Memmy is a local memory hub that gives multiple AI agents one shared, persistent context about their user. It helps people continue work across agents such as Claude Code, Codex, OpenClaw, and Hermes Agent. The catalogue entries are skills for using Memmy with supported agents.
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/memtensor/memmy-agent/full-memorynpx skills add MemTensor/memmy-agent --skill full-memorygit clone --depth 1 https://github.com/MemTensor/memmy-agentWrote 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/memtensor/memmy-agent/full-memory)<a href="https://agentmods.dev/skills/memtensor/memmy-agent/full-memory"><img src="https://agentmods.dev/badge/skills/memtensor/memmy-agent/full-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 | $0.00034 | $0.00726 |
| Opus 5 | $0.00017 | $0.00363 |
| Sonnet 5 | $0.00007 | $0.00145 |
| Haiku 4.5 | $0.00003 | $0.00073 |
Grade A, and why
memmy-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 today.
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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memmy Memory CLI Skill
Use memmy-memory to read and write the shared Memmy memory substrate.
Ground Rules
- Prefer the configured CLI:
memmy-memory .... If the local service is not preconfigured, pass--url,--token, or--config. - The CLI prints JSON. Parse fields such as
sessionId,turnId,injectedContext,hits, and memoryidfrom JSON instead of relying on display formatting. - All agents share one memory database. Always pass
--source {{SOURCE_ARG}}on Memory CLI commands from this agent. - Use useful comma-separated
--tagswhen writing durable memories. - Do not store secrets, tokens, private keys, raw credentials, or bulky logs. Store concise, self-contained facts, decisions, preferences, reusable procedures, and unresolved follow-ups.
- Treat
<memmy_memory_context>as historical memory only and<current_user_request>as the authoritative current task. - Never store the context wrapper tags with
memmy-memory add; store only the durable fact itself. - If the memory service is unavailable, continue the task without inventing memory.
Health
memmy-memory health
Agent Loop
Open or resume a session before a task:
memmy-memory session open --source {{SOURCE_ARG}} --workspace-path "$PWD"
memmy-memory session open --source {{SOURCE_ARG}} --session-id "$SESSION_ID" --workspace-path "$PWD"
At the start of a user turn, retrieve relevant context:
memmy-memory turn start --source {{SOURCE_ARG}} --session-id "$SESSION_ID" --query "$USER_QUERY"
Use returned injectedContext as historical context only. Keep the returned turnId, and keep the current user query separate from recalled memory.
At the end of the turn, write the final interaction:
memmy-memory turn complete "$TURN_ID" --source {{SOURCE_ARG}} --session-id "$SESSION_ID" --query "$USER_QUERY" --answer "$FINAL_ANSWER" --status succeeded
Use --status failed or --status cancelled when the turn did not complete normally. Close the session when the Agent session is done:
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.
- today First seen · 86 lines · 34 tokens per session scan A 5538a7db0242
memmy-memory is a skill published in the GitHub repository MemTensor/memmy-agent (1,259 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 726 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 2026-09-05.
Other skills, from other repositories
add-memory-kind
Add a new business memory kind end-to-end. Pick the storage combination (Markdown / SQLite / LanceDB), pick the markdown strategy (daily-log / skill-named / single-file), then wire up the schema(s), repo(s), and writer(s).
browserwing-executor
Control browser automation through HTTP API. Supports page navigation, element interaction (click, type, select), data extraction, accessibility snapshot analysis, screenshot, JavaScript execution, and batch operations.
release
Cut a versioned release and publish everos to PyPI via the tag-triggered workflow.
dev-browser
Browser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website"…
commit
Stage and create a Conventional Commits message for the current change.
pr
Open a GitHub PR targeting the correct branch with the project template.