Why Praxis
Why not just use Claude's built-in memory?
Fair question. Built-in memory in Claude (and Cursor, and ChatGPT) is genuinely useful. It is zero-setup, free, and good at remembering within a single tool. Here is what it does not do — and where Praxis is a different category of thing rather than a competing feature.
1. Cross-harness
Claude's memory works for Claude. Cursor's memory works for Cursor. Neither works across both. Switch tools, or use different ones for different tasks, and the context does not follow.
Praxis sits one layer up — at the harness level, not inside it. Every tool consults the same sleeve, the same memory corpus, the same distillation pipeline. Switch from Claude to Cursor mid-project and your agent still knows you prefer concise responses, still remembers that the service client was refactored last Tuesday, still defaults to your verbosity setting. Model-agnostic, tool-agnostic, by construction.
2. Team intelligence without surveillance
Built-in memory is per-user. There is no team dimension — it cannot see that your colleague hit the same bug last month, cannot surface that your team usually deletes the default test file when scaffolding a Redux slice, cannot aggregate calibration across people.
Praxis has team-scope shared intelligence (the hivemind) with an architectural 5-developer aggregate floor. Below that threshold, the pipeline refuses to produce a result. The floor is enforced at the query layer, not at the admin settings panel.
The 5-dev floor is epistemic hygiene as much as it is a privacy guarantee. Below 5 developers, aggregates are not statistically meaningful — they are just individual patterns with a team label. The constraint exists because we do not want managers pulling reports that look like team-level insight but are actually surveillance on the one person whose traffic dominates the aggregate.
3. Deliverable attribution
Built-in memory remembers what you talked about. Praxis knows what you shipped.
The distinction is not cosmetic. "We spent $400 on Claude last month" is the question every EM is being asked. "That $400 shipped PRAX-243, PRAX-247, and PRAX-251, two of which were merged on the first review and one of which needed rework" is the answer that changes what the EM says in the next planning meeting. Built-in memory cannot answer that because it has no concept of a shipping artifact, no connection to your VCS, no ticket awareness.
Praxis, via OAuth-connected GitHub and Jira, ties AI spend to merged work. The difference between recall and intelligence.
4. Compounding across the team
Built-in memory does not compound across users. Your teammate solving the bug you are about to hit leaves no trace in your memory bank. The next person who writes a Redux slice re-encounters the same default-test problem your team silently worked around six months ago.
Praxis's hivemind compounds — calibration signal from sleeves similar to yours improves defaults, surfaced patterns get stronger as more people exhibit them, a new hire's first day is not from zero. The more the team uses it, the better it gets for the team.
Honest about the current state: compounding kicks in meaningfully around 5+ developers over 30+ days. Below that, you get a good personal sleeve and calibration — not a flywheel. We are not going to pretend the flywheel is running on day one because that would be obvious the moment you tried it.
Category, not competition
Reading this as "Praxis vs. Claude memory" misses the point. Built-in memory is personal recall inside a single tool. Praxis is team-scale intelligence infrastructure across every tool. Those are not substitutes. The tool-vendors do the first one well and will continue to. The second one is structurally not their product — a tool vendor that built a neutral cross-vendor memory layer would undermine its own lock-in.
That is the gap Praxis exists to fill. Personal memory stays in your tools. Team-scale intelligence lives at the layer that sees across them.