mirror of
https://github.com/openclaw/openclaw.git
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a170e25494
Add task ledger (TASKS.md) parsing and stale-task archival for maintaining agent task state across context compactions. Post-compaction recovery injects memory_recall + TASKS.md read steps after auto-compaction. Sleep cycle gains entity dedup (Phase 1d) and credential scanning. Memory flush now extracts active task checkpoints. Compaction instructions prioritize active tasks. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
637 lines
28 KiB
TypeScript
637 lines
28 KiB
TypeScript
/**
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* CLI command registration for memory-neo4j.
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*
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* Registers the `openclaw memory neo4j` subcommand group with commands:
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* - list: List memory counts by agent and category
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* - search: Search memories via hybrid search
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* - stats: Show memory statistics and configuration
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* - sleep: Run sleep cycle (seven-phase memory consolidation)
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* - promote: Manually promote a memory to core
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* - index: Re-embed all memories after changing embedding model
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* - cleanup: Retroactively apply attention gate to stored memories
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*/
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import type { OpenClawPluginApi } from "openclaw/plugin-sdk";
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import type { ExtractionConfig, MemoryNeo4jConfig } from "./config.js";
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import type { Embeddings } from "./embeddings.js";
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import type { Neo4jMemoryClient } from "./neo4j-client.js";
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import { passesAttentionGate } from "./attention-gate.js";
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import { stripMessageWrappers } from "./message-utils.js";
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import { hybridSearch } from "./search.js";
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import { runSleepCycle } from "./sleep-cycle.js";
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export type CliDeps = {
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db: Neo4jMemoryClient;
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embeddings: Embeddings;
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cfg: MemoryNeo4jConfig;
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extractionConfig: ExtractionConfig;
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vectorDim: number;
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};
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/**
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* Register the `openclaw memory neo4j` CLI subcommand group.
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*/
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export function registerCli(api: OpenClawPluginApi, deps: CliDeps): void {
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const { db, embeddings, cfg, extractionConfig, vectorDim } = deps;
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api.registerCli(
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({ program }) => {
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// Find existing memory command or create fallback
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let memoryCmd = program.commands.find((cmd) => cmd.name() === "memory");
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if (!memoryCmd) {
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// Fallback if core memory CLI not registered yet
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memoryCmd = program.command("memory").description("Memory commands");
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}
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// Add neo4j memory subcommand group
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const memory = memoryCmd.command("neo4j").description("Neo4j graph memory commands");
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memory
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.command("list")
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.description("List memories grouped by agent and category")
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.option("--agent <id>", "Filter by agent id")
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.option("--category <name>", "Filter by category")
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.option("--limit <n>", "Max memories per category (default: 20)")
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.option("--json", "Output as JSON")
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.action(
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async (opts: { agent?: string; category?: string; limit?: string; json?: boolean }) => {
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try {
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await db.ensureInitialized();
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const perCategoryLimit = opts.limit ? parseInt(opts.limit, 10) : 20;
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if (Number.isNaN(perCategoryLimit) || perCategoryLimit <= 0) {
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console.error("Error: --limit must be greater than 0");
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process.exitCode = 1;
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return;
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}
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// Build query with optional filters
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const conditions: string[] = [];
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const params: Record<string, unknown> = {};
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if (opts.agent) {
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conditions.push("m.agentId = $agentId");
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params.agentId = opts.agent;
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}
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if (opts.category) {
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conditions.push("m.category = $category");
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params.category = opts.category;
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}
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const where = conditions.length > 0 ? `WHERE ${conditions.join(" AND ")}` : "";
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const rows = await db.runQuery<{
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agentId: string;
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category: string;
