diff --git a/src/index.ts b/src/index.ts
index 69e1ae8..616eb07 100644
--- a/src/index.ts
+++ b/src/index.ts
@@ -36,6 +36,8 @@ function graceMs(env: Env): number {
 const DUPLICATE_BLOCK_THRESHOLD = 0.95;
 const DUPLICATE_FLAG_THRESHOLD = 0.85;
 const CANDIDATE_SCORE_THRESHOLD = 0.45;
+const TAG_BOOST_STEP = 0.15;
+const TAG_BOOST_MAX = 1.5;
 
 // ─── Model constants ──────────────────────────────────────────────────────────
 
@@ -411,7 +413,8 @@ export function cosineSim(a: ArrayLike<number>, b: ArrayLike<number>): number {
 export function rerankWithTimeDecay(
   matches: VectorizeMatch[],
   recallCounts: Map<string, number> = new Map(),
-  importanceScores: Map<string, number> = new Map()
+  importanceScores: Map<string, number> = new Map(),
+  queryTags: string[] = []
 ): VectorizeMatch[] {
   const now = Date.now();
 
@@ -440,7 +443,12 @@ export function rerankWithTimeDecay(
       const imp = importanceScores.get(parentId) ?? 0;
       const importanceMultiplier = imp === 0 ? 1.0 : 0.8 + (imp / 5) * 0.4;
 
-      return { ...match, score: match.score * combinedMultiplier * appendPenalty * rolledUpPenalty * importanceMultiplier };
+      // Tag boost: applied outside the recency ≤1.0 cap so a tag-relevant memory can
+      // surface above a marginally-closer but irrelevant one.
+      const overlap = queryTags.length ? tags.filter(t => queryTags.includes(t)).length : 0;
+      const tagBoost = overlap ? Math.min(TAG_BOOST_MAX, 1 + overlap * TAG_BOOST_STEP) : 1.0;
+
+      return { ...match, score: match.score * combinedMultiplier * appendPenalty * rolledUpPenalty * importanceMultiplier * tagBoost };
     })
     .sort((a, b) => b.score - a.score);
 }
@@ -520,6 +528,43 @@ export function extractHashtags(content: string): { cleanContent: string; hashta
   return { cleanContent, hashtags };
 }
 
+// ─── Query tag inference ──────────────────────────────────────────────────────
+
+export async function inferQueryTags(query: string, env: Env): Promise<string[]> {
+  const { hashtags } = extractHashtags(query);
+  if (hashtags.length) return hashtags;
+
+  const { results: tagRows } = await env.DB.prepare(
+    `SELECT DISTINCT value FROM entries, json_each(entries.tags) ORDER BY value`
+  ).all();
+  const knownTags = (tagRows as { value: string }[]).map(r => r.value);
+
+  const lowerQuery = query.toLowerCase();
+  const keywordMatches = knownTags.filter(t =>
+    new RegExp(`(?<![\\w-])${t.replace(/[.*+?^${}()|[\]\\]/g, "\\$&")}(?![\\w-])`, "i").test(lowerQuery)
+  );
+
+  if (keywordMatches.length) return keywordMatches;
+
+  if (!knownTags.length) return [];
+
+  try {
+    const stream = await env.AI.run(LLM_MODEL as any, {
+      messages: [{
+        role: "user",
+        content: `From this list of tags: ${knownTags.slice(0, 50).join(", ")}\n\nWhich tags best match this query? Reply with only a comma-separated list of matching tag names from the list, or nothing if none apply.\n\nQuery: ${query.slice(0, 300)}`,
+      }],
+      max_tokens: 100,
+      stream: true,
+    });
+    const text = await readStreamText(stream as ReadableStream);
+    const knownSet = new Set(knownTags);
+    return text.split(",").map(t => t.trim().toLowerCase()).filter(t => t && knownSet.has(t));
+  } catch {
+    return [];
+  }
+}
+
 // ─── Shared entry-listing filter builder ─────────────────────────────────────
 // Builds the WHERE/ORDER/LIMIT clause shared by list_recent and GET /list so
 // both stay in sync on which filters (tag, after, before) are supported.
@@ -932,7 +977,10 @@ export async function recallEntries(
     embedQuery = parsed.cleanQuery;
   }
 
