Agent Evaluation Frameworks in 2026: 6 Picks Compared for Trajectory-First Teams

An agent evaluation framework scores the whole run of an AI agent: the plan, each tool call and its arguments, how the agent used tool results, how it recovered from errors, and whether it finished the task. Most “agent eval” frameworks are LLM eval frameworks with a trajectory bolted on. Pick one designed trajectory-first: a single input-output pair has one rubric, and a multi-step agent needs a score for every layer.

A multi-step agent has a planner that broke the task into substeps and a tool selector that picked seven tools. It also has a retriever that returned stale chunks twice and a final answer that may or may not have satisfied the goal.

Aggregate “did it work” tells you nothing about which layer regressed last Tuesday. Below are six agent evaluation frameworks for that job in 2026, with where each fits, where it falls short and what it costs.

Best agent evaluation framework per use case (TL;DR)

If you only read one row: pick LangSmith if you live in LangGraph, Future AGI when the runtime is mixed and trajectory is the unit, DeepEval when CI is the system of record.

What an agent evaluation framework has to score

An agent evaluation framework has to score six separate dimensions of a run. If a framework cannot score these independently, treat it as LLM eval, not agent eval. Each dimension fails in a different place, so each needs its own score.

• Tool selection. Right tool, or correctly no tool. F1 plus an explicit irrelevance bucket so “called search when it shouldn’t have” is a graded failure.
• Argument extraction. Schema-valid and semantically correct. departure_date="next Friday" schema-validates and fails the user.
• Result utilization. Did the agent use the tool payload, or substitute model knowledge. Tool returns {"amount_cents": 4500}; agent says “your refund of $4,500.00 is processing.” It read cents as dollars.
• Error recovery. On 4xx, timeout, or empty result, did the agent retry, fall back, or escalate. Per-call rubrics never see this; the trajectory metric does.
• Plan coherence. Loop-free, dead-end-free, right depth.
• Task completion. End-to-end on the user’s goal. A 95-percent per-step agent over eight steps lands near 66 percent end-to-end. That compound-error math is why teams ship agents that pass per-turn eval and tank production.

Score the six separately and the diagnostic vocabulary collapses from “the agent failed” to “argument extractor regressed on date strings on the flight-booking path.” One bisect instead of three days. The frameworks below ship varying coverage across these dimensions. Verify per metric, per framework.

The six frameworks at a glance

The split that matters is trajectory-first versus LLM-eval-with-traces, whatever the license. A trajectory-first framework takes a run’s whole step list as its unit of evaluation, while the other kind scores single calls and shows the trace beside them.

1. LangSmith (best when your runtime is LangChain or LangGraph)

Closed platform. Open-source SDKs. Cloud, hybrid, and self-hosted Enterprise.

Quick take. LangSmith is the lowest-friction pick if every agent run is already a LangGraph execution. Native trajectory tracing, evaluators, datasets, prompt management, deployment, and Fleet workflows live on one surface. The framework knows what a Runnable is and what the planner’s state diff means; you do not infer it from spans.

Ideal for. Teams that debug in the LangChain mental model and want trajectory semantics next to deployment, prompts, and the agent IDE. Strong for support copilots, retrieval-heavy agents, and small-to-medium agent fleets where LangGraph is the system of record.

Key strengths.

• Trajectory eval reads LangGraph runs natively, not from generic spans.
• Prompts, datasets, deployment, Fleet, and Studio sit on one surface.
• Per-node evaluator wiring scores the planner, the tool node, and the post-tool LLM call separately without manual span surgery.
• Mature dataset versioning, threaded comments, hosted human review queue.

Honest limitations.

• Strongly opinionated toward LangChain. OTel ingest exists, but non-LangChain agents lose the native trajectory semantics. Custom agents, LiteLLM, and direct provider SDKs sit awkwardly on the surface.
• Per-seat pricing makes cross-functional access expensive.
• Closed platform. SDK is MIT; the platform is not.
• Metric library is shallower than DeepEval’s; you write more custom evaluators than expected.