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id: string;
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text: string;
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importance: number;
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createdAt: string;
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source: string;
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}>(
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`MATCH (m:Memory) ${where}
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WITH m.agentId AS agentId, m.category AS category, m
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ORDER BY m.importance DESC
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WITH agentId, category, collect({
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id: m.id, text: m.text, importance: m.importance,
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createdAt: m.createdAt, source: coalesce(m.source, 'unknown')
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}) AS memories
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UNWIND memories[0..${perCategoryLimit}] AS mem
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RETURN agentId, category,
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mem.id AS id, mem.text AS text,
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mem.importance AS importance,
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mem.createdAt AS createdAt,
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mem.source AS source
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ORDER BY agentId, category, importance DESC`,
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params,
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);
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if (opts.json) {
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console.log(JSON.stringify(rows, null, 2));
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return;
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}
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if (rows.length === 0) {
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console.log("No memories found.");
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return;
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}
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// Group by agent → category → memories
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const byAgent = new Map<
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string,
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Map<
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string,
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Array<{
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id: string;
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text: string;
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importance: number;
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createdAt: string;
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source: string;
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}>
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>
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>();
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for (const row of rows) {
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const agent = (row.agentId as string) ?? "default";
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const cat = (row.category as string) ?? "other";
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if (!byAgent.has(agent)) byAgent.set(agent, new Map());
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const catMap = byAgent.get(agent)!;
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if (!catMap.has(cat)) catMap.set(cat, []);
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catMap.get(cat)!.push({
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id: row.id as string,
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text: row.text as string,
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importance: row.importance as number,
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createdAt: row.createdAt as string,
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source: row.source as string,
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});
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}
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const impBar = (ratio: number) => {
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const W = 10;
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const filled = Math.round(ratio * W);
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return "█".repeat(filled) + "░".repeat(W - filled);
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};
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for (const [agentId, categories] of byAgent) {
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const agentTotal = [...categories.values()].reduce((s, m) => s + m.length, 0);
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console.log(`\n┌─ ${agentId} (${agentTotal} shown)`);
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for (const [category, memories] of categories) {
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console.log(`│\n│ ── ${category} (${memories.length}) ──`);
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for (const mem of memories) {
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const pct = ((mem.importance * 100).toFixed(0) + "%").padStart(4);
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const preview = mem.text.length > 72 ? `${mem.text.slice(0, 69)}...` : mem.text;
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console.log(`│ ${impBar(mem.importance)} ${pct} ${preview}`);
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}
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}
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console.log("└");
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}
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console.log("");
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} catch (err) {
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console.error(`Error: ${err instanceof Error ? err.message : String(err)}`);
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process.exitCode = 1;
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}
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},
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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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.option("--agent <id>", "Agent id (default: default)")
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.action(async (query: string, opts: { limit: string; agent?: string }) => {
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try {
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const results = await hybridSearch(
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db,
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embeddings,
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query,
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parseInt(opts.limit, 10),
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opts.agent ?? "default",
