Truesight vs Galileo vs Braintrust vs DeepEval vs Arize Phoenix: Best AI Agent Evaluation Platform 2026

AI agents are entering production at unprecedented scale. But here’s the problem most teams discover too late: traditional LLM evaluation tools were designed for single-turn chatbots, not for multi-step decision chains, tool selection, and trajectory quality. In March 2025, a fintech AI agent ran a runaway loop for 11 days — accumulating $47,000 in API costs — because nobody had the right evaluation infrastructure to catch the failure pattern. Gartner now predicts over 40% of agentic AI projects will be cancelled by the end of 2027 due to lack of evaluation infrastructure.

If you’re building production AI agents, you need a dedicated agent evaluation platform. We spent 4 weeks testing 7 of the top contenders to find the best in 2026.

Bottom Line Up Front

Galileo wins our 2026 AI agent evaluation comparison. It’s the only platform that closes the loop from offline evaluation to production guardrails, powered by Luna-2 small language models that make 100% traffic evaluation economically viable. Braintrust is the runner-up for engineering teams that need eval-first CI/CD workflows. DeepEval is the best value for Python teams that want the broadest set of open-source agent metrics.

If you’re building production agents and need a complete evaluation platform, go with Galileo. If your primary need is CI/CD evaluation gates, pick Braintrust. If you’re a Python team on a budget, DeepEval is excellent and free.

Why Agent Evaluation Is Different From LLM Evaluation

Before we dive into the tools, it’s worth understanding why this category even exists. LLM observability tools (like LangSmith, Langfuse, and Arize) are great for tracing single LLM calls, measuring latency, and tracking token usage. But AI agents are fundamentally different beasts:

• Multi-step decision chains: An agent makes dozens of decisions per task, and a bad early decision cascades
• Tool selection quality: Did the agent pick the right tool for the job?
• Trajectory quality: Was the agent’s path to the solution efficient or wasteful?
• Task completion: Did the agent actually accomplish what it was asked to do?
• Step efficiency: How many extra steps did the agent take?

Agent evaluation platforms need to score all of these dimensions. That’s what this comparison is about.

Comparison Table

Tool	Starting Price	Free Tier	Best For	Key Strength	Open Source	Agent Metrics
Truesight	$19/mo	No	Domain-specific output quality	Expert-grounded evaluation	No	Custom quality scores
Galileo	$100/mo	5K traces/mo	Enterprise agent production	Luna-2 eval at 97% lower cost	No	9 agentic metrics
Braintrust	$0/mo	1GB data, 10K scores	CI/CD eval pipelines	Loop AI + human review queues	Proxy (MIT)	Custom scorers
DeepEval	$0/mo	Full OSS Apache 2.0	Python agent metrics breadth	50+ metrics, DAG deterministic scoring	Yes (Apache 2.0)	6 agent metrics
Arize Phoenix	$0/mo	Phoenix OSS; AX 25K spans	Vendor-neutral self-hosting	OTel-native tracing	Yes (ELv2)	4 agent evaluators
Comet Opik	$0/mo	Unlimited spans free	Automated prompt optimization	Agent Optimizer (6 algos)	Yes (Apache 2.0)	LLM-as-judge + custom
W&B Weave	$60/mo	Limited traces	W&B ecosystem teams	MCP auto-logging	SDK (Apache 2.0)	Scorers framework

Deep Dives

Galileo

Tagline: AI observability and evaluation with Luna-2 SLMs for 100% traffic coverage

Galileo is our winner, and for good reason. Based in Burlingame, CA with $68M in funding, Galileo built the only platform that makes full-coverage agent evaluation economically possible at scale. The secret sauce is Luna-2 — their family of 3B and 8B small language models that run 10-20 agentic metrics simultaneously at sub-200ms latency for $0.12 per million tokens. Compare that to GPT-5.4 at $5 per million tokens, and you’re looking at a 97% cost reduction.

✅ What we liked

• Luna-2 SLMs are a game changer for cost. Evaluating 100% of production traffic is simply not viable with GPT-based judges. Luna-2 brings that cost down to pocket change while matching accuracy on agent-specific metrics.
• 9 purpose-built agentic metrics covering Tool Selection Quality, Action Completion, Reasoning Coherence, Agent Efficiency, and more. These aren’t generic LLM metrics retrofitted for agents — they were designed for agents from the ground up.
• Eval-to-guardrail lifecycle. The evaluations you build offline automatically become production guardrails. No extra glue code, no duplication of effort.
• Autotune improves metric accuracy by up to 30% from just 2-5 annotated examples. This is huge for teams that don’t have thousands of labeled data points.
• Signals detects unknown failure patterns across production traces automatically — catching failure modes you didn’t even know to look for.
• Runtime Protection blocks unsafe agent outputs before they reach users.
• Three debug views (Graph, Trace, Message) give you comprehensive agent inspection capabilities.