-  const values = await embed(embedQuery, env);
+  const [values, queryTags] = await Promise.all([
+    embed(embedQuery, env),
+    inferQueryTags(embedQuery, env),
+  ]);
 
   let results: { matches: VectorizeMatch[] };
   if (tag) {
@@ -996,7 +1044,7 @@ export async function recallEntries(
   const recallCounts = new Map(rcRows.map(r => [r.id, r.recall_count ?? 0]));
   const importanceScores = new Map(rcRows.map(r => [r.id, r.importance_score ?? 0]));
 
-  const reranked = rerankWithTimeDecay(results.matches as VectorizeMatch[], recallCounts, importanceScores);
+  const reranked = rerankWithTimeDecay(results.matches as VectorizeMatch[], recallCounts, importanceScores, queryTags);
 
   const seen = new Set<string>();
   const deduped = reranked.filter((m) => {
diff --git a/test/integration/recall.test.ts b/test/integration/recall.test.ts
index 652b376..e42022c 100644
--- a/test/integration/recall.test.ts
+++ b/test/integration/recall.test.ts
@@ -278,4 +278,62 @@ describe("GET /recall", () => {
     const scoringCalls = prepareSpy.mock.calls.filter(([sql]) => sql.includes("recall_count, importance_score"));
     expect(scoringCalls).toHaveLength(2);
   });
+
+  it("hashtag or keyword in query skips the LLM during tag inference", async () => {
+    db.entries.push(
+      { id: "entry-1", content: "Work meeting notes", tags: '["work"]', source: "api", created_at: 1000, vector_ids: '["entry-1"]', recall_count: 0, importance_score: 0 },
+    );
+    const aiRun = vi.fn().mockImplementation(async (model: string) => {
+      if (model === "@cf/baai/bge-small-en-v1.5") return { data: [new Array(384).fill(0.1)] };
+      return new ReadableStream({
+        start(c) {
+          c.enqueue(new TextEncoder().encode('data: {"response":"work"}\n\n'));
+          c.enqueue(new TextEncoder().encode("data: [DONE]\n\n"));
+          c.close();
+        },
+      });
+    });
+    env = makeTestEnv(db, {
+      AI: { run: aiRun } as unknown as Ai,
+      VECTORIZE: makeVectorizeMock({
+        query: vi.fn().mockResolvedValue({ matches: [makeMatch("entry-1", 0.9)] }),
+      }),
+    });
+
+    const res = await worker.fetch(req("GET", "/recall?query=work+meeting"), env, ctx);
+    expect(res.status).toBe(200);
+    // "work" is a known tag AND appears as a keyword in the query → LLM not called for inference
+    // (embed call uses BGE model; only LLM calls use other models)
+    const llmCalls = aiRun.mock.calls.filter((args: any[]) => args[0] !== "@cf/baai/bge-small-en-v1.5");
+    expect(llmCalls).toHaveLength(0);
+  });
+
+  it("query with no matching keywords exercises the LLM fallback for tag inference", async () => {
+    db.entries.push(
+      { id: "entry-1", content: "Office lease renewal", tags: '["work"]', source: "api", created_at: 1000, vector_ids: '["entry-1"]', recall_count: 0, importance_score: 0 },
+    );
+    const aiRun = vi.fn().mockImplementation(async (model: string) => {
+      if (model === "@cf/baai/bge-small-en-v1.5") return { data: [new Array(384).fill(0.1)] };
+      return new ReadableStream({
+        start(c) {
+          c.enqueue(new TextEncoder().encode('data: {"response":"work"}\n\n'));
+          c.enqueue(new TextEncoder().encode("data: [DONE]\n\n"));
+          c.close();
+        },
+      });
+    });
+    env = makeTestEnv(db, {
+      AI: { run: aiRun } as unknown as Ai,
+      VECTORIZE: makeVectorizeMock({
+        query: vi.fn().mockResolvedValue({ matches: [makeMatch("entry-1", 0.9)] }),
+      }),
+    });
+
+    // "quarterly planning" — no hashtags, "work" is not a whole word in this query
+    const res = await worker.fetch(req("GET", "/recall?query=quarterly+planning"), env, ctx);
