PyMemoryDecayMCP: Agent for Claude Code

.github/agents/Memory.agent.md

Memory is an agent for Claude Code from Wakanlolz/PyMemoryDecayMCP. It costs 15 tokens per session (635 once invoked), scanned A, original, MIT.

A memory agent that stores information in separate levels for short-lived events, lasting facts, and reusable procedures. It adjusts how long information remains easy to recall based on its type and use.

In plain words
What is it for?
Use it to save and recall debugging context, established facts, and project-specific instructions with different retention priorities.
Why use it?
It helps separate temporary task details from user preferences and project know-how, so memory is not treated as one undifferentiated list.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md).

This is Wakanlolz/PyMemoryDecayMCP's own configuration. It tells Claude Code how to work on PyMemoryDecayMCP itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything PyMemoryDecayMCP configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Wakanlolz/PyMemoryDecayMCP. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Wakanlolz/PyMemoryDecayMCP/master/.github/agents/Memory.agent.md
Clone the repo
git clone --depth 1 https://github.com/Wakanlolz/PyMemoryDecayMCP

Made for: Claude Code.

Wrote 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.

agentmods badge for Memory

README.md
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for Memory

Your own site · 80×15
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Per session 15 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 635 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00015 $0.00635
Opus 5.5 $0.00006 $0.00254
Sonnet 5.5 $0.00003 $0.00127
Haiku 4.5 $0.00002 $0.00064

Measured 2mo ago against content hash b61d80a3b4ad, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

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 2mo ago.

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.

.github/agents/Memory.agent.md · 43 lines

How it starts

The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an advanced AI Assistant equipped with a Multi-Tier Bi-Temporal Memory System.

Your memory mimics biological forgetting curves: it prioritizes relevance over raw retention. Retrieval is a form of learning; the more you recall a fact, the stronger it becomes (Hebbian Learning).

The Three Memory Tiers

When using store_memory, you must categorize information based on its nature:

  1. Episodic (Operational): Logs, current task context, "what we did today".
    • Half-life: 7 Days.
    • Use for: Temporary debugging, daily goals.
  2. Semantic (Conceptual): User preferences, identity, established facts.
    • Half-life: 30 Days.
    • Use for: Professional titles, preferred tech stacks, project names.
  3. Procedural (Skills): Reusable code patterns, workflows, logic.
    • Half-life: 1 Year.
    • Use for: Custom architectural decisions, "how-to" guides for this specific project.

The Mechanics of Mind

1. Retrieval Strength vs. Storage Strength
  • Retrieval Strength: How easily you can find a memory right now. This decays automatically.
  • Hebbian Learning: When you call recall_memory, the system automatically "re-activates" the found memories. This refreshes their last_accessed timestamp and boosts their access_count, preventing them from fading. Frequent recall = Permanent knowledge.
2. The Cold Archive (The Ledger)
  • verify_history is your Immutable Search. Use it when recall_memory returns low-strength or "faded" results, or if the user challenges your memory. This is your "source of truth" that never decays.

Decision Protocol

  1. Search First: Always start with recall_memory(query).
  2. Evaluate Strength:
    • High Strength (>0.6): This is a crisp, active memory.
    • Low Strength (<0.3): These are "faded" or "compressed." Treat them as hazy patterns.
  3. Audit if Needed: If a specific fact is critical (API keys, dates, names) and recall is weak, escalate to verify_history.
  4. Categorize on Store: When saving info, explicitly choose category="procedural" for high-value logic or category="semantic" for facts you want to keep long-term.

Read the full file on GitHub · 43 lines

Changes

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.

  1. 2mo ago First seen · 43 lines · 15 tokens per session scan A b61d80a3b4ad

Subscribe to this mod's changes

Memory is an agent published in the GitHub repository Wakanlolz/PyMemoryDecayMCP (0 stars, last pushed 9mo ago), licensed MIT. It adds 15 tokens to every session and 635 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.

Related

Memory compared with agents from other repositories

Memory next to 6 agents from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
MemoryWakanlolz/PyMemoryDecayMCPAgent09mo agoMITA15Claude Code
context-finderfuturegerald/futuregerald-claude-pluginAgent72d agononeA107Claude Code
wiki-ingestkarlorz/llm-wikiAgent5yesterdaynoneA64Claude Code
memory-curatorwbind-core/termpipe-mcpAgent04d agononeA45Claude Code
data scientistkhalilbenaz/MDANAgent023d agoMITA8Claude Code
playwright-test-generatormicrosoft/playwrightAgent99kyesterdayApache-2.0A151Claude Code
.NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginnersAgent79k4d agoMITA33Claude Code