ebfeb7a6bf
Add plugin lifecycle hooks infrastructure: - before_agent_start: inject context before agent loop - agent_end: analyze conversation after completion - 13 hook types total (message, tool, session, gateway hooks) Memory plugin implementation: - LanceDB vector storage with OpenAI embeddings - kind: "memory" to integrate with upstream slot system - Auto-recall: injects <relevant-memories> when context found - Auto-capture: stores preferences, decisions, entities - Rule-based capture filtering with 0.95 similarity dedup - Tools: memory_recall, memory_store, memory_forget - CLI: clawdbot ltm list|search|stats Plugin infrastructure: - api.on() method for hook registration - Global hook runner singleton for cross-module access - Priority ordering and error catching Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
672 lines
21 KiB
TypeScript
672 lines
21 KiB
TypeScript
/**
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* Clawdbot Memory Plugin
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*
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* Long-term memory with vector search for AI conversations.
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* Uses LanceDB for storage and OpenAI for embeddings.
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* Provides seamless auto-recall and auto-capture via lifecycle hooks.
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*/
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import { Type } from "@sinclair/typebox";
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import * as lancedb from "@lancedb/lancedb";
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import OpenAI from "openai";
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import { randomUUID } from "node:crypto";
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import { homedir } from "node:os";
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import { join } from "node:path";
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import type { ClawdbotPluginApi } from "clawdbot/plugin-sdk";
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// ============================================================================
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// Types
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// ============================================================================
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type MemoryConfig = {
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embedding: {
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provider: "openai";
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model?: string;
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apiKey: string;
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};
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dbPath?: string;
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autoCapture?: boolean;
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autoRecall?: boolean;
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};
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type MemoryEntry = {
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id: string;
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text: string;
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vector: number[];
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importance: number;
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category: "preference" | "fact" | "decision" | "entity" | "other";
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createdAt: number;
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};
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type MemorySearchResult = {
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entry: MemoryEntry;
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score: number;
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};
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// ============================================================================
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// Config Schema
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// ============================================================================
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const memoryConfigSchema = {
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parse(value: unknown): MemoryConfig {
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if (!value || typeof value !== "object" || Array.isArray(value)) {
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throw new Error("memory config required");
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}
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const cfg = value as Record<string, unknown>;
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// Embedding config is required
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const embedding = cfg.embedding as Record<string, unknown> | undefined;
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if (!embedding || typeof embedding.apiKey !== "string") {
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throw new Error("embedding.apiKey is required");
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}
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return {
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embedding: {
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provider: "openai",
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model:
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typeof embedding.model === "string"
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? embedding.model
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: "text-embedding-3-small",
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apiKey: resolveEnvVars(embedding.apiKey),
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},
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dbPath:
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typeof cfg.dbPath === "string"
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? cfg.dbPath
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: join(homedir(), ".clawdbot", "memory", "lancedb"),
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autoCapture: cfg.autoCapture !== false,
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autoRecall: cfg.autoRecall !== false,
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};
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},
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uiHints: {
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"embedding.apiKey": {
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label: "OpenAI API Key",
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sensitive: true,
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placeholder: "sk-proj-...",
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help: "API key for OpenAI embeddings (or use ${OPENAI_API_KEY})",
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},
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"embedding.model": {
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label: "Embedding Model",
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placeholder: "text-embedding-3-small",
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help: "OpenAI embedding model to use",
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},
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dbPath: {
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label: "Database Path",
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placeholder: "~/.clawdbot/memory/lancedb",
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advanced: true,
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},
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autoCapture: {
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label: "Auto-Capture",
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help: "Automatically capture important information from conversations",
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},
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autoRecall: {
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label: "Auto-Recall",
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help: "Automatically inject relevant memories into context",
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},
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},
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};
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function resolveEnvVars(value: string): string {
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return value.replace(/\$\{([^}]+)\}/g, (_, envVar) => {
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const envValue = process.env[envVar];
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if (!envValue) {