+    expect(res.status).toBe(200);
+    // LLM called at least once (for tag inference); embedding uses BGE model (not counted)
+    const llmCalls = aiRun.mock.calls.filter((args: any[]) => args[0] !== "@cf/baai/bge-small-en-v1.5");
+    expect(llmCalls.length).toBeGreaterThanOrEqual(1);
+  });
 });
diff --git a/test/unit/infer-query-tags.test.ts b/test/unit/infer-query-tags.test.ts
new file mode 100644
index 0000000..8144c6c
--- /dev/null
+++ b/test/unit/infer-query-tags.test.ts
@@ -0,0 +1,105 @@
+import { describe, it, expect, vi } from "vitest";
+import { inferQueryTags } from "../../src/index";
+import { makeTestEnv, makeTestDb } from "../helpers/make-env";
+
+function makeSseStream(response: string) {
+  return new ReadableStream({
+    start(c) {
+      c.enqueue(new TextEncoder().encode(`data: {"response":${JSON.stringify(response)}}\n\n`));
+      c.enqueue(new TextEncoder().encode("data: [DONE]\n\n"));
+      c.close();
+    },
+  });
+}
+
+describe("inferQueryTags", () => {
+  it("returns hashtags extracted from the query without hitting the DB", async () => {
+    const db = makeTestDb();
+    const aiRun = vi.fn();
+    const dbPrepareSpy = vi.spyOn(db, "prepare");
+    const env = makeTestEnv(db, { AI: { run: aiRun } as unknown as Ai });
+    const tags = await inferQueryTags("what did I decide about #work today?", env);
+    expect(tags).toEqual(["work"]);
+    // Early return — no DB or LLM call
+    expect(dbPrepareSpy).not.toHaveBeenCalled();
+    expect(aiRun).not.toHaveBeenCalled();
+  });
+
+  it("returns keyword-matched known tags (whole-word match, case-insensitive)", async () => {
+    const db = makeTestDb();
+    db.entries.push({ id: "e1", content: "Office lease note", tags: '["work","legal"]', source: "api", created_at: 1000, vector_ids: "[]", recall_count: 0, importance_score: 0 });
+    const env = makeTestEnv(db);
+    const tags = await inferQueryTags("what work and legal things did I decide?", env);
+    expect(tags).toHaveLength(2);
+    expect(tags).toEqual(expect.arrayContaining(["work", "legal"]));
+  });
+
+  it("does not call the LLM when keyword matches are found", async () => {
+    const db = makeTestDb();
+    db.entries.push({ id: "e1", content: "Note", tags: '["work"]', source: "api", created_at: 1000, vector_ids: "[]", recall_count: 0, importance_score: 0 });
+    const aiRun = vi.fn().mockResolvedValue(makeSseStream("work"));
+    const env = makeTestEnv(db, { AI: { run: aiRun } as unknown as Ai });
+    const tags = await inferQueryTags("work meeting notes", env);
+    expect(tags).toContain("work");
+    expect(aiRun).not.toHaveBeenCalled();
+  });
+
+  it("calls the LLM and intersects with known tags when cheap inference finds nothing", async () => {
+    const db = makeTestDb();
+    db.entries.push({ id: "e1", content: "Note", tags: '["work","personal"]', source: "api", created_at: 1000, vector_ids: "[]", recall_count: 0, importance_score: 0 });
+    const aiRun = vi.fn().mockResolvedValue(makeSseStream("work, personal"));
+    const env = makeTestEnv(db, { AI: { run: aiRun } as unknown as Ai });
+    const tags = await inferQueryTags("quarterly planning session", env);
+    expect(tags).toHaveLength(2);
+    expect(tags).toEqual(expect.arrayContaining(["work", "personal"]));
+    expect(aiRun).toHaveBeenCalledTimes(1);
+  });
+
+  it("filters out unknown tags returned by the LLM (intersects with known set)", async () => {
+    const db = makeTestDb();
+    db.entries.push({ id: "e1", content: "Note", tags: '["work"]', source: "api", created_at: 1000, vector_ids: "[]", recall_count: 0, importance_score: 0 });