❌ What we didn’t

• Enterprise features are gated. VPC, on-prem deployment, and SSO require custom enterprise pricing.
• Luna-2 models need sales enablement for production use — you can’t just flip a switch.
• Focus on agents and RAG means it’s overkill for simple chatbot evaluation. If you’re just doing basic Q&A, there are cheaper options.
• Learning curve. The platform combines evaluation, guardrails, and observability — it takes time to master all three.
• Voice and audio evaluation are not supported yet.

The verdict

Galileo is our winner for enterprise agent evaluation. The Luna-2 cost advantage alone is decisive — paying $0.12/1M tokens instead of $5/1M for equivalent accuracy is not a marginal improvement, it’s a category shift that enables 100% traffic evaluation. Combined with the eval-to-guardrail lifecycle and 9 purpose-built agentic metrics, Galileo is the most complete platform for teams serious about agent quality at scale.

Try Galileo — Start with the free tier (5,000 traces/month).

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Braintrust

Tagline: Eval-first CI/CD workflows for AI teams

Braintrust is our runner-up, and it excels in a specific scenario: engineering teams that want evaluation results to gate their CI/CD pipelines. The architecture is eval-first from day one — every feature is built around the idea that evaluations should prevent bad code from shipping.

✅ What we liked

• Eval-first architecture designed to gate CI/CD pipelines from day one. If you ship frequently, this is exactly what you need.
• Loop AI agent autonomously runs evaluations, generates test cases, and iterates on prompts. It’s like having a QA engineer that works 24/7.
• Span-level human review with tool call inspection and kanban-style triage. When you need human judgment in the loop, the workflow is smooth and intuitive.
• One-click conversion of production failures into regression eval cases. This is the kind of workflow that separates great platforms from good ones.
• Unlimited users and projects on all plans, including free. No per-seat pricing games.
• MIT-licensed proxy for self-hosted tracing if you need it.

❌ What we didn’t

• Pro at $249/month is expensive for small teams. And there’s nothing between the free Starter plan and Pro — the jump is steep.
• Custom scorers require engineering. Unlike Truesight, you can’t have domain experts define quality criteria in natural language.
• No production guardrails. Braintrust is eval-only. You can’t use it to block bad agent outputs in real time like Galileo’s Runtime Protection.
• Fewer pre-built agent-specific metrics than DeepEval or Galileo. You’ll need to build custom scorers for agent-specific behaviors.
• No mid-tier pricing between free and $249/mo.

The verdict

Braintrust is the best choice for engineering teams that ship frequently and want evaluation deeply integrated into their CI/CD workflow. The Loop AI agent and human review queues make it powerful and practical. But it’s eval-only, so if you need production guardrails, you’ll need a companion tool.

Check Braintrust — Free Starter plan with 1GB data and 10K scores.

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DeepEval

Tagline: Open-source LLM evaluation framework with 50+ metrics

DeepEval from Confident AI is our best value pick — and by a wide margin. As an Apache 2.0 open-source project, it offers the broadest set of off-the-shelf evaluation metrics at exactly zero cost.

✅ What we liked

• 50+ built-in metrics including 6 agent-specific ones. Nobody else comes close in metric breadth.
• DAG (Directed Acyclic Graph) metric offers deterministic multi-step scoring without the non-determinism of LLM-as-judge evaluations. This is a genuinely unique and valuable capability.
• Native Pytest integration. If your team already uses pytest, DeepEval drops into your existing workflow effortlessly.
• Apache 2.0 license. Fully open-source, zero vendor lock-in, full community access.
• Synthetic data generation including multi-turn conversational scenarios for testing.
• Red teaming scans for 40+ safety vulnerabilities.
• Active community with frequent updates and improvements.

❌ What we didn’t

• Python only. If your team uses TypeScript, Node.js, or any other language, you’re out of luck.
• Most metrics require LLM API calls, which adds cost at scale. Unlike Galileo’s Luna-2 approach, DeepEval doesn’t have its own evaluation models.
• No built-in UI. Everything is command-line and code-driven. Non-engineers can’t use it.
• No production guardrails or real-time monitoring capabilities. Evaluation only.
• No human-in-the-loop review workflows like Braintrust offers.
• Setup requires engineering effort. This is not a plug-and-play solution.

The verdict

DeepEval is the best value in the agent evaluation space. For Python teams that want comprehensive, open-source agent metrics, nothing else comes close. The DAG metric is genuinely unique and solves the non-determinism problem that plagues LLM-as-judge approaches. The trade-off? You need engineering resources to set it up and maintain it.

Try DeepEval — Free, open-source, Apache 2.0.