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throw new Error(`Environment variable ${envVar} is not set`);
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}
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return envValue;
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});
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}
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// ============================================================================
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// LanceDB Provider
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// ============================================================================
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const TABLE_NAME = "memories";
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const VECTOR_DIM = 1536; // OpenAI text-embedding-3-small
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class MemoryDB {
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private db: lancedb.Connection | null = null;
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private table: lancedb.Table | null = null;
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private initPromise: Promise<void> | null = null;
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constructor(private readonly dbPath: string) {}
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private async ensureInitialized(): Promise<void> {
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if (this.table) return;
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if (this.initPromise) return this.initPromise;
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this.initPromise = this.doInitialize();
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return this.initPromise;
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}
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private async doInitialize(): Promise<void> {
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this.db = await lancedb.connect(this.dbPath);
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const tables = await this.db.tableNames();
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if (tables.includes(TABLE_NAME)) {
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this.table = await this.db.openTable(TABLE_NAME);
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} else {
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this.table = await this.db.createTable(TABLE_NAME, [
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{
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id: "__schema__",
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text: "",
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vector: new Array(VECTOR_DIM).fill(0),
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importance: 0,
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category: "other",
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createdAt: 0,
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},
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]);
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await this.table.delete('id = "__schema__"');
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}
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}
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async store(
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entry: Omit<MemoryEntry, "id" | "createdAt">,
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): Promise<MemoryEntry> {
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await this.ensureInitialized();
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const fullEntry: MemoryEntry = {
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...entry,
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id: randomUUID(),
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createdAt: Date.now(),
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};
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await this.table!.add([fullEntry]);
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return fullEntry;
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}
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async search(
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vector: number[],
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limit = 5,
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minScore = 0.5,
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): Promise<MemorySearchResult[]> {
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await this.ensureInitialized();
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const results = await this.table!.vectorSearch(vector).limit(limit).toArray();
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// LanceDB uses L2 distance by default; convert to similarity score
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const mapped = results.map((row) => {
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const distance = row._distance ?? 0;
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// Use inverse for a 0-1 range: sim = 1 / (1 + d)
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const score = 1 / (1 + distance);
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return {
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entry: {
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id: row.id as string,
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text: row.text as string,
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vector: row.vector as number[],
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importance: row.importance as number,
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category: row.category as MemoryEntry["category"],
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createdAt: row.createdAt as number,
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},
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score,
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};
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});
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return mapped.filter((r) => r.score >= minScore);
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}
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async delete(id: string): Promise<boolean> {
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await this.ensureInitialized();
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// Validate UUID format to prevent injection
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const uuidRegex =
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/^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$/i;
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if (!uuidRegex.test(id)) {
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throw new Error(`Invalid memory ID format: ${id}`);
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}
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await this.table!.delete(`id = '${id}'`);
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return true;
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}
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async count(): Promise<number> {
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await this.ensureInitialized();
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return this.table!.countRows();
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}
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}
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// ============================================================================
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// OpenAI Embeddings
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// ============================================================================
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class Embeddings {
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private client: OpenAI;
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constructor(
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apiKey: string,
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private model: string,
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) {
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this.client = new OpenAI({ apiKey });
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}
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async embed(text: string): Promise<number[]> {
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const response = await this.client.embeddings.create({
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model: this.model,
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input: text,
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});
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return response.data[0].embedding;
