Agentdb Memory Patterns

/============================================================================/ /* AGENTDB-MEMORY-PATTERNS SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: agentdb-memory-patterns version: 1.0.0 description: | [assert|neutral] Apply persistent memory patterns for AI agents using AgentDB. Implement session memory, configure long-term storage, enable pattern learning, and manage context across sessions. Use when building stat [ground:given] [conf:0.95] [state:confirmed] category: platforms tags:

• platforms
• integration
• tools author: ruv cognitive_frame: primary: aspectual goal_analysis: first_order: "Execute agentdb-memory-patterns workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic platforms processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "agentdb-memory-patterns", category: "platforms", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/

[define|neutral] COGNITIVE_FRAME := { frame: "Aspectual", source: "Russian", force: "Complete or ongoing?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/

[define|neutral] TRIGGER_POSITIVE := { keywords: ["agentdb-memory-patterns", "platforms", "workflow"], context: "user needs agentdb-memory-patterns capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/

When NOT to Use This Skill

• Local-only operations with no vector search needs
• Simple key-value storage without semantic similarity
• Real-time streaming data without persistence requirements
• Operations that do not require embedding-based retrieval

Success Criteria

• [assert|neutral] Vector search query latency: <10ms for 99th percentile [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Embedding generation: <100ms per document [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Index build time: <1s per 1000 vectors [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Recall@10: >0.95 for similar documents [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Database connection success rate: >99.9% [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Memory footprint: <2GB for 1M vectors with quantization [ground:acceptance-criteria] [conf:0.90] [state:provisional]

Edge Cases & Error Handling

• Rate Limits: AgentDB local instances have no rate limits; cloud deployments may vary
• Connection Failures: Implement retry logic with exponential backoff (max 3 retries)
• Index Corruption: Maintain backup indices; rebuild from source if corrupted
• Memory Overflow: Use quantization (4-bit, 8-bit) to reduce memory by 4-32x
• Stale Embeddings: Implement TTL-based refresh for dynamic content
• Dimension Mismatch: Validate embedding dimensions (384 for sentence-transformers) before insertion

Guardrails & Safety

• [assert|emphatic] NEVER: expose database connection strings in logs or error messages [ground:policy] [conf:0.98] [state:confirmed]
• [assert|neutral] ALWAYS: validate vector dimensions before insertion [ground:policy] [conf:0.98] [state:confirmed]
• [assert|neutral] ALWAYS: sanitize metadata to prevent injection attacks [ground:policy] [conf:0.98] [state:confirmed]
• [assert|emphatic] NEVER: store PII in vector metadata without encryption [ground:policy] [conf:0.98] [state:confirmed]
• [assert|neutral] ALWAYS: implement access control for multi-tenant deployments [ground:policy] [conf:0.98] [state:confirmed]
• [assert|neutral] ALWAYS: validate search results before returning to users [ground:policy] [conf:0.98] [state:confirmed]

Evidence-Based Validation

• Verify database health: Check connection status and index integrity
• Validate search quality: Measure recall/precision on test queries
• Monitor performance: Track query latency, throughput, and memory usage
• Test failure recovery: Simulate connection drops and index corruption
• Benchmark improvements: Compare against baseline metrics (e.g., 150x speedup claim)

AgentDB Memory Patterns

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

What This Skill Does

Use this skill to implement memory management patterns for AI agents using AgentDB's persistent storage and ReasoningBank integration. Apply these patterns to enable agents to remember conversations, learn from interactions, and maintain context across sessions. Deploy triple-layer retention (24h/7d/30d+) for optimal memory organization.

Performance: 150x-12,500x faster than traditional solutions with 100% backward compatibility.

Prerequisites

Install Node.js 18+ and AgentDB v1.0.7+. Ensure you have AgentDB via agentic-flow or standalone. Review agent architecture patterns before implementing memory systems.

Quick Start with CLI

Initialize AgentDB

Run these commands to set up your AgentDB instance with memory patterns:

# Initialize vector database
npx agentdb@latest init ./agents.db

# Or with custom dimensions
npx agentdb@latest init ./agents.db --dimension 768

# Use preset configurations
npx agentdb@latest init ./agents.db --preset large

# In-memory database for testing
npx agentdb@latest init ./memory.db --in-memory

Start MCP S

/----------------------------------------------------------------------------/ /* S4 SUCCESS CRITERIA / /----------------------------------------------------------------------------*/

[define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S5 MCP INTEGRATION / /----------------------------------------------------------------------------*/

[define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]

/----------------------------------------------------------------------------/ /* S6 MEMORY NAMESPACE / /----------------------------------------------------------------------------*/

[define|neutral] MEMORY_NAMESPACE := { pattern: "skills/platforms/agentdb-memory-patterns/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := { WHO: "agentdb-memory-patterns-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S7 SKILL COMPLETION VERIFICATION / /----------------------------------------------------------------------------*/

[direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S8 ABSOLUTE RULES / /----------------------------------------------------------------------------*/

[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* PROMISE / /----------------------------------------------------------------------------*/

[commit|confident] AGENTDB_MEMORY_PATTERNS_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]