screenpipe, from Screenpipe, is a local-first AI tool that captures and indexes your desktop activity to serve as a searchable personal memory. The app records screen content and audio, builds an index you can query with natural-language queries, and exposes hooks for automation. Key elements include a visual timeline, a plugin system called Pipes, and agent integration for context-aware assistants. It targets knowledge workers, developers, researchers, and people who require reliable meeting documentation or task recall.

Searchable history that reconstructs past sessions

The tool stores indexed traces of what you see and hear so you can retrieve specific moments on demand. It captures screen content and audio and keeps metadata in a local database to let you run natural-language queries against prior activity. A visual timeline interface provides a frame-by-frame rewind of desktop events, which helps when you need to locate a code snippet, a specific document, or an earlier conversation without manually combing file folders.

Transcription and visual indexing produce searchable outputs

The app applies advanced OCR to extract on-screen text and runs real-time speech-to-text for meetings and conversations, including speaker identification for multi-participant calls. Those indexed transcripts and OCR results are directly searchable, making it possible to locate phrases you saw or heard days earlier. Search results are tied to timestamps and visual frames so you can jump back to the original screen state for verification.

Continuous capture has measurable system demands and controls

The capture pipeline is event-driven to limit unnecessary recording and typically uses between five and ten percent CPU during normal operation. System requirements list about eight gigabytes of RAM and an expected five to twenty gigabytes of disk usage per month depending on activity. Cross-platform builds exist for desktop environments, and Linux users can build from source, which affects deployment choices for individual machines or teams.

Privacy posture and automation fit team workflows

Data processing and storage happen on the local device, so stored recordings and indices remain on the user's machine rather than being sent to external servers. The app exposes a plugin architecture for custom automations and an application-facing API for agents to query contextual history. That combination supports integration into workflows that need automated meeting notes or context-aware tooling while keeping raw data on premises.

Practical choice for teams and individuals who accept always-on capture

The app is a practical option for professionals who prioritize auditable, on-device recall and want AI assistants to operate with recorded context, because its core lets agents query local history. Organizations should plan retention and storage policies before wide deployment, and users should evaluate how continuous recording fits privacy norms in shared environments.