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Truesight

Tagline: Expert-grounded evaluation for domain-specific output quality

Truesight (by Goodeye Labs) takes a fundamentally different approach to agent evaluation. Instead of giving engineers more metrics, it gives domain experts (doctors, lawyers, analysts) the ability to define what “good” looks like in natural language. The platform turns stakeholder knowledge into automated, repeatable quality scores.

✅ What we liked

• Domain experts define quality criteria without code. This is the only platform that genuinely solves the stakeholder-engineering translation problem.
• MCP integration lets AI agents self-evaluate against quality standards during execution.
• Proven methodology from 20+ production AI systems generating $25M in revenue.
• Model-agnostic — works with Claude, GPT, Gemini, and open-source models.
• Natural language quality definitions are more intuitive than code-based scoring.

❌ What we didn’t

• No free tier. The Hobby plan starts at $19/month.
• Newer platform (2025/2026) with a smaller community and fewer integrations.
• Focuses on output quality, not execution tracing. You won’t get detailed agent trajectory debug views.
• Limited pre-built agent metrics compared to DeepEval (50+) or Galileo (9 agent-specific).
• Better for domain-specific quality than for debugging agent behavior issues.

The verdict

Truesight is the best choice when your evaluation criteria must be defined by domain experts, not engineers. If a doctor needs to define what good medical AI looks like, Truesight is the only tool designed for that workflow. But if you need execution tracing and deep agent debugging, look elsewhere.

Explore Truesight — Starts at $19/month Hobby plan.

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Arize Phoenix

Tagline: Open-source LLM observability with OTel-native instrumentation

Arize Phoenix is the open-source darling of the LLM observability world, and for good reason. It’s 100% open-source with a single Docker container deployment, making it the simplest self-hosted option by far. The OTel-native instrumentation means your traces are portable — no proprietary vendor lock-in.

✅ What we liked

• 100% open-source — every feature is available without paywalls.
• Simplest self-hosting in the category: a single Docker container vs. multi-service stacks.
• OpenTelemetry standard means your instrumentation is portable and non-proprietary.
• Python, TypeScript, and Java SDKs supported — broader language support than most.
• Free cloud tier with 25,000 spans per month.

❌ What we didn’t

• No auto-fix or automated remediation. Phoenix tells you what’s broken but won’t fix it.
• Fewer pre-built evaluators than competitors. You’ll need more custom logic.
• Engineering-centric — no no-code UI for product or business teams.
• Advanced capabilities gated behind commercial tiers, with only 14 days retention on free.
• Agent-specific metrics are limited compared to Galileo or DeepEval.

The verdict

Arize Phoenix is the best choice for teams that prioritize open standards and self-hosting above all else. If avoiding vendor lock-in is a core requirement, Phoenix gives you OTel-native tracing with a simple Docker deployment. But be prepared to build custom evaluations on top of the solid tracing infrastructure — the pre-built agent metrics won’t take you as far as Galileo or DeepEval will.

Try Arize Phoenix — Free open-source or AX Free with 25K spans/month.

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Comet Opik

Tagline: Open-source LLM evaluation with automated agent optimization

Comet Opik (from Comet ML, based in New York) is an Apache 2.0 open-source platform that goes beyond evaluation into automated optimization. The Agent Optimizer uses 6 different algorithms to automatically refine prompts and tool configurations — a capability that none of the other tools in this comparison offer.

✅ What we liked

• Agent Optimizer automatically refines prompts and tool configurations with 6 optimization algorithms. This is genuinely unique in the category.
• Apache 2.0 open-source with full feature set — no cloud-exclusive features.
• Over 40 million daily traces processed — battle-tested at scale.
• 150,000+ developers in the community.
• Free cloud plan with unlimited spans — the most generous free tier in this comparison.
• Cost Intelligence feature (launched June 2026) tracks Claude Code and Codex spend.
• Guardrails for PII detection, content filtering, and off-topic management.
• Self-hosting supported via Kubernetes.

❌ What we didn’t

• Agent evaluation is part of broader LLM observability — not agent-first. The agent-specific features are layered on top of a general LLM platform.
• Fewer pre-built agent-specific metrics than DeepEval or Galileo.
• Requires technical expertise to self-host effectively (Kubernetes, not Docker compose).
• Younger project (mid-2024) compared to Arize or LangSmith.
• Prompt optimization is stronger than trajectory or debugging analysis.

The verdict

Comet Opik offers incredible value with its generous free tier and unique Agent Optimizer. It’s the best choice if you want open-source LLM observability with automated optimization capabilities. But if your primary need is deep agent evaluation with pre-built agent-specific metrics, Galileo or DeepEval will take you further out of the box.

Try Comet Opik — Free plan with unlimited spans.

