How long does the integration take?

Change one base URL in an OpenAI-compatible client, or install the Python or TypeScript SDK; the quickstart routes a first request in a few minutes.

Will this lock us into another vendor?

No. The router is Apache-2.0 open source, your provider keys stay yours, and the API is OpenAI-compatible, so leaving is a change of base URL.

What happens when a provider fails?

The router falls back to the next model your policy allows and records the fallback in the trace, so an outage costs a retry, not an incident.

agentdb-vector-search-optimization - Skill

Name: agentdb-vector-search-optimization
Author: aiskillstore

Imported from aiskillstore/marketplace (skills/dnyoussef/agentdb-vector-search-optimization/SKILL.md). Install upstream with npx skills add aiskillstore/marketplace --skill agentdb-vector-search-optimization. Copyright stays with the author.

AgentDB Vector Search Optimization

Overview

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations for scaling to millions of vectors.

SOP Framework: 5-Phase Optimization

Phase 1: Baseline Performance (1 hour)

Measure current metrics (latency, throughput, memory)
Identify bottlenecks
Set optimization targets

Phase 2: Apply Quantization (1-2 hours)

Configure product quantization
Train codebooks
Apply compression
Validate accuracy

Phase 3: Implement HNSW Indexing (1-2 hours)

Build HNSW index
Tune parameters (M, efConstruction, efSearch)
Benchmark speedup

Phase 4: Configure Caching (1 hour)

Implement query cache
Set TTL and eviction policies
Monitor hit rates

Phase 5: Benchmark Results (1-2 hours)

Run comprehensive benchmarks
Compare before/after
Validate improvements

Quick Start

import { AgentDB, Quantization, QueryCache } from 'agentdb-optimization';

const db = new AgentDB({ name: 'optimized-db', dimensions: 1536 });

// Quantization (4x memory reduction)
const quantizer = new Quantization({
  method: 'product-quantization',
  compressionRatio: 4
});
await db.applyQuantization(quantizer);

// HNSW indexing (150x speedup)
await db.createIndex({
  type: 'hnsw',
  params: { M: 16, efConstruction: 200 }
});

// Caching
db.setCache(new QueryCache({
  maxSize: 10000,
  ttl: 3600000
}));

Optimization Techniques

Quantization

Product Quantization: 4-8x compression
Scalar Quantization: 2-4x compression
Binary Quantization: 32x compression

Indexing

HNSW: 150x faster, high accuracy
IVF: Fast, partitioned search
LSH: Approximate search

Caching

Query Cache: LRU eviction
Result Cache: TTL-based
Embedding Cache: Reuse embeddings

Success Metrics

Memory reduction: 4-32x
Search speedup: 150x
Accuracy maintained: > 95%
Cache hit rate: > 70%

Additional Resources

Full docs: SKILL.md
AgentDB Optimization: https://agentdb.dev/docs/optimization

Install agentdb-vector-search-optimization and it becomes one more candidate the router can pick - when it fits.

A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.

{ "ocm": "1", "id": "aiskillstore-marketplace-agentdb-vector-search-optimization", "kind": "skill", "name": "agentdb-vector-search-optimization", "description": "Optimize AgentDB vector search performance using quantization for 4-32x memory reduction, HNSW indexing for 150x faster search, caching, and batch operations for scaling to millions of vectors.", "publisher": "aiskillstore", "version": "1.0.0", "capabilities": { "domains": [ "math" ], "tags": [ "skill-md", "agentdb", "optimization", "quantization", "hnsw-indexing", "performance", "skills-sh" ], "languages": [ "en" ] }, "quality_prior": 0.6, "examples": [ "Optimize AgentDB vector search performance using quantization for 4-32x memory reduction, HNSW indexing for 150x faster search, caching, and batch operations for scaling to millions of vectors." ], "primary": false, "metadata": { "source": { "provider": "skills.sh", "repository": "https://github.com/aiskillstore/marketplace", "path": "skills/dnyoussef/agentdb-vector-search-optimization/SKILL.md", "ref": "HEAD", "url": "https://github.com/aiskillstore/marketplace/blob/HEAD/skills/dnyoussef/agentdb-vector-search-optimization/SKILL.md", "key": "aiskillstore/marketplace/skills/dnyoussef/agentdb-vector-search-optimization/SKILL.md" } }, "instructions": "# AgentDB Vector Search Optimization\n\n## Overview\n\nOptimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations for scaling to millions of vectors.\n\n## SOP Framework: 5-Phase Optimization\n\n### Phase 1: Baseline Performance (1 hour)\n- Measure current metrics (latency, throughput, memory)\n- Identify bottlenecks\n- Set optimization targets\n\n### Phase 2: Apply Quantization (1-2 hours)\n- Configure product quantization\n- Train codebooks\n- Apply compression\n- Validate accuracy\n\n### Phase 3: Implement HNSW Indexing (1-2 hours", "cost": { "context_tokens": 530 } }

agentdb-vector-search-optimization

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