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}
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}
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// ============================================================================
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// Rule-based capture filter
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// ============================================================================
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const MEMORY_TRIGGERS = [
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/zapamatuj si|pamatuj|remember/i,
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/preferuji|radši|nechci|prefer/i,
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/rozhodli jsme|budeme používat/i,
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/\+\d{10,}/,
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/[\w.-]+@[\w.-]+\.\w+/,
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/můj\s+\w+\s+je|je\s+můj/i,
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/my\s+\w+\s+is|is\s+my/i,
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/i (like|prefer|hate|love|want|need)/i,
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/always|never|important/i,
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];
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function shouldCapture(text: string): boolean {
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if (text.length < 10 || text.length > 500) return false;
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// Skip injected context from memory recall
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if (text.includes("<relevant-memories>")) return false;
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// Skip system-generated content
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if (text.startsWith("<") && text.includes("</")) return false;
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// Skip agent summary responses (contain markdown formatting)
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if (text.includes("**") && text.includes("\n-")) return false;
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// Skip emoji-heavy responses (likely agent output)
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const emojiCount = (text.match(/[\u{1F300}-\u{1F9FF}]/gu) || []).length;
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if (emojiCount > 3) return false;
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return MEMORY_TRIGGERS.some((r) => r.test(text));
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}
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function detectCategory(
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text: string,
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): "preference" | "fact" | "decision" | "entity" | "other" {
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const lower = text.toLowerCase();
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if (/prefer|radši|like|love|hate|want/i.test(lower)) return "preference";
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if (/rozhodli|decided|will use|budeme/i.test(lower)) return "decision";
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if (/\+\d{10,}|@[\w.-]+\.\w+|is called|jmenuje se/i.test(lower))
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return "entity";
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if (/is|are|has|have|je|má|jsou/i.test(lower)) return "fact";
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return "other";
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}
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// ============================================================================
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// Plugin Definition
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// ============================================================================
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const memoryPlugin = {
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id: "memory",
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name: "Memory (Vector)",
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description: "Long-term memory with vector search and seamless auto-recall/capture",
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kind: "memory" as const,
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configSchema: memoryConfigSchema,
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register(api: ClawdbotPluginApi) {
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const cfg = memoryConfigSchema.parse(api.pluginConfig);
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const db = new MemoryDB(cfg.dbPath!);
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const embeddings = new Embeddings(cfg.embedding.apiKey, cfg.embedding.model!);
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api.logger.info(`memory: plugin registered (db: ${cfg.dbPath}, lazy init)`);
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// ========================================================================
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// Tools
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// ========================================================================
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api.registerTool(
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{
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name: "memory_recall",
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label: "Memory Recall",
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description:
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"Search through long-term memories. Use when you need context about user preferences, past decisions, or previously discussed topics.",
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parameters: Type.Object({
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query: Type.String({ description: "Search query" }),
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limit: Type.Optional(Type.Number({ description: "Max results (default: 5)" })),
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}),
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async execute(_toolCallId, params) {
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const { query, limit = 5 } = params as { query: string; limit?: number };
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const vector = await embeddings.embed(query);
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const results = await db.search(vector, limit, 0.1);
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if (results.length === 0) {
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return {
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content: [{ type: "text", text: "No relevant memories found." }],
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details: { count: 0 },
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};
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}
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const text = results
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.map(
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(r, i) =>
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`${i + 1}. [${r.entry.category}] ${r.entry.text} (${(r.score * 100).toFixed(0)}%)`,
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)
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.join("\n");
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// Strip vector data for serialization (typed arrays can't be cloned)
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const sanitizedResults = results.map((r) => ({
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id: r.entry.id,
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text: r.entry.text,
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category: r.entry.category,
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importance: r.entry.importance,
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score: r.score,
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}));
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return {
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content: [
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{ type: "text", text: `Found ${results.length} memories:\n\n${text}` },
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],
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details: { count: results.length, memories: sanitizedResults },
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};
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},
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},
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{ name: "memory_recall" },
