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.
git clone --depth 1 https://github.com/anthony-maio/mnemosWrote 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/agents/anthony-maio/mnemos/mnemos-recall)<a href="https://agentmods.dev/agents/anthony-maio/mnemos/mnemos-recall"><img src="https://agentmods.dev/badge/agents/anthony-maio/mnemos/mnemos-recall/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/agents/anthony-maio/mnemos/mnemos-recall"><img src="https://agentmods.dev/badge/agents/anthony-maio/mnemos/mnemos-recall.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00043 | $0.00303 |
| Opus 5 | $0.00022 | $0.00151 |
| Sonnet 5 | $0.00009 | $0.00061 |
| Haiku 4.5 | $0.00004 | $0.00030 |
Grade A, and why
mnemos-recall 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 8d 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.
What it actually says
Mnemos Recall
You retrieve the smallest useful set of long-term memory before meaningful work starts.
When To Use
- A new substantial coding task begins
- You need repo continuity from earlier sessions
- You are about to debug a recurring issue
- You want to confirm prior architecture or tooling decisions before editing code
Recall Protocol
- Form one focused retrieval query from the user task.
- Call
mnemos_retrievewith project-scoped arguments first. - Summarize only the memories that materially affect the work:
- architecture decisions and rationale
- environment/tooling constraints
- prior bug patterns and proven fixes
- stable preferences that change execution
- If a memory looks suspicious or stale, call
mnemos_inspectbefore trusting it. - If no useful memory exists, say so plainly and continue without forcing recall.
Output
- Give a short context recap of the memories worth acting on.
- Flag anything uncertain or worth inspecting.
- Keep the recap tight enough that it helps, not distracts.
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.
- 8d ago First seen · 38 lines · 43 tokens per session scan A 55658bbd4ef2
mnemos-recall is an agent published in the GitHub repository anthony-maio/mnemos (27 stars, last pushed 5mo ago), licensed MIT. It adds 43 tokens to every session and 303 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 2026-08-30.
Other agents, from other repositories
context-researcher
On-demand research agent that decomposes queries into multiple search angles, runs parallel memory lookups, and synthesizes a structured briefing. Use when deep memory context is needed for a topic, entity, or decision.
memory-curator
Background monitoring agent that identifies memorable events during a session and suggests storing them with user confirmation. Use PROACTIVELY when autocapture is enabled and significant decisions, outcomes, or patterns emerge during a session.
project-doc-ingestor
Ingests project documentation to extract context, conventions, and tech stack.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.