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@sebbsssss/clude-brain
Advanced tools
Cognitive memory engine for AI agents — store, recall, and learn from experience
Cognitive memory engine for AI agents. The core of Clude — extracted as a standalone, provider-agnostic package.
@clude/brain is the memory engine that powers Clude. It handles:
npm install @clude/brain
import { CludeEngine } from '@clude/brain';
import type { StorageProvider, EmbeddingProvider } from '@clude/brain';
// Bring your own providers
const engine = new CludeEngine({
storage: myStorageProvider,
embeddings: myEmbeddingProvider,
});
// Store a memory
await engine.store({
content: "Auth bug was caused by expired JWT refresh token",
type: "procedural",
tags: ["auth", "debugging"],
importance: 0.8,
});
// Recall relevant memories
const memories = await engine.recall({
query: "authentication issues",
limit: 5,
});
Implement these interfaces to connect any storage or embedding backend:
interface StorageProvider {
store(memory: Memory): Promise<string>;
recall(options: RecallOptions): Promise<Memory[]>;
get(id: string): Promise<Memory | null>;
update(id: string, patch: Partial<Memory>): Promise<void>;
delete(id: string): Promise<boolean>;
stats(): Promise<MemoryStats>;
}
interface EmbeddingProvider {
embed(text: string): Promise<number[]>;
embedBatch(texts: string[]): Promise<(number[] | null)[]>;
readonly dimensions: number;
}
| Type | Purpose | Decay Rate |
|---|---|---|
episodic | Events, conversations | Fast |
semantic | Facts, knowledge | Slow |
procedural | How-to, patterns | Very slow |
self_model | Self-awareness | Very slow |
Memories are ranked by a weighted combination of:
@clude/brain — This package. The memory engine.@clude/cloud — Supabase + Voyage providers.@clude/local — SQLite + local embeddings.@clude/mcp — MCP server for Claude Desktop / Cursor.clude — Python SDK (pip install clude)clude-bot — Full bot with X integration, dream cycles, etc.MIT — Pixl64 LLP
FAQs
Cognitive memory engine for AI agents — store, recall, and learn from experience
We found that @sebbsssss/clude-brain demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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