Pricing intel. Developer is $0 with 1 seat and up to 5K base traces a month. Plus is $39 per seat per month with 10K base traces, unlimited seats and one free small serverless deployment. Enterprise is custom, with hybrid and self-hosted options.

Expert verdict. The default for LangChain shops. Picking anything else when your runtime is LangGraph means rebuilding the trajectory layer the framework already gives you. Picking LangSmith when your runtime is not LangChain is paying for semantics you cannot use.

2. Future AGI (best for trajectory-first eval across mixed runtimes)

Apache-2.0 core. Self-hostable. Hosted cloud option.

Quick take. Future AGI takes a run’s whole trajectory as its unit of evaluation. The AgentTrajectoryInput type ingests the full step list, the available tools, and the expected goal as one object, and the trajectory metrics (TaskCompletion, StepEfficiency, ToolSelectionAccuracy, TrajectoryScore, GoalProgress, ActionSafety, ReasoningQuality) operate on it directly. Task completion and function-calling evals also attach to live spans as EvalTag scorers, on the same plane as simulation, span-attached online eval, prompt optimization and gateway-level guardrails.

Ideal for. Teams whose runtime is mixed (some LangGraph, some custom, some direct provider SDKs) and who want trajectory eval, tool-call scoring, simulated personas, and span-attached production scores on one platform. Strong fit for RAG agents, voice agents, support automation, and internal copilots.

Key strengths.

• AgentTrajectoryInput is a first-class type. The trajectory metric takes the full step list as input, not the final string.
• traceAI auto-instruments 50+ AI frameworks across Python, TypeScript, Java, and C# (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Pydantic AI, DSPy, Mastra, Vercel AI SDK, Spring AI, LangChain4j). Sessions and an agent graph view show multi-agent dispatch.
• Eval stack ships as a package: the agent-learning-kit SDK (Apache-2.0), the Future AGI platform for evals on datasets and live traces, and Error Feed, which groups traces that failed the same way into one issue tagged with a fix layer.
• Agent Command Center on the same plane: 100+ providers, built-in Tool Permissions and MCP Security guardrails, per-virtual-key allowed_tools / denied_tools.
• Six prompt optimizers (GEPA, PromptWizard, ProTeGi, Bayesian, Meta-Prompt, Random) use production traces as training data.
• SOC 2 Type II, ISO 27001, GDPR and CCPA certified, HIPAA compliant with a BAA, per futureagi.com/trust.

Pricing intel. Free tier for every team, no card required; usage-based billing at scale. SOC 2, HIPAA BAA, SAML SSO + SCIM, and dedicated CSM available as add-ons. Pricing.

Expert verdict. The strongest pick when the runtime is mixed and the trajectory is the unit. One platform covers trace, eval, simulate, optimize, and gateway.

3. Braintrust (best hosted experiment UI)

Closed platform. Hosted SaaS; on-prem on Enterprise.

Quick take. Braintrust’s distinguishing capability is the surface. Dataset diffing, experiment cards side by side, prompt and dataset versioning, and a fast hosted human review workflow are the cleanest in this category. Eval primitives are competent; teams pick Braintrust because the UI does not get in the way of running 40 experiments in an afternoon.

Ideal for. Teams that buy for the dashboard. Cross-functional eval workflows where PMs, prompt engineers, and applied scientists read the same experiment results. Strong fit when “the platform is the system of record” matters more than self-hosting.

Key strengths.

• Hosted experiment UI is the most polished in this category.
• Dataset diffing across experiments is strong for prompt regression review.
• Native LLM-as-judge runner with built-in rubric authoring.
• Solid OTel ingest; not LangChain-locked.
• Strong prompt and dataset versioning. Experiments can pin a dataset version, and environment tags mark which version runs in production.

Honest limitations.

