Top 5 platforms for agent evals in 2025

Your voice agent just handled a 12-turn conversation with a customer, bouncing between a knowledge base, a calendar API, and a payment processor before finally booking an appointment. It felt smooth. The customer seemed happy. But was it actually good?

You listen to the recording. The agent asked for the customer's preferred date three times because it kept forgetting context. It pulled from an outdated help article when a newer one existed. It almost charged the wrong credit card before course-correcting. The call ended successfully, but the path was a mess.

This is the challenge with agentic AI. Manual review doesn't scale, and traditional testing can't catch multi-step failures. You need to move from vibes to verified, from "it seemed fine" to "we measured it." Systematic agent evaluation is the difference between teams that ship with confidence and teams that discover problems through customer complaints.

What is agent evaluation?

Agent evaluation measures how well autonomous AI systems perform across multi-turn interactions, decision chains, and tool usage. Unlike single-turn LLM evaluation that checks one response, agent eval assesses entire trajectories: whether an AI agent chose the right tools, constructed valid parameters, handled errors appropriately, and synthesized accurate final answers across potentially dozens of steps.

When evaluation is just a feature, basic logging and manual review suffice. You might check a handful of outputs, spot obvious errors, and move on. But when agents handle complex workflows with branching logic, external tool calls, and stateful memory, simple logging becomes inadequate. That's when agent evaluation becomes a category-defining capability requiring dedicated platforms with multi-turn scoring, trajectory analysis, and production monitoring at scale.

Key features to consider in agent eval platforms

Choosing the right platform for evaluations for AI agents requires understanding six critical capabilities:

Multi-turn evaluation capabilities: The platform must assess complete agent conversations, not just individual responses. Look for support for trajectory scoring, step-by-step analysis, and the ability to validate decision chains across dozens of interactions.

Code-based and LLM-as-a-judge scorers: The best platforms offer both deterministic code-based metrics for precise validation and LLM-as-a-judge evaluations for nuanced, subjective assessment. Pre-built scorer libraries accelerate implementation while custom scorer support enables domain-specific evaluation.

Observability and tracing depth: Deep visibility into agent behavior is non-negotiable. Platforms should provide span-level tracing, nested execution graphs, and the ability to replay entire agent sessions. Understanding why an AI agent failed matters as much as knowing that it failed.

Integration ecosystem and SDK quality: Framework-agnostic design ensures the platform works with your existing stack, whether you're using LangChain, raw API calls, or custom frameworks. Native SDKs for TypeScript and Python with comprehensive documentation reduce implementation friction.

Team collaboration features: Modern agentic eval requires cross-functional input. Look for intuitive interfaces that enable product managers and domain experts to review outputs, annotate failures, and contribute to evaluation criteria without writing code.

Cost transparency and scalability: As evaluation volume grows, pricing must scale predictably. Clear visibility into costs per evaluation, flexible sampling strategies for production monitoring, and the ability to balance thoroughness with budget constraints separate production-ready platforms from experimental tools.

The 5 best agent eval platforms in 2025

1. Braintrust

Best for: Production-grade agentic systems requiring effortless custom scorer creation, unified evaluation, and deep observability.

Braintrust stands apart as the most comprehensive platform for agent eval, built by engineers who scaled LLM applications at Google and Stripe. The platform combines evaluation, observability, and optimization in a unified system that eliminates the tooling fragmentation plaguing most AI teams.

Creating scorers with Loop

Braintrust's standout feature is Loop, its built-in AI assistant that writes custom scorers for you. Instead of spending hours coding evaluation logic, simply describe what you want to measure in natural language. Loop generates production-ready scorers tailored to your specific use case, whether you're validating tool selection accuracy, measuring conversation quality, or checking domain-specific business rules. Braintrust also includes pre-built scorers for common patterns like factuality checking and context usage when you need them.

