Best LLM Evaluation Tools for AI Agents in 2026

AI agents don't fail like traditional LLM applications. A RAG pipeline either retrieves the right context or it doesn't. A chatbot either stays on topic or drifts. But an agent makes a sequence of decisions — which tool to call, what parameters to pass, how to interpret the result, when to retry, when to stop — and a failure at any step can cascade through the entire execution.

Evaluating only the final output of an agent is like grading a math exam by checking the last answer. You miss the reasoning errors, the wrong formulas, the correct intermediate steps that happened to produce a wrong conclusion. Agent evaluation requires scoring each decision point independently.

Most LLM evaluation tools weren't built for this. They were designed for single-turn prompt-response pairs or simple chain evaluations. When applied to agents, they log traces — which tools were called, in what order — but don't score whether the agent made the right decisions. That's the difference between agent tracing and agent evaluation. Tracing tells you what happened. Evaluation tells you whether it was correct.

This guide compares the tools that matter for agent evaluation in 2026, ranked by their ability to evaluate agent behavior at the step level — not just observe it.

What Makes Agent Evaluation Different

Agent evaluation requires capabilities that most LLM evaluation tools don't have. Before comparing platforms, it's worth understanding what separates agent evaluation from standard LLM evaluation:

Span-Level Scoring

Agents produce traces with multiple spans — tool calls, LLM completions, retrieval steps, planning decisions. Useful evaluation means scoring each span independently. Did the agent select the right tool? Was the retrieved context relevant to the query? Did the planning step produce a coherent strategy? Platforms that only score the final output miss the 90% of failure modes that happen mid-execution.

Agent-Specific Metrics

Standard metrics like faithfulness and relevance were designed for RAG pipelines. Agents need metrics for tool selection accuracy, planning quality, step-level faithfulness, reasoning coherence, and task completion across multi-step workflows. Repurposing RAG metrics for agent evaluation produces misleading scores.

Graph Visualization

Agent execution isn't linear. Tools call other tools, LLM calls branch into parallel paths, and retry loops create complex execution trees. Debugging agent failures requires graph visualization that shows exactly which path the agent took and where it diverged from expected behavior.

Multi-Turn Agent Simulation

Testing agents on static datasets doesn't capture real-world behavior. Agents interact with users across multiple turns, make tool calls based on conversation history, and adapt their strategy based on results. Evaluation platforms need to simulate these dynamic interactions — not replay historical conversations.

CI/CD Regression Detection

Agent behavior changes when models update, prompts change, or tool APIs evolve. Catching regressions — wrong tool selected, degraded planning quality, broken reasoning chains — requires automated evaluation in the deployment pipeline, not manual spot-checking after release.

Our Evaluation Criteria

We evaluated each platform against seven criteria specific to agent evaluation:

• Span-level evaluation — Can you score individual agent steps (tool calls, reasoning, retrieval) independently?
• Agent-specific metrics — Does the platform include metrics designed for agentic workflows, or just repurposed RAG metrics?
• Graph visualization — Can you visualize agent execution as a tree/graph for debugging cascading failures?
• Multi-turn simulation — Can you simulate realistic user-agent conversations with tool use and branching paths?
• CI/CD integration — Can you run agent evaluations automatically in your deployment pipeline?
• Collaboration — Can PMs, QA, and domain experts review agent traces and participate in evaluation without engineering involvement?
• Security testing — Can you test agents for prompt injection, unauthorized tool use, and data exfiltration?

1. Confident AI

Confident AI evaluates AI agents at the span level — scoring individual tool calls, reasoning steps, and retrieval decisions within a single agent trace, not just the final output. It combines evaluation, observability, and security testing in one platform designed for cross-functional teams.

The platform provides 50+ research-backed metrics including ones purpose-built for agentic workflows: tool selection accuracy, planning quality, step-level faithfulness, and reasoning coherence. These aren't repurposed RAG metrics — they're designed for how agents actually fail.

Customers include Panasonic, Toshiba, Amdocs, BCG, and CircleCI.

Best for: Teams building production AI agents that need to evaluate every decision an agent makes — not just trace what happened — with workflows accessible to engineers, PMs, and QA alike.

