Screenpipe is a local-first platform designed to record, index, and analyze desktop activity. By capturing screen, audio, and keyboard input, it creates a comprehensive and searchable history of computer usage. The system functions as an activity recorder and automation framework, providing a persistent, context-aware memory that allows artificial intelligence agents to observe and interact with local desktop environments.
The platform distinguishes itself through a privacy-focused architecture that processes all data locally. It utilizes on-device computer vision and speech recognition to transcribe audio and extract metadata from screen content, while simultaneously applying automated redaction to filter sensitive information before storage. All captured logs are maintained in a local relational database, ensuring that user data remains under local control and is protected by authenticated encryption at rest.
Beyond simple recording, the platform supports complex workflow automation and business process analysis. It exposes captured activity data to external tools through standardized protocols, enabling the execution of custom scripts and agents that can trigger actions across third-party software. These capabilities allow for the mapping of work patterns, the identification of process bottlenecks, and the generation of structured datasets for training computer-use models.
The software provides a command-line interface for managing capture policies and supports fleet-wide deployment within organizational environments. All processing, including data sanitization and indexing, occurs on the host machine to maintain security and compliance.
