agentMemory

A persistent, searchable knowledge store for AI agents that saves project information and decisions alongside project documentation.

In plain words
What is it for?
Use it to save and retrieve patterns, decisions, and other project knowledge with searches, tags, and categories.
Why use it?
It reduces the need to rediscover the same project context in later sessions by keeping useful information available for search.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/webzler/agentmemory/agentmemory
Any agent
npx skills add webzler/agentMemory --skill agentmemory
Clone the repo
git clone --depth 1 https://github.com/webzler/agentMemory

Made for: Claude Code, Codex.

Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 553 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 86% copy Near-identical to another mod 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 $0.00018 $0.00553
Opus 5 $0.00009 $0.00277
Sonnet 5 $0.00004 $0.00111
Haiku 4.5 $0.00002 $0.00055

Measured 2d ago against content hash 17160641b60b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agentMemory 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 2d 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.

Origin

This is a copy

86% identical to agent-memory — 19 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

agentMemory Skill

This skill extends your capabilities by providing a persistent, searchable memory bank that automatically syncs with project documentation.

Prerequisites

  • Node.js installed
  • Check if agentMemory is already installed in the project:
    ls -la .agentMemory
    

Setup

  1. Install Dependencies:

    npm install
    
  2. Build the Project:

    npm run compile
    
  3. Start the Memory Server: You need to run the MCP server to interact with the memory bank.

    npm run start-server <project_id> <absolute_path_to_workspace>
    

    Note: This skill typically runs as a background process or via an mcp-server configuration. ensuring it is running is key.

Capabilities (MCP Tools)

Once the server is running, you can use these tools:

memory_search

Search for memories by query, type, or tags.

  • Args: query (string), type? (string), tags? (string[])
  • Usage: "Find all authentication patterns" -> memory_search({ query: "authentication", type: "pattern" })

memory_write

Record new knowledge or decisions.

  • Args: key (string), type (string), content (string), tags? (string[])
  • Usage: "Save this architecture decision" -> memory_write({ key: "auth-v1", type: "decision", content: "..." })

memory_read

Retrieve specific memory content by key.

  • Args: key (string)
  • Usage: "Get the auth design" -> memory_read({ key: "auth-v1" })

memory_stats

View analytics on memory usage.

  • Usage: "Show memory statistics" -> memory_stats({})

Workflow

  1. Initialization: The first time you run this in a project, it may attempt to import existing markdown memory banks from .kilocode/, .clinerules/, or .roo/.
  2. Development Loop:
    • Before Task: Search memory for relevant context.
    • During Task: Use read/search to answer questions.
    • After Task: Write new findings to memory.
  3. Sync: Your writes are automatically synced to standard markdown files in the project.

Read the full file on GitHub · 68 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. 2d ago First seen · 68 lines · 18 tokens per session scan A 17160641b60b

Subscribe to this mod's changes

agentMemory is a skill published in the GitHub repository webzler/agentMemory (32 stars, last pushed 7mo ago), licensed MIT. It adds 18 tokens to every session and 553 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to agent-memory, differing in 19 lines, and is treated as a copy.

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