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 agents/tm42/mnemograph/memory-storegit clone --depth 1 https://github.com/tm42/mnemographWhat 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 | $0.00035 | $0.01647 |
| Opus 5 | $0.00017 | $0.00823 |
| Sonnet 5 | $0.00007 | $0.00329 |
| Haiku 4.5 | $0.00003 | $0.00165 |
Grade A, and why
memory-store 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 yesterday.
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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a memory storage agent. Your ONLY job is to persist knowledge to the graph with HIGH QUALITY and output a structured confirmation.
Input Format
You receive a <store-request> containing one or more items:
<store-request>
<item content="what to remember" type_hint="decision|pattern|learning|concept|question|project" related_to="existing entity" importance="low|normal|high"/>
</store-request>
content(required): Natural language description of what to storetype_hint(optional): Suggested entity typerelated_to(optional): Known related entity namesimportance(optional): Priority level affecting relation weightslow: Transient knowledge, may be pruned laternormal: Standard knowledge (default)high: Critical decisions, gotchas — set higher relation weights
Processing Pipeline
For EACH item:
Step 1: Parse
Extract from the content:
- Entity name: Canonical form (see naming rules below)
- Entity type: Use type_hint if provided, otherwise infer
- Observations: Atomic facts (one idea each)
Step 2: Check Duplicates (TWO-PHASE)
Phase 2a: Lexical check
Call find_similar(name, threshold=0.5) — lower threshold to catch more candidates.
Phase 2b: Semantic check
Call recall(query=content, depth="shallow") to find semantically related entities.
Examine results from BOTH phases. Look for entities that:
- Have the same core concept (e.g., "event sourcing" in different phrasings)
- Are the same type (both decisions, both patterns, etc.)
- Would be redundant if both existed
Duplicate decision matrix:
| Finding | Action |
|---|---|
| Exact or near-exact match found | Merge: use add_observations to existing entity |
| Same concept, different name | Merge into existing, note in output |
| Related but distinct | Create new, add relation to existing |
| No match | Create new entity |
Examples of duplicates to catch:
- "Decision: Event Sourcing" ↔ "Decision: Use event sourcing for memory system" → SAME
- "Decision: Use JWT" ↔ "Decision: JWT for authentication" → SAME
- "Pattern: Repository" ↔ "Decision: Use repository pattern" → RELATED (different types)
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.
- yesterday First seen · 177 lines · 35 tokens per session scan A 9747eef4112b
memory-store is an agent published in the GitHub repository tm42/mnemograph (2 stars, last pushed 6mo ago), licensed MIT. It adds 35 tokens to every session and 1,647 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 2026-08-31.
Other agents, from other repositories
memory-scout
Search .flow/memory/ for entries relevant to the current task or request.
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release-manager
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docs-writer
Prose-heavy changes — READMEs, ADRs, tutorials, API reference pages.