feat: optional LLM enhancement + noise filter for topic detection

- Add llm-enhance.ts: optional OpenAI-compatible LLM for deeper analysis
  - Supports any provider: Ollama, OpenAI, OpenRouter, vLLM, etc.
  - Batched calls (configurable batchSize, default 3 messages)
  - Cooldown + timeout + graceful degradation (falls back to regex)
  - JSON structured output: threads, decisions, closures, mood

- Add noise filter (isNoiseTopic):
  - Rejects short/blacklisted/pronoun-starting fragments
  - Fixes 'nichts gepostet habe' type garbage threads

- Improve patterns:
  - Topic regex: min 3 chars, max 40 (was 2-30)
  - Add 'let's talk/discuss/look at' and 'lass uns über/mal' triggers
  - German patterns handle optional articles (dem/die/das)

- Wire LLM into hooks:
  - Regex runs first (zero cost, always)
  - LLM batches and enhances on top (async, fire-and-forget)
  - ThreadTracker.applyLlmAnalysis() merges LLM findings
  - DecisionTracker.addDecision() for direct LLM-detected decisions

- Config: new 'llm' section (disabled by default)
- 288 tests passing (18 new)
- Version 0.2.0

BREAKING: None — LLM is opt-in, regex behavior unchanged

package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@vainplex/openclaw-cortex",
-  "version": "0.1.2",
+  "version": "0.2.0",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@vainplex/openclaw-cortex",
-      "version": "0.1.2",
+      "version": "0.2.0",
       "license": "MIT",
       "devDependencies": {
         "@types/node": "^22.0.0",

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@vainplex/openclaw-cortex",
-  "version": "0.1.2",
+  "version": "0.2.0",
   "description": "OpenClaw plugin: conversation intelligence — thread tracking, decision extraction, boot context, pre-compaction snapshots",
   "type": "module",
   "main": "dist/index.js",

src/config.ts

@@ -31,6 +31,14 @@ export const DEFAULTS: CortexConfig = {
   patterns: {
     language: "both",
   },
+  llm: {
+    enabled: false,
+    endpoint: "http://localhost:11434/v1",
+    model: "mistral:7b",
+    apiKey: "",
+    timeoutMs: 15000,
+    batchSize: 3,
+  },
 };
 
 function bool(value: unknown, fallback: boolean): boolean {
@@ -59,6 +67,7 @@ export function resolveConfig(pluginConfig?: Record<string, unknown>): CortexCon
   const pc = (raw.preCompaction ?? {}) as Record<string, unknown>;
   const nr = (raw.narrative ?? {}) as Record<string, unknown>;
   const pt = (raw.patterns ?? {}) as Record<string, unknown>;
+  const lm = (raw.llm ?? {}) as Record<string, unknown>;
 
   return {
     enabled: bool(raw.enabled, DEFAULTS.enabled),
@@ -91,6 +100,14 @@ export function resolveConfig(pluginConfig?: Record<string, unknown>): CortexCon
     patterns: {
       language: lang(pt.language),
     },
+    llm: {
+      enabled: bool(lm.enabled, DEFAULTS.llm.enabled),
+      endpoint: str(lm.endpoint, DEFAULTS.llm.endpoint),
+      model: str(lm.model, DEFAULTS.llm.model),
+      apiKey: str(lm.apiKey, DEFAULTS.llm.apiKey),
+      timeoutMs: int(lm.timeoutMs, DEFAULTS.llm.timeoutMs),
+      batchSize: int(lm.batchSize, DEFAULTS.llm.batchSize),
+    },
   };
 }
 

