nocmem: qa-style extraction prompt, multi-cue variants, claude history importer

- Switch extraction prompt to qa-style (80% recall vs 60% baseline)
- Semicolon-separated cues in extraction become paraphrase variants
- Add import_claude.py to bulk-import Claude Code conversation history
- Fix LLM model name in systemd service, add logging basicConfig

.gitignore

@@ -7,3 +7,5 @@ state.*.json
 target/
 data/
 noc.service
+tools/manage_todo
+mem/benchmarks/longmemeval.json

Makefile

@@ -34,7 +34,8 @@ deploy: test build
 	scp target/release/noc $(SUITE):~/bin/
 	scp config.suite.yaml $(SUITE):/data/noc/config.yaml
 	scp noc.service.in $(SUITE):/data/noc/
-	scp -r tools/ $(SUITE):/data/noc/tools/
+	rsync -a tools/ $(SUITE):/data/noc/tools/
+	rsync -a assets/ $(SUITE):/data/noc/assets/
 	ssh $(SUITE) 'bash -lc "\
 		cd /data/noc \
 		&& sed -e \"s|@REPO@|/data/noc|g\" -e \"s|@PATH@|\$$PATH|g\" noc.service.in > ~/.config/systemd/user/noc.service \

context.md

@@ -7,9 +7,10 @@
 - **LLM**: vLLM on ailab (100.84.7.49:8000), gemma-4-31B-it-AWQ
 - **Claude Code**: ~/.local/bin/claude (子代<E5AD90><E4BBA3>执行引擎)
 - **uv**: ~/.local/bin/uv (Python 包管理)
+- **Hugo**: /usr/local/bin/hugo (静态博客生成器)
 
 ### 域名路由 (Caddy)
-- famzheng.me — 主站（占位）
+- famzheng.me → Hugo 博客 (/data/www/blog/public/)
 - git.famzheng.me → Gitea (localhost:3000)
 - 新增子域名：编辑 /etc/caddy/Caddyfile，然后 `sudo systemctl reload caddy`
 
@@ -31,6 +32,30 @@ api.famzheng.me {
 修改后执行 `sudo systemctl reload caddy` 生效。
 Caddy 自动申请和续期 Let's Encrypt 证书，无需手动管理。
 
+### 博客
+Fam 的博客:
+- 站点: https://famzheng.me, 源码: /data/www/blog/
+- Repo: https://git.famzheng.me/fam/blog
+- 这是 Fam 的个人博客，不要在上面写东西
+
+你的博客 (AI 日记/随想):
+- 站点: https://noc.famzheng.me, 源码: /data/www/noc-blog/
+- Repo: https://git.famzheng.me/noc/diary
+- 这是你自己的空间，可以自由写日记、随想、技术笔记
+- 写新文章: 在 content/posts/ 下创建 .md 文件，运行 `cd /data/www/noc-blog && hugo`，然后 git commit + push
+
+Hugo 写文章格式:
+```markdown
+---
+title: "标题"
+date: 2026-04-10T22:00:00+01:00
+draft: false
+summary: "一句话摘要"
+---
+
+正文内容，支持 Markdown。
+```
+
 ### Gitea
 - URL: https://git.famzheng.me
 - Admin: noc (token 在 /data/noc/gitea-token)