Key Capabilities

• Span-level evaluation: Score each agent step independently — tool calls, reasoning, retrieval, planning — so you know exactly where an agent failed, not just that it failed.
• Graph visualization: Tree view of agent execution showing tool call sequences, branching paths, and step-level outputs. Critical for debugging multi-step agents where failures cascade.
• Agent-specific metrics via DeepEval: 50+ metrics including tool selection accuracy, planning quality, step-level faithfulness, and reasoning coherence. Metrics are open-source and used by top AI companies.
• Multi-turn agent simulation: Simulate realistic user-agent conversations with tool use, branching paths, and multi-step reasoning. Generate dynamic test scenarios that mirror production behavior — don't rely on static datasets.
• CI/CD regression testing: Catch agent regressions before deployment. Integrates with pytest and popular testing frameworks — evaluation results flow back as testing reports with regression tracking.
• Red teaming for agents: Test for prompt injection, jailbreaks, unauthorized tool use, and data exfiltration across agent steps. Based on OWASP Top 10 and NIST AI RMF.
• Cross-functional collaboration: PMs and QA review agent traces, annotate tool call decisions, and trigger evaluation cycles via AI connections (HTTP-based, no code) — without engineering involvement.

Pros

• Evaluates agent decisions at the span level, not just final outputs — the only platform on this list that does this comprehensively
• 50+ research-backed metrics through DeepEval, including purpose-built agent metrics, not repurposed RAG scoring
• Multi-turn simulation generates dynamic agent test scenarios instead of replaying static datasets
• Cross-functional workflows — PMs and QA participate in agent quality without engineering bottlenecks
• Native red teaming covers agent-specific attack vectors like unauthorized tool use
• Framework-agnostic — works with LangChain, LangGraph, CrewAI, Pydantic AI, OpenAI, Vercel AI SDK, and more via native SDKs (Python, TypeScript) plus OTEL and OpenInference

Cons

• Cloud-based and not open-source, though enterprise self-hosting is available
• The breadth of the platform may be more than what's needed for teams only doing lightweight agent tracing
• Usage-based pricing at $1/GB is among the cheapest on the list, but teams new to this kind of tooling may need a ramp-up period to forecast costs

Pricing starts at $0 (Free), $19.99/seat/month (Starter), $49.99/seat/month (Premium), with custom pricing for Team and Enterprise plans. Tracing at $1/GB-month — no hidden retention gimmicks.

2. Arize AI

Arize AI brings ML monitoring heritage to LLM observability, offering span-level tracing and real-time dashboards for agent workflows. Through its open-source Phoenix library, it provides agent trace capture and visualization. For agent evaluation, Arize supports custom evaluators but the depth of built-in agent-specific metrics is limited compared to evaluation-first platforms.

Best for: Large engineering organizations already using Arize for ML monitoring that want to extend coverage to LLM agents without adding another vendor.

Key Capabilities

• Span-level tracing with custom metadata tagging for agent workflows
• Real-time performance dashboards tracking latency, error rates, and token consumption
• Visual agent workflow maps for understanding multi-step execution
• ML and LLM monitoring in one platform via Phoenix (open-source)
• Custom evaluators for scoring agent outputs

Pros

• Enterprise-scale infrastructure handles high-throughput agent workloads
• Combines ML and LLM monitoring, reducing vendor count for teams running both
• Phoenix is open-source, giving teams flexibility over their tracing setup
• Real-time telemetry gives immediate visibility into agent operational health

Cons

• The LLM evaluation layer is shallow — built for ML monitoring first and extended to LLMs second. Agent-specific metrics for tool selection, planning quality, and reasoning are limited
• Engineer-only UX limits involvement from PMs, QA, and domain experts in agent quality workflows
• No multi-turn agent simulation — you can't generate dynamic test scenarios
• No collaboration workflows — evaluation and debugging require engineering at every step
• Advanced capabilities gated behind commercial tiers with only 14 days of retention

Pricing starts at $0 (Phoenix, open-source), $0 (AX Free), $50/month (AX Pro), with custom pricing for AX Enterprise.

3. Galileo AI

Galileo AI positions itself as an evaluation intelligence platform with a dedicated Agentic Evaluations feature. It provides hallucination detection through its Hallucination Index, evaluation scoring, and an Observe/Evaluate/Protect product suite. For agents, it offers evaluation scoring alongside a public Agent Leaderboard integrated with Hugging Face.

Best for: Teams that want a structured evaluation platform with hallucination detection and agentic evaluation features, particularly those that value benchmarking against public agent leaderboards.