src/decision-tracker.ts

@@ -156,6 +156,29 @@ export class DecisionTracker {
     }
   }
 
+  /**
+   * Add a decision directly (from LLM analysis). Deduplicates and persists.
+   */
+  addDecision(what: string, who: string, impact: ImpactLevel | string): void {
+    const now = new Date();
+    if (this.isDuplicate(what, now)) return;
+
+    const validImpact = (["critical", "high", "medium", "low"].includes(impact) ? impact : "medium") as ImpactLevel;
+
+    this.decisions.push({
+      id: randomUUID(),
+      what: what.slice(0, 200),
+      date: now.toISOString().slice(0, 10),
+      why: `LLM-detected decision (${who})`,
+      impact: validImpact,
+      who,
+      extracted_at: now.toISOString(),
+    });
+
+    this.enforceMax();
+    this.persist();
+  }
+
   /**
    * Get all decisions (in-memory).
    */

src/hooks.ts

@@ -9,6 +9,7 @@ import { ThreadTracker } from "./thread-tracker.js";
 import { DecisionTracker } from "./decision-tracker.js";
 import { BootContextGenerator } from "./boot-context.js";
 import { PreCompaction } from "./pre-compaction.js";
+import { LlmEnhancer, resolveLlmConfig } from "./llm-enhance.js";
 
 /**
  * Extract message content from a hook event using the fallback chain.
@@ -29,6 +30,7 @@ type HookState = {
   workspace: string | null;
   threadTracker: ThreadTracker | null;
   decisionTracker: DecisionTracker | null;
+  llmEnhancer: LlmEnhancer | null;
 };
 
 function ensureInit(state: HookState, config: CortexConfig, logger: OpenClawPluginApi["logger"], ctx?: HookContext): void {
@@ -41,20 +43,40 @@ function ensureInit(state: HookState, config: CortexConfig, logger: OpenClawPlug
   if (!state.decisionTracker && config.decisionTracker.enabled) {
     state.decisionTracker = new DecisionTracker(state.workspace, config.decisionTracker, config.patterns.language, logger);
   }
+  if (!state.llmEnhancer && config.llm.enabled) {
+    state.llmEnhancer = new LlmEnhancer(config.llm, logger);
+  }
 }
 
 /** Register message hooks (message_received + message_sent). */
 function registerMessageHooks(api: OpenClawPluginApi, config: CortexConfig, state: HookState): void {
   if (!config.threadTracker.enabled && !config.decisionTracker.enabled) return;
 
-  const handler = (event: HookEvent, ctx: HookContext, senderOverride?: string) => {
+  const handler = async (event: HookEvent, ctx: HookContext, senderOverride?: string) => {
     try {
       ensureInit(state, config, api.logger, ctx);
       const content = extractContent(event);
       const sender = senderOverride ?? extractSender(event);
       if (!content) return;
+
+      // Regex-based processing (always runs — zero cost)
       if (config.threadTracker.enabled && state.threadTracker) state.threadTracker.processMessage(content, sender);
       if (config.decisionTracker.enabled && state.decisionTracker) state.decisionTracker.processMessage(content, sender);
+
+      // LLM enhancement (optional — batched, async, fire-and-forget)
+      if (state.llmEnhancer) {
+        const role = senderOverride ? "assistant" as const : "user" as const;
+        const analysis = await state.llmEnhancer.addMessage(content, sender, role);
+        if (analysis) {
+          // Apply LLM findings on top of regex results
+          if (state.threadTracker) state.threadTracker.applyLlmAnalysis(analysis);
+          if (state.decisionTracker) {
+            for (const dec of analysis.decisions) {
+              state.decisionTracker.addDecision(dec.what, dec.who, dec.impact);
+            }
+          }
+        }
+      }
     } catch (err) {
       api.logger.warn(`[cortex] message hook error: ${err}`);
     }
@@ -109,13 +131,13 @@ function registerCompactionHooks(api: OpenClawPluginApi, config: CortexConfig, s
  * Each handler is wrapped in try/catch — never throws.
  */
 export function registerCortexHooks(api: OpenClawPluginApi, config: CortexConfig): void {
-  const state: HookState = { workspace: null, threadTracker: null, decisionTracker: null };
+  const state: HookState = { workspace: null, threadTracker: null, decisionTracker: null, llmEnhancer: null };
 
   registerMessageHooks(api, config, state);
   registerSessionHooks(api, config, state);
   registerCompactionHooks(api, config, state);
 