doc/nocmem.md

@@ -0,0 +1,277 @@
+# nocmem — NOC 自动记忆系统
+
+## 动机
+
+NOC 现有记忆：100 个文本槽位（200 字符/槽）+ 滑动窗口摘要。全部塞在 system prompt 里，每次对话都带着。
+
+问题：
+- 没有语义检索，无关记忆浪费 token
+- 槽位容量有限，不可扩展
+- 没有联想能力（A 提到 → 想起 B → 引出 C）
+
+nocmem 用 NuoNuo 的 Hopfield-Hebbian 混合记忆网络替代朴素文本槽位，实现**自动召回**和**自动存储**。
+
+## 核心技术
+
+### NuoNuo Hippocampal Memory
+
+生物启发的双层记忆架构（详见 `../nuonuo/doc/architecture.md`）：
+
+**Layer 1 — Hopfield（单跳，噪声容忍）**
+
+存储 (cue, target) embedding 对。召回时两阶段：
+
+1. **NN 预过滤**：cosine similarity 找 top-K 候选（K=20）
+2. **Hopfield settle**：β-scaled softmax attention 迭代收敛（3 步）
+
+关键特性：**paraphrase 容忍** — 用户换一种说法问同样的事，照样能召回。通过存储 cue variants（同一条记忆的多种表述）实现，attention 按 memory_id 聚合。
+
+**Layer 2 — Hebbian（多跳，联想链）**
+
+WTA pattern separation（384D → 16384D 稀疏码，k=50，稀疏度 0.3%）+ 外积权重矩阵 W。
+
+Hopfield 找到起点后，Hebbian 通过 `W @ code` 沿关联链前进：A → B → C。
+
+这是传统 RAG 做不到的——向量搜索只能找"相似"，Hebbian 能找"相关但不相似"的东西。
+
+**性能指标**
+
+| 指标 | 数值 |
+|------|------|
+| Paraphrase recall（+augmentation, 2K bg） | 95-100% |
+| Multi-hop（3 hops, 500 bg） | 100% |
+| Scale（20K memories, no augmentation） | 80% |
+| Recall 延迟 @ 20K | 4ms |
+| VRAM | ~1 GB |
+
+### Embedding
+
+使用 `all-MiniLM-L6-v2`（384 维），CPU/GPU 均可。选择理由：
+
+- NuoNuo 实验（P1）验证：**gap metric（相关与不相关的分数差）比绝对相似度更重要**
+- MiniLM 在 gap metric 上优于 BGE-large 等更大模型
+- 推理快：GPU ~1ms，CPU ~10ms per query
+
+### 记忆提取
+
+对话结束后，用 LLM 从 (user_msg, assistant_msg) 中提取 (cue, target, importance) 三元组：
+
+- **cue**：什么情况下应该回忆起这条记忆（触发短语）
+- **target**：记忆内容本身
+- **importance**：0-1 重要度评分
+
+LLM 不可用时回退到 heuristic（问答模式检测 + 技术关键词匹配）。
+
+提取后，LLM 为每个 cue 生成 3 个 paraphrase，作为 cue_variants 存入，提升召回鲁棒性。
+
+## 架构
+
+```
+              ┌─────────────┐
+              │   Telegram   │
+              │    User      │
+              └──────┬───────┘
+                     │ message
+                     ▼
+              ┌─────────────┐
+              │     NOC      │
+              │   (Rust)     │
+              │              │
+              │ 1. 收到 user │
+              │    message   │
+              │              │
+              │ 2. HTTP POST ├──────────────────┐
+              │    /recall   │                  │
+              │              │                  ▼
+              │              │         ┌─────────────────┐
+              │              │         │     nocmem      │
+              │              │         │   (Python)      │
+              │              │         │                 │
+              │              │         │ embed(query)    │
+              │              │◄────────┤ hippocampus     │
+              │   recalled   │         │   .recall()     │
+              │   memories   │         │ format results  │
+              │              │         └─────────────────┘
+              │ 3. 构建 messages:       
+              │    [...history,         
+              │     user_msg,           
+              │     {role:system,       
+              │      recalled memories}]
+              │              │
+              │ 4. 调 LLM    │
+              │    (stream)  │
+              │              │
+              │ 5. 得到      │
+              │    response  │
+              │              │
+              │ 6. 异步 POST ├──────────────────┐
+              │    /ingest   │                  │
+              │              │                  ▼
+              │              │         ┌─────────────────┐
+              │              │         │     nocmem      │
+              │              │         │                 │
+              │              │         │ LLM extract     │
+              │              │         │ embed + store   │
