Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/plur-ai/plur/plur-memorynpx skills add plur-ai/plur --skill plur-memorygit clone --depth 1 https://github.com/plur-ai/plurWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/plur-ai/plur/plur-memory)<a href="https://agentmods.dev/skills/plur-ai/plur/plur-memory"><img src="https://agentmods.dev/badge/skills/plur-ai/plur/plur-memory.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.01538 |
| Opus 5 | $0.00015 | $0.00769 |
| Sonnet 5 | $0.00006 | $0.00308 |
| Haiku 4.5 | $0.00003 | $0.00154 |
Grade A, and why
plur-memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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_learnwith a clear statement - When corrected by the user → call
plur_learnimmediately with the correction - When an injected engram was helpful → call
plur_feedbackwith signal "positive" - When an injected engram was wrong or stale → call
plur_feedbackwith signal "negative" - When a memory is no longer true → call
plur_forgetwith 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_learnfor every correction the user makes - Call
plur_learnfor stated preferences ("always use X", "never do Y") - Call
plur_learnfor 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed f4d03be487d0
- 5d ago First seen · 129 lines · 31 tokens per session scan A e9b5c14d59cc
plur-memory is a skill published in the GitHub repository plur-ai/plur (245 stars, last pushed today), licensed Apache-2.0. It adds 31 tokens to every session and 1,538 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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honcho-integration
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verify
Build, launch, and drive a local Honcho stack to verify a change at its runtime surface (the /v3 HTTP API and the deriver queue). Use when verifying a diff or confirming a change works in the running app.
honcho-memory
Gives AI agents persistent memory across conversations using Honcho. Automatically saves and retrieves user context so the AI remembers preferences, history, and facts between sessions. Use when you need the AI to remember past conversations, recall what a user has told it, inject relevant context into prompts, or…
honcho-cli
Inspect and debug Honcho workspaces via the honcho CLI. Use when investigating peer representations, memory state, session context, or dialectic quality — any task that requires introspection of a Honcho deployment, including verifying that a recall/record memory loop is actually working.