Key Capabilities

• Agentic Evaluations feature for scoring multi-step agent workflows
• Hallucination detection via Galileo's Hallucination Index
• Evaluate, Observe, and Protect product suite covering the full lifecycle
• Agent Leaderboard integrated with Hugging Face for benchmarking
• Support for multi-modal and conversation evaluations

Pros

• Dedicated agentic evaluation feature signals investment in the agent evaluation space
• Hallucination Index provides a standardized way to measure and track hallucination rates
• Agent Leaderboard gives teams external benchmarks for comparing agent performance
• Covers evaluation, monitoring, and protection in one platform

Cons

• Narrower metric coverage than DeepEval-powered platforms — fewer research-backed metrics available for agent-specific workflows like tool selection accuracy and planning quality
• No cross-functional collaboration workflows — evaluation is engineering-driven
• No multi-turn agent simulation for generating dynamic test scenarios
• Less proven for span-level evaluation of individual agent decisions compared to platforms built specifically for this

Pricing is custom — contact for details.

4. Langfuse

Langfuse is an open-source tracing platform that logs agent sessions and tool calls. It provides session-level grouping and a trace explorer for debugging, with strong OpenTelemetry integration. For agents, Langfuse captures execution traces and organizes them by session, but has no built-in evaluation metrics — scoring agent decisions requires custom implementation or external tooling.

Best for: Engineering teams that want open-source agent tracing with full control over their data, and are comfortable building evaluation logic themselves or integrating external eval libraries.

Key Capabilities

• OpenTelemetry-native agent trace capture with rich metadata
• Session-level grouping for multi-turn agent conversations
• Token usage and cost attribution across agent runs
• Searchable trace explorer for debugging agent execution
• Self-hosting option for full data ownership

Pros

• Fully open-source with self-hosting — complete control over agent trace data
• Strong OpenTelemetry foundation integrates into existing infrastructure
• Large community and active development with frequent releases
• Good fit if you already have internal agent evaluation pipelines and just need a tracing backend

Cons

• No built-in evaluation metrics — no scoring for agent decisions, tool calls, or reasoning quality out of the box
• No span-level evaluation — traces are logged but individual agent steps aren't scored automatically
• No multi-turn agent simulation for generating dynamic test scenarios
• No cross-functional workflows — requires engineering for everything, from trace review to evaluation setup
• Logs agent traces without evaluating them — observability without quality assessment means you see what happened but not whether it was correct

Pricing starts at $0 (Free / self-hosted), $29/month (Pro), with custom pricing for Enterprise.

5. Evidently AI

Evidently AI is an open-source platform for ML and LLM testing with synthetic data generation. It provides evaluation reports, data drift detection, and test suites that work across traditional ML and LLM use cases. For agents, it offers coverage through its general evaluation framework, but agent-specific features like span-level scoring and tool call evaluation are limited.

Best for: Teams that want open-source ML/LLM testing with synthetic data generation and data drift detection, and are comfortable building agent-specific evaluation on top.

Key Capabilities

• Open-source evaluation and testing framework
• Synthetic data generation for creating agent test scenarios
• Data and prediction drift detection across model versions
• Evaluation reports and test suites with CI integration
• Dashboard for tracking quality metrics over time

Pros

• Fully open-source with strong community adoption
• Synthetic data generation is useful for creating agent test scenarios
• Combines data monitoring with LLM evaluation in one toolkit
• Drift detection catches silent quality degradation across agent model updates

Cons

• More focused on data and model monitoring than agent-specific evaluation — agent eval requires significant custom work on top of the general framework
• Limited production agent tracing — you'll need a separate observability tool for live agent debugging
• No span-level evaluation for scoring individual agent steps like tool calls and reasoning
• No graph visualization for debugging agent execution paths
• No cross-functional collaboration workflows for non-technical team members

Pricing starts at $0 (open-source), with Evidently Cloud for managed hosting.

6. Deepchecks

Deepchecks comes from a traditional ML testing background and has expanded into LLM evaluation. Its standout feature is flexible deployment — VPC, on-premise, and bare metal options give enterprises full control over where evaluation runs. For agent evaluation, it provides LLM-as-a-judge scoring alongside infrastructure-level testing, but agent-specific capabilities are secondary to its core ML testing heritage.

Best for: Enterprise teams with strict deployment requirements (VPC, on-prem, bare metal) that need LLM evaluation alongside traditional ML testing in a single platform.

