ScreenJournal vs Pieces

Updated on 10 July 2026

Pieces is a personal AI memory: it captures your own workflow context so your assistant can recall it for you. ScreenJournal is a team work visibility tool: it reads on-screen work across a whole team, writes a timeline of what each person did, then deletes the raw screen data.

The two sound related because both turn on-screen activity into a memory an AI can use. The difference is who that memory serves. Pieces feeds your own assistant. ScreenJournal informs the people running the team, and the team itself.

ScreenJournal is an AI work visibility tool that reads on-screen work as it happens, turns it into a detailed timeline of what each person actually did, and then deletes the raw screen data. Timelines accumulate into a searchable chronicle of everyone's work history, and from them ScreenJournal generates timesheets and reports automatically and drafts standup summaries on request, answering questions about any of it in plain English.

It also records and transcribes call and meeting audio, which it keeps as a business record and analyses alongside the on-screen work.

What is Pieces?

Pieces, long marketed as Pieces for Developers and now positioned more broadly as an AI memory companion, is a desktop app built around a Long-Term Memory engine that captures workflow context from the apps, browsers and IDEs you use. Its documentation describes capture at the operating-system level using OCR among other techniques, with vision, clipboard and audio listed as sources, and summaries created every 20 minutes. Memories are stored locally, retained for around nine months per its published materials, and power a timeline, conversational search and a copilot chat that typically uses cloud models, with local and bring-your-own-model options publicly described. Users can pause capture, disable it, or exclude individual apps, and Pieces states that it filters secrets and PII from captured text, never trains on user data, and holds SOC 2 Type II certification per its docs. It runs on Windows, macOS and Linux, ships plugins for VS Code, JetBrains and the browser, and exposes its memory to tools like GitHub Copilot, Cursor and Claude over MCP. A Teams tier with shared memory is publicly offered via sales contact.

How do Pieces and ScreenJournal compare?

Both build a durable memory from on-screen work, so the comparison comes down to who the memory serves and what happens to the raw data.

Assistant memory vs team visibility

Pieces exists to make your own AI assistant smarter about you; ScreenJournal exists to make a team's work visible to the people responsible for it. Everything in Pieces points at its owner: the memories live on your machine, the timeline is yours, and the MCP integrations feed your context to your coding assistant. Its Teams tier is publicly described as shared memory and context, and no manager dashboards, per-person reporting or aggregate analytics are publicly documented, so even the team offering is about shared knowledge rather than oversight. ScreenJournal is the other product: per-person timelines written from the work, roles and permissions, a role-normalised Effort Score, weekly AI reports with rankings, risks and action items, and timesheets prepared from the same record. Both expose MCP, and the difference carries through it: Pieces hands your assistant your own memory, while ScreenJournal answers questions about the whole team's work, permission-scoped by role.

What happens to the raw screen data?

Pieces keeps a personal memory on your device; ScreenJournal keeps a team record and deletes the raw screen data. Pieces publicly describes on-device processing, a local store of memories retained for around nine months, and filtering of secrets and PII from captured text; it does not publicly detail whether raw screen images are kept after the text is extracted. ScreenJournal is explicit about the whole path: the screen is recorded as short-lived video, the work is read from it, and the video is deleted immediately during processing. What survives is the derived timeline, and the team's history accumulates as the most recent 12 months of derived work history in the chronicle, searchable in plain English and permission-scoped by role. Call and meeting audio is the exception on both sides: Pieces lists audio among its capture sources per its docs, and ScreenJournal retains call and meeting audio as a business record rather than discarding it. The screen-data principle is described in derive and discard.

When is Pieces the right choice?

Pieces is the right choice when you want your own assistant to remember your work.

• You are a developer or knowledge worker who wants AI recall of your own workflow context.
• You want that memory kept local-first on your own machine, with pause and per-app exclusions under your control.
• You want to feed your context to your coding assistant over MCP, in your editor or browser.
• Your team wants shared knowledge and context rather than work visibility, reporting or timesheets.

When is ScreenJournal the right choice?

ScreenJournal is the right choice when the team's work, not one person's context, is what needs remembering.

• You manage a team and need to know what was produced, across everyone.
• You want timesheets, weekly reports and plain-English answers generated from the work itself.
• You run a call centre or outsourcing operation where voice is part of the work.
• You want a record employees can also use and contest, with redaction that erases entries entirely.
• You want derived data with the raw screen data deleted, not an archive to secure.

Frequently asked questions

Is Pieces a team monitoring or work-visibility tool?

No. Pieces is a personal AI memory companion: it captures your own workflow context so your assistant can recall it for you. Its Teams tier is publicly described as shared memory and context, and no manager dashboards, per-person reporting or aggregate analytics are publicly documented. ScreenJournal is built for team work visibility, with per-person timelines, roles, reports and timesheets.

Does ScreenJournal keep a long-term memory like Pieces?

Yes, but for a different audience. Pieces keeps around nine months of personal memories on your device, per its published materials, to give your own AI assistant context. ScreenJournal's chronicle keeps the most recent 12 months of derived work history for the whole team, searchable in plain English through chat and MCP and permission-scoped by role.

Does Pieces store screen recordings?

Pieces publicly describes capturing text from your screen with OCR and storing memories locally; it does not publicly detail whether raw screen images are retained. ScreenJournal is explicit: the screen is recorded as short-lived video, the work is read from it, and that raw screen data is deleted immediately during processing, leaving only the derived timeline.

Does ScreenJournal delete call and meeting audio the way it deletes screen data?

No. Derive-and-discard applies only to the screen. Call and meeting audio is captured, transcribed and retained as a business record, typically 12 months by default and adjustable where a client's compliance requires. Employees can redact voice entries and switch capture off, and playback is scoped by role and logged.

Memory for your assistant, or memory for the team

Pieces is a thoughtful take on personal AI memory, and for an individual developer it solves a real problem: your assistant finally knows what you were doing. But a team's work deserves a record of its own, one that managers can report from, employees can contest, and the business can search long after everyone has forgotten the details. ScreenJournal reads the work, writes the timeline, deletes the raw screen data and keeps the answers a team actually needs. For the wider market view, see ScreenJournal vs the alternatives.