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/secondlifes/code-intel/memory-usage-optimizationnpx skills add SecondLifes/code-intel --skill memory-usage-optimizationgit clone --depth 1 https://github.com/SecondLifes/code-intelWhat 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 | $0.00082 | $0.01411 |
| Opus 5 | $0.00041 | $0.00705 |
| Sonnet 5 | $0.00016 | $0.00282 |
| Haiku 4.5 | $0.00008 | $0.00141 |
Grade A, and why
qdrant-memory-usage-optimization 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.
This is a copy
100% identical to qdrant-memory-usage-optimization — 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.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Understanding memory usage
Qdrant operates with two types of memory:
-
Resident memory (aka RSSAnon) - memory used for internal data structures like the ID tracker, plus components that stay fully in RAM. On Qdrant 1.19 or newer this is controlled per-component with
memory: pinned(e.g. quantized vectors, payload indexes); on 1.18 or older the equivalent isalways_ram: true. -
OS page cache - memory used for caching disk reads, which can be released when needed. Original vectors are normally stored in page cache, so the service won't crash if RAM is full, but performance may degrade. On Qdrant 1.19 or newer this corresponds to
memory: cached(pre-warmed into page cache at startup) ormemory: cold(lazy disk reads, not pre-warmed); on 1.18 or older it's controlled via theon_diskboolean on vectors, HNSW config, sparse vector index, and payload index. See Memory Tiers docs (available on 1.19+).
It is normal for the OS page cache to occupy all available RAM, but if resident memory is above 80% of total RAM, it is a sign of a problem.
Memory usage monitoring
- Qdrant exposes memory usage through the
/metricsendpoint. See Monitoring docs.
How much memory is needed for Qdrant?
Optimal memory usage depends on the use case.
- For regular search scenarios, general guidelines are provided in the Capacity planning docs.
For a detailed breakdown of memory usage at large scale, see Large scale memory usage example.
Payload indexes and HNSW graph also require memory, along with vectors themselves, so it's important to consider them in calculations.
Additionally, Qdrant requires some extra memory for optimizations. During optimization, optimized segments are fully loaded into RAM, so it is important to leave enough headroom.
The larger max_segment_size is, the more headroom is needed.
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 · 68 lines · 82 tokens per session scan A 1915009666b5
qdrant-memory-usage-optimization is a skill published in the GitHub repository SecondLifes/code-intel (2 stars, last pushed 21d ago), licensed Apache-2.0. It adds 82 tokens to every session and 1,411 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to qdrant-memory-usage-optimization, differing in 0 lines, and is treated as a copy.
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