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/rmarquesa/agentmemory-offline/agentmemory-agentsnpx skills add rmarquesa/agentmemory-offline --skill agentmemory-agentsgit clone --depth 1 https://github.com/rmarquesa/agentmemory-offlineWrote 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/rmarquesa/agentmemory-offline/agentmemory-agents)<a href="https://agentmods.dev/skills/rmarquesa/agentmemory-offline/agentmemory-agents"><img src="https://agentmods.dev/badge/skills/rmarquesa/agentmemory-offline/agentmemory-agents.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 | $0.00046 | $0.00371 |
| Opus 5 | $0.00023 | $0.00186 |
| Sonnet 5 | $0.00009 | $0.00074 |
| Haiku 4.5 | $0.00005 | $0.00037 |
Grade A, and why
agentmemory-agents 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 3d 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.
This is a copy
100% identical to agentmemory-agents — 0 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.
What it actually says
agentmemory connect <agent> merges the memory server into a host agent's config and preserves any existing servers. REST is the underlying protocol; for MCP-only hosts the adapter wires the stdio MCP bridge.
Quick start
agentmemory connect claude-code # or cursor, codex, gemini-cli, ...
After wiring, restart the host or run its MCP reload (for example /mcp in Claude Code) so it picks up the server. Then confirm the agent lists agentmemory's tools.
Workflow
- Detect the calling agent. If unknown, default to
claude-code. - Run
agentmemory connect <name>using a name from the table in REFERENCE.md. - Verify: the host should show the full tool set with a server running. Only 7 tools means the MCP shim could not reach a server (see ../_shared/TROUBLESHOOTING.md).
Notes
- The action skills (remember, recall, and the rest) are installed separately with
npx skills add rohitg00/agentmemory.connectmakes tools available; skills teach the agent when to use them. - Windows: use WSL2. Native Windows runs the server but
connectis not supported there.
See also
- agentmemory-mcp-tools, agentmemory-rest-api, agentmemory-hooks.
Reference
The full adapter list with display names and protocol notes lives in REFERENCE.md, generated from src/cli/connect/.
What ships with it
1 file 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.
- 3d ago First seen · 35 lines · 46 tokens per session scan A 208cbde6e1c0
agentmemory-agents is a skill published in the GitHub repository rmarquesa/agentmemory-offline (0 stars, last pushed 3d ago), licensed Apache-2.0. It adds 46 tokens to every session and 371 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agentmemory-agents, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
plur-memory
Persistent learning for AI agents. Open engram format. Your agent learns from corrections, remembers across sessions, and transfers knowledge across domains.
plur-session-end
Extract durable learnings at the end of a session. Saves corrections, preferences, and codebase patterns as engrams — nothing ephemeral, nothing sensitive.
plur-memory
Your memory stays on your machine. No cloud, no tracking, no API key. PLUR makes your OpenClaw remember — and shares that memory with every other tool you use.
compartmentalize
Sweep this conversation and save everything potentially worth knowing again into Compartment, the encrypted memory vault. Run it before compacting or summarizing so nothing is lost to the summary, or on its own at any point to bank the session.
paxm
Recall or remember durable project context with paxm.
reduce
Use when asked to simplify, clean up, tidy, or refactor code for clarity without changing what it does, or when the user says "simplify this", "clean this up", "make it readable", "reduce the complexity", or "tidy this". Behavior-preserving only; not a bug or security audit (use bug-hunt or security-sweep for those).