Key Capabilities

• LLM evaluation with customizable LLM-as-a-judge scoring
• Flexible deployment: cloud, VPC, on-premise, bare metal
• Traditional ML testing alongside LLM evaluation in one platform
• Version comparison and auto-scoring for tracking model changes
• Production monitoring and tracing

Pros

• Deployment flexibility is unmatched — bare metal and VPC options serve highly regulated industries
• Combines ML and LLM testing, reducing tool sprawl for teams running both
• Strong enterprise security posture with on-prem options
• Version comparison helps track quality across model updates

Cons

• Traditional ML testing heritage means LLM agent evaluation is secondary, not the core product
• Agent-specific metrics and span-level evaluation are limited — not designed for scoring individual agent decisions like tool selection or planning quality
• No multi-turn agent simulation for generating dynamic test scenarios
• No graph visualization for debugging agent execution paths
• No cross-functional collaboration workflows — primarily built for engineering teams

Pricing is custom for enterprise deployments.

Agent Evaluation Tools Comparison Table

Feature	Confident AI	Arize AI	Galileo AI	Langfuse	Evidently AI	Deepchecks
Span-level agent evaluation Score individual tool calls, reasoning steps, and retrieval within a trace					Limited	Limited
Agent-specific metrics Tool selection accuracy, planning quality, reasoning coherence	50+ via DeepEval	Custom evaluators	Agentic evals		Open-source suite	Custom LLM-as-judge
Graph visualization Tree view of agent execution for debugging cascading failures					Limited	Limited
Multi-turn agent simulation Simulate dynamic user-agent conversations with tool use
Built-in eval metrics Research-backed metrics available out of the box	50+ via DeepEval	Custom evaluators	Hallucination Index + evaluators		Open-source suite	Custom LLM-as-judge
CI/CD integration Run agent evaluations in your deployment pipeline
Cross-functional workflows PMs and QA can review traces and run evals without engineering
Red teaming for agents Test for prompt injection, unauthorized tool use, data exfiltration
Agent tracing Log tool calls, LLM completions, and execution flow					Limited
Open-source option Self-host or inspect the codebase	Limited		Limited			Limited

How to Choose the Best Agent Evaluation Tool

The decision comes down to what you actually need: agent tracing or agent evaluation.

If you just need to see what your agent did — which tools it called, in what order, and how long each step took — most platforms on this list will work. Arize gives you that at enterprise scale, Langfuse gives you that with open-source flexibility, and Galileo gives you that alongside hallucination detection.

But if you need to know whether your agent made the right decisions, the field narrows dramatically. Here's how to think about it:

• Do you need span-level evaluation? Most agent failures happen mid-execution, not at the final output. If you need to score individual tool calls, reasoning steps, and retrieval decisions, Confident AI is the only platform that does this comprehensively with research-backed metrics.

• Is agent safety a primary concern? Galileo AI offers protection features through Galileo Protect. But if you need safety testing alongside evaluation and observability in one platform, Confident AI covers red teaming natively — including agent-specific attack vectors like unauthorized tool use and data exfiltration.

• Do non-engineers need to participate? If PMs, QA, or domain experts need to review agent traces, annotate decisions, and trigger evaluation cycles, Confident AI is the only option with cross-functional workflows. Every other platform on this list is engineer-only.

• Do you need open-source? Langfuse and Evidently AI offer fully open-source options with self-hosting. Arize's Phoenix library is also open-source. These are good choices if data sovereignty and code transparency are non-negotiable — but expect to build your own agent evaluation layer on top.

• Are you testing agents in CI/CD? Confident AI integrates with pytest and runs span-level agent evaluations in the pipeline — catching regressions in tool selection, planning, and reasoning before deployment. Deepchecks and Evidently also integrate with CI, though their agent-specific evaluation depth is more limited.

• Do you have strict deployment requirements? Deepchecks offers VPC, on-premise, and bare metal deployment for highly regulated industries. Confident AI offers enterprise self-hosting. Langfuse and Evidently can be self-hosted as open-source.

For production agent teams that need the complete picture — evaluation at every decision point, observability on production traffic, simulation for dynamic testing, and security testing for agent-specific attack vectors — Confident AI is the only platform that brings all of this together. Other tools cover one or two of these concerns. None cover all of them, and none make it accessible to the whole team.

For teams just starting with agents that want lightweight tracing before committing to a full evaluation platform, Langfuse or Arize Phoenix provide a low-friction starting point — but expect to outgrow them as your agent evaluation needs mature.

Why Confident AI is the Best Tool for AI Agent Evaluation

Most tools on this list were built for something else first — ML monitoring, tracing, or single-turn LLM evaluation — and extended to agents later. Confident AI was built around the premise that evaluation is the product, and agent evaluation is where that matters most.