• Closed platform. Self-hosting needs the Enterprise plan. Regulated workloads with data-residency constraints push through procurement.
• Agent-trajectory layer is less first-class than LangSmith or Future AGI. You score per-span more than per-trajectory.
• Tool-call and multi-agent trace rendering trail LangSmith and Phoenix in the deep-debug case.

Pricing intel. Starter is $0 a month with 1 GB of processed data, 10K scores, 14-day retention and unlimited users. Pro is $249 a month with 5 GB, 50K scores and 30-day retention. Enterprise is custom, with on-prem or hosted deployment.

Expert verdict. The right pick when the UI is the product. Teams that need a hosted experiment surface PMs can use without an SDK should pick Braintrust. Teams that need an open-source license or a first-class trajectory type should look elsewhere.

4. DeepEval (best for pytest-style component eval)

Open source (Apache 2.0). Confident AI is the hosted cloud.

Quick take. DeepEval is an open-source LLM eval framework with a broad, publicly documented metric library. Pytest ergonomics: write assert metric.score > 0.7, run deepeval test run, get a regression suite that fits your existing CI. For agents, component-level eval via @observe makes each trajectory step a unit test.

Ideal for. AI/ML teams that already write pytest suites and want eval as a CI job, not a separate dashboard. Strong when CI is the system of record and you want broad open metric coverage with zero vendor lock.

Key strengths.

• Broad open metric library: AnswerRelevancy, GEval (research-backed custom-criteria scorer), Faithfulness, ContextualPrecision, Bias, Toxicity, Hallucination, ConversationCompleteness, plus agent metrics such as Task Completion, Tool Correctness and Plan Adherence.
• pytest-native ergonomics. Fits existing CI without a new runner.
• @observe component-level eval works with any tracing backend.
• Confident AI hosted cloud adds a dashboard if you want one later.

Honest limitations.

• Trace UI is less polished than purpose-built agent platforms. Flame-graph trajectory replay across sub-agents is thin.
• Python is the most complete implementation; the TypeScript SDK (npm deepeval) still has feature gaps.
• Multi-agent supervisor-worker rendering is weaker than LangSmith or Future AGI.

Pricing intel. The framework is free under Apache-2.0. Confident AI Free is $0 with 2 seats, 1 project and 5 test runs a week. Starter is $200 a month with unlimited seats, Team is $2,000 a month with SSO and SOC 2, and Enterprise is custom with on-prem deployment.

Expert verdict. The right pick when eval lives in CI and the metric library matters more than the UI. Pair it with Phoenix when you need a polished trajectory replay UI on top.

5. Arize Phoenix (best for OTel-native agent traces)

Source available (Elastic License 2.0). Self-hostable. Arize AX is the hosted path.

Quick take. Phoenix is the right pick when open standards are the binding constraint. It ships agent trace rendering, eval scores attached to spans, datasets, experiments, and prompt iteration without buying the full Arize AX product. OpenInference instrumentation covers 14+ agent frameworks.

Ideal for. Platform-engineering teams that care about OpenTelemetry and OpenInference, already use Arize for ML observability, or want a self-hosted lab with a path into the broader Arize platform when procurement asks.

Key strengths.

• OpenTelemetry and OpenInference first-class. OTLP ingest, standard span attributes, no proprietary lock.
• OpenInference auto-instrumentation for LangChain, LlamaIndex, DSPy, OpenAI, Bedrock, Anthropic, CrewAI, OpenAI Agents, AutoGen, Pydantic AI in Python, plus JavaScript (with the Vercel AI SDK), Java and Go packages.
• phoenix-evals ships prebuilt and custom scorers as a separate package; clean separation between tracing and eval.
• Self-hostable for free; you manage span volume and retention.
• Arize AX upgrade path when procurement requires a closed contract.

Honest limitations.

• Elastic License 2.0, not OSI-open. Call it source available if your legal team uses OSI definitions.
• Agent-trajectory metrics are partial. The workbench is strong; the per-trajectory rubric library is shallower than what Future AGI ships natively.
• AX is a separate closed product with a different SKU.
• Self-host requires you to operate the data plane (SQLite by default, PostgreSQL for production, plus retention and scaling).

