Screenpipe just launched local screen recording for AI agents, turning your entire desktop session into a searchable memory layer that lives entirely on your machine. The Y Combinator S26 startup, founded by Louis, ships a desktop app and CLI that capture screenshots, operating system accessibility trees, and audio transcripts, then index everything into a local SQLite database. An AI-friendly API on port 3030 lets agents query that history in milliseconds. The launch matters because it solves the context problem that has plagued agent frameworks: models need to know what you actually do across apps, not just inside a single chat window or MCP server. With support for OpenClaw, Claude, ChatGPT, and custom agents, Screenpipe is building the bridge between human computer use and autonomous agent workflows without shipping your data anywhere. The tool is already drawing attention from privacy advocates and automation engineers who want agent memory without cloud dependency.
What Did Screenpipe Just Launch?
Screenpipe shipped a desktop application and CLI that records your screen and audio, then structures that stream into a queryable memory bank for AI agents. The tool runs locally on macOS, Windows, and experimentally on Linux, capturing app switches, clicks, typing pauses, scrolls, and idle states. Each meaningful event triggers a screenshot paired with the OS accessibility tree, while audio is transcribed locally via Parakeet or Whisper. Everything lands in a local SQLite database alongside MP4 and Markdown files. The project graduated into Y Combinator’s S26 batch after starting as a personal CLI in 2024. Louis, the founder, previously built Ava, an Obsidian AI plugin, and Embedbase, a RAG API. Screenpipe is source-available under a custom commercial license: free for personal and research use, paid for commercial deployments. The CLI requires no signup, runs immediately after installation, and outputs structured data that agents can consume without additional ETL pipelines. You can install it without an account using a single npx command. The core is written in Rust for performance, while the desktop interface uses a lightweight web layer, giving users a fast native feel without sacrificing cross-platform reach.
Why Does Local-Only Recording Matter for AI Agent Memory?
Agent frameworks have spent years trying to give models context about your work. MCP servers connect to specific apps, skills handle predefined tasks, and tool calling lets agents read files, but none of them see the full picture across your desktop. You switch between Slack, Figma, a terminal, and a browser, and the agent stays blind to most of that context. Local screen recording for AI agents fixes this by capturing the actual pixels and audio of your workflow, then making that history searchable. Because Screenpipe never uploads your screen to the cloud, you avoid the compliance nightmare of sending proprietary designs, customer chats, or financial data to a SaaS vendor. This matters for teams in regulated industries and for anyone who treats their desktop as a sensitive workspace. Healthcare engineers bound by HIPAA, financial analysts under FINRA guidelines, and legal teams handling privileged documents can all use Screenpipe without triggering data residency violations. It also removes latency. Agents query a local SQLite database on port 3030 instead of waiting for a remote vector store. That local query model eliminates network round trips and keeps your workflow history available even when you are offline or working from an air-gapped environment.
How Does Screenpipe Capture Desktop Context Without Burning CPU?
Early prototypes of Screenpipe ran OCR on every video frame, which turned laptops into space heaters and produced mountains of duplicate text. The current architecture is smarter. It listens for OS-level events such as app switches, mouse clicks, scrolls, typing pauses, and idle fallbacks. When a meaningful change occurs, Screenpipe grabs a single screenshot and pairs it with the operating system’s accessibility tree at that exact timestamp. The accessibility tree already contains structured UI data, so OCR is only a fallback when that tree is unavailable. This event-driven approach slashes CPU usage. The team targets under 1 percent CPU and under 400 MB RAM for background indexing and local PII redaction. Audio runs continuously but is transcribed in chunks using local models through Apple MLX or Windows DirectML. By respecting the structure the OS already provides, Screenpipe avoids the brute-force approach that kills battery life and makes continuous recording practical for daily work. Additional deduplication logic hashes frame content so identical screenshots sitting in the same static window do not generate redundant database rows.
What Is the AI-Friendly API Running on Port 3030?
