Build, test, and improveAI in one workflow.
Compare prompts, models, and agents, catch regressions, and debug failures using the SDK, CLI, IDE, and Git workflow your team already uses.
See development in action
Building AI is easy. Improving it is the hard part.
Teams can build an AI prototype in days, but turning it into a reliable production system requires hundreds of decisions across prompts, models, datasets, tools, and architecture. Without a shared development workflow, each change becomes another experiment.
The teams that win are measuring every change, learning from failures, and carrying what works directly into production.
EU AI Act
ISO/IEC 42001
NIST AI RMF
OSFI E-23
<5min
from an AI change to a measurable pass or fail result
One
command to test prompts, models, and agents before they ship
10x
iteration velocity from testing hypotheses instead of changing prompts blindly
Between prototypeand production,developers areflying blind.
Teams change prompts, swap models, update datasets, and rework agent workflows across notebooks and homegrown tools. Results are difficult to compare, successful configurations are hard to reproduce, and production failures rarely inform the next version.
No reliable baseline for the next experiment.
Prompt, model, and agent changes are difficult to compare when every experiment uses different datasets, metrics, and configurations.
Experiments and results scattered across notebooks and docs.
Each team maintains its own evaluation harness, prompt registry, datasets, and results. Successful experiments are difficult to reproduce, share, or maintain.
Production failures never make it back into development as test cases.
Incidents are patched and forgotten instead of becoming regression tests. The same failure can return because the development workflow never learned from production.
One development workflow from prototype to production.
Openlayer connects experiments, evaluations, traces, regression tests, and production feedback in one developer workflow. Teams can test every change, understand why it passed or failed, and keep the same standards running after deployment.
Every result stays connected to the version, dataset, and configuration that produced it. Governance evidence builds automatically in the background.
From prototype to production
01
One test suite across development and production
Run the same tests locally, in CI/CD, and against production traffic, so the standards used before launch continue measuring the system after deployment.
02
175+ out-of-the-box evals
Evaluate quality, safety, security, performance, cost, RAG, and agent behavior using configurable tests ready to run in a few clicks.
03
Experiment tracking and version comparison
Track every prompt, model, dataset, and architecture change. Compare results across versions and return to any previous configuration with complete history.
04
Custom metrics
Define business-specific metrics in code or natural language, including custom rubrics, deterministic checks, and adversarial tests.
05
Regression testing
Compare every new version against an approved baseline and catch regressions in quality, safety, cost, latency, or performance before deployment.
06
Dataset slicing and cohort analysis
Analyze performance across dataset versions, time periods, user cohorts, and subpopulations to find failures hidden by aggregate scores.
07
Full agent trace visibility
Inspect every step an agent takes, including model calls, tool selection, arguments, handoffs, latency, token usage, and evaluation results.
08
Build across the agent frameworks you already use
Connects directly to OpenAI Agents SDK, LiteLLM, and other agent frameworks.
09
Git-native CI/CD with test gating
Run tests automatically with pull requests and builds. Block changes when required thresholds fail using GitHub Actions, Jenkins, CircleCI, or the REST API.
10
Debug without leaving your development environment
Inspect failed tests and traces directly from supported IDEs, or integrate through SDKs for Python, TypeScript, Go, Ruby, and Java.
11
Production-to-dev feedback loop
Bring failed production traces directly into development and convert them into regression tests, so every incident improves the next release.
“Without Openlayer, we’d be blind to how our LLM outputs behave at scale. It’s saved us months of engineering time and given us a repeatable way to keep phishing simulations reliable and realistic.”
Daniel Chyan, CTO, Jericho Security
Trusted by regulated leaders: Sun Life and Gallagher (insurance); Rogers, KPN, and Comcast (telecom and media).
Backed by Y Combinator and Race Capital. SOC 2 Type II.
Founded by ex-Apple/Siri ML engineers.
Jericho Security: 6x deployment frequency and +53% throughput after standardizing on Openlayer.
Named in the 2026 Gartner Market Guide for AI Evaluation and Observability Platforms.
Endorsed by Guillermo Rauch (Vercel CEO) and Max Mullen (Instacart founder).