Pricing intel. Phoenix is free to self-host. Arize AX Free covers 25K spans and 1 GB of ingestion a month with 15-day retention and unlimited users. AX Pro is $50 a month with 50K spans, 10 GB and 30-day retention, and AX Enterprise is custom, SaaS or self-hosted.

Expert verdict. The strongest open-standards pick. Teams that want OTel-native agent tracing under one self-hostable lab should pick Phoenix. For first-class trajectory metrics, pair phoenix-evals with a heavier eval framework.

6. OpenAI Evals (best as the floor for OpenAI-only stacks)

Open source (MIT). YAML-defined. Run-it-yourself.

Quick take. OpenAI’s official eval library. YAML-defined evals run against OpenAI completions; deterministic and model-graded checks both supported. The floor option for OpenAI-first teams who want a single-vendor eval surface and do not need a dashboard, trajectory metrics, or multi-agent rendering.

Ideal for. Single-vendor OpenAI stacks where the workload is single-call completions, CI is the only surface that matters, and the team can wire YAML configs and small Python harnesses themselves.

Key strengths.

• Free, MIT, run wherever your CI runs.
• Tight OpenAI API integration; model-graded checks use OpenAI judges by default with one config line.
• YAML eval definitions are easy to review, diff, and store in Git alongside the prompt.
• Strong floor for “we want to start scoring something on every PR.”

Honest limitations.

• Not an agent framework. No trajectory type, no multi-agent rendering, no span ingest, no UI.
• Strongly OpenAI-centric. Anthropic, Gemini, Bedrock, or open-weight judges are bridge work.
• Multi-step agent eval support is community-contributed and thin.
• The open-source library has no dashboard, dataset versioning, prompt management, or human review queue. OpenAI now points users to Evals in the OpenAI Dashboard for a hosted option.

Pricing intel. Free under the MIT license. You pay only for the OpenAI tokens your graders use.

Expert verdict. The right pick when the runtime is single-vendor OpenAI and the team wants the smallest possible eval surface. It has no trajectory type or span ingest, so a production agent fleet outgrows it.

τ-bench and BFCL are benchmarks, not frameworks

τ-bench evaluates multi-turn agents in airline and retail environments with an LLM-simulated user, and its headline metric is pass^k across k independent rollouts. In the original 2024 paper, gpt-4o scored under 25 percent at pass^8 on retail. Sierra now maintains it as τ-bench 2.0 (the tau2-bench repo), which covers airline, retail and telecom, runs full-duplex voice, and localizes tasks into six languages.

BFCL (Berkeley Function Calling Leaderboard) V4 scores function calling with AST matching, an irrelevance category for calls the model should not make, multi-turn tasks, and agentic web-search and memory tasks. A model that aces AST and tanks irrelevance overcalls on your registry.

Use them as model-selection signals. They tell you nothing about your tool registry, argument schemas, error codes, or business policy, the same gap that makes public benchmarks mislead on production behavior. Gate production on your own private eval set.

Other open-source agent evaluation frameworks

Four more open-source projects show up next to these six in searches for an agent evaluation framework. All four are Apache-2.0. Ragas and AWS Agent Evaluation score agent runs, MLflow traces, evaluates and monitors them, and Harbor runs agents in benchmark environments.

How to choose an agent evaluation framework

• LangSmith if your runtime is LangChain or LangGraph. Buying signal: every agent run is already a LangGraph execution.
• Future AGI if your runtime is mixed and trajectory is the unit. Buying signal: your team runs three or four tools (traces, offline tests, gateway, guardrails) and still cannot reproduce production failures before release.
• Braintrust if the UI is the product. Buying signal: PMs and prompt engineers need to read the same experiment results without an SDK.
• DeepEval if CI is the system of record. Buying signal: you want assert metric.score > 0.7 in pytest, no separate dashboard.
• Phoenix if open standards are the binding constraint. Buying signal: your platform team cares about OpenTelemetry and OpenInference, not vendor SDKs.
• OpenAI Evals if the stack is single-vendor and small. Buying signal: a YAML in Git is the eval surface you want. Avoid for production agent fleets.

