What did Hlido score screenpipe?

screenpipe scored 81/100 (STEADY) in Hlido's independent, hands-on review.

How much does screenpipe cost?

Hlido recorded the pricing model as: Usage-based. Derived from evidence Hlido holds, not vendor-supplied; verify current prices on the vendor's pricing page.

Does any vendor pay Hlido for placement?

No. Hlido takes no money from the agents it rates — scoring weights stay private and the evidence behind every verdict is public.

screenpipe

Name: screenpipe review
Item: screenpipe
Rating: 81
Author: Hlido Editor

Productivity · tested 2026-08-23 · re-test due 2026-11-23 · by the Hlido desk, not the vendor

In short: Local-first computer history that any approved AI can search — strong traction, an explicit privacy default, and the rare case where the scary version of the product is the one they chose not to build.

5 PASS · 0 FAIL of 5 public-surface claims

Visit screenpipe → scorecard.json

Quick answer

screenpipe scores 81/100 (STEADY) on Hlido’s independent, hands-on test (reviewed 2026-08-23). STEADY (81) on strong external corroboration (21k+ GitHub stars, Y Combinator backing), an explicit local-by-default privacy posture stated up front, a dedicated Security surface, and an agent model that shows trigger, c Pricing: Usage-based.

Hlido Editor · 2026-08-23

screenpipe records screen, audio and application activity into a local history, then lets an approved AI agent search it, reason over it, and act. The proposition is straightforward and the demonstration on the surface is unusually concrete: the same prompt and the same model, run with and without screenpipe's context, with the difference shown side by side — four Linear tickets updated with linked PRs and a feedback log, drawn from 47 days of memory. Showing the delta rather than asserting it is the right way to make this argument, and most tools in the category do not bother. The trust posture is the deciding factor. "Keeping the raw history local by default" is stated in the opening paragraph, not buried, and the product carries a dedicated Security section alongside Enterprise and Pricing. For a tool that by design sees everything on your screen and hears everything in your calls, local-by-default is not a feature — it is the only defensible architecture, and choosing it costs the vendor the data. Traction corroborates: 21,187 GitHub stars and Y Combinator backing are external signals, not self-assertions. The agent story is the second half. Recorded context feeds background agents — meeting notes, CRM updates, SOP generation, time tracking — with the surface showing what triggered each agent, what context it used, what it did, and where the result went. That per-run visibility is exactly the audit trail most agent products omit. What is not resolved on the surface: what "approved agent" means in enforcement terms, what leaves the machine when a cloud model is used, and how the local/cloud boundary is drawn in practice. For a product with this blast radius, that boundary is the thing to verify before deployment.

Why STEADY

STEADY (81) on strong external corroboration (21k+ GitHub stars, Y Combinator backing), an explicit local-by-default privacy posture stated up front, a dedicated Security surface, and an agent model that shows trigger, context, action and destination per run. Held below VITAL because the enforcement mechanics of 'approved agent' and the precise local/cloud boundary are not specified — and on a tool that captures screen and audio continuously, that boundary is the single most consequential unresolved detail.

Public-surface checklist

PASS Homepage loads (required)
PASS Primary value prop (required) — Your computer history. Open to any AI.
PASS Cta present (required) — Download for free
PASS Pricing or access — Pricing nav item present; free download path
PASS Evidence or demo — 4 screenshots captured; 44-second product film and interactive with/without comparison on page

What we saw

4 screenshots captured by the Hlido engine during the reviewed run (run-b20bfc2b11d515d1-screenpipe-com). Our own captures — not vendor marketing material.

screenpipe — run screenshot 1 (home.png) — home.png

screenpipe — run screenshot 2 (page_.png) — page_.png

screenpipe — run screenshot 3 (page_use-cases.png) — page_use-cases.png

screenpipe — run screenshot 4 (page_onboarding.png) — page_onboarding.png

What it does well

Local-by-default raw history, stated in the opening paragraph rather than buried
Demonstrates value by A/B-ing the same prompt with and without context, instead of asserting it
21,187 GitHub stars and Y Combinator backing — external, checkable traction signals
Dedicated Security and Enterprise sections alongside Pricing
Per-agent run visibility: what triggered it, what context it used, what it did, where the result went
Open to any AI rather than locking the history to one vendor's model
Free download with a published pricing path

What it fails at

'Approved agent' is not defined in enforcement terms on the surface
The local/cloud boundary — what leaves the machine when a cloud model is used — is unspecified
No independent security audit linked for a tool with continuous screen and audio capture
Retention, deletion and redaction controls are not described on the reviewed surface
Consent handling for other participants captured in recorded calls is not addressed
Product demonstrations are vendor-produced rather than third-party evaluated

Best for

Individuals who want searchable recall of everything they have seen and heard at work
Teams automating meeting notes, CRM updates and follow-ups from real activity rather than manual entry
Privacy-conscious users who want AI context without shipping raw history to a vendor
Developers building agents that need grounded personal work context

Not recommended for

Regulated environments without a documented retention, redaction and consent model
Organisations requiring an independent security audit before deploying continuous capture
Anyone recording calls where all-party consent cannot be established
Users unwilling to accept the blast radius of a continuous screen and audio recorder

Pricing & access

ModelUsage-based
Pricing findable on the public surfacePASS Pricing nav item present; free download path (tested 2026-08-23)

Derived from Hlido-held evidence only (engine checklist + editorial text); quotes are verbatim from the scorecard; not vendor-supplied; re-derived daily. Verify current prices on the vendor's pricing page. Last verified 2026-08-23.

Compared to

Otter.ai local-total-recall
Otter captures and transcribes meetings into a cloud service; screenpipe captures everything — screen, audio, apps — and keeps it local by default. Otter for a narrow, mature, compliance-friendly meeting record; screenpipe for total recall with the data staying on your machine.
Superhuman cross-application-memory
Superhuman optimises one channel (email) with a polished client; screenpipe indexes everything you did and lets any AI query it. Different scopes entirely — Superhuman for inbox throughput, screenpipe for cross-application memory.

Agent relevance

API SDK

Agentic-Commerce Readiness 56/100 · INTEGRABLE

Independent readiness for agent delegation & transaction. How it’s scored · check live

Explicitly positioned as context infrastructure for agents — 'computer history for every AI agent' — with background agents consuming the local index and a documented per-run trace of trigger, context, action and destination. The surface advertises openness to any AI and shows connected integrations, but does not publish an MCP server or a named public API endpoint on the reviewed page; the integration contract must be confirmed in the developer documentation.

Agent-friendly score: 7/10

Evidence

Records screen, audio and app activity, keeping raw history local by default — source (2026-08-23) verified
21,187 GitHub stars at time of review — source (2026-08-23) verified
Backed by Y Combinator — source (2026-08-23) verified
Dedicated Security, Enterprise and Pricing sections — source (2026-08-23) verified
History is open to any AI rather than a single vendor's model — source (2026-08-23) verified
Agent runs show trigger, context used, action taken and destination — source (2026-08-23) verified
Free download available — source (2026-08-23) verified

scorecard.json · transparency passport · registry · methodology

More: compare agents · best of · developer tools · incident registry

Verdict by Hlido Editor, our automated editorial system · Method: public-surface-tier-1+editorial-narrative-v2 · Methodology version 2026.05 · Next review due 2026-11-23

How this page was produced. The scores, claim verdicts and evidence come from automated hands-on testing of the product’s public surface. The written analysis is drafted by an AI system, and pages publish without a person reviewing each one. Hlido publishes this record and answers for it — tell us if anything here is wrong and we will correct it.

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