+    const aiRun = vi.fn().mockResolvedValue(makeSseStream("work, invented-tag, random"));
+    const env = makeTestEnv(db, { AI: { run: aiRun } as unknown as Ai });
+    const tags = await inferQueryTags("quarterly planning session", env);
+    expect(tags).toEqual(["work"]);
+  });
+
+  it("returns empty array when the LLM throws — never propagates error", async () => {
+    const db = makeTestDb();
+    db.entries.push({ id: "e1", content: "Note", tags: '["work"]', source: "api", created_at: 1000, vector_ids: "[]", recall_count: 0, importance_score: 0 });
+    const aiRun = vi.fn().mockRejectedValue(new Error("AI unavailable"));
+    const env = makeTestEnv(db, { AI: { run: aiRun } as unknown as Ai });
+    await expect(inferQueryTags("quarterly planning session", env)).resolves.toEqual([]);
+  });
+
+  it("returns empty array when DB has no entries (no vocabulary to match against)", async () => {
+    const db = makeTestDb();
+    const env = makeTestEnv(db);
+    const tags = await inferQueryTags("quarterly planning session", env);
+    expect(tags).toEqual([]);
+  });
+
+  it("does not partially match — 'networking' does not match tag 'net'", async () => {
+    const db = makeTestDb();
+    db.entries.push({ id: "e1", content: "Note", tags: '["net"]', source: "api", created_at: 1000, vector_ids: "[]", recall_count: 0, importance_score: 0 });
+    const env = makeTestEnv(db);
+    const tags = await inferQueryTags("networking event", env);
+    expect(tags).not.toContain("net");
+  });
+
+  it("does not match a hyphenated compound — 'my-claude-response-thing' does not match tag 'claude-response'", async () => {
+    const db = makeTestDb();
+    db.entries.push({ id: "e1", content: "Note", tags: '["claude-response"]', source: "api", created_at: 1000, vector_ids: "[]", recall_count: 0, importance_score: 0 });
+    const env = makeTestEnv(db);
+    const tags = await inferQueryTags("my-claude-response-thing happened", env);
+    expect(tags).not.toContain("claude-response");
+  });
+
+  it("matches a hyphenated tag that appears standalone in the query", async () => {
+    const db = makeTestDb();
+    db.entries.push({ id: "e1", content: "Note", tags: '["claude-response"]', source: "api", created_at: 1000, vector_ids: "[]", recall_count: 0, importance_score: 0 });
+    const env = makeTestEnv(db);
+    const tags = await inferQueryTags("what claude-response notes do I have", env);
+    expect(tags).toContain("claude-response");
+  });
+});
diff --git a/test/unit/rerank.test.ts b/test/unit/rerank.test.ts
index 84ce068..78d1704 100644
--- a/test/unit/rerank.test.ts
+++ b/test/unit/rerank.test.ts
@@ -106,4 +106,19 @@ describe("rerankWithTimeDecay", () => {
     const result = rerankWithTimeDecay([old, fresh], new Map(), importance);
     expect(result[0].id).toBe("old");
   });
+
+  it("tag-overlapping entry outranks equal-vector-score entry without matching tag", () => {
+    const withTag = match("tagged", 0.9, NOW - 5 * MS_DAY, ["work"]);
+    const withoutTag = match("untagged", 0.9, NOW - 5 * MS_DAY, ["personal"]);
+    const result = rerankWithTimeDecay([withoutTag, withTag], new Map(), new Map(), ["work"]);
+    expect(result[0].id).toBe("tagged");
+    expect(result[0].score).toBeGreaterThan(result[1].score);
+  });
+
+  it("queryTags=[] produces identical scores to no queryTags argument (backward compat)", () => {
+    const m = match("entry", 0.9, NOW - 5 * MS_DAY, ["work"]);
+    const [withEmpty] = rerankWithTimeDecay([m], new Map(), new Map(), []);
+    const [withDefault] = rerankWithTimeDecay([m]);
+    expect(withEmpty.score).toBeCloseTo(withDefault.score, 6);
+  });
 });