Problem solved

Screenpipe addresses the lack of contextual memory in AI assistants. Traditional AI agents only know what users type or explicitly provide, missing the broader context of what they have seen, said, or heard throughout the day. Screenpipe fills this gap by continuously recording screen and audio, creating a searchable, local database of personal activity that AI can use to provide more informed and personalized responses.

Use cases

• Personal AI assistant memory
• Meeting transcription and summarization
• Automated documentation of computer activity
• Context-aware agent interaction
• Productivity analysis and time tracking
• Research data collection from screen content

Audience

• Developers
• ML Engineers
• Researchers
• Hobbyists
• Startups

Pros

• Local-first and privacy-preserving
• Extensive integration with AI agents and apps
• High-performance Rust implementation
• Open source and actively maintained
• YC-backed project

Cons

• Continuous recording may raise privacy concerns
• Requires significant storage for long-term recordings
• Potential performance overhead on low-end machines
• Setup and configuration may be complex for non-technical users