--- name: agentdb-memory-patterns-516a5357177c description: "Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants." --- # AgentDB Memory Patterns ## What This Skill Does Provides memory management patterns for AI agents using AgentDB's persistent storage and ReasoningBank integration. Enables agents to remember conversations, learn from interactions, and maintain context across sessions. **Performance**: 150x-12,500x faster than traditional solutions with 100% backward compatibility. ## Prerequisites - Node.js 18+ - AgentDB v1.0.7+ (via agentic-flow or standalone) - Understanding of agent architectures ## Quick Start with CLI ### Initialize AgentDB ```bash # 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 Server for Claude Code ```bash # Start MCP server (integrates with Claude Code) npx agentdb@latest mcp # Add to Claude Code (one-time setup) claude mcp add agentdb npx agentdb@latest mcp ``` ### Create Learning Plugin ```bash # Interactive plugin wizard npx agentdb@latest create-plugin # Use template directly npx agentdb@latest create-plugin -t decision-transformer -n my-agent # Available templates: # - decision-transformer (sequence modeling RL) # - q-learning (value-based learning) # - sarsa (on-policy TD learning) # - actor-critic (policy gradient) # - curiosity-driven (exploration-based) ``` ## Quick Start with API ```typescript import { createAgentDBAdapter } from 'agentic-flow/reasoningbank'; // Initialize with default configuration const adapter = await createAgentDBAdapter({ dbPath: '.agentdb/reasoningbank.db', enableLearning: true, // Enable learning plugins enableReasoning: true, // Enable reasoning agents quantizationType: 'scalar', // binary | scalar | product | none cacheSize: 1000, // In-memory cache }); // Store interaction memory const patternId = await adapter.insertPattern({ id: '', type: 'pattern', domain: 'conversation', pattern_data: JSON.stringify({ embedding: await computeEmbedding('What is the capital of France?'), pattern: { user: 'What is the capital of France?', assistant: 'The capital of France is Paris.', timestamp: Date.now() } }), confidence: 0.95, usage_count: 1, success_count: 1, created_at: Date.now(), last_used: Date.now(), }); // Retrieve context with reasoning const context = await adapter.retrieveWithReasoning(queryEmbedding, { domain: 'conversation', k: 10, useMMR: true, // Maximal Marginal Relevance synthesizeContext: true, // Generate rich context }); ``` ## Memory Patterns ### 1. Session Memory ```typescript class SessionMemory { async storeMessage(role: string, content: string) { return await db.storeMemory({ sessionId: this.sessionId, role, content, timestamp: Date.now() }); } async getSessionHistory(limit = 20) { return await db.query({ filters: { sessionId: this.sessionId }, orderBy: 'timestamp', limit }); } } ``` ### 2. Long-Term Memory ```typescript // Store important facts await db.storeFact({ category: 'user_preference', key: 'language', value: 'English', confidence: 1.0, source: 'explicit' }); // Retrieve facts const prefs = await db.getFacts({ category: 'user_preference' }); ``` ### 3. Pattern Learning ```typescript // Learn from successful interactions await db.storePattern({ trigger: 'user_asks_time', response: 'provide_formatted_time', success: true, context: { timezone: 'UTC' } }); // Apply learned patterns const pattern = await db.matchPattern(currentContext); ``` ## Advanced Patterns ### Hierarchical Memory ```typescript // Organize memory in hierarchy await memory.organize({ immediate: recentMessages, // Last 10 messages shortTerm: sessionContext, // Current session longTerm: importantFacts, // Persistent facts semantic: embeddedKnowledge // Vector search }); ``` ### Memory Consolidation ```typescript // Periodically consolidate memories await memory.consolidate({ strategy: 'importance', // Keep important memories maxSize: 10000, // Size limit minScore: 0.5 // Relevance threshold }); ``` ## CLI Operations ### Query Database ```bash # Query with vector embedding npx agentdb@latest query ./agents.db "[0.1,0.2,0.3,...]" # Top-k results npx agentdb@latest query ./agents.db "[0.1,0.2,0.3]" -k 10 # With similarity threshold npx agentdb@latest query ./agents.db "0.1 0.2 0.3" -t 0.75 # JSON output npx agentdb@latest query ./agents.db "[...]" -f json ``` ### Import/Export Data ```bash # Export vectors to file npx agentdb@latest export ./agents.db ./backup.json # Import vectors from file npx agentdb@latest import ./backup.json # Get database statistics npx agentdb@latest stats ./agents.db ``` ### Performance Benchmarks ```bash # Run performance benchmarks npx agentdb@latest benchmark # Results show: # - Pattern Search: 150x faster (100µs vs 15ms) # - Batch Insert: 500x faster (2ms vs 1s) # - Large-scale Query: 12,500x faster (8ms vs 100s) ``` ## Integration with ReasoningBank ```typescript import { createAgentDBAdapter, migrateToAgentDB } from 'agentic-flow/reasoningbank'; // Migrate from legacy ReasoningBank const result = await migrateToAgentDB( '.swarm/memory.db', // Source (legacy) '.agentdb/reasoningbank.db' // Destination (AgentDB) ); console.log(`✅ Migrated ${result.patternsMigrated} patterns`); // Train learning model const adapter = await createAgentDBAdapter({ enableLearning: true, }); await adapter.train({ epochs: 50, batchSize: 32, }); // Get optimal strategy with reasoning const result = await adapter.retrieveWithReasoning(queryEmbedding, { domain: 'task-planning', synthesizeContext: true, optimizeMemory: true, }); ``` ## Learning Plugins ### Available Algorithms (9 Total) 1. **Decision Transformer** - Sequence modeling RL (recommended) 2. **Q-Learning** - Value-based learning 3. **SARSA** - On-policy TD learning 4. **Actor-Critic** - Policy gradient with baseline 5. **Active Learning** - Query selection 6. **Adversarial Training** - Robustness 7. **Curriculum Learning** - Progressive difficulty 8. **Federated Learning** - Distributed learning 9. **Multi-task Learning** - Transfer learning ### List and Manage Plugins ```bash # List available plugins npx agentdb@latest list-plugins # List plugin templates npx agentdb@latest list-templates # Get plugin info npx agentdb@latest plugin-info ``` ## Reasoning Agents (4 Modules) 1. **PatternMatcher** - Find similar patterns with HNSW indexing 2. **ContextSynthesizer** - Generate rich context from multiple sources 3. **MemoryOptimizer** - Consolidate similar patterns, prune low-quality 4. **ExperienceCurator** - Quality-based experience filtering ## Best Practices 1. **Enable quantization**: Use scalar/binary for 4-32x memory reduction 2. **Use caching**: 1000 pattern cache for <1ms retrieval 3. **Batch operations**: 500x faster than individual inserts 4. **Train regularly**: Update learning models with new experiences 5. **Enable reasoning**: Automatic context synthesis and optimization 6. **Monitor metrics**: Use `stats` command to track performance ## Troubleshooting ### Issue: Memory growing too large ```bash # Check database size npx agentdb@latest stats ./agents.db # Enable quantization # Use 'binary' (32x smaller) or 'scalar' (4x smaller) ``` ### Issue: Slow search performance ```bash # Enable HNSW indexing and caching # Results: <100µs search time ``` ### Issue: Migration from legacy ReasoningBank ```bash # Automatic migration with validation npx agentdb@latest migrate --source .swarm/memory.db ``` ## Performance Characteristics - **Vector Search**: <100µs (HNSW indexing) - **Pattern Retrieval**: <1ms (with cache) - **Batch Insert**: 2ms for 100 patterns - **Memory Efficiency**: 4-32x reduction with quantization - **Backward Compatibility**: 100% compatible with ReasoningBank API ## Learn More - GitHub: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb - Documentation: node_modules/agentic-flow/docs/AGENTDB_INTEGRATION.md - MCP Integration: `npx agentdb@latest mcp` for Claude Code - Website: https://agentdb.ruv.io --- Source attribution: Activer007 Original project: https://github.com/Activer007/ordinary-claude-skills/blob/e3e1b3d55f9a132a68d242040d23048a7ba8f85c/skills_all/agentdb-memory-patterns/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. i did not write most of these. i just collected them. * **anthropic skills:** mit license (mostly) * **community skills:** check the `LICENSE` file in each specific folder. credits go to https://github.com/anthropics, https://github.com/obra, https://github.com/ComposioHQ, and the other people listed in the source tables. if you own one of these and want me to take it down, just open an issue and i will nuke it. # Mega AI Hub resource metadata Creator / publisher: Activer007 Original project: https://github.com/Activer007/ordinary-claude-skills/blob/e3e1b3d55f9a132a68d242040d23048a7ba8f85c/skills_all/agentdb-memory-patterns/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Product Management, Customer Support Roles: Customer Experience, Data Scientist, ML / AI Engineer, Product Manager, Technical Product Manager Specialties: API Tools: Agentdb, Agentic Flow Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 8a1d5cd9ecdb41dc11c5cd1c6c3e7243d66daee2c3b6268be1790ccbb3cb0447