Screenpipe exposes an HTTP API on localhost port 3030 that any agent can query to retrieve context about your past activity. The API supports authentication and ships with MCP and skills integrations, so you do not need to write custom adapters. Agents can ask for everything you saw between 8 AM and 4 PM, pull up tasks you started in a specific app, or gather context about a project before drafting a response. Because the data is stored locally in SQLite, queries return in milliseconds. The API surface is designed for LLM consumption, meaning it returns structured data that is easy to inject into prompts or RAG pipelines. A typical response looks like this:
{
"timestamp": "2026-07-24T14:32:00Z",
"app": "vscode",
"window": "api.rs",
"text": "pub fn record() -> Result<Pipe, Error>"
}You can filter by time range, application name, or content substring using standard query parameters. Pagination keeps large result sets manageable when an agent requests an entire afternoon of context. You can connect Claude, ChatGPT, Hermes, or OpenClaw to this endpoint. For builders running local agent stacks, port 3030 becomes a single source of truth about human activity that sits right next to your model inference layer.
How Does Screenpipe Compare to MCP and Static Skills?
MCP servers gave agents a standard way to call tools, but they are static and bound to individual applications. You need a separate MCP server for Slack, another for GitHub, and another for your CRM. Static skills work the same way: pre-coded actions that do not adapt to what you are actually doing right now. Screenpipe takes a different approach. It observes the entire desktop session, independent of any single app’s API, and feeds that timeline directly into the agent’s context window. You do not need to build a new integration every time your team adopts a tool. Unlike Nucleus MCP, which focuses on secure local memory for agents, Screenpipe generates that memory by observing the user instead of requiring manual imports. Screenpipe does not replace MCP; it complements it by supplying the ambient context that individual servers cannot see. When you need to perform an action inside an app, MCP is still the right choice. When you need to understand what the user was doing across ten apps over the last three hours, Screenpipe fills the gap. Most production setups will use both: Screenpipe for memory and context, MCP servers for execution.
| Feature | Screenpipe | MCP Servers | Static Skills |
|---|---|---|---|
| Data source | Screen, audio, OS events | Single app/API | Predefined commands |
| Scope | Cross-desktop | Per-integration | Task-specific |
| Setup complexity | Install one app | Configure many servers | Write or import code |
| Privacy | Local by default | Depends on server | Depends on host |
| Real-time context | Continuous | On-demand | On-demand |
What Can You Automate with Screenpipe and OpenClaw?
Because Screenpipe sees everything you do, it opens automation patterns that were impossible with app-specific APIs alone. You can build an OpenClaw agent that watches your LinkedIn profile visits and updates your personal CRM automatically. Another agent can review your day and generate a list of automation opportunities for your team. You can also build SOPs by asking the agent to summarize how you completed a task yesterday, then replay those steps tomorrow. A third pattern involves automatic time tracking: the agent reads window titles and meeting transcripts, then logs billable hours to your project management tool without manual timers. The integration works because OpenClaw can query port 3030 as a memory source, pulling structured context into its reasoning loop. OpenClaw agents gain a persistent, personalized memory without requiring cloud storage or expensive vector databases. This goes beyond reactive tools like Dorabot by giving the agent memory of your entire desktop, not just your IDE. This is not just about logging clicks. It is about giving your agent the same situational awareness you have when you sit down at your desk.
How Does the Accessibility Tree Reduce Signal-to-Noise?
Raw screenshots are noisy. A single frame might contain a cluttered IDE, a Slack notification, and a YouTube tab, and OCR would extract all of it with no hierarchy. Screenpipe solves this by reading the operating system’s accessibility tree, a structured representation of the UI that the OS already maintains for assistive technologies. When you switch apps or click a button, the tree updates with element names, roles, and values. Screenpipe pairs this structured data with the screenshot at the same timestamp, so the agent knows not just what the screen looked like, but what UI elements were active. OCR still runs when the tree is incomplete, but it is no longer the primary data source. This hybrid approach cuts duplicate data and gives agents semantic context instead of flat text soup. The result is a cleaner memory layer where queries like “what Jira ticket was I editing at 2 PM” return precise answers instead of irrelevant pixel text. Without the accessibility tree, agents would drown in banner ads, system tray icons, and background browser tabs every time they searched your history.
What Privacy Controls Are Built Into Screenpipe?
Privacy is not an afterthought in Screenpipe; it is the foundation. The app stores all recordings locally in SQLite, MP4, and Markdown files. It does not require an account to run the CLI. You can filter recording by specific apps, windows, or URLs, and it automatically ignores browser incognito sessions. Recording schedules let you define when capture is active, so you can shut it off during personal hours. You can also pause recording globally with a hotkey or configure automatic stop triggers when specific apps gain focus. For enterprises, Screenpipe built a local AI PII redaction model that runs on Apple MLX or