Universal Agent Evaluation Platform

Quality Intelligence for

Test, benchmark, and continuously improve AI agents across retrieval, reasoning, tools, and generation — without changing your agent code.

Model-agnostic

Reproducible benchmarks

Enterprise-ready security

SDK & API-first

Evaluating AI Agents Is
Broken Today

Most teams are flying blind, relying on manual testing or basic metrics that don't capture the complexity of agentic workflows.

Surface-Level Evaluation

Teams only evaluate final answers, missing critical failures in retrieval, reasoning, or tool usage steps.

Black Box Context

No visibility into what context was retrieved or how the agent decided to use specific tools.

No Standard Benchmarks

Lack of standardized datasets makes it impossible to compare performance across different models or versions.

Silent Regressions

Updates to prompts or models often cause silent regressions that aren't caught until production.

A Complete Evaluation Platform
Not Just Metrics

SupaEval provides the infrastructure to run repeatable evaluations, produce comparable benchmarks, and generate actionable insights for your AI agents.

Datasets

Standardized, versioned datasets

Config

Declarative evaluation setup

Execution

Scalable, deterministic runs

Scoring

Multi-layered metric computation

Insights

Root-cause analysis

Improvement

Feedback loops & RLHF

The SupaEval Platform

Five pillars of quality intelligence to ensure your agents are production-ready.

Data Foundation

Standardized, versioned datasets for AI evaluation. Manage prompts, conversations, and multi-turn tasks in one place.

Evaluation Definition

Declarative configs without code changes. Define metrics, judges, and pass/fail criteria in a version-controlled format.

Execution & Benchmarking

Scalable, deterministic evaluation runs. Run thousands of tests in parallel with reproducible results.

Insights & Dashboards

Root-cause analysis across agent layers. Drill down from overall scores to specific retrieval or generation failures.

Learning & Optimization

Feedback loops and RLHF-ready outputs. Turn evaluation insights into training data for continuous improvement.

How It Works

Start evaluating your agents in minutes, not weeks.

Connect your agent

Integrate via SDK or API. We support any LLM or agent framework.

npm install supaeval

Select datasets

Choose from our standardized benchmarks or upload your own custom datasets.

supaeval.datasets.list()

Define metrics

Configure retrieval, generation, and tool usage metrics without writing code.

metrics: ['accuracy', 'hallucination']

Run evaluations

Execute scalable, parallel evaluation runs to get results in minutes.

supaeval.run({ dataset: 'v1' })

Analyze failures

Drill down into specific traces to understand why an agent failed.

supaeval.analysis.get_failures()

Improve and re-run

Fix the issues, update your agent, and verify improvements immediately.

supaeval.compare(run_a, run_b)

Why SupaEval?

See how we compare to traditional logging and monitoring tools.

Feature	Traditional Eval	SupaEval
Full Agent Lifecycle Evaluation	Limited to specific stages	Comprehensive, end-to-end evaluation
Continuous Benchmarking	Manual, ad-hoc testing	Automated, ongoing performance tracking
Platform-Computed Metrics	Basic logging, manual analysis	Advanced, AI-driven metric generation
Enterprise-Grade Reliability	Best-effort, often lacking scalability	Built for scale, high availability, and security
Retrieval & Tool Usage Analysis	Difficult to track, no dedicated tools	Detailed insights into RAG and tool interactions
No Code Changes Required	Requires significant code instrumentation	Integrates seamlessly with minimal setup

Full Agent Lifecycle Evaluation

Limited to specific stages

Comprehensive, end-to-end evaluation

Continuous Benchmarking

Manual, ad-hoc testing

Automated, ongoing performance tracking

Platform-Computed Metrics

Basic logging, manual analysis

Advanced, AI-driven metric generation

Enterprise-Grade Reliability

Best-effort, often lacking scalability

Built for scale, high availability, and security

Retrieval & Tool Usage Analysis

Difficult to track, no dedicated tools

Detailed insights into RAG and tool interactions

No Code Changes Required

Requires significant code instrumentation

Integrates seamlessly with minimal setup

Enterprise-Grade Trust

Built for security-conscious teams. We take data privacy and security seriously.

Secure Agent Invocation

We invoke your agents securely via encrypted channels. Your API keys and secrets are stored in a vault.

Tenant Isolation

Strict logical separation of data. Your datasets and evaluation results are never accessible by other tenants.

No Training on Data

We guarantee that your data is never used to train our models or any third-party models.

Audit-Friendly

Comprehensive logs of every evaluation run, including who ran it, what config was used, and the results.

Ready to evaluate your agents?

Join forward-thinking engineering teams building reliable AI agents with SupaEval.