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W&B Weave

Tagline: Agent trace observability with MCP auto-logging

Weights & Biases is a well-known name in ML experiment tracking, and Weave extends that ecosystem into agent observability. The standout feature is MCP auto-logging — it captures agent tool calls without manual instrumentation.

✅ What we liked

• MCP auto-logging captures agent tool calls without manual instrumentation. If you’re using MCP-based agents, this is seamless.
• Deep integration with the W&B ecosystem (experiment tracking, model registry, datasets).
• Scorers framework allows custom evaluation criteria.
• Good for teams already invested in W&B for ML workflows — one less vendor to manage.
• SDK available under Apache 2.0.

❌ What we didn’t

• Not an agent-first platform. Weave is observability added to an ML tracking tool — it wasn’t designed for agents from the ground up.
• Fewer pre-built agent metrics than dedicated tools.
• Less mature for multi-agent system evaluation.
• Limited CI/CD eval gating compared to Braintrust.
• Agent evaluation features are less deep than Galileo or DeepEval.

The verdict

W&B Weave is the right choice if you’re already using Weights & Biases for ML experiment tracking and need basic agent observability without adding another vendor. But if agent evaluation is a priority, purpose-built tools like Galileo, Braintrust, or DeepEval offer far more depth.

Check W&B Weave — Free tier available.

Pricing Breakdown

Tool	Free Tier	Pro/Growth	Enterprise	Notes
Galileo	5K traces/mo	$100/mo (50K traces)	Custom	Most affordable pro tier for agent evaluation
Braintrust	1GB data, 10K scores	$249/mo (5GB, 50K scores)	Custom	Sharp jump from free to pro
DeepEval	Full OSS (Apache 2.0)	$19.99/user/mo (cloud)	Custom	Free if self-hosted
Truesight	None	$19/mo Hobby	Custom	No free tier at all
Arize Phoenix	Phoenix OSS; AX 25K spans	$50/mo (AX Pro)	Custom	OSS free, cloud tiers add features
Comet Opik	Unlimited spans	$19/mo (25K spans)	Custom	Most generous free tier
W&B Weave	Limited traces	$60/mo (Teams)	Custom	Best for existing W&B customers

Bottom Line

After 4 weeks of testing 7 AI agent evaluation platforms in 2026, here’s our final recommendation:

If you’re building production AI agents and need a complete evaluation platform that covers offline eval, production guardrails, and 100% traffic monitoring at reasonable cost — go with Galileo. The Luna-2 SLMs make full-coverage evaluation economically viable for the first time, and the 9 purpose-built agentic metrics cover the full spectrum of agent quality.

If your primary need is CI/CD evaluation gates and human review workflows — go with Braintrust. The Loop AI agent and kanban-style review queues are best-in-class for engineering teams that ship frequently.

If you’re a Python team on a budget — go with DeepEval. It’s free, open-source, and offers 50+ metrics including the unique DAG deterministic scoring. You can’t beat the value.

Frequently Asked Questions

What’s the difference between agent evaluation and LLM observability?

LLM observability tracks single LLM calls — latency, tokens, basic response quality. Agent evaluation goes deeper: it scores multi-step decision chains, tool selection quality, trajectory efficiency, and task completion. An LLM observability tool can tell you that a call took 2 seconds and cost $0.05. An agent evaluation tool can tell you that your agent chose the wrong tool on step 4, took 3 unnecessary steps, and still failed to complete the task.

Do I need both an evaluation platform and an observability platform?

Not necessarily. Galileo combines evaluation and observability into one platform. Braintrust focuses on evaluation but connects to your existing observability stack. DeepEval is purely evaluation. If you need both, Galileo is the most integrated option — your offline evaluations become production guardrails automatically.

Can I use these tools with any LLM provider?

Yes — all the tools in this comparison are provider-agnostic. They work with OpenAI, Anthropic, Google, open-source models, and self-hosted models. Truesight and Galileo in particular emphasize model-agnostic evaluation.

Is open-source (DeepEval, Arize Phoenix, Comet Opik) or commercial (Galileo, Braintrust) better for agent evaluation?

It depends on your team. Open-source gives you full control, no vendor lock-in, and zero licensing cost — but requires engineering resources for setup, maintenance, and customization. Commercial platforms like Galileo give you more out-of-the-box capabilities, dedicated support, and integrated workflows (like eval-to-guardrail) that would take months to build yourself with open-source tools.

What’s the minimum budget I need for agent evaluation?

You can start for free with DeepEval (open-source), Braintrust (free tier with 1GB data), Comet Opik (free tier with unlimited spans), or Arize Phoenix (open-source self-hosted). For a more complete commercial solution, Galileo’s Pro plan at $100/month is the most affordable entry point for production-grade agent evaluation.

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