+              │              │         │ save checkpoint │
+              │              │         └─────────────────┘
+              │ 7. 回复用户  │
+              └──────────────┘
+```
+
+## 消息注入策略
+
+**关键设计**：recalled memories 注入在 user message **之后**，作为独立的 system message。
+
+```json
+[
+  {"role": "system", "content": "persona + memory_slots + ..."},   // 不变
+  {"role": "user", "content": "历史消息1"},                         // 历史
+  {"role": "assistant", "content": "历史回复1"},
+  ...
+  {"role": "user", "content": "当前用户消息"},                      // 当前轮
+  {"role": "system", "content": "[相关记忆]\n- 记忆1\n- 记忆2"}    // ← nocmem 注入
+]
+```
+
+为什么不放 system prompt 里？
+
+**KV cache 友好**。System prompt 是所有对话共享的前缀，如果每条消息都改 system prompt 的内容（注入不同的 recalled memories），整个 KV cache 前缀失效，前面几千 token 全部重算。
+
+放在 user message 之后，前缀（system prompt + 历史消息 + 当前 user message）保持稳定，只有尾部的 recalled memories 是变化的，KV cache 命中率最大化。
+
+**临时性**。Recalled memories 不持久化到对话历史数据库。每轮对话独立召回，下一轮消息进来时重新召回当时相关的记忆。这避免了历史消息中堆积大量冗余的记忆注入。
+
+## HTTP API
+
+### POST /recall
+
+请求：
+```json
+{"text": "数据库最近是不是很慢"}
+```
+
+响应：
+```json
+{
+  "memories": "[相关记忆]\n- 上次数据库慢是因为缺少索引 (hop=1)\n- PostgreSQL 跑在 5432 端口 (hop=2)",
+  "count": 2
+}
+```
+
+- 如果没有相关记忆，返回 `{"memories": "", "count": 0}`
+- NOC 检查 count > 0 才注入，避免空消息
+
+### POST /ingest
+
+请求：
+```json
+{
+  "user_msg": "帮我看看数据库为什么慢",
+  "assistant_msg": "检查了一下，是 users 表缺少 email 字段的索引..."
+}
+```
+
+响应：
+```json
+{"stored": 2}
+```
+
+- fire-and-forget，NOC 不等响应
+- 内部流程：LLM 提取 → embed → generate paraphrases → store → save checkpoint
+
+### GET /stats
+
+```json
+{
+  "num_memories": 1234,
+  "num_cue_entries": 4500,
+  "augmentation_ratio": 3.6,
+  "vram_mb": 1024,
+  "embedding_model": "all-MiniLM-L6-v2"
+}
+```
+
+## NOC 侧改动
+
+### config.yaml
+
+```yaml
+nocmem:
+  endpoint: "http://127.0.0.1:9820"
+```
+
+### Rust 改动（最小化）
+
+**`config.rs`**：加一个可选字段
+
+```rust
+#[serde(default)]
+pub nocmem: Option<NocmemConfig>,
+
+#[derive(Deserialize, Clone)]
+pub struct NocmemConfig {
+    pub endpoint: String,
+}
+```
+
+**`main.rs`**（主消息处理路径）：
+
+在 `api_messages.push(user_msg)` 之后、`run_openai_with_tools` 之前：
+
+```rust
+// auto recall from nocmem
+if let Some(ref nocmem) = config.nocmem {
+    if let Ok(recalled) = nocmem_recall(&nocmem.endpoint, &prompt).await {
+        if !recalled.is_empty() {
+            api_messages.push(serde_json::json!({
+                "role": "system",
+                "content": recalled
+            }));
+        }
+    }
+}
+```
+
+在 LLM 回复之后（`push_message` 之后）：
+
+```rust
+// async ingest to nocmem (fire-and-forget)
+if let Some(ref nocmem) = config.nocmem {
+    let endpoint = nocmem.endpoint.clone();
+    let u = prompt.clone();
+    let a = response.clone();
+    tokio::spawn(async move {
+        let _ = nocmem_ingest(&endpoint, &u, &a).await;
+    });
+}
+```
+
+`nocmem_recall` 和 `nocmem_ingest` 是两个简单的 HTTP 调用函数。recall 设 500ms 超时（失败就跳过，不影响正常对话）。
+
+### 同步覆盖的调用点
+
+| 位置 | 场景 | recall | ingest |
+|------|------|--------|--------|
+| `main.rs` handle_message | 用户聊天 | ✅ | ✅ |
+| `life.rs` AgentDone | 子代理完成通知 | ✅ | ❌ |
+| `life.rs` run_timer | 定时器触发 | ❌ | ❌ |
+| `http.rs` api_chat | HTTP API 聊天 | ✅ | ✅ |
+| `gitea.rs` | Gitea webhook | ❌ | ❌ |
+
+## 部署
+
+nocmem 作为独立 Python 服务运行：
+
+```bash
+cd /data/src/noc/mem
+uv run uvicorn server:app --host 127.0.0.1 --port 9820
+```
+
+可配 systemd 管理。checkpoint 持久化到 `./data/hippocampus.pt`（相对于 mem 目录）。
+
+## 未来方向
+
+- **重要度衰减**：长期不被召回的记忆自动降权
+- **矛盾检测**：新记忆与旧记忆冲突时自动替换
+- **记忆整合（sleep consolidation）**：定期合并碎片记忆为更紧凑的表示
+- **和 memory slot 融合**：逐步迁移 slot 内容到 nocmem，最终淘汰 slot 系统