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extractionConfig.enabled,
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{ graphSearchDepth: cfg.graphSearchDepth },
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);
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const output = results.map((r) => ({
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id: r.id,
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text: r.text,
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category: r.category,
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importance: r.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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} catch (err) {
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console.error(`Error: ${err instanceof Error ? err.message : String(err)}`);
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process.exitCode = 1;
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}
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});
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memory
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.command("stats")
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.description("Show memory statistics and configuration")
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.action(async () => {
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try {
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await db.ensureInitialized();
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const stats = await db.getMemoryStats();
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const total = stats.reduce((sum, s) => sum + s.count, 0);
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console.log("\nMemory (Neo4j) Statistics");
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console.log("─────────────────────────");
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console.log(`Total memories: ${total}`);
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console.log(`Neo4j URI: ${cfg.neo4j.uri}`);
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console.log(`Embedding: ${cfg.embedding.provider}/${cfg.embedding.model}`);
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console.log(
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`Extraction: ${extractionConfig.enabled ? extractionConfig.model : "disabled"}`,
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);
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console.log(`Auto-capture: ${cfg.autoCapture ? "enabled" : "disabled"}`);
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console.log(`Auto-recall: ${cfg.autoRecall ? "enabled" : "disabled"}`);
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console.log(`Core memory: ${cfg.coreMemory.enabled ? "enabled" : "disabled"}`);
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if (stats.length > 0) {
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const BAR_WIDTH = 20;
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const bar = (ratio: number) => {
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const filled = Math.round(ratio * BAR_WIDTH);
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return "█".repeat(filled) + "░".repeat(BAR_WIDTH - filled);
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};
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// Group by agentId
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const byAgent = new Map<
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string,
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Array<{ category: string; count: number; avgImportance: number }>
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>();
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for (const row of stats) {
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const list = byAgent.get(row.agentId) || [];
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list.push({
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category: row.category,
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count: row.count,
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avgImportance: row.avgImportance,
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});
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byAgent.set(row.agentId, list);
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}
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for (const [agentId, categories] of byAgent) {
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const agentTotal = categories.reduce((sum, c) => sum + c.count, 0);
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const maxCatCount = Math.max(...categories.map((c) => c.count));
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const catLabelLen = Math.max(...categories.map((c) => c.category.length));
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console.log(`\n┌─ ${agentId} (${agentTotal} memories)`);
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console.log("│");
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console.log(
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`│ ${"Category".padEnd(catLabelLen)} ${"Count".padStart(5)} ${"".padEnd(BAR_WIDTH)} ${"Importance".padStart(10)}`,
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);
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console.log(`│ ${"─".repeat(catLabelLen + 5 + BAR_WIDTH * 2 + 18)}`);
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for (const { category, count, avgImportance } of categories) {
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const cat = category.padEnd(catLabelLen);
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const cnt = String(count).padStart(5);
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const pct = ((avgImportance * 100).toFixed(0) + "%").padStart(10);
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console.log(
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`│ ${cat} ${cnt} ${bar(count / maxCatCount)} ${pct} ${bar(avgImportance)}`,
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);
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}
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console.log("└");
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}
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console.log(`\nAgents: ${byAgent.size} (${[...byAgent.keys()].join(", ")})`);
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}
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console.log("");
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} catch (err) {
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console.error(`Error: ${err instanceof Error ? err.message : String(err)}`);
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process.exitCode = 1;
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}
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});
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memory
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.command("sleep")
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.description("Run sleep cycle — consolidate memories with Pareto-based promotion")
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.option("--agent <id>", "Agent id (default: all agents)")
