agentmemory is a persistent-memory system that lets AI coding agents retain and search information across sessions. It connects with several coding agents and MCP clients, storing knowledge with confidence scoring, lifecycle handling, knowledge graphs, and hybrid search. The catalogue contains agentmemory integrations such as skills, hooks, commands, a plugin, and an MCP component.
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 skills add rohitg00/agentmemory --skill remembergit clone --depth 1 https://github.com/rohitg00/agentmemoryWrote 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/rohitg00/agentmemory/remember)<a href="https://agentmods.dev/skills/rohitg00/agentmemory/remember"><img src="https://agentmods.dev/badge/skills/rohitg00/agentmemory/remember/github.svg" alt="Measured on agentmods" height="20"></a>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.
<a href="https://agentmods.dev/skills/rohitg00/agentmemory/remember"><img src="https://agentmods.dev/badge/skills/rohitg00/agentmemory/remember.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00052 | $0.00594 |
| Opus 5.5 | $0.00021 | $0.00238 |
| Sonnet 5.5 | $0.00010 | $0.00119 |
| Haiku 4.5 | $0.00005 | $0.00059 |
Grade A, and why
remember 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The user wants to save this to long-term memory: $ARGUMENTS
Quick start
memory_save {
"content": "We rotate JWT refresh tokens on every use; the old token is revoked server-side in auth/refresh.ts.",
"concepts": "jwt-refresh-rotation, token-revocation, auth-flow",
"files": "src/auth/refresh.ts"
}
Expected output:
Saved memory abc12345 with 3 concepts: jwt-refresh-rotation, token-revocation, auth-flow.
Why
A memory is only as useful as the terms that retrieve it. Tag with specific
concepts so a future recall finds it, and preserve the user's own phrasing.
Workflow
- Pull the core insight, decision, or fact out of
$ARGUMENTS. - Extract 2-5 lowercased concept phrases. Prefer specific over generic
(
jwt-refresh-rotationbeatsauth). - Extract referenced file paths (absolute or repo-relative). Empty if none.
- Call
memory_savewithcontent,concepts(comma-separated string), andfiles(comma-separated string). In a multi-agent setup passagentIdso the memory lands in the right agent's scope. - Confirm the save and echo the concepts so the user knows the retrieval terms.
- To update a fact, save the corrected version outright: near-duplicate content supersedes the old record, which leaves recall but stays in the version chain.
Anti-patterns
WRONG: concepts: "stuff, code, notes" (generic tags nothing can find later).
RIGHT: concepts: "jwt-refresh-rotation, token-revocation" (specific, retrievable).
Checklist
- Content preserves the user's phrasing, not a paraphrase.
- Concepts are specific, lowercased, 2-5 items.
- File paths are real references, not guesses.
- Confirmation echoes the exact concepts tagged.
See also
recall: retrieve what you save here (the pair to this skill).forget: remove a memory you saved by mistake.lesson: behavioral rules from corrections; memories are for facts.memory-discipline: when to save unprompted.
Troubleshooting
See TROUBLESHOOTING.md if memory_save is not available.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago Changed 78fb0b4c239a
- 1mo ago First seen · 67 lines · 52 tokens per session scan A d9b6facf4cbe
remember is a skill published in the GitHub repository rohitg00/agentmemory (29,653 stars, last pushed yesterday), licensed Apache-2.0. It adds 52 tokens to every session and 594 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
hindsight-local
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user).
memory-retain
Save what was learned to Hindsight long-term memory. Use when a task finishes, when the user states a fact, decision or preference, and when you discover something a later run or another Bot would need.
last30Days
Resolve "last30Days" to a concrete ISO date range relative to your run time — a rolling 30-day window ending today. Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a "last 30 days" / trailing-month task…
thisWeek
Resolve "thisWeek" to a concrete ISO date range relative to your run time — this week so far (Monday → today). Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a week-to-date task (this week's activity…
kb-research
Structured approach to finding and synthesizing information from the user's knowledge base.
sl_capture
How to capture new reusable patterns into ktx's semantic layer - when a measure, segment, or join belongs in the catalog and how to write it generically so it stays small and useful over time. Loaded by the post-turn memory-agent only. The research agent does not write to the SL.