — a workflow that does not transfer to production agents. A single prompt goes in, a single response comes out, and you score the response. That model worked for early LLM applications: chatbots, summarizers, classifiers where quality is determined entirely by a single output.
Modern AI agents are different in every dimension that matters for evaluation. An agent produces a sequence of decisions across a full session: which tool to call, what arguments to use, how to incorporate the tool's response into the next reasoning step, whether the current plan still aligns with the original goal. According to research on LLM agent benchmarks, agents evaluated only on final-output quality pass 20–40% more test cases than they would under full trajectory evaluation (Wei et al., 2023). That gap represents real failures — failures that only step-level evaluation can catch.
The practical consequence: most evaluation platforms require significant workarounds to handle multi-turn conversation flows, tool call sequences, and state management across steps. Some have added agent support as a layer on top of their existing LLM-first architecture. Others were designed for agents from the beginning. This guide clarifies which is which, and which tool fits which use case.
Comparison Matrix: 7 AI Evaluation Tools for Agents
The following criteria are selected specifically for teams building agents — not single-turn LLM applications.
Tool Deep Dives
1. Latitude
Best for: Production multi-turn agents
Latitude models agent execution as a causal trace of dependent steps — each tool call, reasoning step, and state transition captured in relation to what came before and after it. This architecture enables two capabilities that are unique in this comparison: automatic issue clustering (related failures across sessions are grouped into addressable patterns, not surfaced as individual incidents) and eval auto-generation via GEPA (production failures become regression tests automatically, without manual test authoring).
Latitude tracks the full issue lifecycle: first observation → root cause investigation → fix deployment → verified resolution. Issue clustering turns hundreds of failed traces into a prioritized queue. GEPA measures eval quality using Matthews Correlation Coefficient (MCC), tracking how accurately each generated eval predicts real production failures — so teams know which tests are actually catching problems, not just running.
For multi-turn evaluation specifically, Latitude supports simulation-based testing — running agents against synthetic conversation flows before deployment — and continuous scoring of production sessions against quality criteria. Context retention accuracy drops 15–30% in sessions exceeding 10 turns; Latitude surfaces these degradation patterns automatically rather than requiring manual session-by-session review.
Strengths: Agent-native causal trace architecture; automatic issue clustering; GEPA eval auto-generation from production data; multi-turn simulation testing; MCC-based eval quality measurement
Limitations: Narrower integration surface than LangSmith or Langfuse for non-standard frameworks; GEPA requires structured annotation workflow to work well
Pricing: 30-day free trial (no credit card required); usage-based paid plans; enterprise custom
2. Braintrust
Best for: Eval-driven development and pre-deployment experiments
Braintrust treats the eval workflow — not observability — as the primary interface. Prompts are versioned objects. All experiment data is stored in Brainstore, an OLAP database optimized for AI interaction queries. The workflow is designed around running evals, comparing results across prompt versions, and blocking deploys when scores regress. For teams with clearly defined quality criteria and mature eval culture, Braintrust executes this workflow better than any other tool in this comparison.
Braintrust has solid support for multi-turn conversation evaluation and handles tool call logging. Its strongest differentiated value is dataset management and systematic pre-deployment experimentation. Where it's less strong is automatic issue discovery from production — failure pattern clustering and eval auto-generation from production data are not native. Teams whose primary need is detecting unexpected failure patterns in live traffic will find the platform requires more manual analysis than agent-native alternatives.
Strengths: Best eval experiment UI in this comparison; excellent CI/CD integration for regression-gated deploys; strong prompt versioning and dataset management; LLM-as-judge and custom Python scorer support
Limitations: Static evaluation surface — you measure what you defined, not what production reveals; issue discovery requires manual trace review
Pricing: Hobby tier free (limited); Teams $200/month; enterprise custom
3. Langfuse
Best for: Open-source observability and self-hosted deployment
Langfuse is the most widely deployed open-source LLM observability platform. Its ClickHouse-backed data infrastructure (following a 2026 architectural update), widest framework integration coverage in this comparison, and self-hosted deployment option make it the default choice for teams with data residency requirements or open-source mandates.