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.option("--dedup-threshold <n>", "Vector similarity threshold for dedup (default: 0.95)")
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.option("--pareto <n>", "Top N% for core memory (default: 0.2 = top 20%)")
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.option("--promotion-min-age <days>", "Min age in days before promotion (default: 7)")
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.option("--decay-threshold <n>", "Decay score threshold for pruning (default: 0.1)")
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.option("--decay-half-life <days>", "Base half-life in days (default: 30)")
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.option("--batch-size <n>", "Extraction batch size (default: 50)")
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.option("--delay <ms>", "Delay between extraction batches in ms (default: 1000)")
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.option("--max-semantic-pairs <n>", "Max LLM-checked semantic dedup pairs (default: 500)")
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.option("--concurrency <n>", "Parallel LLM calls — match OLLAMA_NUM_PARALLEL (default: 8)")
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.option(
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"--skip-semantic",
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"Skip LLM-based semantic dedup (Phase 1b) and conflict detection (Phase 1c)",
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)
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.option("--workspace <dir>", "Workspace directory for TASKS.md cleanup")
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.action(
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async (opts: {
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agent?: string;
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dedupThreshold?: string;
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pareto?: string;
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promotionMinAge?: string;
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decayThreshold?: string;
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decayHalfLife?: string;
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batchSize?: string;
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delay?: string;
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maxSemanticPairs?: string;
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concurrency?: string;
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skipSemantic?: boolean;
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workspace?: string;
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}) => {
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console.log("\n🌙 Memory Sleep Cycle");
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console.log("═════════════════════════════════════════════════════════════");
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console.log("Multi-phase memory consolidation (Pareto-based):\n");
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console.log(" Phase 1: Deduplication — Merge near-duplicate memories");
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console.log(
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" Phase 1b: Semantic Dedup — LLM-based paraphrase detection (0.75–0.95 band)",
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);
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console.log(" Phase 1c: Conflict Detection — Resolve contradictory memories");
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console.log(" Phase 1d: Entity Dedup — Merge duplicate entity nodes");
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console.log(
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" Phase 2: Pareto Scoring — Calculate effective scores for all memories",
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);
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console.log(
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" Phase 3: Core Promotion — Regular memories above threshold → core",
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);
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console.log(
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" Phase 4: Core Demotion — Core memories below threshold → regular",
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);
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console.log(" Phase 5: Extraction — Extract entities and categorize");
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console.log(" Phase 6: Decay & Pruning — Remove stale low-importance memories");
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console.log(" Phase 7: Orphan Cleanup — Remove disconnected nodes");
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console.log(" Phase 7b: Credential Scan — Remove memories with leaked secrets");
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console.log(" Phase 8: Task Ledger Cleanup — Archive stale tasks in TASKS.md\n");
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try {
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// Validate sleep cycle CLI parameters before running
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const batchSize = opts.batchSize ? parseInt(opts.batchSize, 10) : undefined;
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const delay = opts.delay ? parseInt(opts.delay, 10) : undefined;
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const decayHalfLife = opts.decayHalfLife
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? parseInt(opts.decayHalfLife, 10)
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: undefined;
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const decayThreshold = opts.decayThreshold
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? parseFloat(opts.decayThreshold)
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: undefined;
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const pareto = opts.pareto ? parseFloat(opts.pareto) : undefined;
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const promotionMinAge = opts.promotionMinAge
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? parseInt(opts.promotionMinAge, 10)
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: undefined;
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if (batchSize != null && (Number.isNaN(batchSize) || batchSize <= 0)) {
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console.error("Error: --batch-size must be greater than 0");
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process.exitCode = 1;
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return;
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}
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if (delay != null && (Number.isNaN(delay) || delay < 0)) {
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console.error("Error: --delay must be >= 0");
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process.exitCode = 1;
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return;
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}