mem/benchmarks/efficiency_bench.py

@@ -0,0 +1,345 @@
+"""Efficiency benchmark for nocmem vs ChromaDB baseline.
+
+Measures: storage size, memory usage, query latency, ingest throughput
+at various scales (100, 1K, 5K, 10K, 20K memories).
+
+Usage:
+    uv run python benchmarks/efficiency_bench.py
+"""
+
+import gc
+import os
+import json
+import shutil
+import tempfile
+import time
+
+import torch
+import psutil
+from sentence_transformers import SentenceTransformer
+
+from nuonuo.hippocampus import HippocampalMemory
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+EMBED_MODEL = "all-MiniLM-L6-v2"
+EMBED_DIM = 384
+
+DATA_FILE = "benchmarks/longmemeval.json"
+
+# ── helpers ─────────────────────────────────────────────────────────
+
+def get_process_mem_mb():
+    return psutil.Process(os.getpid()).memory_info().rss / 1024**2
+
+def get_gpu_mem_mb():
+    if DEVICE != "cuda":
+        return 0.0
+    return torch.cuda.memory_allocated() / 1024**2
+
+def file_size_mb(path):
+    if os.path.exists(path):
+        return os.path.getsize(path) / 1024**2
+    return 0.0
+
+def dir_size_mb(path):
+    total = 0
+    for dirpath, _, filenames in os.walk(path):
+        for f in filenames:
+            total += os.path.getsize(os.path.join(dirpath, f))
+    return total / 1024**2
+
+
+# ── extract chunks from LongMemEval ────────────────────────────────
+
+def load_chunks(max_chunks=25000):
+    """Extract turn-level chunks from LongMemEval data."""
+    with open(DATA_FILE) as f:
+        data = json.load(f)
+
+    chunks = []
+    seen = set()
+    for item in data:
+        for sid, sess in zip(item["haystack_session_ids"], item["haystack_sessions"]):
+            for i in range(0, len(sess) - 1, 2):
+                key = (sid, i)
+                if key in seen:
+                    continue
+                seen.add(key)
+                user = sess[i]["content"]
+                asst = sess[i + 1]["content"] if i + 1 < len(sess) else ""
+                text = f"{user}\n{asst}"[:1000]
+                chunks.append(text)
+                if len(chunks) >= max_chunks:
+                    return chunks
+    return chunks
+
+
+# ── nocmem benchmark ────────────────────────────────────────────────
+
+def bench_nocmem(encoder, chunks, n, query_texts):
+    """Benchmark nocmem at scale n."""
+    torch.cuda.empty_cache()
+    gc.collect()
+
+    subset = chunks[:n]
+    gpu_before = get_gpu_mem_mb()
+    ram_before = get_process_mem_mb()
+
+    # batch embed
+    t0 = time.monotonic()
+    embeddings = encoder.encode(
+        subset, convert_to_tensor=True, normalize_embeddings=True,
+        device=DEVICE, batch_size=256, show_progress_bar=False,
+    )
+    embed_time = time.monotonic() - t0
+
+    # store
+    hip = HippocampalMemory(embed_dim=EMBED_DIM, device=DEVICE)
+    t1 = time.monotonic()
+    for i in range(n):
+        hip.store(embeddings[i], embeddings[i], metadata={"id": i})
+    store_time = time.monotonic() - t1
+
+    gpu_after = get_gpu_mem_mb()
+    ram_after = get_process_mem_mb()
+
+    # save to measure file size
+    tmp = tempfile.mktemp(suffix=".pt")
+    hip.save(tmp)
+    disk_mb = file_size_mb(tmp)
+    os.unlink(tmp)
+
+    # query latency — multiple queries, measure p50/p99
+    query_embs = encoder.encode(
+        query_texts, convert_to_tensor=True, normalize_embeddings=True,
+        device=DEVICE, show_progress_bar=False,
+    )
+    latencies = []
+    for qe in query_embs:
+        t = time.monotonic()
+        hip.recall(qe, top_k=5)
+        latencies.append((time.monotonic() - t) * 1000)
+
+    latencies.sort()
+    p50 = latencies[len(latencies) // 2]
+    p99 = latencies[int(len(latencies) * 0.99)]
+    avg = sum(latencies) / len(latencies)
+
+    # cleanup
+    del hip, embeddings
+    torch.cuda.empty_cache()
+
+    return {
+        "n": n,
+        "embed_time_s": embed_time,
+        "store_time_s": store_time,
+        "ingest_rate": n / (embed_time + store_time),  # memories/sec
+        "disk_mb": disk_mb,
+        "gpu_delta_mb": gpu_after - gpu_before,
+        "ram_delta_mb": ram_after - ram_before,
+        "latency_avg_ms": avg,
+        "latency_p50_ms": p50,
+        "latency_p99_ms": p99,
+    }
+
+
+# ── chromadb benchmark ──────────────────────────────────────────────
+
+def bench_chromadb(encoder, chunks, n, query_texts):
+    """Benchmark ChromaDB (MemPalace's backend) at scale n."""
+    import chromadb
+
+    subset = chunks[:n]
+    ram_before = get_process_mem_mb()
+
+    tmpdir = tempfile.mkdtemp()
+    client = chromadb.PersistentClient(path=tmpdir)
+    collection = client.create_collection(
+        name="bench",
+        metadata={"hnsw:space": "cosine"},
+    )
+
+    # embed
+    t0 = time.monotonic()
+    embeddings_np = encoder.encode(
+        subset, normalize_embeddings=True,
+        batch_size=256, show_progress_bar=False,
+    )
+    embed_time = time.monotonic() - t0
+
+    # store — chromadb takes numpy/list
+    t1 = time.monotonic()
+    batch = 5000
+    for start in range(0, n, batch):
+        end = min(start + batch, n)
+        collection.add(
+            ids=[str(i) for i in range(start, end)],
+            embeddings=embeddings_np[start:end].tolist(),
+            documents=subset[start:end],
+        )
+    store_time = time.monotonic() - t1
+
+    ram_after = get_process_mem_mb()
+    disk_mb = dir_size_mb(tmpdir)
+
+    # query latency
+    query_np = encoder.encode(
+        query_texts, normalize_embeddings=True, show_progress_bar=False,
+    )
+    latencies = []
+    for qe in query_np:
+        t = time.monotonic()
+        collection.query(query_embeddings=[qe.tolist()], n_results=5)
+        latencies.append((time.monotonic() - t) * 1000)
+
+    latencies.sort()
+    p50 = latencies[len(latencies) // 2]
+    p99 = latencies[int(len(latencies) * 0.99)]
+    avg = sum(latencies) / len(latencies)
+
+    # cleanup
+    del client, collection
+    shutil.rmtree(tmpdir)
+
+    return {
+        "n": n,
