Plan and implement a unified AgentDB memory backend for agents. Migrate SQLite and Markdown data, add HNSW indexing for similarity search, and support shared memory and SONA learning patterns while preserving compatibility with existing systems.
中文简介正在准备中。当前显示英语简介。
Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).
安装前可以查看交给 AI 代理的指令内容。
Consolidates disparate memory systems into unified AgentDB backend with HNSW vector search, achieving 150x-12,500x search performance improvements while maintaining backward compatibility.
# Initialize memory unification
Task("Memory architecture", "Design AgentDB unification strategy", "v3-memory-specialist")
# AgentDB integration
Task("AgentDB setup", "Configure HNSW indexing and vector search", "v3-memory-specialist")
# Data migration
Task("Memory migration", "Migrate SQLite/Markdown to AgentDB", "v3-memory-specialist")
┌─────────────────────────────────────────┐
│ • MemoryManager (basic operations) │
│ • DistributedMemorySystem (clustering) │
│ • SwarmMemory (agent-specific) │
│ • AdvancedMemoryManager (features) │
│ • SQLiteBackend (structured) │
│ • MarkdownBackend (file-based) │
│ • HybridBackend (combination) │
└─────────────────────────────────────────┘
↓
┌─────────────────────────────────────────┐
│ 🚀 AgentDB with HNSW │
│ • 150x-12,500x faster search │
│ • Unified query interface │
│ • Cross-agent memory sharing │
│ • SONA learning integration │
└─────────────────────────────────────────┘
class UnifiedMemoryService implements IMemoryBackend {
constructor(
private agentdb: AgentDBAdapter,
private indexer: HNSWIndexer,
private migrator: DataMigrator
) {}
async store(entry: MemoryEntry): Promise<void> {
await this.agentdb.store(entry);
await this.indexer.index(entry);
}
async query(query: MemoryQuery): Promise<MemoryEntry[]> {
if (query.semantic) {
return this.indexer.search(query); // 150x-12,500x faster
}
return this.agentdb.query(query);
}
}
class HNSWIndexer {
constructor(dimensions: number = 1536) {
this.index = new HNSWIndex({
dimensions,
efConstruction: 200,
M: 16,
speedupTarget: '150x-12500x'
});
}
async search(query: MemoryQuery): Promise<MemoryEntry[]> {
const embedding = await this.embedContent(query.content);
const results = this.index.search(embedding, query.limit || 10);
return this.retrieveEntries(results);
}
}
// AgentDB adapter setup
const agentdb = new AgentDBAdapter({
dimensions: 1536,
indexType: 'HNSW',
speedupTarget: '150x-12500x'
});
// SQLite → AgentDB
const migrateFromSQLite = async () => {
const entries = await sqlite.getAll();
for (const entry of entries) {
const embedding = await generateEmbedding(entry.content);
await agentdb.store({ ...entry, embedding });
}
};
// Markdown → AgentDB
const migrateFromMarkdown = async () => {
const files = await glob('**/*.md');
for (const file of files) {
const content = await fs.readFile(file, 'utf-8');
await agentdb.store({
id: generateId(),
content,
embedding: await generateEmbedding(content),
metadata: { originalFile: file }
});
}
};
class SONAMemoryIntegration {
async storePattern(pattern: LearningPattern): Promise<void> {
await this.memory.store({
id: pattern.id,
content: pattern.data,
metadata: {
sonaMode: pattern.mode,
reward: pattern.reward,
adaptationTime: pattern.adaptationTime
},
embedding: await this.generateEmbedding(pattern.data)
});
}
async retrieveSimilarPatterns(query: string): Promise<LearningPattern[]> {
return this.memory.query({
type: 'semantic',
content: query,
filters: { type: 'learning_pattern' }
});
}
}
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
中文简介正在准备中。当前显示英语简介。
Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
中文简介正在准备中。当前显示英语简介。
Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
中文简介正在准备中。当前显示英语简介。
Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
中文简介正在准备中。当前显示英语简介。
Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
中文简介正在准备中。当前显示英语简介。
Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.
中文简介正在准备中。当前显示英语简介。