agentdb-persistent-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB - session memory, long-term storage, pattern learning, and context management for stateful agents, chat systems, and intelligent assistants
Best use case
agentdb-persistent-memory-patterns is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Implement persistent memory patterns for AI agents using AgentDB - session memory, long-term storage, pattern learning, and context management for stateful agents, chat systems, and intelligent assistants
Implement persistent memory patterns for AI agents using AgentDB - session memory, long-term storage, pattern learning, and context management for stateful agents, chat systems, and intelligent assistants
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "agentdb-persistent-memory-patterns" skill to help with this workflow task. Context: Implement persistent memory patterns for AI agents using AgentDB - session memory, long-term storage, pattern learning, and context management for stateful agents, chat systems, and intelligent assistants
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agentdb-persistent-memory-patterns/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agentdb-persistent-memory-patterns Compares
| Feature / Agent | agentdb-persistent-memory-patterns | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Implement persistent memory patterns for AI agents using AgentDB - session memory, long-term storage, pattern learning, and context management for stateful agents, chat systems, and intelligent assistants
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AgentDB Persistent Memory Patterns
## Overview
Implement persistent memory patterns for AI agents using AgentDB - session memory, long-term storage, pattern learning, and context management for stateful agents, chat systems, and intelligent assistants.
## SOP Framework: 5-Phase Memory Implementation
### Phase 1: Design Memory Architecture (1-2 hours)
- Define memory schemas (episodic, semantic, procedural)
- Plan storage layers (short-term, working, long-term)
- Design retrieval mechanisms
- Configure persistence strategies
### Phase 2: Implement Storage Layer (2-3 hours)
- Create memory stores in AgentDB
- Implement session management
- Build long-term memory persistence
- Setup memory indexing
### Phase 3: Test Memory Operations (1-2 hours)
- Validate store/retrieve operations
- Test memory consolidation
- Verify pattern recognition
- Benchmark performance
### Phase 4: Optimize Performance (1-2 hours)
- Implement caching layers
- Optimize retrieval queries
- Add memory compression
- Performance tuning
### Phase 5: Document Patterns (1 hour)
- Create usage documentation
- Document memory patterns
- Write integration examples
- Generate API documentation
## Quick Start
```typescript
import { AgentDB, MemoryManager } from 'agentdb-memory';
// Initialize memory system
const memoryDB = new AgentDB({
name: 'agent-memory',
dimensions: 768,
memory: {
sessionTTL: 3600,
consolidationInterval: 300,
maxSessionSize: 1000
}
});
const memoryManager = new MemoryManager({
database: memoryDB,
layers: ['episodic', 'semantic', 'procedural']
});
// Store memory
await memoryManager.store({
type: 'episodic',
content: 'User preferred dark theme',
context: { userId: '123', timestamp: Date.now() }
});
// Retrieve memory
const memories = await memoryManager.retrieve({
query: 'user preferences',
type: 'episodic',
limit: 10
});
```
## Memory Patterns
### Session Memory
```typescript
const session = await memoryManager.createSession('user-123');
await session.store('conversation', messageHistory);
await session.store('preferences', userPrefs);
const context = await session.getContext();
```
### Long-Term Storage
```typescript
await memoryManager.consolidate({
from: 'working-memory',
to: 'long-term-memory',
strategy: 'importance-based'
});
```
### Pattern Learning
```typescript
const patterns = await memoryManager.learnPatterns({
memory: 'episodic',
algorithm: 'clustering',
minSupport: 0.1
});
```
## Success Metrics
- Memory persists across agent restarts
- Retrieval latency < 50ms (p95)
- Pattern recognition accuracy > 85%
- Context maintained with 95% accuracy
- Memory consolidation working
## Additional Resources
- Full documentation: SKILL.md
- Process guide: PROCESS.md
- AgentDB Memory Docs: https://agentdb.dev/docs/memoryRelated Skills
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