Agentdb Performance Optimization
- 984 installs
- 67k repo stars
- Updated August 4, 2026
- ruvnet/ruflo
agentdb performance optimization is a Claude Code skill that reduces memory usage and accelerates vector search and pattern retrieval in AgentDB-powered applications for developers hitting agent storage bottlenecks.
About
agentdb performance optimization focuses on lowering memory consumption and speeding vector search and pattern retrieval in AgentDB-backed agent applications. The skill guides tuning strategies for embedding indexes, cache layers, and retrieval hot paths so multi-agent systems sustain lower RAM footprints and faster query latency under load. Developers reach for agentdb performance optimization when AgentDB memory growth or slow similarity search blocks production agent deployments. Catalog listing describes performance-oriented AgentDB workflows; confirm the local skill readme matches before applying version-specific tuning parameters.
- Achieves 150x–12,500x performance gains through quantization, HNSW indexing, caching, and batch operations
- Delivers 4-32x memory reduction while preserving retrieval accuracy
- Enables sub-100µs vector search and sub-1ms pattern retrieval
- Provides one-command benchmark suite that surfaces exact speed and memory metrics
- Includes ready-to-use TypeScript configuration patterns for optimized adapters
Agentdb Performance Optimization by the numbers
- 984 all-time installs (skills.sh)
- +3 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #1,111 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/ruvnet/ruflo --skill agentdb-performance-optimizationAdd your badge
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| Installs | 984 |
|---|---|
| repo stars | ★ 67k |
| Security audit | 3 / 3 scanners passed |
| Last updated | August 4, 2026 |
| Repository | ruvnet/ruflo ↗ |
How do you optimize AgentDB vector search performance?
Dramatically reduce memory usage and accelerate vector search and pattern retrieval inside AgentDB-powered applications.
Who is it for?
Engineers operating AgentDB-backed agents who need measurable memory reduction and faster vector or pattern retrieval at scale.
Skip if: Early prototypes without performance constraints or teams not yet running AgentDB vector workloads in production.
When should I use this skill?
AgentDB memory usage is high or vector search and pattern retrieval latency blocks agent performance in production.
What you get
Optimized index configs, reduced memory profiles, and faster vector retrieval benchmarks for AgentDB workloads.
- tuned index configs
- memory profiles
- retrieval benchmarks
Files
Claims Authorization Skill
Purpose
Claims-based authorization for secure agent operations and access control.
Claim Types
| Claim | Description |
|---|---|
read | Read file access |
write | Write file access |
execute | Command execution |
spawn | Agent spawning |
memory | Memory access |
network | Network access |
admin | Administrative operations |
Commands
Check Claim
npx claude-flow claims check --agent agent-123 --claim writeGrant Claim
npx claude-flow claims grant --agent agent-123 --claim write --scope "/src/**"Revoke Claim
npx claude-flow claims revoke --agent agent-123 --claim writeList Claims
npx claude-flow claims list --agent agent-123Scope Patterns
| Pattern | Description |
|---|---|
* | All resources |
/src/** | All files in src |
/config/*.toml | TOML files in config |
memory:patterns | Patterns namespace |
Security Levels
| Level | Claims |
|---|---|
minimal | read only |
standard | read, write, execute |
elevated | + spawn, memory |
admin | all claims |
Best Practices
1. Follow principle of least privilege 2. Scope claims to specific resources 3. Audit claim usage regularly 4. Revoke claims when no longer needed
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Use agentdb performance optimization after basic AgentDB integration when memory or retrieval latency is the bottleneck, not during initial memory schema design.
FAQ
What does agentdb performance optimization improve?
agentdb performance optimization improves AgentDB memory usage and speeds vector search plus pattern retrieval. Use it when agent applications show high RAM consumption or slow similarity queries under production load.
When should agentdb performance optimization run?
agentdb performance optimization fits post-build tuning before or after shipping AgentDB-powered agents. Invoke it when storage growth or retrieval latency becomes a measurable bottleneck.
Is Agentdb Performance Optimization safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.