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);
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api.registerTool(
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{
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name: "memory_store",
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label: "Memory Store",
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description:
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"Save important information in long-term memory. Use for preferences, facts, decisions.",
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parameters: Type.Object({
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text: Type.String({ description: "Information to remember" }),
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importance: Type.Optional(
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Type.Number({ description: "Importance 0-1 (default: 0.7)" }),
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),
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category: Type.Optional(
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Type.Union([
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Type.Literal("preference"),
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Type.Literal("fact"),
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Type.Literal("decision"),
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Type.Literal("entity"),
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Type.Literal("other"),
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]),
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),
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}),
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async execute(_toolCallId, params) {
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const {
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text,
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importance = 0.7,
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category = "other",
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} = params as {
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text: string;
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importance?: number;
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category?: MemoryEntry["category"];
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};
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const vector = await embeddings.embed(text);
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// Check for duplicates
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const existing = await db.search(vector, 1, 0.95);
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if (existing.length > 0) {
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return {
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content: [
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{ type: "text", text: `Similar memory already exists: "${existing[0].entry.text}"` },
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],
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details: { action: "duplicate", existingId: existing[0].entry.id, existingText: existing[0].entry.text },
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};
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}
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const entry = await db.store({
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text,
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vector,
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importance,
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category,
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});
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return {
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content: [{ type: "text", text: `Stored: "${text.slice(0, 100)}..."` }],
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details: { action: "created", id: entry.id },
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};
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},
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},
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{ name: "memory_store" },
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);
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api.registerTool(
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{
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name: "memory_forget",
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label: "Memory Forget",
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description: "Delete specific memories. GDPR-compliant.",
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parameters: Type.Object({
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query: Type.Optional(Type.String({ description: "Search to find memory" })),
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memoryId: Type.Optional(Type.String({ description: "Specific memory ID" })),
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}),
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async execute(_toolCallId, params) {
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const { query, memoryId } = params as { query?: string; memoryId?: string };
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if (memoryId) {
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await db.delete(memoryId);
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return {
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content: [{ type: "text", text: `Memory ${memoryId} forgotten.` }],
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details: { action: "deleted", id: memoryId },
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};
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}
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if (query) {
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const vector = await embeddings.embed(query);
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const results = await db.search(vector, 5, 0.7);
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if (results.length === 0) {
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return {
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content: [{ type: "text", text: "No matching memories found." }],
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details: { found: 0 },
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};
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}
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if (results.length === 1 && results[0].score > 0.9) {
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await db.delete(results[0].entry.id);
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return {
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content: [
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{ type: "text", text: `Forgotten: "${results[0].entry.text}"` },
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],
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details: { action: "deleted", id: results[0].entry.id },
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};
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}
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const list = results
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.map((r) => `- [${r.entry.id.slice(0, 8)}] ${r.entry.text.slice(0, 60)}...`)
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.join("\n");
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// Strip vector data for serialization
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const sanitizedCandidates = results.map((r) => ({
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id: r.entry.id,
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text: r.entry.text,
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category: r.entry.category,
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score: r.score,
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}));
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return {
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content: [
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{
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type: "text",
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text: `Found ${results.length} candidates. Specify memoryId:\n${list}`,
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},
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],
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details: { action: "candidates", candidates: sanitizedCandidates },
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};
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}
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return {
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content: [{ type: "text", text: "Provide query or memoryId." }],