   api.logger.info(
-    `[cortex] Hooks registered — threads:${config.threadTracker.enabled} decisions:${config.decisionTracker.enabled} boot:${config.bootContext.enabled} compaction:${config.preCompaction.enabled}`,
+    `[cortex] Hooks registered — threads:${config.threadTracker.enabled} decisions:${config.decisionTracker.enabled} boot:${config.bootContext.enabled} compaction:${config.preCompaction.enabled} llm:${config.llm.enabled}${config.llm.enabled ? ` (${config.llm.model}@${config.llm.endpoint})` : ""}`,
   );
 }

src/llm-enhance.ts

@@ -0,0 +1,258 @@
+import { request } from "node:http";
+import { URL } from "node:url";
+import type { PluginLogger } from "./types.js";
+
+/**
+ * LLM Enhancement — optional AI-powered analysis layered on top of regex patterns.
+ *
+ * When enabled, sends conversation snippets to a local or remote LLM for deeper
+ * thread/decision/closure detection. Falls back gracefully to regex-only on failure.
+ *
+ * Supports any OpenAI-compatible API (Ollama, vLLM, OpenRouter, OpenAI, etc.)
+ */
+
+export type LlmConfig = {
+  enabled: boolean;
+  /** OpenAI-compatible endpoint, e.g. "http://localhost:11434/v1" */
+  endpoint: string;
+  /** Model identifier, e.g. "mistral:7b" or "gpt-4o-mini" */
+  model: string;
+  /** API key (optional, for cloud providers) */
+  apiKey: string;
+  /** Timeout in ms for LLM calls */
+  timeoutMs: number;
+  /** Minimum message count before triggering LLM (batches for efficiency) */
+  batchSize: number;
+};
+
+export const LLM_DEFAULTS: LlmConfig = {
+  enabled: false,
+  endpoint: "http://localhost:11434/v1",
+  model: "mistral:7b",
+  apiKey: "",
+  timeoutMs: 15000,
+  batchSize: 3,
+};
+
+export type LlmAnalysis = {
+  threads: Array<{
+    title: string;
+    status: "open" | "closed";
+    summary?: string;
+  }>;
+  decisions: Array<{
+    what: string;
+    who: string;
+    impact: "high" | "medium" | "low";
+  }>;
+  closures: string[];
+  mood: string;
+};
+
+const SYSTEM_PROMPT = `You are a conversation analyst. Given a snippet of conversation between a user and an AI assistant, extract:
+
+1. **threads**: Active topics being discussed. Each has a title (short, specific) and status (open/closed).
+2. **decisions**: Any decisions made. Include what was decided, who decided, and impact (high/medium/low).
+3. **closures**: Thread titles that were completed/resolved in this snippet.
+4. **mood**: Overall conversation mood (neutral/frustrated/excited/tense/productive/exploratory).
+
+Rules:
+- Only extract REAL topics, not meta-conversation ("how are you", greetings, etc.)
+- Thread titles should be specific and actionable ("auth migration to OAuth2", not "the thing")
+- Decisions must be actual commitments, not questions or suggestions
+- Be conservative — when in doubt, don't extract
+
+Respond ONLY with valid JSON matching this schema:
+{"threads":[{"title":"...","status":"open|closed","summary":"..."}],"decisions":[{"what":"...","who":"...","impact":"high|medium|low"}],"closures":["thread title"],"mood":"neutral"}`;
+
+/**
+ * Call an OpenAI-compatible chat completion API.
+ */
+function callLlm(
+  config: LlmConfig,
+  messages: Array<{ role: string; content: string }>,
+  logger: PluginLogger,
+): Promise<string | null> {
+  return new Promise((resolve) => {
+    try {
+      const url = new URL(`${config.endpoint}/chat/completions`);
+      const body = JSON.stringify({
+        model: config.model,
+        messages,
+        temperature: 0.1,
+        max_tokens: 1000,
+        response_format: { type: "json_object" },
+      });
+
+      const headers: Record<string, string> = {
+        "Content-Type": "application/json",
+        "Content-Length": String(Buffer.byteLength(body)),
+      };
+      if (config.apiKey) {
+        headers["Authorization"] = `Bearer ${config.apiKey}`;
+      }
+
+      const proto = url.protocol === "https:" ? require("node:https") : require("node:http");
+      const req = proto.request(
+        {
+          hostname: url.hostname,
+          port: url.port || (url.protocol === "https:" ? 443 : 80),
+          path: url.pathname,
+          method: "POST",
+          headers,
+          timeout: config.timeoutMs,
+        },
+        (res: any) => {
+          let data = "";
+          res.on("data", (chunk: string) => (data += chunk));
+          res.on("end", () => {
+            try {
+              const parsed = JSON.parse(data);
+              const content = parsed?.choices?.[0]?.message?.content;
