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 skills add ayushcodes10/echo-mem --skill agentdb-optimizationgit clone --depth 1 https://github.com/ayushcodes10/echo-memWrote 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/ayushcodes10/echo-mem/agentdb-optimization)<a href="https://agentmods.dev/skills/ayushcodes10/echo-mem/agentdb-optimization"><img src="https://agentmods.dev/badge/skills/ayushcodes10/echo-mem/agentdb-optimization/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/skills/ayushcodes10/echo-mem/agentdb-optimization"><img src="https://agentmods.dev/badge/skills/ayushcodes10/echo-mem/agentdb-optimization.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 | $0.00053 | $0.03268 |
| Opus 5.5 | $0.00021 | $0.01307 |
| Sonnet 5.5 | $0.00011 | $0.00654 |
| Haiku 4.5 | $0.00005 | $0.00327 |
Grade A, and why
AgentDB Performance 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 1mo 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.
This is a copy
100% identical to AgentDB Performance 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 — 510 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentDB Performance Optimization
What This Skill Does
Provides comprehensive performance optimization techniques for AgentDB vector databases. Achieve 150x-12,500x performance improvements through quantization, HNSW indexing, caching strategies, and batch operations. Reduce memory usage by 4-32x while maintaining accuracy.
Performance: <100µs vector search, <1ms pattern retrieval, 2ms batch insert for 100 vectors.
Prerequisites
- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow)
- Existing AgentDB database or application
Quick Start
Run Performance Benchmarks
# Comprehensive performance benchmarking
npx agentdb@latest benchmark
# Results show:
# ✅ Pattern Search: 150x faster (100µs vs 15ms)
# ✅ Batch Insert: 500x faster (2ms vs 1s for 100 vectors)
# ✅ Large-scale Query: 12,500x faster (8ms vs 100s at 1M vectors)
# ✅ Memory Efficiency: 4-32x reduction with quantization
Enable Optimizations
import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';
// Optimized configuration
const adapter = await createAgentDBAdapter({
dbPath: '.agentdb/optimized.db',
quantizationType: 'binary', // 32x memory reduction
cacheSize: 1000, // In-memory cache
enableLearning: true,
enableReasoning: true,
});
Quantization Strategies
1. Binary Quantization (32x Reduction)
Best For: Large-scale deployments (1M+ vectors), memory-constrained environments Trade-off: ~2-5% accuracy loss, 32x memory reduction, 10x faster
const adapter = await createAgentDBAdapter({
quantizationType: 'binary',
// 768-dim float32 (3072 bytes) → 96 bytes binary
// 1M vectors: 3GB → 96MB
});
Use Cases:
- Mobile/edge deployment
- Large-scale vector storage (millions of vectors)
- Real-time search with memory constraints
Performance:
- Memory: 32x smaller
- Search Speed: 10x faster (bit operations)
- Accuracy: 95-98% of original
2. Scalar Quantization (4x Reduction)
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.
- 1mo ago First seen · 510 lines · 53 tokens per session scan A 0438ee74207a
AgentDB Performance Optimization is a skill published in the GitHub repository ayushcodes10/echo-mem (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 53 tokens to every session and 3,268 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to AgentDB Performance Optimization, differing in 0 lines, and is treated as a copy.
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