Launch HN: Screenpipe (YC S26) – Empowering AI Agents via Continuous Screen Capture

Screenpipe is introducing a paradigm shift in how AI agents interact with your digital life. By implementing a 24/7 recording of your screen and audio, it provides a comprehensive "digital memory" that allows AI to understand exactly what you've seen, heard, and done.

"The goal is to give AI agents the same context that a human colleague would have if they sat next to you all day."

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🛠️ The Core Concept

Most AI assistants suffer from a context gap. They know what you tell them in a prompt, but they don't know the specific Slack thread you read ten minutes ago or the exact line of code you were debugging in a niche IDE.

Manual copy-pasting of context $\rightarrow$ Automatic, continuous context ingestion.

How it Works: The Technical Pipeline

Screenpipe doesn't just record a video file; it transforms raw pixels and sound into structured, searchable data.

1. Capture: It utilizes ffmpeg to capture the screen and system audio.
2. Processing: It runs OCR (Optical Character Recognition) on visual frames and Speech-to-Text on audio.
3. Storage: All data is indexed into a local SQLite database.
4. Access: An API allows LLMs to query this database to retrieve specific moments in time.

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🔒 Privacy and Architecture

A primary concern with 24/7 recording is security. Screenpipe addresses this by being local-first.

The system is designed to be open source, ensuring transparency in how your data is handled.

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🚀 Powering the Next Generation of Agents

By providing a continuous stream of data, Screenpipe enables agents to perform tasks that were previously impossible. Instead of asking, "What did I do yesterday?", an agent can calculate the exact time spent on a project using a formula like:

$\text{Total Focus Time} = \sum_{i=1}^{n} (t_{end, i} - t_{start, i})$

where $i$ represents distinct sessions of a specific application being active on screen.

Example API Implementation

Developers can integrate Screenpipe into their own tools using simple curl requests or SDKs:

# Example: Fetching the last 5 minutes of screen text
curl -X GET "http://localhost:3030/api/screen?limit=5m" \
     -H "Authorization: Bearer YOUR_TOKEN"

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🖼️ Visualizing the Experience

Figure 1: A conceptual view of the timeline-based search and data extraction.

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✅ Roadmap & Getting Started

If you are looking to integrate your workflow with a "perfect memory" AI, here is the current checklist for adoption:

• Install the Screenpipe background daemon.
• Configure local storage paths for the SQLite database.
• Connect a local LLM (like Llama 3 via Ollama) to the API.
• Build a custom agent to automate repetitive reporting tasks.

Key Use Cases

• Instant Recall: Find that one website you saw three days ago but forgot to bookmark.
• Automatic Journaling: Let an AI summarize your day based on your actual activity.
• Contextual Coding: An AI that knows exactly which documentation page you are looking at while you write code.

Screenpipe (YC S26) is effectively building the "hard drive" for the AI's working memory, ensuring that no digital interaction is ever truly lost.