Langfuse has added nested trace support for agents, representing multi-step workflows as parent-child span relationships. The underlying model is LLM-first, however — each span is an independent event, and causal relationships between steps must be inferred manually rather than being queryable as first-class objects. For teams debugging complex agent failures, the manual correlation required across nested traces becomes a bottleneck at scale. Eval generation from production data requires manual authoring.
Strengths: Full data sovereignty via self-hosting; widest framework integration (OpenAI, Anthropic, LangChain, LlamaIndex, Bedrock, and more); active open-source community; accessible pricing
Limitations: LLM-first architecture limits causal agent trace analysis; no automatic issue clustering or pattern discovery; eval generation is manual
Pricing: Free self-hosted (open-source); cloud hobby free; Teams ~$49/month; enterprise custom
4. LangSmith
Best for: LangChain and LangGraph teams
LangSmith is the observability and evaluation platform built by LangChain for LangChain teams. For this specific stack, it's the right default: automatic tracing requires near-zero additional instrumentation, LangGraph workflows are natively supported, and the eval framework integrates cleanly with LangChain's testing utilities. The trace tree view provides full execution path visibility. Human review queues and annotation workflows are polished and well-integrated.
The limitation is the reverse of the strength: LangSmith's observability is deeply coupled to LangChain's abstractions. Teams not on LangChain face significant integration overhead. Teams considering migrating away from LangChain face rebuilding their observability layer from scratch. Issue clustering and automatic failure discovery are not native — the platform excels at showing you traces you choose to examine, not at surfacing patterns across traces you haven't examined.
Strengths: Zero-config full tracing for LangChain/LangGraph agents; mature eval and annotation framework; trace tree execution path visualization
Limitations: LangChain lock-in risk; high integration overhead for non-LangChain stacks; issue discovery is manual
Pricing: Developer free (limited); Plus $39/month; enterprise custom
5. Arize AI
Best for: Enterprise ML teams and RAG-heavy agents
Arize comes from ML monitoring — it was built to track model performance, data drift, and data quality in production ML systems — and has extended those capabilities into LLMs and agents. The result is an enterprise-grade platform with strong compliance, access control, and integration with existing ML infrastructure. Arize's Phoenix project (open-source, OTel-native) provides a self-hosted entry point for teams that want Arize-quality tracing without enterprise pricing.
For RAG-heavy agents, Arize provides depth that other tools in this comparison don't match: context relevance, faithfulness, completeness, and embedding drift detection. For complex multi-turn agent debugging, Arize's heritage means its strongest capabilities are in model-level and data-level metrics rather than step-level causal trace analysis.
Strengths: Enterprise-grade security and compliance; strong ML monitoring heritage and data distribution monitoring; Phoenix open-source option with OTel-native integration; best RAG evaluation depth in this comparison
Limitations: Less emphasis on multi-step agent trace causality; enterprise cloud pricing is opaque; auto-generated evals from production data are not supported
Pricing: Phoenix fully open-source (free, self-hosted); Arize cloud on request
6. Maxim AI
Best for: Full-lifecycle eval coverage and multi-framework environments
Maxim is an end-to-end evaluation and observability platform covering the full AI development lifecycle: pre-release simulation, evaluation, and production monitoring in a single interface. Its notable differentiator is HTTP API endpoint-based testing — teams evaluate agents through their APIs without modifying source code, which is valuable for no-code platforms, proprietary frameworks, or teams maintaining multiple agent architectures simultaneously. Maxim also emphasizes cross-functional collaboration, with a UX designed for both engineering and product teams.
Maxim is a newer entrant that has invested in agent-specific capabilities including multi-step simulation and agent workflow testing. Issue clustering and eval auto-generation from production data remain less developed than purpose-built agent platforms.