Common mistakes when picking agent evaluation frameworks

• Scoring final answer only. A correct-looking response can come from a 12-step trajectory that should have been 4 steps. Tool-call accuracy, argument correctness, and result utilization are first-class metrics.
• Treating LLM eval and agent eval as the same problem. AnswerRelevancy on the final response misses tool-call mistakes. Tool-selection accuracy on individual steps misses goal completion. You need both layers.
• Ignoring online scoring cost. Three judges per step on a 10-step trace fires 30 judge calls per request. Use a distilled small judge or sample by failure signal.
• Mismatched framework and runtime. LangSmith on a non-LangChain runtime loses native semantics. DeepEval on a multi-agent supervisor pattern works but the trace UI is thin. OpenAI Evals on anything non-OpenAI is bridge work. Pick by where your runtime already lives.
• No CI gating, or aggregate-only thresholds. An aggregate 0.85 hides a 0.62 on argument extraction behind a 0.97 on tool selection. Wire per-dimension assertions from week one.
• Conflating offline eval and online scoring. Offline catches regressions before release; online catches drift after. Different rubrics, different sample sizes, different cost budgets. Two workflows, not one.

How to test a framework before you commit

1. Run a domain reproduction. Export a representative slice of real agent traces (failures, long-tail prompts, tool calls, retrieval misses, hand-labeled outcomes). Instrument each candidate with your OTel payload shape and your judge model. Do not accept a demo dataset.
2. Measure trajectory eval cost. Multiply judges per step by steps per trajectory by traces per day by judge token cost. If the result is more than 10 percent of your LLM bill, switch to a distilled small judge or sample by failure signal.
3. Test multi-agent rendering. Take a real run with branching and supervisor-worker dispatch. Verify the trace tree renders the actual graph, not a flat span list.

How Future AGI ships the trajectory-first stack

Future AGI ships the eval stack as a package. Start with the SDK for code-defined per-layer scoring; graduate to the Platform when the loop needs evaluators that learn from your corrections. The SDK scores trajectories in code, traceAI carries evals onto live spans, and Error Feed turns failing traces into issues you can triage.

• agent-learning-kit (Apache-2.0): pip install agent-learning-kit; trajectory metrics on AgentTrajectoryInput such as TaskCompletion and ToolSelectionAccuracy; deterministic function-call metrics such as FunctionNameMatch; and evaluate() scores you can replay in CI.
• Future AGI platform: self-improving evaluators that learn from your corrections, which come back as few-shot examples on later runs; built-in checks such as Trajectory Match compare an agent’s actions with the expected sequence.
• traceAI (Apache-2.0): 50+ AI frameworks across Python, TypeScript, Java and C#; 14 span kinds with first-class TOOL, AGENT, RETRIEVER, GUARDRAIL; EvalTag attaches evals such as task completion to live spans.
• Error Feed: reads sampled traces, groups traces that failed the same way into one issue, and tags each with a fix layer (Prompt, Tools, Orchestration or Guardrails).
• Agent Command Center: 100+ providers; built-in Tool Permissions and MCP Security guardrails; per-virtual-key allowed_tools / denied_tools.

Finding the layer that broke last Tuesday

The agent that regressed last Tuesday left a trace. Scored on six dimensions, that trace stops saying “the agent failed” and names the layer that moved: the planner, a tool argument, a retrieval result or the final answer. That diagnosis is the whole job of an agent evaluation framework, whichever one you pick.

Score your next agent run layer by layer. Run pip install agent-learning-kit, load one real trajectory into AgentTrajectoryInput, then start free on Future AGI to attach evals such as task completion to live traces.

Sources

• LangSmith pricing
• Braintrust pricing
• Confident AI pricing
• Arize pricing