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if (decayHalfLife != null && (Number.isNaN(decayHalfLife) || decayHalfLife <= 0)) {
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console.error("Error: --decay-half-life must be greater than 0");
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process.exitCode = 1;
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return;
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}
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if (
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decayThreshold != null &&
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(Number.isNaN(decayThreshold) || decayThreshold < 0 || decayThreshold > 1)
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) {
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console.error("Error: --decay-threshold must be between 0 and 1");
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process.exitCode = 1;
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return;
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}
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if (pareto != null && (Number.isNaN(pareto) || pareto < 0 || pareto > 1)) {
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console.error("Error: --pareto must be between 0 and 1");
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process.exitCode = 1;
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return;
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}
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if (
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promotionMinAge != null &&
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(Number.isNaN(promotionMinAge) || promotionMinAge < 0)
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) {
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console.error("Error: --promotion-min-age must be >= 0");
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process.exitCode = 1;
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return;
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}
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const maxSemanticPairs = opts.maxSemanticPairs
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? parseInt(opts.maxSemanticPairs, 10)
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: undefined;
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if (
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maxSemanticPairs != null &&
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(Number.isNaN(maxSemanticPairs) || maxSemanticPairs <= 0)
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) {
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console.error("Error: --max-semantic-pairs must be greater than 0");
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process.exitCode = 1;
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return;
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}
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const concurrency = opts.concurrency ? parseInt(opts.concurrency, 10) : undefined;
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if (concurrency != null && (Number.isNaN(concurrency) || concurrency <= 0)) {
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console.error("Error: --concurrency must be greater than 0");
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process.exitCode = 1;
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return;
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}
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await db.ensureInitialized();
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// Resolve workspace dir for task ledger cleanup
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const resolvedWorkspace = opts.workspace?.trim() || undefined;
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const result = await runSleepCycle(db, embeddings, extractionConfig, api.logger, {
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agentId: opts.agent,
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dedupThreshold: opts.dedupThreshold ? parseFloat(opts.dedupThreshold) : undefined,
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skipSemanticDedup: opts.skipSemantic === true,
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maxSemanticDedupPairs: maxSemanticPairs,
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llmConcurrency: concurrency,
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paretoPercentile: pareto,
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promotionMinAgeDays: promotionMinAge,
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decayRetentionThreshold: decayThreshold,
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decayBaseHalfLifeDays: decayHalfLife,
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decayCurves: Object.keys(cfg.decayCurves).length > 0 ? cfg.decayCurves : undefined,
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extractionBatchSize: batchSize,
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extractionDelayMs: delay,
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workspaceDir: resolvedWorkspace,
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onPhaseStart: (phase) => {
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const phaseNames: Record<string, string> = {
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dedup: "Phase 1: Deduplication",
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semanticDedup: "Phase 1b: Semantic Deduplication",
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conflict: "Phase 1c: Conflict Detection",
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entityDedup: "Phase 1d: Entity Deduplication",
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pareto: "Phase 2: Pareto Scoring",
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promotion: "Phase 3: Core Promotion",
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extraction: "Phase 4: Extraction",
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decay: "Phase 5: Decay & Pruning",
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cleanup: "Phase 6: Orphan Cleanup",
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noiseCleanup: "Phase 7: Noise Cleanup",
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credentialScan: "Phase 7b: Credential Scan",
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taskLedger: "Phase 8: Task Ledger Cleanup",
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};
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console.log(`\n▶ ${phaseNames[phase] ?? phase}`);
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console.log("─────────────────────────────────────────────────────────────");
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},
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onProgress: (_phase, message) => {
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console.log(` ${message}`);
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},
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});
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console.log("\n═════════════════════════════════════════════════════════════");
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console.log(`✅ Sleep cycle complete in ${(result.durationMs / 1000).toFixed(1)}s`);