// Ask Loop to create a custom scorer:
// "Create a scorer that checks if the agent correctly
// identified the user's appointment preference and
// selected the right calendar slot"

// Loop generates a scorer function instantly
const appointmentScore = await CustomScorer({
  output: agentResponse,
  expected: correctSlot,
  context: availableSlots,
});

Remote evals in playgrounds

Braintrust Playgrounds make agent evaluation effortless with remote evals, the easiest way to test your agents. Simply configure your agent's endpoint, define test cases, and run evaluations directly in the UI. No SDK integration required. Braintrust's playground automatically handles multi-turn conversations, tracks all tool calls, and scores outputs using your custom Loop-generated scorers or pre-built metrics. This visual, no-code approach lets product managers and non-technical team members run comprehensive agent evaluations without writing a single line of test code.

Online and offline evaluation

Braintrust supports both development-time experimentation and production monitoring. Offline evals run against curated test datasets during development, catching regressions before deployment. Online evaluation scores production traffic asynchronously, enabling teams to monitor quality at scale with configurable sampling rates.

from braintrust import Eval

# Offline evaluation during development
Eval(
    "Agent Quality Check",
    {
        "data": lambda: test_dataset,
        "task": lambda input: run_agent(input),
        "scores": [Factuality, ToolSelectionAccuracy],
    },
)

Production-grade observability

Every agent interaction generates detailed traces with span-level visibility. Teams can replay entire sessions, inspect intermediate tool calls, and understand decision chains that led to specific outputs. The platform tracks latency, cost per request, and custom quality metrics, making it easy to identify performance bottlenecks and cost optimization opportunities.

AI-powered log analysis

Loop excels at analyzing production logs to surface important insights and capture common failure modes. Instead of manually reviewing thousands of traces, ask Loop to identify patterns, categorize issues, or explain what went wrong in failed interactions. Loop can automatically detect recurring problems, suggest improvements, and help teams understand agent behavior at scale, providing deeper insights than rule-based pattern matching approaches.

Real impact

Teams using Braintrust report accuracy improvements exceeding 30% within weeks of implementation. One customer service application handling 10,000 daily queries reduced escalations by 3,000 after implementing systematic evaluation, saving hundreds of hours weekly. Development velocity increases up to 10x compared to teams relying on ad-hoc production monitoring, translating directly to faster feature delivery and competitive advantage.

Pros

• Loop creates custom scorers instantly from natural language descriptions
• Remote evals in playgrounds for no-code agent testing without SDK integration
• AI-powered log analysis with Loop to identify failure modes and surface insights at scale
• Unified platform combining evaluation, observability, and prompt optimization
• TypeScript and Python SDKs with framework-agnostic design
• Native CI/CD integration via GitHub Actions for automated regression testing
• Online and offline evaluation with configurable sampling for production monitoring
• Deep tracing with span-level visibility and session replay
• Pre-built scorer library for common patterns when needed
• Brainstore purpose-built database for searching and analyzing AI interactions at scale
• SOC 2 compliance with enterprise security, RBAC, and self-hosting options

Cons

• Learning curve for teams new to systematic evaluation practices
• Advanced features require understanding of evaluation methodologies

Pricing

Free tier includes unlimited users, 1 GB of processed data monthly, and 10,000 scores. Pro plan starts at $249/month for small teams with increased quotas and extended data retention. Enterprise pricing available for large-scale deployments with custom security requirements and on-premises deployment options.

2. Galileo AI: Vendor-managed agent reliability for teams who don't want to build their own

Best for: Teams that want prebuilt evaluators and runtime guardrails handed to them, especially in regulated or high-risk agent deployments.

Galileo is a managed, proprietary AI evaluation and observability platform built around what it calls an Agent Reliability Platform. That covers agent observability, automatic failure detection, and guardrails that can step in before a tool even executes. Instead of writing your own scorers first, you start from more than 20 vendor-maintained metrics, including hallucination detection, Context Adherence, Chunk Attribution, Completeness, and Correctness.

Galileo's Luna-2 small language models run that scoring inline and low-latency without spinning up a heavyweight LLM judge, and Galileo Insights flags failure modes and root causes across eval runs on its own. Continuous Learning via Human Feedback (CLHF) lets teams tune how a metric behaves from annotated examples, and integrations with CrewAI, LangGraph, OpenAI Agents SDK, LlamaIndex, Strands, and OpenTelemetry instrument agent traces across different stacks.