+              resolve(content ?? null);
+            } catch {
+              logger.warn(`[cortex-llm] Failed to parse LLM response`);
+              resolve(null);
+            }
+          });
+        },
+      );
+
+      req.on("error", (err: Error) => {
+        logger.warn(`[cortex-llm] Request error: ${err.message}`);
+        resolve(null);
+      });
+
+      req.on("timeout", () => {
+        req.destroy();
+        logger.warn(`[cortex-llm] Request timed out (${config.timeoutMs}ms)`);
+        resolve(null);
+      });
+
+      req.write(body);
+      req.end();
+    } catch (err) {
+      logger.warn(`[cortex-llm] Exception: ${err}`);
+      resolve(null);
+    }
+  });
+}
+
+/**
+ * Parse LLM JSON response into structured analysis.
+ * Returns null on any parse failure (graceful degradation).
+ */
+function parseAnalysis(raw: string, logger: PluginLogger): LlmAnalysis | null {
+  try {
+    const parsed = JSON.parse(raw);
+    return {
+      threads: Array.isArray(parsed.threads)
+        ? parsed.threads.filter(
+            (t: any) => typeof t.title === "string" && t.title.length > 2,
+          )
+        : [],
+      decisions: Array.isArray(parsed.decisions)
+        ? parsed.decisions.filter(
+            (d: any) => typeof d.what === "string" && d.what.length > 5,
+          )
+        : [],
+      closures: Array.isArray(parsed.closures)
+        ? parsed.closures.filter((c: any) => typeof c === "string")
+        : [],
+      mood: typeof parsed.mood === "string" ? parsed.mood : "neutral",
+    };
+  } catch {
+    logger.warn(`[cortex-llm] Failed to parse analysis JSON`);
+    return null;
+  }
+}
+
+/**
+ * Message buffer for batching LLM calls.
+ */
+export class LlmEnhancer {
+  private buffer: Array<{ role: string; content: string; sender: string }> = [];
+  private readonly config: LlmConfig;
+  private readonly logger: PluginLogger;
+  private lastCallMs = 0;
+  private readonly cooldownMs = 5000;
+
+  constructor(config: LlmConfig, logger: PluginLogger) {
+    this.config = config;
+    this.logger = logger;
+  }
+
+  /**
+   * Buffer a message. Returns analysis when batch is full, null otherwise.
+   */
+  async addMessage(
+    content: string,
+    sender: string,
+    role: "user" | "assistant",
+  ): Promise<LlmAnalysis | null> {
+    if (!this.config.enabled) return null;
+
+    this.buffer.push({ role, content, sender });
+
+    if (this.buffer.length < this.config.batchSize) return null;
+
+    // Cooldown check
+    const now = Date.now();
+    if (now - this.lastCallMs < this.cooldownMs) return null;
+    this.lastCallMs = now;
+
+    // Flush buffer
+    const batch = this.buffer.splice(0);
+    return this.analyze(batch);
+  }
+
+  /**
+   * Force-analyze remaining buffer (e.g. before compaction).
+   */
+  async flush(): Promise<LlmAnalysis | null> {
+    if (!this.config.enabled || this.buffer.length === 0) return null;
+    const batch = this.buffer.splice(0);
+    return this.analyze(batch);
+  }
+
+  private async analyze(
+    messages: Array<{ role: string; content: string; sender: string }>,
+  ): Promise<LlmAnalysis | null> {
+    const snippet = messages
+      .map((m) => `[${m.sender}]: ${m.content}`)
+      .join("\n\n");
+
+    const raw = await callLlm(
+      this.config,
+      [
+        { role: "system", content: SYSTEM_PROMPT },
+        { role: "user", content: snippet },
+      ],
+      this.logger,
+    );
+
+    if (!raw) return null;
+
+    const analysis = parseAnalysis(raw, this.logger);
+    if (analysis) {
+      const stats = `threads=${analysis.threads.length} decisions=${analysis.decisions.length} closures=${analysis.closures.length}`;
+      this.logger.info(`[cortex-llm] Analysis: ${stats}`);
+    }
+    return analysis;
+  }
+}
+
+/**
+ * Resolve LLM config from plugin config.
+ */
+export function resolveLlmConfig(raw?: Record<string, unknown>): LlmConfig {
+  if (!raw) return { ...LLM_DEFAULTS };
+  return {
+    enabled: typeof raw.enabled === "boolean" ? raw.enabled : LLM_DEFAULTS.enabled,
+    endpoint: typeof raw.endpoint === "string" ? raw.endpoint : LLM_DEFAULTS.endpoint,
+    model: typeof raw.model === "string" ? raw.model : LLM_DEFAULTS.model,
+    apiKey: typeof raw.apiKey === "string" ? raw.apiKey : LLM_DEFAULTS.apiKey,
+    timeoutMs: typeof raw.timeoutMs === "number" ? raw.timeoutMs : LLM_DEFAULTS.timeoutMs,
+    batchSize: typeof raw.batchSize === "number" ? raw.batchSize : LLM_DEFAULTS.batchSize,
+  };
+}

