Most agent frameworks treat memory as an afterthought. They store conversation history in ephemeral context windows, dump JSON blobs into vector databases, or sync everything to a cloud service that becomes a compliance headache. Screenpipe takes a different approach: continuous local recording of screen and audio, indexed on-device, exposed to agents through a search API that never leaves your machine.
The YC S26 company launched on Hacker News with a straightforward pitch: record everything you do, make it searchable, and let agents query that history to automate repetitive workflows. No cloud sync. No third-party storage. The architecture raises three immediate questions for anyone building agent infrastructure: how does continuous capture scale without destroying disk and performance, what does the agent API surface actually look like, and how do you extract reusable procedures from raw recordings without leaking sensitive data?
The Recording Pipeline
Screenpipe runs a capture loop that samples screen content and audio streams at configurable intervals. The default is one frame per second for screen, continuous for audio. Each frame gets OCR’d locally (Tesseract or similar), audio gets transcribed (Whisper or a lightweight alternative), and both text streams feed into a local SQLite database with full-text search indexes.
The disk problem is real. At 1 FPS with OCR metadata, you generate roughly 50-100 MB per hour of active work. A typical workday produces 500 MB to 1 GB. Screenpipe handles this with:
- Deduplication: consecutive identical frames are hashed and stored once with a time range.
- Compression: text is cheap, images are not. Only keyframes or user-triggered snapshots persist as PNGs. Everything else is text.
- Retention policies: configurable auto-delete after N days, or manual archival to external drives.
Audio transcription runs in batches every few seconds. The transcription model runs on-device (CPU or GPU depending on hardware). This keeps latency under 5 seconds for recent queries but means older audio takes longer to search if it hasn’t been pre-indexed.
Agent API Surface
Agents interact with Screenpipe through a local HTTP API (default port 3030) or a language-specific SDK. The core endpoints:
GET /search?q=<query>&start=<timestamp>&end=<timestamp>: full-text search across OCR and transcription data.GET /timeline?start=<timestamp>&end=<timestamp>: chronological list of captured events (app switches, window titles, audio segments).POST /tag: user or agent can tag specific time ranges with labels (e.g., “meeting with client X”).GET /sop?tag=<label>: retrieve tagged sequences as structured workflows.
The search API returns JSON with text snippets, timestamps, and context (which app was active, which window). Agents can use this to answer questions like “what was the error message I saw in terminal yesterday at 3 PM” or “find all Slack conversations about the deployment pipeline.”
The security boundary is the localhost interface. No external network access by default. Agents running on the same machine can query freely. Remote agents need SSH tunneling or a user-configured reverse proxy, which makes accidental leaks harder but not impossible if an agent’s prompt injection or tool call goes rogue.
SOP Extraction and Workflow Automation
Screenpipe’s automation angle is turning recorded sessions into Standard Operating Procedures. The flow:
- User performs a task (e.g., deploying a service, processing an invoice).
- Screenpipe records every screen, every command, every click.
- User tags the session:
POST /tagwith label “deploy-backend-service”. - An LLM (local or API-based) ingests the tagged timeline and generates a step-by-step SOP in Markdown or JSON.
- The SOP becomes an agent prompt or a workflow template in a tool like n8n or Temporal.
The LLM step is where things get interesting. Screenpipe doesn’t ship with a built-in LLM. You bring your own (OpenAI, Anthropic, local Llama). The SDK includes a helper that formats timeline data into a prompt:
from screenpipe import ScreenpipeClient, generate_sop
client = ScreenpipeClient(base_url="http://localhost:3030")
timeline = client.get_timeline(tag="deploy-backend-service")
sop = generate_sop(
timeline=timeline,
llm_provider="openai",
model="gpt-4",
output_format="markdown"
)
print(sop)The generate_sop function sends the timeline (text, timestamps, app names) to the LLM with a system prompt like:
You are analyzing a recorded work session. Extract a step-by-step procedure that can be followed by a human or automated by an agent. Include commands, UI actions, and decision points. Omit sensitive data like passwords or API keys.