Strengths: Full-lifecycle coverage from simulation to production; API endpoint-based testing without source code changes; cross-functional UX
Limitations: Less mature than established platforms; issue discovery capabilities limited compared to agent-native tools
Pricing: Moderate; contact for details
7. Galileo
Best for: High-volume production deployments with eval cost constraints
Galileo's standout capability is its Luna evaluation models — proprietary models that distill expensive LLM-as-judge evaluators into compact models running at sub-200ms latency and significantly lower cost per evaluation. This changes the economics of production-scale eval: assessments that would cost prohibitively at GPT-4 pricing become viable at high volume using Luna. Galileo also automatically converts pre-production evals into production guardrails, providing a structured path from testing to production quality enforcement.
Galileo's guardrails-first approach suits teams with compliance or safety requirements that need real-time enforcement. It's less suited for teams whose primary need is understanding why an agent is failing — trace-level debugging and failure pattern discovery are not Galileo's core capability.
Strengths: Luna models enable cost-efficient production eval at scale; guardrail framework with real-time enforcement; research-backed metrics
Limitations: Enterprise pricing; less focused on trace-level failure debugging and issue clustering
Pricing: Enterprise — contact for pricing
Which Tool Should You Use?
The Architectural Divide: Agent-Native vs LLM-First
The most important distinction in this comparison is not features — it is architecture. Most platforms in this list were designed when "AI evaluation" meant scoring single LLM responses. That is a well-solved problem with established tooling.
Production agents with multi-turn workflows, tool use, and autonomous decision chains are a structurally different problem. The failure modes are different — goal alignment drift, context loss across turns, tool argument errors that silently corrupt downstream steps. The detection methods are different — trace-level analysis across full sessions, not single-response quality scores. The evaluation infrastructure required is different — session-level scoring of complete execution trajectories, not prompt-response pair assessment.
Teams that evaluate production agents with LLM-first tools typically find themselves doing a lot of manual work that the tool was not designed to automate: correlating log events across steps, building custom eval pipelines on top of raw traces, debugging failures by reading JSON manually. That work is feasible at small scale. It does not scale to production volume, where hundreds of concurrent sessions may be exhibiting related failure patterns that only a clustering-capable system can surface as a single actionable issue.
Agents that have moved beyond demos into production workflows with real users require tools that were designed for that complexity — not tools that have added agent support as a secondary layer on an LLM-first architecture.
Frequently Asked Questions
What is the difference between an agent-first and an LLM-first evaluation tool?
An agent-first tool models the agent's full execution as a causal trace of dependent steps — each tool call, reasoning step, and state transition captured in relation to prior steps. An LLM-first tool evaluates individual model responses in isolation and treats multi-turn sessions as sequences of independent events. The practical difference: agent-first tools can pinpoint that a wrong tool argument at step 2 caused a cascading failure at step 7. LLM-first tools can only see that step 7's output was poor — they cannot trace the root cause across steps.
Can I use Braintrust for multi-turn agent evaluation?
Yes. Braintrust supports multi-turn conversation evaluation and tool call logging. Its strongest use case is systematic pre-deployment experimentation — structured experiments, prompt version comparison, eval dataset management. Where it's less strong is automatic issue discovery from production traffic: failure pattern clustering and auto-generated evals from production data are not native capabilities. Teams whose primary need is detecting unexpected failures in live agents will find Latitude's production-first architecture a better fit.
What is the best free AI evaluation tool for agents?
Langfuse (self-hosted, fully open-source) and Arize Phoenix (open-source, OTel-native) are the strongest free options. Langfuse provides prompt management, LLM call logging, and annotation workflows at no cost. Phoenix adds embedding drift detection and RAG-specific metrics. Both require manual effort for failure pattern discovery — automatic issue clustering and production-derived eval generation are not available in these free tiers. Latitude offers a 30-day free trial with full feature access.
Which platform is best for a team switching away from LangSmith?
Teams switching away from LangSmith typically migrate to Latitude (for production agent observability with automatic issue discovery), Langfuse (for framework-agnostic open-source tracing), or Braintrust (for eval-driven development). The right choice depends on why you're switching: if LangSmith's LangChain coupling is the issue, any of the three provides framework-agnostic instrumentation. If production issue discovery is the gap, Latitude is the best fit.
Related: Multi-turn conversation tracing in Latitude · Auto-generated evals with GEPA · Latitude Evals product page