+        "embed_time_s": embed_time,
+        "store_time_s": store_time,
+        "ingest_rate": n / (embed_time + store_time),
+        "disk_mb": disk_mb,
+        "gpu_delta_mb": 0,
+        "ram_delta_mb": ram_after - ram_before,
+        "latency_avg_ms": avg,
+        "latency_p50_ms": p50,
+        "latency_p99_ms": p99,
+    }
+
+
+# ── main ────────────────────────────────────────────────────────────
+
+def main():
+    print("nocmem efficiency benchmark")
+    print(f"device: {DEVICE}")
+    print()
+
+    # check chromadb available
+    has_chromadb = False
+    try:
+        import chromadb
+        has_chromadb = True
+        print("chromadb: available (will compare)")
+    except ImportError:
+        print("chromadb: not installed (nocmem only)")
+    print()
+
+    print("loading data...")
+    chunks = load_chunks(25000)
+    print(f"  {len(chunks)} unique chunks extracted")
+
+    print("loading encoder...")
+    encoder = SentenceTransformer(EMBED_MODEL, device=DEVICE)
+
+    # query texts — mix of English and Chinese
+    query_texts = [
+        "What degree did I graduate with?",
+        "How to deploy the application?",
+        "What was the database error we fixed last week?",
+        "Tell me about the meeting schedule",
+        "What programming language should I learn?",
+        "数据库密码在哪里",
+        "部署到生产环境的步骤",
+        "上次讨论的性能优化方案",
+        "项目的技术栈是什么",
+        "最近的待办事项有哪些",
+        "How do I configure the server?",
+        "What's the API endpoint for user authentication?",
+        "Can you recommend some books on machine learning?",
+        "What was the root cause of the production incident?",
+        "How much memory does the GPU have?",
+        "VR设备的兼容性问题",
+        "模型推理的延迟是多少",
+        "代码仓库的结构是怎样的",
+        "如何解决内存泄漏",
+        "上次会议的结论是什么",
+    ]
+
+    scales = [100, 500, 1000, 5000, 10000, 20000]
+    # filter to what we have
+    scales = [s for s in scales if s <= len(chunks)]
+
+    nocmem_results = []
+    chroma_results = []
+
+    for n in scales:
+        print(f"\n── scale: {n:,} memories ──")
+
+        print(f"  nocmem...", end="", flush=True)
+        r = bench_nocmem(encoder, chunks, n, query_texts)
+        nocmem_results.append(r)
+        print(f" done  (R: {r['latency_avg_ms']:.1f}ms, disk: {r['disk_mb']:.1f}MB)")
+
+        if has_chromadb:
+            print(f"  chromadb...", end="", flush=True)
+            r2 = bench_chromadb(encoder, chunks, n, query_texts)
+            chroma_results.append(r2)
+            print(f" done  (R: {r2['latency_avg_ms']:.1f}ms, disk: {r2['disk_mb']:.1f}MB)")
+
+    # ── report ──────────────────────────────────────────────────────
+
+    print(f"\n{'='*80}")
+    print(f"EFFICIENCY BENCHMARK RESULTS")
+    print(f"{'='*80}")
+
+    # table header
+    if has_chromadb:
+        print(f"\n{'Scale':>8} | {'--- nocmem ---':^40} | {'--- ChromaDB ---':^40}")
+        print(f"{'':>8} | {'Latency':>8} {'p99':>8} {'Disk':>8} {'VRAM':>8} {'Rate':>8} | {'Latency':>8} {'p99':>8} {'Disk':>8} {'RAM':>8} {'Rate':>8}")
+        print(f"{'':>8} | {'(ms)':>8} {'(ms)':>8} {'(MB)':>8} {'(MB)':>8} {'(/s)':>8} | {'(ms)':>8} {'(ms)':>8} {'(MB)':>8} {'(MB)':>8} {'(/s)':>8}")
+        print("-" * 100)
+        for nm, cr in zip(nocmem_results, chroma_results):
+            print(
+                f"{nm['n']:>8,} | "
+                f"{nm['latency_avg_ms']:>8.1f} {nm['latency_p99_ms']:>8.1f} {nm['disk_mb']:>8.1f} {nm['gpu_delta_mb']:>8.1f} {nm['ingest_rate']:>8.0f} | "
+                f"{cr['latency_avg_ms']:>8.1f} {cr['latency_p99_ms']:>8.1f} {cr['disk_mb']:>8.1f} {cr['ram_delta_mb']:>8.1f} {cr['ingest_rate']:>8.0f}"
+            )
+    else:
+        print(f"\n{'Scale':>8} | {'Latency':>8} {'p99':>8} {'Disk':>8} {'VRAM':>8} {'Ingest':>8}")
+        print(f"{'':>8} | {'(ms)':>8} {'(ms)':>8} {'(MB)':>8} {'(MB)':>8} {'(/s)':>8}")
+        print("-" * 60)
+        for nm in nocmem_results:
+            print(
+                f"{nm['n']:>8,} | "
+                f"{nm['latency_avg_ms']:>8.1f} {nm['latency_p99_ms']:>8.1f} {nm['disk_mb']:>8.1f} {nm['gpu_delta_mb']:>8.1f} {nm['ingest_rate']:>8.0f}"
+            )
+
+    # summary
+    if nocmem_results:
+        biggest = nocmem_results[-1]
+        print(f"\nnocmem @ {biggest['n']:,}:")
+        print(f"  Query latency:  avg {biggest['latency_avg_ms']:.1f}ms, p99 {biggest['latency_p99_ms']:.1f}ms")
+        print(f"  Disk:           {biggest['disk_mb']:.1f} MB")
+        print(f"  VRAM delta:     {biggest['gpu_delta_mb']:.1f} MB")
+        print(f"  Ingest rate:    {biggest['ingest_rate']:.0f} memories/sec")
+
+    if chroma_results:
+        biggest = chroma_results[-1]
+        print(f"\nChromaDB @ {biggest['n']:,}:")
+        print(f"  Query latency:  avg {biggest['latency_avg_ms']:.1f}ms, p99 {biggest['latency_p99_ms']:.1f}ms")
+        print(f"  Disk:           {biggest['disk_mb']:.1f} MB")
+        print(f"  RAM delta:      {biggest['ram_delta_mb']:.1f} MB")
+        print(f"  Ingest rate:    {biggest['ingest_rate']:.0f} memories/sec")
+
+    if has_chromadb and nocmem_results and chroma_results:
+        nm = nocmem_results[-1]
+        cr = chroma_results[-1]
+        print(f"\n── nocmem vs ChromaDB @ {nm['n']:,} ──")
+        lat_ratio = cr['latency_avg_ms'] / nm['latency_avg_ms'] if nm['latency_avg_ms'] > 0 else float('inf')
+        disk_ratio = cr['disk_mb'] / nm['disk_mb'] if nm['disk_mb'] > 0 else float('inf')
+        rate_ratio = nm['ingest_rate'] / cr['ingest_rate'] if cr['ingest_rate'] > 0 else float('inf')
+        print(f"  Latency:  nocmem {lat_ratio:.1f}x faster" if lat_ratio > 1 else f"  Latency:  ChromaDB {1/lat_ratio:.1f}x faster")
+        print(f"  Disk:     nocmem {disk_ratio:.1f}x smaller" if disk_ratio > 1 else f"  Disk:     ChromaDB {1/disk_ratio:.1f}x smaller")
+        print(f"  Ingest:   nocmem {rate_ratio:.1f}x faster" if rate_ratio > 1 else f"  Ingest:   ChromaDB {1/rate_ratio:.1f}x faster")
+
+
+if __name__ == "__main__":
+    main()