The LLM returns a structured SOP. The quality depends on how noisy the timeline is. If the user switched tabs 50 times during the task, the LLM has to infer which actions were essential. This is where tagging and manual cleanup matter.
Architecture Comparison
| Component | Screenpipe (Local-First) | Cloud Agent Memory (e.g., Rewind, Mem0) | Vector DB + Embeddings (Pinecone, Weaviate) |
|---|---|---|---|
| Storage | SQLite on-device | Cloud blob storage + metadata DB | Managed vector index in cloud |
| Privacy | Data never leaves machine | Data synced to vendor servers | Embeddings and metadata in third-party infra |
| Search Latency | <100ms for recent, seconds for old | Network-dependent (50-500ms) | 10-50ms for vector similarity |
| Retention Cost | Disk space (user-controlled) | Monthly subscription per GB | Per-vector pricing, scales with usage |
| Agent Access | Local API, no auth by default | OAuth + API keys | API keys, rate limits |
| Failure Mode | Disk full, indexing lag | Service downtime, API rate limits | Embedding drift, stale data |
Observability and Debugging
Screenpipe logs every capture event, OCR result, and transcription to a local log file. Agents can query /logs to see what was recorded and when. This is useful for debugging “why didn’t the agent find this?” issues.
The indexing lag is the main observability gap. If OCR or transcription falls behind (CPU-bound), the search API returns incomplete results. Screenpipe exposes a /status endpoint that shows:
- Last indexed timestamp
- Queue depth (frames waiting for OCR)
- Disk usage
- Active recording state (paused, running, error)
Agents should check /status before querying to avoid stale results. A simple retry loop with backoff handles transient lag.
Deployment Shape
Screenpipe is a desktop app (Electron wrapper around a Rust core). It runs as a background service on macOS, Windows, or Linux. The Rust core handles capture, OCR, transcription, and indexing. The Electron UI is a settings panel and search interface for humans.
For agent deployments, you skip the UI and run the Rust binary headless:
screenpipe --headless --port 3030 --retention-days 30This starts the HTTP API without the Electron window. Agents connect via http://localhost:3030. For multi-machine setups (e.g., a team of agents), you’d run Screenpipe on each developer’s machine and aggregate SOPs in a shared repository (Git, S3, etc.). The recordings stay local; only the extracted SOPs move.
Likely Failure Modes
- Disk exhaustion: default retention is infinite. Users forget to set
--retention-daysand fill their SSD. - OCR accuracy: screenshots of code, terminals, or design tools produce noisy text. Search returns false positives.
- Audio transcription drift: background noise, accents, or low-quality mics degrade transcription. Agents query for “deploy script” but the transcription says “deep ploy script.”
- Sensitive data leakage: an agent’s tool call sends a Screenpipe search result (containing a password or API key) to an external API. The local boundary doesn’t prevent this; it just makes it less automatic.
- Indexing lag under load: heavy multitasking (10+ apps, video calls, screen sharing) can overwhelm the OCR pipeline. The queue grows, search results lag by minutes.
Technical Verdict
Use Screenpipe when:
- You need agent memory that never touches a third-party server.
- Your workflows are repetitive and screen-based (support tickets, data entry, deployments).
- You want to extract SOPs from real work sessions without manual documentation.
- You’re building agents for compliance-heavy environments (healthcare, finance) where cloud sync is a non-starter.
Avoid Screenpipe when:
- Your agents need to share memory across machines or users (local-only is a hard constraint).
- Your workflows are API-driven or headless (no screen to record).
- You need sub-second search across months of data (SQLite full-text search doesn’t scale like Elasticsearch).
- You’re on a resource-constrained device (OCR and transcription are CPU-heavy).
The local-first architecture is the feature and the limitation. It solves the privacy problem by making cloud sync impossible. It creates the scalability problem by putting all storage and compute on one machine. For single-user agent workflows where data sovereignty matters, that trade-off makes sense. For multi-agent systems or team collaboration, you’ll need a different memory layer.