OpenInterpretability

OpenInterpretability provides a robust toolkit for examining how language models process information. Key features include: * Production-grade probes for model behavior * Integration with popular coding environments like Claude Code and Cursor * Benchmarking and leaderboard for interpretability methods * Tools for training and analyzing model components * Open-source and privacy-focused design This framework allows developers and researchers to deploy and validate interpretability methods at scale. It offers specialized guard probes, such as FabricationGuard for detecting incorrect outputs and agent-probe-guard for identifying agent failures and processing issues. These probes operate with high accuracy and minimal latency, making them suitable for real-time inference environments. OpenInterpretability emphasizes reproducibility and standardization, ensuring that all probes and methodologies can be inspected and re-executed. Its ProbeBench leaderboard includes anti-Goodhart norms to provide reliable metrics, while SAE training resources range from quick Colab notebooks to comprehensive, paper-grade setups. This promotes transparent evaluation and continuous improvement for understanding and validating model operations. Built for engineering teams, researchers, and developers focused on ensuring the reliability and safety of advanced language models. It helps in auditing model knowledge, catching erroneous outputs, and validating reasoning processes in a transparent and reproducible manner within existing development workflows.