mem/benchmarks/longmemeval_bench.py

@@ -0,0 +1,239 @@
+"""LongMemEval benchmark for nocmem.
+
+Evaluates retrieval quality: given a question, can nocmem find the correct
+session(s) from a haystack of ~50 conversation sessions?
+
+Uses HippocampalMemory directly (no HTTP) for speed.
+Compares against MemPalace's 96.6% R@5 baseline.
+
+Usage:
+    uv run python benchmarks/longmemeval_bench.py [--limit N] [--granularity session|turn]
+"""
+
+import argparse
+import json
+import math
+import sys
+import time
+
+import torch
+from sentence_transformers import SentenceTransformer
+
+from nuonuo.hippocampus import HippocampalMemory
+
+# ── setup ───────────────────────────────────────────────────────────
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+EMBED_MODEL = "all-MiniLM-L6-v2"
+EMBED_DIM = 384
+
+
+def load_encoder():
+    print(f"loading {EMBED_MODEL} on {DEVICE}...")
+    return SentenceTransformer(EMBED_MODEL, device=DEVICE)
+
+
+def embed_batch(encoder, texts: list[str]) -> torch.Tensor:
+    """Batch embed, returns (N, dim) tensor."""
+    return encoder.encode(
+        texts, convert_to_tensor=True, normalize_embeddings=True,
+        device=DEVICE, batch_size=128, show_progress_bar=False,
+    )
+
+
+# ── granularity: how to chunk sessions ──────────────────────────────
+
+def sessions_to_chunks_turn(session_ids, sessions):
+    """Each user-assistant turn becomes a separate chunk."""
+    chunks = []  # (text, session_id)
+    for sid, sess in zip(session_ids, sessions):
+        for i in range(0, len(sess) - 1, 2):
+            user = sess[i]["content"]
+            asst = sess[i + 1]["content"] if i + 1 < len(sess) else ""
+            text = f"{user}\n{asst}"
+            # truncate long turns to avoid embedding issues
+            chunks.append((text[:1000], sid))
+        # handle odd-numbered turns
+        if len(sess) % 2 == 1:
+            chunks.append((sess[-1]["content"][:1000], sid))
+    return chunks
+
+
+def sessions_to_chunks_session(session_ids, sessions):
+    """Each session becomes a single chunk (concatenated turns)."""
+    chunks = []
+    for sid, sess in zip(session_ids, sessions):
+        text = "\n".join(m["content"] for m in sess)
+        # truncate to fit embedding model's context
+        chunks.append((text[:2000], sid))
+    return chunks
+
+
+# ── evaluate one question ───────────────────────────────────────────
+
+def evaluate_question(encoder, item, granularity, ks=(5, 10)):
+    """Store haystack, query, check if answer session in top-K.
+
+    Returns dict with R@5, R@10, NDCG@10, timings.
+    """
+    # chunk the haystack
+    if granularity == "turn":
+        chunks = sessions_to_chunks_turn(
+            item["haystack_session_ids"], item["haystack_sessions"])
+    else:
+        chunks = sessions_to_chunks_session(
+            item["haystack_session_ids"], item["haystack_sessions"])
+
+    texts = [c[0] for c in chunks]
+    sids = [c[1] for c in chunks]
+    answer_sids = set(item["answer_session_ids"])
+
+    # batch embed all chunks
+    t0 = time.monotonic()
+    embeddings = embed_batch(encoder, texts)
+    embed_time = time.monotonic() - t0
+
+    # build memory
+    t1 = time.monotonic()
+    hip = HippocampalMemory(embed_dim=EMBED_DIM, device=DEVICE)
+    for i in range(len(chunks)):
+        hip.store(
+            embeddings[i], embeddings[i],
+            metadata={"session_id": sids[i]},
+        )
+    store_time = time.monotonic() - t1
+
+    # query
+    t2 = time.monotonic()
+    query_emb = encoder.encode(
+        [item["question"]], convert_to_tensor=True,
+        normalize_embeddings=True, device=DEVICE,
+    )[0]
+
+    max_k = max(ks)
+    results = hip.recall(query_emb, top_k=max_k)
+    recall_time = time.monotonic() - t2
+
+    # deduplicate by session_id, preserving rank order
+    seen = set()
+    ranked_sids = []
+    for r in results:
+        sid = r.metadata["session_id"]
+        if sid not in seen:
+            seen.add(sid)
+            ranked_sids.append(sid)
+
+    # compute metrics
+    metrics = {}
+    for k in ks:
+        top_k_sids = set(ranked_sids[:k])
+        hit = bool(answer_sids & top_k_sids)
+        metrics[f"R@{k}"] = 1.0 if hit else 0.0
+
+    # NDCG@10
+    ndcg = compute_ndcg(ranked_sids[:10], answer_sids)
+    metrics["NDCG@10"] = ndcg
+
+    metrics["embed_ms"] = embed_time * 1000
+    metrics["store_ms"] = store_time * 1000
+    metrics["recall_ms"] = recall_time * 1000
+    metrics["n_chunks"] = len(chunks)
+
+    return metrics
+
+
+def compute_ndcg(ranked_sids, answer_sids, k=10):
+    """Normalized Discounted Cumulative Gain."""
+    dcg = 0.0
+    for i, sid in enumerate(ranked_sids[:k]):
+        if sid in answer_sids:
+            dcg += 1.0 / math.log2(i + 2)  # i+2 because rank starts at 1
+
+    # ideal: all answer sessions at top
+    n_relevant = min(len(answer_sids), k)
+    idcg = sum(1.0 / math.log2(i + 2) for i in range(n_relevant))
+
+    return dcg / idcg if idcg > 0 else 0.0
+
+
+# ── main ───<E29480><E29480>────────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data", default="benchmarks/longmemeval.json")
+    parser.add_argument("--limit", type=int, default=0, help="limit number of questions (0=all)")
+    parser.add_argument("--granularity", choices=["session", "turn"], default="turn")
+    args = parser.parse_args()
+
+    print(f"LongMemEval benchmark for nocmem")
+    print(f"granularity: {args.granularity}")
+    print(f"device: {DEVICE}")
+    print()
+
+    with open(args.data) as f:
+        data = json.load(f)
+
+    if args.limit:
+        data = data[:args.limit]
+
+    encoder = load_encoder()
+
+    print(f"evaluating {len(data)} questions...\n")
+
+    all_metrics = []
+    by_type = {}
+
+    for i, item in enumerate(data):
+        metrics = evaluate_question(encoder, item, args.granularity)
+        all_metrics.append(metrics)
+
+        qtype = item["question_type"]
+        if qtype not in by_type:
+            by_type[qtype] = []
+        by_type[qtype].append(metrics)
+
+        # progress
+        if (i + 1) % 10 == 0 or i == len(data) - 1:
+            r5 = sum(m["R@5"] for m in all_metrics) / len(all_metrics) * 100
+            r10 = sum(m["R@10"] for m in all_metrics) / len(all_metrics) * 100
+            avg_recall = sum(m["recall_ms"] for m in all_metrics) / len(all_metrics)
+            print(f"  [{i+1:3d}/{len(data)}] R@5={r5:.1f}%  R@10={r10:.1f}%  recall={avg_recall:.1f}ms")
+
+    # final results
+    n = len(all_metrics)
+    r5 = sum(m["R@5"] for m in all_metrics) / n * 100
+    r10 = sum(m["R@10"] for m in all_metrics) / n * 100
+    ndcg = sum(m["NDCG@10"] for m in all_metrics) / n * 100
+    avg_embed = sum(m["embed_ms"] for m in all_metrics) / n
+    avg_store = sum(m["store_ms"] for m in all_metrics) / n
+    avg_recall = sum(m["recall_ms"] for m in all_metrics) / n
+    avg_chunks = sum(m["n_chunks"] for m in all_metrics) / n
+
+    print(f"\n{'='*60}")
+    print(f"nocmem LongMemEval Results ({args.granularity} granularity)")
+    print(f"{'='*60}")
+    print(f"  Questions:     {n}")
+    print(f"  Avg chunks:    {avg_chunks:.0f}")
+    print(f"")
+    print(f"  R@5:           {r5:.1f}%")
+    print(f"  R@10:          {r10:.1f}%")
+    print(f"  NDCG@10:       {ndcg:.1f}%")
+    print(f"")
+    print(f"  Avg embed:     {avg_embed:.0f}ms")
+    print(f"  Avg store:     {avg_store:.0f}ms")
+    print(f"  Avg recall:    {avg_recall:.1f}ms")
+
+    print(f"\n── by question type ──")
+    for qtype, ms in sorted(by_type.items()):
+        nt = len(ms)
+        tr5 = sum(m["R@5"] for m in ms) / nt * 100
+        tr10 = sum(m["R@10"] for m in ms) / nt * 100
+        print(f"  {qtype:30s}  n={nt:3d}  R@5={tr5:.1f}%  R@10={tr10:.1f}%")
+
+    print(f"\n── comparison ──")
+    print(f"  MemPalace (raw, session):   R@5=96.6%")
+    print(f"  nocmem ({args.granularity:7s}):         R@5={r5:.1f}%")
+
+
+if __name__ == "__main__":
+    main()