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console.log("─────────────────────────────────────────────────────────────");
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console.log(
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` Deduplication: ${result.dedup.clustersFound} clusters → ${result.dedup.memoriesMerged} merged`,
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);
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console.log(
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` Conflicts: ${result.conflict.pairsFound} pairs, ${result.conflict.resolved} resolved, ${result.conflict.invalidated} invalidated`,
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);
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console.log(
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` Semantic Dedup: ${result.semanticDedup.pairsChecked} pairs checked, ${result.semanticDedup.duplicatesMerged} merged`,
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||
);
|
||
console.log(
|
||
` Pareto: ${result.pareto.totalMemories} total (${result.pareto.coreMemories} core, ${result.pareto.regularMemories} regular)`,
|
||
);
|
||
console.log(
|
||
` Threshold: ${result.pareto.threshold.toFixed(4)} (top 20%)`,
|
||
);
|
||
console.log(
|
||
` Promotion: ${result.promotion.promoted}/${result.promotion.candidatesFound} promoted to core`,
|
||
);
|
||
console.log(` Decay/Pruning: ${result.decay.memoriesPruned} memories pruned`);
|
||
console.log(
|
||
` Extraction: ${result.extraction.succeeded}/${result.extraction.total} extracted` +
|
||
(result.extraction.failed > 0 ? ` (${result.extraction.failed} failed)` : ""),
|
||
);
|
||
console.log(
|
||
` Cleanup: ${result.cleanup.entitiesRemoved} entities, ${result.cleanup.tagsRemoved} tags removed`,
|
||
);
|
||
console.log(
|
||
` Task Ledger: ${result.taskLedger.archivedCount} stale tasks archived` +
|
||
(result.taskLedger.archivedIds.length > 0
|
||
? ` (${result.taskLedger.archivedIds.join(", ")})`
|
||
: ""),
|
||
);
|
||
if (result.aborted) {
|
||
console.log("\n⚠️ Sleep cycle was aborted before completion.");
|
||
}
|
||
console.log("");
|
||
} catch (err) {
|
||
console.error(
|
||
`\n❌ Sleep cycle failed: ${err instanceof Error ? err.message : String(err)}`,
|
||
);
|
||
process.exitCode = 1;
|
||
}
|
||
},
|
||
);
|
||
|
||
memory
|
||
.command("promote")
|
||
.description("Manually promote a memory to core status")
|
||
.argument("<id>", "Memory ID to promote")
|
||
.action(async (id: string) => {
|
||
try {
|
||
await db.ensureInitialized();
|
||
const promoted = await db.promoteToCore([id]);
|
||
if (promoted > 0) {
|
||
console.log(`✅ Memory ${id} promoted to core.`);
|
||
} else {
|
||
console.log(`❌ Memory ${id} not found.`);
|
||
process.exitCode = 1;
|
||
}
|
||
} catch (err) {
|
||
console.error(`Error: ${err instanceof Error ? err.message : String(err)}`);
|
||
process.exitCode = 1;
|
||
}
|
||
});
|
||
|
||
memory
|
||
.command("index")
|
||
.description(
|
||
"Re-embed all memories and entities — use after changing embedding model/provider",
|
||
)
|
||
.option("--batch-size <n>", "Embedding batch size (default: 50)")
|
||
.action(async (opts: { batchSize?: string }) => {
|
||
const batchSize = opts.batchSize ? parseInt(opts.batchSize, 10) : 50;
|
||
if (Number.isNaN(batchSize) || batchSize <= 0) {
|
||
console.error("Error: --batch-size must be greater than 0");
|
||
process.exitCode = 1;
|
||
return;
|
||
}
|
||
|
||
console.log("\nMemory Neo4j — Reindex Embeddings");
|
||
console.log("═════════════════════════════════════════════════════════════");
|
||
console.log(`Model: ${cfg.embedding.provider}/${cfg.embedding.model}`);
|
||
console.log(`Dimensions: ${vectorDim}`);
|
||
console.log(`Batch size: ${batchSize}\n`);
|
||
|
||
try {
|
||
const startedAt = Date.now();
|
||
const result = await db.reindex((texts) => embeddings.embedBatch(texts), {
|
||
batchSize,
|
||
onProgress: (phase, done, total) => {
|
||
if (phase === "drop-indexes" && done === 0) {
|
||
console.log("▶ Dropping old vector index…");
|
||
} else if (phase === "memories") {
|
||
console.log(` Memories: ${done}/${total}`);
|
||
} else if (phase === "create-indexes" && done === 0) {
|
||
console.log("▶ Recreating vector index…");
|
||
}
|
||
},
|
||
});
|
||
|
||
const elapsed = ((Date.now() - startedAt) / 1000).toFixed(1);
|
||
console.log("\n═════════════════════════════════════════════════════════════");
|
||
console.log(`✅ Reindex complete in ${elapsed}s — ${result.memories} memories`);
|
||
console.log("");
|
||
} catch (err) {
|
||
console.error(
|
||
`\n❌ Reindex failed: ${err instanceof Error ? err.message : String(err)}`,
|
||
);
|
||
process.exitCode = 1;
|
||
}
|
||
});
|
||
|
||
memory
|
||
.command("cleanup")
|
||
.description(
|
||
"Retroactively apply the attention gate — find and remove low-substance memories",
|
||
)
|
||
.option("--execute", "Actually delete (default: dry-run preview)")
|
||
.option("--all", "Include explicitly-stored memories (default: auto-capture only)")
|
||
.option("--agent <id>", "Only clean up memories for a specific agent")
|
||
.action(async (opts: { execute?: boolean; all?: boolean; agent?: string }) => {
|
||
try {
|
||
await db.ensureInitialized();
|
||
|
||
// Fetch memories — by default only auto-capture (explicit stores are trusted)
|
||
const conditions: string[] = [];
|
||
if (!opts.all) {
|
||
conditions.push("m.source = 'auto-capture'");
|
||
}
|
||
if (opts.agent) {
|
||
conditions.push("m.agentId = $agentId");
|
||
}
|
||
const where = conditions.length > 0 ? `WHERE ${conditions.join(" AND ")}` : "";
|
||
const allMemories = await db.runQuery<{
|
||
id: string;
|
||
text: string;
|
||
source: string;
|
||
}>(
|
||
`MATCH (m:Memory) ${where}
|
||
RETURN m.id AS id, m.text AS text, COALESCE(m.source, 'unknown') AS source
|
||
ORDER BY m.createdAt ASC`,
|
||
opts.agent ? { agentId: opts.agent } : {},
|
||
);
|
||
|
||
// Strip channel metadata wrappers (same as the real pipeline) then gate
|
||
const noise: Array<{ id: string; text: string; source: string }> = [];
|
||
for (const mem of allMemories) {
|
||
const stripped = stripMessageWrappers(mem.text);
|
||
if (!passesAttentionGate(stripped)) {
|
||
noise.push(mem);
|
||
}
|
||
}
|
||
|
||
if (noise.length === 0) {
|
||
console.log("\nNo low-substance memories found. Everything passes the gate.");
|
||
return;
|
||
}
|
||
|
||
console.log(
|
||
`\nFound ${noise.length}/${allMemories.length} memories that fail the attention gate:\n`,
|
||
);
|
||
|
||
for (const mem of noise) {
|
||
const preview = mem.text.length > 80 ? `${mem.text.slice(0, 77)}...` : mem.text;
|
||
console.log(` [${mem.source}] "${preview}"`);
|
||
}
|
||
|
||
if (!opts.execute) {
|
||
console.log(
|
||
`\nDry run — ${noise.length} memories would be removed. Re-run with --execute to delete.\n`,
|
||
);
|
||
return;
|
||
}
|
||
|
||
// Delete in batch
|
||
const deleted = await db.pruneMemories(noise.map((m) => m.id));
|
||
console.log(`\nDeleted ${deleted} low-substance memories.\n`);
|
||
} catch (err) {
|
||
console.error(`Error: ${err instanceof Error ? err.message : String(err)}`);
|
||
process.exitCode = 1;
|
||
}
|
||
});
|
||
},
|
||
{ commands: [] }, // Adds subcommands to existing "memory" command, no conflict
|
||
);
|
||
}
|