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/matrixorigin/memoria/api-referencenpx skills add matrixorigin/memoria --skill api-referencegit clone --depth 1 https://github.com/matrixorigin/memoriaWhat 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 | $0.00027 | $0.02678 |
| Opus 5 | $0.00014 | $0.01339 |
| Sonnet 5 | $0.00005 | $0.00536 |
| Haiku 4.5 | $0.00003 | $0.00268 |
Grade A, and why
api-reference 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 — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory CRUD
List: GET /v1/memories?limit=50&cursor=...&memory_type=semantic
Response: { "items": [...], "next_cursor": "..." }
Store: POST /v1/memories
{ "content": "...", "memory_type": "semantic", "session_id": null }
Returns 201 with MemoryResponse.
Types: semantic (default), profile, procedural, working, tool_result
Batch Store: POST /v1/memories/batch
{ "memories": [{ "content": "..." }, { "content": "...", "memory_type": "profile" }] }
Retrieve: POST /v1/memories/retrieve
Hybrid vector + fulltext search, ranked by relevance.
{ "query": "...", "top_k": 10, "memory_types": ["semantic"], "session_id": null, "explain": false }
explain: false | true (timing) | "verbose" (detailed) | "analyze" (full diagnostics)
Search: POST /v1/memories/search
{ "query": "...", "top_k": 10, "explain": false }
Same as retrieve but without session prioritization.
Full-text Search: POST /v1/memories/fulltext-search
Pure MatrixOne lexical full-text search with optional exact SQL pre-filters. It does not generate embeddings or run vector, graph, hybrid, temporal, or confidence scoring and is intentionally not exposed as an MCP tool.
{
"query": "MatrixOne database",
"extra_metadata_filter": {"scene": "incident", "rank": 2},
"subject_id": "subject-123",
"memory_types": ["semantic"],
"session_id": "session-123",
"trust_tier": "T2",
"branch": "main",
"limit": 20
}
All supplied filters use AND. session_id is strict: unscoped memories with
session_id: null are not included. This differs from retrieve/search session
scoping, which can include unscoped memories. Metadata equality preserves JSON
type families: number 2 may equal 2.0, while string "2" does not equal
number 2. Query length is limited to 4096 UTF-8 bytes and limit to 1–100.
Returns a plain memory array ordered by MatrixOne full-text score and then
memory_id; each result exposes the score as retrieval_score.
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 · 277 lines · 27 tokens per session scan A 57ff95849738
api-reference is a skill published in the GitHub repository matrixorigin/memoria (591 stars, last pushed 6d ago), licensed Apache-2.0. It adds 27 tokens to every session and 2,678 once invoked, about $0.0001 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.
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