mem/benchmarks/noise_vs_scale.py

@@ -0,0 +1,178 @@
+"""Does recall noise decrease as memory count grows?
+
+At various scales, measure:
+1. Recall accuracy (R@3) for relevant queries
+2. Max cosine similarity for irrelevant queries
+3. Separation gap between relevant and irrelevant
+
+If nocmem works well at scale, the gap should widen — relevant queries
+should score much higher than irrelevant ones as the memory pool grows.
+"""
+
+import json
+import time
+import torch
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from nuonuo.hippocampus import HippocampalMemory
+
+DEVICE = "cuda"
+EMBED_DIM = 384
+DATA_FILE = "benchmarks/longmemeval.json"
+
+IRRELEVANT_QUERIES = [
+    "今天天气怎么样",
+    "你喜欢吃什么",
+    "嗨",
+    "讲个笑话",
+    "明天会下雨吗",
+    "你觉得猫可爱还是狗可爱",
+    "人生的意义是什么",
+    "帮我写一首诗",
+    "地球到月球有多远",
+    "如何学会游泳",
+]
+
+BETA_CONFIGS = [16.0, 32.0, 64.0]
+SCALES = [50, 200, 500, 1000, 3000]
+
+
+def main():
+    print("noise vs scale benchmark\n")
+    print("loading encoder...")
+    encoder = SentenceTransformer("all-MiniLM-L6-v2", device=DEVICE)
+
+    def emb(text):
+        return encoder.encode([text], convert_to_tensor=True,
+                              normalize_embeddings=True, device=DEVICE)[0]
+
+    def emb_batch(texts):
+        return encoder.encode(texts, convert_to_tensor=True,
+                              normalize_embeddings=True, device=DEVICE,
+                              batch_size=256, show_progress_bar=False)
+
+    # load data
+    print("loading data...")
+    with open(DATA_FILE) as f:
+        data = json.load(f)
+
+    # collect unique chunks with their source question index
+    all_chunks = []  # (text, question_idx, session_id)
+    seen = set()
+    for qi, item in enumerate(data):
+        for sid, sess in zip(item["haystack_session_ids"], item["haystack_sessions"]):
+            for i in range(0, len(sess) - 1, 2):
+                key = (sid, i)
+                if key in seen:
+                    continue
+                seen.add(key)
+                user = sess[i]["content"]
+                asst = sess[i + 1]["content"] if i + 1 < len(sess) else ""
+                text = f"{user}\n{asst}"[:1000]
+                all_chunks.append((text, qi, sid))
+    print(f"  {len(all_chunks)} unique chunks")
+
+    # pre-embed irrelevant queries
+    irrel_embs = [emb(q) for q in IRRELEVANT_QUERIES]
+
+    # collect relevant queries: for each question, we know the answer session
+    # pick first 50 questions that have at least one answer session
+    relevant_queries = []
+    for item in data[:100]:
+        answer_sids = set(item["answer_session_ids"])
+        relevant_queries.append((item["question"], answer_sids))
+    rel_query_embs = emb_batch([q for q, _ in relevant_queries])
+
+    print(f"  {len(relevant_queries)} relevant queries")
+    print(f"  {len(IRRELEVANT_QUERIES)} irrelevant queries")
+
+    # filter scales to what we have
+    scales = [s for s in SCALES if s <= len(all_chunks)]
+
+    for beta in BETA_CONFIGS:
+        print(f"\n{'='*70}")
+        print(f"  β = {beta}")
+        print(f"{'='*70}")
+        print(f"{'Scale':>7} | {'R@3':>6} | {'Rel maxcos':>10} {'Irrel maxcos':>12} {'Gap':>8} | {'Rel attn':>9} {'Irrel attn':>11}")
+        print("-" * 80)
+
+        for n in scales:
+            subset = all_chunks[:n]
+            texts = [c[0] for c in subset]
+            sids = [c[2] for c in subset]
+
+            # embed and build memory
+            embeddings = emb_batch(texts)
+            hip = HippocampalMemory(
+                embed_dim=EMBED_DIM, beta=beta, hopfield_top_k=10, device=DEVICE,
+            )
+            for i in range(n):
+                hip.store(embeddings[i], embeddings[i],
+                          metadata={"session_id": sids[i]})
+
+            cue_mat = hip._get_cue_matrix()
+
+            # --- relevant queries ---
+            rel_max_cos = []
+            rel_top_attn = []
+            hits = 0
+            tested = 0
+
+            for qi in range(len(relevant_queries)):
+                question, answer_sids = relevant_queries[qi]
+                qe = rel_query_embs[qi]
+
+                # check if any answer session is in this subset
+                subset_sids = set(sids)
+                if not (answer_sids & subset_sids):
+                    continue
+                tested += 1
+
+                # cosine sim
+                cos_sims = qe @ cue_mat.T
+                rel_max_cos.append(cos_sims.max().item())
+
+                # recall
+                results = hip.recall(qe, top_k=3)
+                top_attn = results[0].similarity if results else 0
+                rel_top_attn.append(top_attn)
+
+                recalled_sids = {r.metadata["session_id"] for r in results}
+                if answer_sids & recalled_sids:
+                    hits += 1
+
+            r3 = hits / tested * 100 if tested > 0 else 0
+            avg_rel_cos = np.mean(rel_max_cos) if rel_max_cos else 0
+            avg_rel_attn = np.mean(rel_top_attn) if rel_top_attn else 0
+
+            # --- irrelevant queries ---
+            irrel_max_cos = []
+            irrel_top_attn = []
+            for qe in irrel_embs:
+                cos_sims = qe @ cue_mat.T
+                irrel_max_cos.append(cos_sims.max().item())
+
+                results = hip.recall(qe, top_k=3)
+                top_attn = results[0].similarity if results else 0
+                irrel_top_attn.append(top_attn)
+
+            avg_irrel_cos = np.mean(irrel_max_cos)
+            avg_irrel_attn = np.mean(irrel_top_attn)
+
+            gap = avg_rel_cos - avg_irrel_cos
+
+            print(f"{n:>7,} | {r3:>5.1f}% | {avg_rel_cos:>10.3f} {avg_irrel_cos:>12.3f} {gap:>8.3f} | {avg_rel_attn:>8.0%} {avg_irrel_attn:>10.0%}")
+
+            del hip
+            torch.cuda.empty_cache()
+
+    print(f"\n── 解读 ──")
+    print(f"Rel maxcos:   相关查询的最大余弦相似度（越高越好）")
+    print(f"Irrel maxcos: 无关查询的最大余弦相似度（越低越好）")
+    print(f"Gap:          两者之差（越大越好 = 越容易区分）")
+    print(f"Rel attn:     相关查询 top1 的 Hopfield attention 权重")
+    print(f"Irrel attn:   无关查询 top1 的 Hopfield attention 权重（越低 = 越少噪音）")
+
+
+if __name__ == "__main__":
+    main()