Pros

• Agent Reliability Platform with automatic failure detection and pre-tool-execution guardrails
• 20+ vendor-maintained built-in metrics for fast, code-free scoring
• Luna-2 evaluation models for low-latency inline scoring
• Galileo Insights for automatic failure-mode detection and root-cause analysis
• Broad agent-framework integrations including CrewAI, LangGraph, and OpenAI Agents SDK

Cons

• Closed and proprietary platform with no source to audit, fork, or extend
• Runtime guardrails are Enterprise-only
• No native CI/CD deployment blocking for regression gating
• Small free tier (~5,000 traces/month) and self-hosting reserved for Enterprise

Pricing

Free tier covers roughly 5,000 traces per month. Pro starts around $100/month for about 50,000 traces with usage-based overages. Enterprise pricing is custom and required for both runtime guardrails and self-hosting (VPC or on-prem).

Read our guide on Galileo AI vs. Braintrust.

3. Agenta

Best for: Teams that want open-source agent evaluation paired with observability and a prompt playground, accessible to both PMs and engineers.

Agenta is an open-source LLMOps platform that evaluates agents across their full trace, not just single responses. Evaluation runs with LLM-as-a-judge and custom evaluators and supports human annotation, so teams can score every step an agent takes and flag where it went wrong. The observability layer traces each request and lets you annotate traces directly, which makes failures easier to find.

The prompt playground rounds out the workflow, with side-by-side prompt and model comparison and complete version history, and you can deploy changes without writing code. Agenta integrates with LangChain, LlamaIndex, and OpenAI, and the functional features are MIT-licensed and self-hostable, so PMs and engineers can both work in it without committing to a closed platform.

Pros

• Open-source and self-hostable, with full-trace agent evaluation
• LLM-as-a-judge, custom evaluators, and human annotation
• Built-in observability with request tracing and trace annotation
• Prompt playground with side-by-side comparison and version history
• Integrates with LangChain, LlamaIndex, and OpenAI

Cons

• No native CI/CD deployment blocking to gate regressions
• Free tier caps usage at 2 seats and 5,000 traces per month
• Production traces do not convert into regression datasets automatically

Pricing

Hobby tier is free with 2 seats and 5,000 traces per month. Pro starts at $49/month, and Business at $399/month with RBAC and SOC2. Self-hosting the open-source core is free.

4. Maxim AI

Best for: Teams requiring end-to-end agent lifecycle coverage with simulation and comprehensive observability.

Maxim AI positions itself as a full-stack platform covering the complete agentic lifecycle from prompt engineering through simulation, evaluation, and real-time production monitoring. The platform emphasizes simulation capabilities that enable testing agents against synthetic scenarios before production exposure.

The unified interface brings pre-release experimentation, agent simulations, offline and online evals, and production observability into a single workflow. Teams can run complex multi-turn simulations spanning different personas, tools, and decision trajectories to stress-test agent behavior under varied conditions.

Pros

• End-to-end lifecycle coverage from development to production
• Advanced simulation capabilities for pre-deployment testing
• Unified platform reduces tool fragmentation
• Real-time monitoring with drift detection and alerting
• Multi-provider model support and routing

Cons

• Higher complexity due to comprehensive feature set
• Newer in market compared to established competitors
• Steeper learning curve for full platform utilization

Pricing

Contact sales for pricing. Enterprise-focused with custom deployment options.

5. Langfuse

Best for: Teams requiring open-source, self-hosted evaluation solutions with complete data control.

Langfuse delivers transparency and flexibility through its open-source model. The MIT-licensed core includes all essential features without usage limits or feature gates, enabling teams to self-host on their own infrastructure and maintain complete control over evaluation data.

The platform provides comprehensive tracing with visual execution graphs, prompt management with versioning, and flexible evaluation through both automated scoring and human annotation. Open-source transparency enables deep customization and audit capabilities critical for regulated industries.

Pros

• Fully open-source core with MIT license and no usage limits
• Complete self-hosting capability for data sovereignty
• Active community with frequent updates and integrations
• Flexible evaluation framework supporting custom metrics
• Cost-effective for high-volume usage

Cons

• Requires engineering resources for setup and maintenance
• Manual-first evaluation approach may slow iteration
• Limited automation compared to managed platforms
• Self-hosting operational overhead

Pricing

Free with self-hosting or hobby. SaaS starting at $29 monthly.

