mediar-ai/screenpipe

ScreenPipe – An AI‑Powered Desktop History Engine

ScreenPipe addresses a core limitation in contemporary AI assistants: the absence of persistent, contextual awareness of a user’s local environment. Traditional language models can answer questions and generate content based on the text they receive, but they cannot “see” what a user is doing on their own machine. ScreenPipe solves this by continuously recording the desktop, microphone, and application history in a local, privacy‑preserving database. This stream of real‑time data becomes the context that an AI assistant can query, allowing it to respond with actions or insights that are tightly coupled to the user’s current workflow.

At its heart, ScreenPipe is a modular MCP server that exposes several key capabilities to AI clients. Developers can retrieve the latest screen capture, audio snippets, or a structured timeline of application events through simple resource calls. The server also offers a lightweight SDK—screenpipe terminator—which replaces vision‑based automation with OS‑level APIs, delivering up to 100× faster and more reliable interactions. By keeping all data local, ScreenPipe eliminates latency associated with cloud uploads and ensures compliance with strict privacy requirements.

The practical value of this architecture is evident across a range of scenarios. A content creator can ask the assistant to “summarize the last video editing session,” and the model will pull timestamps, window titles, and even audio transcriptions to generate a concise report. A developer can trigger a build pipeline by simply saying “start the CI run,” and ScreenPipe will execute the appropriate shell command in the correct context. In enterprise settings, auditors can query historical screen activity to verify compliance with security policies without exposing sensitive screenshots externally.

Integration into existing AI workflows is straightforward: the MCP server registers its resources and tools with any compliant client, enabling declarative prompts that reference live desktop data. Because the server is open source and 100 % local, teams can host it behind firewalls or on edge devices, sidestepping the need for third‑party cloud services. The inclusion of a marketplace—“AI app store powered by 24/7 desktop history”—further lowers the barrier to entry, allowing developers to share reusable tools that consume ScreenPipe’s streams.

Unique advantages of ScreenPipe include its zero‑latency, privacy‑first design and the terminator SDK that bypasses computer vision entirely. By leveraging native OS APIs, it achieves deterministic performance and avoids the brittleness of image‑based automation. Coupled with an active community that contributes plugins, integrations, and hackathon challenges, ScreenPipe is rapidly becoming the foundational layer for desktop‑centric AI applications.