src/patterns.ts

@@ -32,13 +32,23 @@ const WAIT_PATTERNS_DE = [
 ];
 
 const TOPIC_PATTERNS_EN = [
-  /(?:back to|now about|regarding)\s+(\w[\w\s-]{2,30})/i,
+  /(?:back to|now about|regarding|let's (?:talk|discuss|look at))\s+(?:the\s+)?(\w[\w\s-]{3,40})/i,
 ];
 
 const TOPIC_PATTERNS_DE = [
-  /(?:zurück zu|jetzt zu|bzgl\.?|wegen)\s+(\w[\w\s-]{2,30})/i,
+  /(?:zurück zu|jetzt zu|bzgl\.?|wegen|lass uns (?:über|mal))\s+(?:dem?|die|das)?\s*(\w[\w\s-]{3,40})/i,
 ];
 
+/** Words that should never be thread titles (noise filter) */
+const TOPIC_BLACKLIST = new Set([
+  "it", "that", "this", "the", "them", "what", "which", "there",
+  "das", "die", "der", "es", "was", "hier", "dort",
+  "nothing", "something", "everything", "nichts", "etwas", "alles",
+  "me", "you", "him", "her", "us", "mir", "dir", "ihm", "uns",
+  "today", "tomorrow", "yesterday", "heute", "morgen", "gestern",
+  "noch", "schon", "jetzt", "dann", "also", "aber", "oder",
+]);
+
 const MOOD_PATTERNS: Record<Exclude<Mood, "neutral">, RegExp> = {
   frustrated: /(?:fuck|shit|mist|nervig|genervt|damn|wtf|argh|schon wieder|zum kotzen|sucks)/i,
   excited: /(?:geil|nice|awesome|krass|boom|läuft|yes!|🎯|🚀|perfekt|brilliant|mega|sick)/i,
@@ -115,6 +125,24 @@ export function detectMood(text: string): Mood {
   return lastMood;
 }
 
