OpenClaw has built-in memory now. Do you still need PLUR?
OpenClaw has had built-in cross-session memory since mid-2026 — per-session notes indexed in a local SQLite database, keyword and vector search, and one-way imports from Codex and Claude Code. For basic session continuity inside OpenClaw, you no longer need a memory plugin. But OpenClaw's memory stays inside OpenClaw. If you use more than one AI tool, if you want memory that improves with feedback rather than just accumulates, or if you ever want to share or sell what your agent has learned, PLUR is still the layer you need.
What OpenClaw's built-in memory covers
OpenClaw's native memory stores observations from your sessions — what the agent did, what you asked for, project-level context — and makes them available in future conversations within OpenClaw. This is a genuine improvement: you no longer have to re-explain your codebase conventions to an OpenClaw agent at the start of every session.
What it does not do:
- Cross-tool sync — OpenClaw memory is scoped to OpenClaw. Your Claude Code agent, Cursor, Codex, and Hermes agents each start from scratch unless you run PLUR alongside.
- Learning over time — OpenClaw stores what happened; it does not improve the signal. PLUR's ACT-R-inspired activation and decay model strengthens frequently-confirmed facts and quietly fades outdated ones. The memory gets better with use.
-
Editable, auditable format — OpenClaw memory is opaque to outside tools. PLUR engrams are plain YAML files in
~/.plur/— readable, editable, version-controllable, portable. - Pack marketplace — PLUR's exchange layer lets you install pre-trained context packs (for a framework, a domain, a methodology) and, eventually, sell your own. OpenClaw has no equivalent.
The architecture difference
OpenClaw's native memory and PLUR solve adjacent but different problems:
| OpenClaw native memory | PLUR | |
|---|---|---|
| Scope | Inside OpenClaw | Shared across Claude Code, Cursor, Codex, Hermes, OpenClaw |
| Format | Platform-managed |
~/.plur/ YAML, open spec |
| Learning | Accumulates | ACT-R activation + decay — improves with feedback |
| Inspectable | Yes — Markdown files (MEMORY.md, memory/*.md); SQLite index is opaque | Yes — edit or delete any engram |
| Exchange | No | Pack marketplace (Phase 3) |
| Enterprise governance | No | Team scopes, provenance, org-level isolation |
PLUR and OpenClaw native memory are not mutually exclusive. Developers who install PLUR alongside OpenClaw get both: OpenClaw's fast in-platform recall, plus engrams that carry institutional knowledge across every tool they use.
When you only need OpenClaw memory
If you work exclusively in OpenClaw, your context is simple, and you have no interest in sharing knowledge across tools or agents, OpenClaw's built-in memory is likely enough. It is free, requires no setup, and covers the most common case: not re-explaining yourself at the start of a new conversation.
When PLUR adds the most value
- You use Claude Code, Cursor, Codex, or Hermes alongside OpenClaw and want a unified memory layer.
- Your team has hard-won conventions, decisions, or domain knowledge you want every agent to know — regardless of which tool they run in.
- You want memory that improves: confirmed facts activate, stale facts fade.
- You want to inspect, edit, or selectively forget what the AI remembers — with full auditability.
- You want to install a pre-trained context pack for a framework or domain instead of bootstrapping from scratch.
Getting started
npx @plur-ai/mcp init
This installs PLUR's MCP server, creates ~/.plur/, and wires the session hooks for your configured tools. Engrams written by one tool are readable by all of them. Full install guide →
If you're using OpenClaw specifically, you can also install PLUR as a native OpenClaw plugin: openclaw plugins install @plur-ai/claw — this wires PLUR's engram layer directly into OpenClaw's Context Engine.
Sources: OpenClaw memory concepts; OpenClaw built-in memory; PLUR engram spec; github.com/plur-ai/plur