Summary comparison table

Upgrade your agent evaluation workflow with Braintrust. Start free today.

Why Braintrust is leading the way

Braintrust's unique position stems from three core differentiators that matter most for production agentic systems. First, Loop and remote evals eliminate barriers to agent evaluation entirely. While competitors require teams to write complex evaluation code from scratch, Loop lets you describe what you want to measure in plain English and generates production-ready scorers instantly. Loop also excels at analyzing production logs to identify failure modes and surface insights at scale, providing AI-powered pattern detection that goes far beyond basic rule-based approaches. Remote evals take it further by letting anyone run comprehensive agent tests directly in the playground UI. No SDK integration, no test code, just configure and run. This democratizes evaluation. Product managers and domain experts can evaluate agents without writing code, dramatically accelerating iteration.

Second, the unified platform approach eliminates tooling fragmentation. Teams don't need separate systems for evaluation, monitoring, and optimization. Everything flows through a single interface with shared datasets, reducing context switching and accelerating iteration cycles.

Third, production-readiness separates theory from practice. Online evaluation with configurable sampling, deep tracing with span-level visibility, and native CI/CD integration mean teams can evaluate rigorously without slowing deployment velocity. The result: 30%+ accuracy improvements and 10x faster development cycles compared to manual approaches.

FAQs

What is agent evaluation?

Agent evaluation systematically measures how AI systems perform across multi-turn interactions, assessing tool selection, decision chains, and output quality at each step. Braintrust's Loop makes it easy to create custom scorers for agent trajectories by describing evaluation criteria in natural language, eliminating the need to write complex scoring code.

How do I choose the right agent eval platform?

Look for multi-turn evaluation support, ease of creating custom scorers, and observability depth that reveals decision chains. Braintrust offers the most comprehensive solution with Loop for instant scorer generation, remote evals for no-code testing in playgrounds, unified evaluation and monitoring, and framework-agnostic SDKs that work with any tech stack.

Do I need runtime guardrails for agent evals?

Runtime guardrails that intercept unsafe or off-policy outputs before users see them matter most in regulated or high-risk deployments. Galileo offers them as part of its Agent Reliability Platform, though they are Enterprise-only. For most teams, open and extensible evaluation does more. Braintrust pairs framework-agnostic scoring with Loop for instant custom scorers and AI-powered log analysis, remote evals for no-code testing, and native CI/CD deployment blocking. That lets you gate regressions before they ship rather than only catching them at runtime, and you get it without a closed platform or an Enterprise-tier paywall.

How does agent evaluation differ from LLM evaluation?

LLM evaluation measures single-turn completions, while agent evaluation assesses multi-step workflows where agents plan, select tools, and adapt across dozens of interactions. Braintrust specifically addresses agent complexity with trajectory-level scoring and step-by-step analysis tools.

If I'm successful with traditional testing, should I invest in agent evals?

Traditional testing validates deterministic code, but agents introduce non-deterministic behavior where inputs produce different valid outputs. Braintrust enables teams to maintain quality standards as they scale from prototypes to production with data-driven insights.

How quickly can I see results from agent evaluation?

Teams typically implement basic evaluation within hours using Loop to generate custom scorers, with quality improvements appearing within days. Braintrust customers report 30%+ accuracy improvements within weeks of implementation.

What's the difference between online and offline evaluation?

Offline evaluation runs during development against test datasets, while online evaluation scores production traffic asynchronously. Braintrust supports both modes with the same scorer library and configurable sampling rates.

What are the best alternatives to Galileo AI?

Braintrust leads as the most comprehensive alternative with Loop for effortless scorer creation and AI-powered log analysis, remote evals for no-code testing, and unified observability. Galileo is closed and proprietary, and it keeps guardrails and self-hosting on its Enterprise tier. Braintrust is open and extensible, with native CI/CD deployment blocking and workflows that PMs and engineers share. Loop's ability to generate custom scorers from natural language descriptions and analyze production logs to identify failure modes at scale, combined with playground-based agent testing and production-grade evaluation features, makes Braintrust ideal for teams shipping agents at scale.