+/**
+ * Check if a topic candidate is noise (too short, blacklisted, or garbage).
+ */
+export function isNoiseTopic(topic: string): boolean {
+  const trimmed = topic.trim();
+  if (trimmed.length < 4) return true;
+  // Single word that's in blacklist
+  const words = trimmed.toLowerCase().split(/\s+/);
+  if (words.length === 1 && TOPIC_BLACKLIST.has(words[0])) return true;
+  // All words are blacklisted
+  if (words.every(w => TOPIC_BLACKLIST.has(w) || w.length < 3)) return true;
+  // Looks like a sentence fragment (starts with pronoun or blacklisted word)
+  if (/^(ich|i|we|wir|du|er|sie|he|she|it|es|nichts|nothing|etwas|something)\s/i.test(trimmed)) return true;
+  // Contains line breaks or is too long for a title
+  if (trimmed.includes("\n") || trimmed.length > 60) return true;
+  return false;
+}
+
 /** High-impact keywords for decision impact inference */
 export const HIGH_IMPACT_KEYWORDS = [
   "architecture", "architektur", "security", "sicherheit",

src/thread-tracker.ts

@@ -7,7 +7,7 @@ import type {
   ThreadPriority,
   PluginLogger,
 } from "./types.js";
-import { getPatterns, detectMood, HIGH_IMPACT_KEYWORDS } from "./patterns.js";
+import { getPatterns, detectMood, HIGH_IMPACT_KEYWORDS, isNoiseTopic } from "./patterns.js";
 import type { PatternLanguage } from "./patterns.js";
 import { loadJson, saveJson, rebootDir, ensureRebootDir } from "./storage.js";
 
@@ -127,9 +127,10 @@ export class ThreadTracker {
     this.sessionMood = data.session_mood ?? "neutral";
   }
 
-  /** Create new threads from topic signals. */
+  /** Create new threads from topic signals (with noise filtering). */
   private createFromTopics(topics: string[], sender: string, mood: string, now: string): void {
     for (const topic of topics) {
+      if (isNoiseTopic(topic)) continue;
       const exists = this.threads.some(
         t => t.title.toLowerCase() === topic.toLowerCase() || matchesThread(t, topic),
       );
@@ -143,6 +144,52 @@ export class ThreadTracker {
     }
   }
 
+  /**
+   * Apply LLM analysis results — creates threads, closes threads, adds decisions.
+   * Called from hooks when LLM enhance is enabled.
+   */
+  applyLlmAnalysis(analysis: {
+    threads: Array<{ title: string; status: "open" | "closed"; summary?: string }>;
+    closures: string[];
+    mood: string;
+  }): void {
+    const now = new Date().toISOString();
+
+    // Create threads from LLM
+    for (const lt of analysis.threads) {
+      if (isNoiseTopic(lt.title)) continue;
+      const exists = this.threads.some(
+        t => t.title.toLowerCase() === lt.title.toLowerCase() || matchesThread(t, lt.title),
+      );
+      if (!exists) {
+        this.threads.push({
+          id: randomUUID(), title: lt.title, status: lt.status,
+          priority: inferPriority(lt.title), summary: lt.summary ?? "LLM-detected",
+          decisions: [], waiting_for: null, mood: analysis.mood ?? "neutral",
+          last_activity: now, created: now,
+        });
+      }
+    }
+
+    // Close threads from LLM closures
+    for (const closure of analysis.closures) {
+      for (const thread of this.threads) {
+        if (thread.status === "open" && matchesThread(thread, closure)) {
+          thread.status = "closed";
+          thread.last_activity = now;
+        }
+      }
+    }
+
+    // Update session mood
+    if (analysis.mood && analysis.mood !== "neutral") {
+      this.sessionMood = analysis.mood;
+    }
+
+    this.dirty = true;
+    this.persist();
+  }
+
   /** Close threads matching closure signals. */
   private closeMatching(content: string, closures: boolean[], now: string): void {
     if (closures.length === 0) return;

src/types.ts

@@ -245,6 +245,14 @@ export type CortexConfig = {
   patterns: {
     language: "en" | "de" | "both";
   };
+  llm: {
+    enabled: boolean;
+    endpoint: string;
+    model: string;
+    apiKey: string;
+    timeoutMs: number;
+    batchSize: number;
+  };
 };
 
