| name | plur-memory |
| description | Persistent learning for AI agents. Open engram format. Your agent learns from corrections, remembers across sessions, and transfers knowledge across domains. |
| version | 0.9.0 |
| metadata | {"hermes":{"tags":["memory","learning","knowledge","engrams"],"category":"productivity","requires_toolsets":[]}} |
PLUR Memory
Persistent memory for AI agents. Corrections, preferences, and patterns are stored as engrams that strengthen with use and decay when irrelevant. The system gets smarter the longer you use it.
When to Use
Always. Memory is not a feature you toggle — it's a layer that runs continuously.
The plugin automatically injects relevant engrams into every conversation turn via the pre_llm_call hook. You don't need to call plur_inject manually unless you want full hybrid search (the automatic path uses fast BM25 search).
Memory Lifecycle
- Automatic injection runs every turn — relevant engrams appear in your context as
<plur-memory> blocks
- When you discover something worth remembering → call
plur_learn with a clear statement
- When corrected by the user → call
plur_learn immediately with the correction
- When an injected engram was helpful → call
plur_feedback with signal "positive"
- When an injected engram was wrong or stale → call
plur_feedback with signal "negative"
- When a memory is no longer true → call
plur_forget with the engram ID
The Learning Protocol
End your responses with a learning section when you discover reusable insights:
---
🧠 I learned:
- Insight one (min 10 characters)
- Insight two
The plugin auto-captures these — no manual plur_learn call needed. This is a convenience fallback; calling plur_learn directly is preferred for important learnings.
Getting Started
On first install, PLUR has zero engrams — injection returns empty. This is expected.
Your first 5 sessions are the bootstrap period. Actively learn:
- Call
plur_learn for every correction the user makes
- Call
plur_learn for stated preferences ("always use X", "never do Y")
- Call
plur_learn for discovered patterns and conventions
After ~20 engrams, injection starts returning useful context automatically. To accelerate, install a community pack via plur_packs_install.
Meta-Engram Extraction
Periodically run plur_extract_meta to distill cross-domain principles from your engrams.
The extraction is a multi-turn conversation:
- Call
plur_extract_meta — returns analysis prompts with "status": "prompts_ready"
- Process each prompt using your reasoning
- Call
plur_meta_submit_analysis with your responses as {"responses": [...]}
- Repeat steps 2-3 until you receive
{"status": "complete"}
If you call plur_meta_submit_analysis with no active pipeline, you'll get {"status": "no_active_pipeline"} — call plur_extract_meta first.
Meta-engrams are the highest-value knowledge: principles that transfer across domains.
What NOT to Learn
- Trivial facts ("the user said hello")
- Things already in the codebase (file paths, function names — those change)
- Session-specific state ("we're working on X right now")
- Anything you're not confident about
What to Learn
- Corrections: "The API returns snake_case, not camelCase"
- Preferences: "User prefers TypeScript over JavaScript"
- Patterns: "This codebase uses repository pattern for data access"
- Decisions: "We chose PostgreSQL for ACID compliance"
- Conventions: "Always run lint before committing"