Agent failures are sequential. A wrong tool call in step two corrupts every step that follows. Scoring only the final output is like checking a patient's temperature after surgery and calling it a full diagnosis. Confident AI evaluates each span independently — tool calls, reasoning steps, retrieval decisions, planning outputs — with metrics designed specifically for how agents fail. No other platform on this list does this with the same depth.

The metrics aren't generic either. DeepEval's 50+ research-backed metrics include tool selection accuracy, planning quality, step-level faithfulness, and reasoning coherence — built for agentic workflows, not repurposed from RAG evaluation. These are open-source, used by OpenAI, Google, and Microsoft, and continuously updated as agent architectures evolve.

Where Confident AI pulls furthest ahead is making agent quality a team concern, not just an engineering task. PMs review agent traces and annotate tool call decisions. QA triggers full evaluation cycles through AI connections — HTTP-based, no code. Domain experts flag edge cases in shared workspaces. On every other platform, agent evaluation requires engineering at every step. Confident AI removes that bottleneck.

Multi-turn simulation generates dynamic agent test scenarios with tool use, branching paths, and multi-step reasoning — testing how agents behave in realistic conditions rather than replaying static datasets. Red teaming covers agent-specific attack vectors like unauthorized tool use and data exfiltration, based on OWASP Top 10 and NIST AI RMF. CI/CD integration catches regressions in tool selection, planning, and reasoning before deployment. At $1/GB-month with no evaluation caps, it's also the most cost-effective platform on this list for teams running agents at scale.

Frequently Asked Questions

What is the difference between agent tracing and agent evaluation?

Agent tracing logs what happened — which tools were called, what the LLM generated at each step, how long each operation took. Agent evaluation scores whether those decisions were correct. Tracing tells you an agent called a search tool with specific parameters. Evaluation tells you whether the search tool was the right choice, whether the parameters were appropriate, and whether the result was used correctly. Most platforms do tracing. Few do evaluation.

Can I evaluate individual tool calls within an agent trace?

Most platforms only score the final output of an agent run. Confident AI evaluates individual spans within a trace — tool calls, reasoning steps, retrieval decisions — with metrics designed for agentic workflows. This span-level evaluation is critical because agent failures typically happen mid-execution, not at the final output.

How do I test multi-step AI agents before deployment?

Static test datasets don't capture agent behavior because agents make dynamic decisions based on context. Multi-turn simulation generates realistic user-agent conversations with tool use and branching paths, testing agents in scenarios that mirror production. Confident AI provides this natively. Running metrics on historical conversations tells you about past performance — simulation tells you about future behavior.

What metrics matter for AI agent evaluation?

Standard LLM metrics (faithfulness, relevance) are necessary but not sufficient. Agent evaluation needs: tool selection accuracy (did it pick the right tool?), planning quality (was the strategy coherent?), step-level faithfulness (was each reasoning step grounded?), reasoning coherence (did the logic hold across steps?), and task completion (did the agent achieve the goal?). Confident AI provides these through DeepEval's 50+ metrics.

Can non-engineers evaluate AI agent quality?

On most platforms, no — agent evaluation requires engineering involvement at every step. Confident AI is the exception. PMs, QA, and domain experts can review agent traces, annotate tool call decisions, and trigger full evaluation cycles through AI connections (HTTP-based, no code). Engineers do initial setup, then the whole team owns agent quality.

How do I catch agent regressions before deployment?

Integrate agent evaluation into your CI/CD pipeline. When models update, prompts change, or tool APIs evolve, automated evaluations catch regressions — wrong tool selected, degraded planning, broken reasoning chains — before they reach production. Confident AI integrates with pytest and flows evaluation results back as testing reports with regression tracking.

What agent-specific security risks should I test for?

Beyond standard LLM risks (prompt injection, jailbreaks, PII leakage), agents face unique attack vectors: unauthorized tool use (agent calls tools it shouldn't), data exfiltration (agent leaks sensitive data through tool calls), privilege escalation (agent accesses resources beyond its scope), and infinite loop exploitation. Confident AI's red teaming covers these based on OWASP Top 10 and NIST AI RMF.

Do I need a separate tool for agent tracing and agent evaluation?

Ideally, no. Using one tool for tracing and another for evaluation creates a fragmented workflow — you're switching between platforms to debug a single agent failure. Confident AI combines tracing, span-level evaluation, simulation, and security testing in one platform. If you're using a tracing-only tool like Langfuse or Arize Phoenix, you'll need to add an evaluation layer on top.