 // ============================================================

test/llm-enhance.test.ts

@@ -0,0 +1,97 @@
+import { describe, it, expect } from "vitest";
+import { resolveLlmConfig, LlmEnhancer, LLM_DEFAULTS } from "../src/llm-enhance.js";
+
+const mockLogger = {
+  info: () => {},
+  warn: () => {},
+  error: () => {},
+  debug: () => {},
+};
+
+describe("resolveLlmConfig", () => {
+  it("returns defaults when no config provided", () => {
+    const config = resolveLlmConfig(undefined);
+    expect(config).toEqual(LLM_DEFAULTS);
+    expect(config.enabled).toBe(false);
+  });
+
+  it("returns defaults for empty object", () => {
+    const config = resolveLlmConfig({});
+    expect(config).toEqual(LLM_DEFAULTS);
+  });
+
+  it("merges partial config with defaults", () => {
+    const config = resolveLlmConfig({
+      enabled: true,
+      model: "qwen2.5:7b",
+    });
+    expect(config.enabled).toBe(true);
+    expect(config.model).toBe("qwen2.5:7b");
+    expect(config.endpoint).toBe(LLM_DEFAULTS.endpoint);
+    expect(config.timeoutMs).toBe(LLM_DEFAULTS.timeoutMs);
+    expect(config.batchSize).toBe(LLM_DEFAULTS.batchSize);
+  });
+
+  it("respects custom endpoint and apiKey", () => {
+    const config = resolveLlmConfig({
+      enabled: true,
+      endpoint: "https://api.openai.com/v1",
+      model: "gpt-4o-mini",
+      apiKey: "sk-test",
+      timeoutMs: 30000,
+      batchSize: 5,
+    });
+    expect(config.endpoint).toBe("https://api.openai.com/v1");
+    expect(config.apiKey).toBe("sk-test");
+    expect(config.timeoutMs).toBe(30000);
+    expect(config.batchSize).toBe(5);
+  });
+
+  it("ignores invalid types", () => {
+    const config = resolveLlmConfig({
+      enabled: "yes" as any,
+      model: 42 as any,
+      timeoutMs: "fast" as any,
+    });
+    expect(config.enabled).toBe(LLM_DEFAULTS.enabled);
+    expect(config.model).toBe(LLM_DEFAULTS.model);
+    expect(config.timeoutMs).toBe(LLM_DEFAULTS.timeoutMs);
+  });
+});
+
+describe("LlmEnhancer", () => {
+  it("returns null when disabled", async () => {
+    const enhancer = new LlmEnhancer({ ...LLM_DEFAULTS, enabled: false }, mockLogger);
+    const result = await enhancer.addMessage("test message", "user1", "user");
+    expect(result).toBeNull();
+  });
+
+  it("buffers messages until batchSize", async () => {
+    const enhancer = new LlmEnhancer(
+      { ...LLM_DEFAULTS, enabled: true, batchSize: 3 },
+      mockLogger,
+    );
+    // First two messages should buffer (no LLM call)
+    const r1 = await enhancer.addMessage("hello", "user1", "user");
+    expect(r1).toBeNull();
+    const r2 = await enhancer.addMessage("world", "assistant", "assistant");
+    expect(r2).toBeNull();
+    // Third would trigger LLM but will fail gracefully (no server)
+    const r3 = await enhancer.addMessage("test", "user1", "user");
+    // Returns null because localhost:11434 is not guaranteed
+    // The important thing is it doesn't throw
+    expect(r3 === null || typeof r3 === "object").toBe(true);
+  });
+
+  it("flush returns null when no messages buffered", async () => {
+    const enhancer = new LlmEnhancer({ ...LLM_DEFAULTS, enabled: true }, mockLogger);
+    const result = await enhancer.flush();
+    expect(result).toBeNull();
+  });
+
+  it("flush returns null when disabled", async () => {
+    const enhancer = new LlmEnhancer({ ...LLM_DEFAULTS, enabled: false }, mockLogger);
+    const result = await enhancer.flush();
+    expect(result).toBeNull();
+  });
+});

