Model Behavior Analysis
Evaluate how models respond across prompts, perturbations, edge cases, and reasoning tasks.
AI interpretability platform
Understand how AI models think, reason, and behave.
OpenInterp helps developers and researchers inspect model behavior, analyze internal representations, evaluate reasoning patterns, and build more transparent AI systems.
Behavior analysisActivation viewsTransparent reportsDeveloper tooling
Why it matters
Modern AI systems are powerful, but difficult to understand.
As models become more capable, developers and researchers need reliable ways to inspect behavior, understand failures, compare outputs, and reason about why models respond the way they do.
Platform
Interpretability tools for understanding AI systems.
OpenInterp focuses on the core work of model understanding: observing behavior, inspecting internals, testing hypotheses, and communicating results clearly.
Internal Representation Inspection
Explore activations, attention patterns, embeddings, and supported model internals.
Interpretability Experiments
Run structured experiments to test hypotheses about model behavior and decision patterns.
Transparent Reports
Generate clear summaries and visualizations that make model behavior easier to communicate.
Use cases
For teams that need evidence, not just outputs.
OpenInterp is designed for people who need to understand model behavior before they rely on it in research, development, safety, or product work.
AI researchers
Study model internals, compare representations, and document findings with reproducible analysis.
Model developers
Debug unexpected behavior, inspect reasoning patterns, and evaluate changes across model versions.
Safety and reliability teams
Investigate failures, assess robustness, and communicate evidence behind model decisions.
AI product builders
Compare how models reason across tasks before shipping systems that depend on their outputs.
Research and developer tooling
Built for research workflows and developer tooling.
OpenInterp is shaped around practical model analysis: structured experiments, clean APIs, repeatable outputs, and workflows that fit modern AI development pipelines.
Python-first tooling
Designed for analysis workflows researchers and ML engineers already use.
Reproducible experiments
Structure model analysis so results can be rerun, compared, and reviewed.
Clean APIs
A platform direction centered on clear interfaces for model traces, metrics, and reports.
Extensible architecture
Built to fit modern AI development pipelines without forcing a single analysis style.
Get started
Start building more interpretable AI systems.
Use OpenInterp to inspect, evaluate, and understand model behavior with tools designed for modern AI workflows.