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details: { error: "missing_param" },
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};
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},
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},
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{ name: "memory_forget" },
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);
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// ========================================================================
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// CLI Commands
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// ========================================================================
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api.registerCli(
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({ program }) => {
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const memory = program
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.command("ltm")
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.description("Long-term memory plugin commands");
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memory
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.command("list")
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.description("List memories")
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.action(async () => {
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const count = await db.count();
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console.log(`Total memories: ${count}`);
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});
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memory
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.command("search")
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.description("Search memories")
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.argument("<query>", "Search query")
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.option("--limit <n>", "Max results", "5")
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.action(async (query, opts) => {
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const vector = await embeddings.embed(query);
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const results = await db.search(vector, parseInt(opts.limit), 0.3);
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// Strip vectors for output
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const output = results.map((r) => ({
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id: r.entry.id,
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text: r.entry.text,
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category: r.entry.category,
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importance: r.entry.importance,
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score: r.score,
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}));
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console.log(JSON.stringify(output, null, 2));
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});
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memory
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.command("stats")
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.description("Show memory statistics")
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.action(async () => {
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const count = await db.count();
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console.log(`Total memories: ${count}`);
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});
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},
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{ commands: ["ltm"] },
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);
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// ========================================================================
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// Lifecycle Hooks
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// ========================================================================
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// Auto-recall: inject relevant memories before agent starts
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if (cfg.autoRecall) {
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api.on("before_agent_start", async (event) => {
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if (!event.prompt || event.prompt.length < 5) return;
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|
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try {
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const vector = await embeddings.embed(event.prompt);
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const results = await db.search(vector, 3, 0.3);
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if (results.length === 0) return;
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|
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const memoryContext = results
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.map((r) => `- [${r.entry.category}] ${r.entry.text}`)
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.join("\n");
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|
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api.logger.info?.(
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`memory: injecting ${results.length} memories into context`,
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);
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|
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return {
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prependContext: `<relevant-memories>\nThe following memories may be relevant to this conversation:\n${memoryContext}\n</relevant-memories>`,
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};
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} catch (err) {
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api.logger.warn(`memory: recall failed: ${String(err)}`);
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}
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});
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|
}
|
|
|
|
// Auto-capture: analyze and store important information after agent ends
|
|
if (cfg.autoCapture) {
|
|
api.on("agent_end", async (event) => {
|
|
if (!event.success || !event.messages || event.messages.length === 0) {
|
|
return;
|
|
}
|
|
|
|
try {
|
|
// Extract text content from messages (handling unknown[] type)
|
|
const texts: string[] = [];
|
|
for (const msg of event.messages) {
|
|
// Type guard for message object
|
|
if (!msg || typeof msg !== "object") continue;
|
|
const msgObj = msg as Record<string, unknown>;
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|
|
|
// Only process user and assistant messages
|
|
const role = msgObj.role;
|
|
if (role !== "user" && role !== "assistant") continue;
|
|
|
|
const content = msgObj.content;
|
|
|
|
// Handle string content directly
|
|
if (typeof content === "string") {
|
|
texts.push(content);
|
|
continue;
|
|
}
|
|
|
|
// Handle array content (content blocks)
|
|
if (Array.isArray(content)) {
|
|
for (const block of content) {
|
|
if (
|
|
block &&
|
|
typeof block === "object" &&
|
|
"type" in block &&
|
|
(block as Record<string, unknown>).type === "text" &&
|
|
"text" in block &&
|
|
typeof (block as Record<string, unknown>).text === "string"
|
|
) {
|
|
texts.push((block as Record<string, unknown>).text as string);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Filter for capturable content
|
|
const toCapture = texts.filter(
|
|
(text) => text && shouldCapture(text),
|
|
);
|
|
if (toCapture.length === 0) return;
|
|
|
|
// Store each capturable piece (limit to 3 per conversation)
|
|
let stored = 0;
|
|
for (const text of toCapture.slice(0, 3)) {
|
|
const category = detectCategory(text);
|
|
const vector = await embeddings.embed(text);
|
|
|
|
// Check for duplicates (high similarity threshold)
|
|
const existing = await db.search(vector, 1, 0.95);
|
|
if (existing.length > 0) continue;
|
|
|
|
await db.store({
|
|
text,
|
|
vector,
|
|
importance: 0.7,
|
|
category,
|
|
});
|
|
stored++;
|
|
}
|
|
|
|
if (stored > 0) {
|
|
api.logger.info(`memory: auto-captured ${stored} memories`);
|
|
}
|
|
} catch (err) {
|
|
api.logger.warn(`memory: capture failed: ${String(err)}`);
|
|
}
|
|
});
|
|
}
|
|
|
|
// ========================================================================
|
|
// Service
|
|
// ========================================================================
|
|
|
|
api.registerService({
|
|
id: "memory",
|
|
start: () => {
|
|
api.logger.info(
|
|
`memory: initialized (db: ${cfg.dbPath}, model: ${cfg.embedding.model})`,
|
|
);
|
|
},
|
|
stop: () => {
|
|
api.logger.info("memory: stopped");
|
|
},
|
|
});
|
|
},
|
|
};
|
|
|
|
export default memoryPlugin;
|