test/noise-filter.test.ts

@@ -0,0 +1,57 @@
+import { describe, it, expect } from "vitest";
+import { isNoiseTopic } from "../src/patterns.js";
+
+describe("isNoiseTopic", () => {
+  it("rejects short strings", () => {
+    expect(isNoiseTopic("foo")).toBe(true);
+    expect(isNoiseTopic("ab")).toBe(true);
+    expect(isNoiseTopic("")).toBe(true);
+  });
+
+  it("rejects single blacklisted words", () => {
+    expect(isNoiseTopic("that")).toBe(true);
+    expect(isNoiseTopic("this")).toBe(true);
+    expect(isNoiseTopic("nichts")).toBe(true);
+    expect(isNoiseTopic("alles")).toBe(true);
+  });
+
+  it("rejects all-blacklisted multi-word", () => {
+    expect(isNoiseTopic("das was es")).toBe(true);
+    expect(isNoiseTopic("the that it")).toBe(true);
+  });
+
+  it("rejects sentence fragments starting with pronouns", () => {
+    expect(isNoiseTopic("ich habe nichts gepostet")).toBe(true);
+    expect(isNoiseTopic("we should do something")).toBe(true);
+    expect(isNoiseTopic("er hat gesagt")).toBe(true);
+    expect(isNoiseTopic("I think maybe")).toBe(true);
+  });
+
+  it("rejects topics with newlines", () => {
+    expect(isNoiseTopic("line one\nline two")).toBe(true);
+  });
+
+  it("rejects topics longer than 60 chars", () => {
+    const long = "a".repeat(61);
+    expect(isNoiseTopic(long)).toBe(true);
+  });
+
+  it("accepts valid topic names", () => {
+    expect(isNoiseTopic("Auth Migration")).toBe(false);
+    expect(isNoiseTopic("Plugin-Repo Setup")).toBe(false);
+    expect(isNoiseTopic("NATS Event Store")).toBe(false);
+    expect(isNoiseTopic("Cortex Demo")).toBe(false);
+    expect(isNoiseTopic("Security Audit")).toBe(false);
+    expect(isNoiseTopic("Deployment Pipeline")).toBe(false);
+  });
+
+  it("accepts german topic names", () => {
+    expect(isNoiseTopic("Darkplex Analyse")).toBe(false);
+    expect(isNoiseTopic("Credential Rotation")).toBe(false);
+    expect(isNoiseTopic("Thread Tracking Qualität")).toBe(false);
+  });
+
+  it("rejects 'nichts gepostet habe' (real-world noise)", () => {
+    expect(isNoiseTopic("nichts gepostet habe")).toBe(true);
+  });
+});

test/patterns.test.ts

@@ -260,10 +260,10 @@ describe("topic patterns", () => {
       expect(anyMatch(topic, "just a random sentence")).toBe(false);
     });
 
-    it("limits captured topic to 30 chars", () => {
+    it("limits captured topic to 40 chars", () => {
-      const topics = captureTopics(topic, "back to the very long topic name that exceeds thirty characters limit here");
+      const topics = captureTopics(topic, "back to the very long topic name that exceeds forty characters limit here and keeps going");
       if (topics.length > 0) {
-        expect(topics[0].length).toBeLessThanOrEqual(31);
+        expect(topics[0].length).toBeLessThanOrEqual(41);
       }
     });
   });
@@ -274,7 +274,7 @@ describe("topic patterns", () => {
     it("captures topic after 'zurück zu'", () => {
       const topics = captureTopics(topic, "Zurück zu der Auth-Migration");
       expect(topics.length).toBeGreaterThan(0);
-      expect(topics[0]).toContain("der Auth-Migration");
+      expect(topics[0]).toContain("Auth-Migration");
     });
 
     it("captures topic after 'jetzt zu'", () => {