screenpipe

Product Introduction

1. Definition: Screenpipe is an open-source, locally processed AI screen recorder and automation tool. It captures screen activity (video, audio, application states) and uses Optical Character Recognition (OCR), speech-to-text, and local Large Language Models (LLMs) to index, search, and automate workflows.
2. Core Value Proposition: Screenpipe provides 24/7 digital memory recall and AI automation while guaranteeing 100% local data processing and zero data uploads, addressing critical privacy concerns inherent in cloud-based alternatives like Rewind.ai or Microsoft Recall.

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Main Features

1. 24/7 Screen Recording with OCR:

   • How it works: Continuously captures screen video at configurable resolutions/framerates. Uses Tesseract OCR (or similar open-source engines) to extract visible text from frames. Applies ultra-optimized compression (~63MB/hour) storing screenshots, audio, and text separately.
   • Tech: Frame differencing for compression, GPU acceleration support, exclusion zones/apps.

2. Real-Time Audio Transcription & AI Meeting Notes:

   • How it works: Microphone audio is transcribed locally using offline Automatic Speech Recognition (ASR) models (e.g., Whisper.cpp). AI summarizes conversations, extracts action items, and syncs transcriptions with screen context.
   • Tech: Voice Activity Detection (VAD), speaker diarization (optional), local LLM summarization (Ollama, Llama.cpp).

3. Natural Language Search (Local RAG):

   • How it works: Indexes OCR text, transcripts, app metadata, and timestamps into a local vector database (e.g., Chroma DB). Users query via natural language ("What did Sarah say about the budget?"). LLMs (local ChatGPT, Claude, Ollama) retrieve relevant screen segments with video playback.
   • Tech: Retrieval-Augmented Generation (RAG), sentence transformers for embeddings, temporal context linking.

4. Privacy-First Architecture:

   • How it works: All data (video, audio, text) is stored exclusively on-device. Sensitive data (PII) like credit cards, emails, and passwords is automatically redacted before being processed by AI models using regex and NER techniques.
   • Tech: On-device encryption, configurable data retention policies, telemetry opt-out.

5. Cross-Platform & Open API:

   • How it works: Native apps for macOS, Windows, Linux. Developers can extend functionality via REST API or direct code access (Apache 2.0 license). Integrates with local LLMs, Obsidian, VSCode.
   • Tech: Electron (UI), Rust/C++ core modules, comprehensive SDK.

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Problems Solved

1. Pain Point: Professionals lose critical information from meetings, workflows, or research due to fragmented digital tools and human memory limits. Cloud-based recall systems risk sensitive data exposure.
2. Target Audience:
   • Security-Conscious Professionals: Lawyers, healthcare workers, finance analysts requiring compliance (HIPAA/GDPR).
   • Developers: Needing context switching between coding, documentation, and debugging sessions.
   • ADHD/Neurodiverse Users: Reducing cognitive load by retrieving past information effortlessly.
   • Researchers: Aggregating knowledge from disparate sources (PDFs, web pages, lectures).
3. Use Cases:
   • Generate AI meeting minutes with source video proof.
   • Retrospectively find lost code snippets or design assets via natural language.
   • Automate repetitive tasks using desktop context-aware workflows (e.g., "Summarize all Slack discussions about Project X last week").

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Unique Advantages

1. Differentiation vs. Competitors:

   Feature	Screenpipe	Rewind.ai	Microsoft Recall
   Data Location	100% Local	Cloud	Local + Cloud*
   Open Source	Yes (Apache 2.0)	No	No
   PII Redaction	Pre-AI Processing	Limited	Partial
   Offline Use	Fully Supported	Limited	Partial
   API Access	Full SDK & REST API	Restricted	None
   ***Recall uploads data to Azure if Copilot used.*

2. Key Innovation:

   • Local RAG Pipeline: Executes full Retrieval-Augmented Generation (text extraction → embedding → LLM querying) entirely on-device, eliminating cloud dependencies.
   • PII-Aware AI: Redacts sensitive data before it reaches AI models, a critical privacy safeguard competitors lack.

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Frequently Asked Questions (FAQ)

1. Does Screenpipe upload my data to the cloud?
   No. Screenpipe processes all data locally—OCR, transcription, AI search, and storage occur entirely on your device. Zero data is uploaded externally.

2. Can Screenpipe work completely offline?
   Yes. Core functionality (recording, OCR, local LLM queries) works offline. Optional cloud integrations (e.g., ChatGPT) require internet but are not mandatory.

3. How much storage does 24/7 recording consume?
   Screenpipe uses ~0.5GB/day (15GB/month) via optimized compression. This equals roughly 4 standard-definition movies monthly. Users can adjust retention periods.

4. Is Screenpipe’s transcription accurate with background noise?
   Local ASR models (e.g., Whisper) achieve near-human accuracy in quiet environments. Performance may dip with heavy accents or loud backgrounds—hardware microphones impact results.

5. Can developers build custom tools with Screenpipe?
   Yes. The open-source API allows creating plugins, custom automations, or integrations with tools like VSCode, Obsidian, or private LLMs (via Ollama).