mem/benchmarks/prompt_experiment.py

@@ -0,0 +1,217 @@
+"""Test different extraction prompts for ingest quality.
+
+Takes a few real conversation turns, tries different prompts,
+compares what gets extracted, then tests recall against target queries.
+"""
+
+import json
+import time
+import requests
+import torch
+from sentence_transformers import SentenceTransformer
+from nuonuo.hippocampus import HippocampalMemory
+from openai import OpenAI
+
+DEVICE = "cuda"
+LLM = OpenAI(base_url="http://100.84.7.49:8000/v1", api_key="unused")
+MODEL = "gemma4-31b"
+encoder = SentenceTransformer("all-MiniLM-L6-v2", device=DEVICE)
+
+# real conversation turns from Claude Code history
+TURNS = [
+    ("装好了tailscale。你帮忙生成吧，用新的bot名叫yoyo",
+     "好，Tailscale IP `100.116.213.25`，yoyo 用户创建成功。继续搞 token 和部署。"),
+    ("能来一个org共享的gitea ci cd runner 不，就跑在这台机器上就行。",
+     "搞起来！装 act_runner，注册到 euphon org，以后每个仓库直接加 `.gitea/workflows/` 就能跑 CI 了。"),
+    ("能不能给heradev一个bridge的nic，secondary的，这样就有192.168.1.0/24的地址可以用了",
+     "饭团，你是要给 heradev 这个 KVM 虚拟机加一个桥接网卡对吧？这样它就能直接拿到物理网段 192.168.1.0/24 的 IP。"),
+    ("i7 bot也不好，直接叫@ms吧",
+     "好，@ms 改好了。现在来研究全局 webhook 的问题——之前系统级 webhook 没触发，让我深入看看。"),
+    ("AI/ML 工具克隆 全部删掉吧，emblemcv，visint也删掉",
+     "好嘞，这一波删掉能省 ~62G。动手了。"),
+]
+
+# queries we expect to recall
+TARGET_QUERIES = [
+    ("yoyo是什么", ["yoyo"]),
+    ("act runner怎么装的", ["act_runner", "runner"]),
+    ("heradev网络怎么配的", ["heradev", "桥接", "bridge", "192.168"]),
+    ("gitea bot叫什么", ["ms", "@ms", "麻薯"]),
+    ("清理了哪些项目", ["emblemcv", "visint", "62G", "删"]),
+]
+
+# different extraction prompts to test
+PROMPTS = {
+    "baseline": """From this conversation turn, extract key facts worth remembering for future conversations.
+For each fact, provide a "cue" (what would trigger recalling this) and a "target" (the fact itself).
+Rate importance 0-1 (1 = critical fact, 0 = trivial).
+
+User: {user}
+Assistant: {assistant}
+
+Output format (one per line):
+CUE: <trigger phrase> | TARGET: <fact> | IMPORTANCE: <0-1>
+
+Only extract genuinely useful facts. If nothing worth remembering, output NONE.""",
+
+    "entity_focused": """从这段对话中提取值得记住的事实。重点关注：
+- 名称、代号、别名（谁叫什么）
+- 配置、参数、端口、地址
+- 做了什么操作、改了什么
+- 决策和原因
+
+每条事实用以下格式输出（每行一条）：
+CUE: <用什么问题能想起这件事> | TARGET: <事实本身，要具体> | IMPORTANCE: <0-1>
+
+User: {user}
+Assistant: {assistant}
+
+如果没有值得记住的，输出 NONE。""",
+
+    "multi_cue": """从这段对话中提取值得长期记住的事实。
+
+要求：
+1. 每条事实提供 2-3 个不同的触发短语（cue），用分号分隔
+2. target 要具体、独立可理解（不依赖上下文）
+3. 包含所有出现的名称、代号、配置值
+
+格式（每行一条）：
+CUE: <触发短语1>; <触发短语2>; <触发短语3> | TARGET: <具体事实> | IMPORTANCE: <0-1>
+
+User: {user}
+Assistant: {assistant}
+
+没有值得记住的则输出 NONE。""",
+
+    "qa_style": """你是一个记忆提取器。把这段对话变成若干个"问答对"——未来有人问这个问题时，能直接给出答案。
+
+要求：
+- 问题要自然，像人真的会这么问
+- 答案要具体完整，包含关键细节（名称、数字、地址等）
+- 同一个事实可以从不同角度提问
+
+格式（每行一条）：
+CUE: <自然的提问方式> | TARGET: <完整的回答> | IMPORTANCE: <0-1>
+
+User: {user}
+Assistant: {assistant}
+
+没有值得记住的则输出 NONE。""",
+}
+
+import re
+
+def extract_with_prompt(prompt_template, user_msg, asst_msg):
+    prompt = prompt_template.format(user=user_msg, assistant=asst_msg)
+    try:
+        resp = LLM.chat.completions.create(
+            model=MODEL,
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.3, max_tokens=512,
+        )
+        result = resp.choices[0].message.content
+    except Exception as e:
+        return []
+
+    memories = []
+    for line in result.strip().split("\n"):
+        if line.strip() == "NONE":
+            break
+        m = re.match(r"CUE:\s*(.+?)\s*\|\s*TARGET:\s*(.+?)\s*\|\s*IMPORTANCE:\s*([\d.]+)", line)
+        if m:
+            memories.append({
+                "cue": m.group(1).strip(),
+                "target": m.group(2).strip(),
+                "importance": float(m.group(3)),
+            })
+    return memories
+
+
+def emb(text):
+    return encoder.encode([text], convert_to_tensor=True, normalize_embeddings=True, device=DEVICE)[0]
+
+
+def test_recall(memories_list, queries):
+    """Build a memory from extracted memories and test recall."""
+    hip = HippocampalMemory(embed_dim=384, beta=32.0, hopfield_top_k=10, device=DEVICE)
+
+    for mem in memories_list:
+        cue_text = mem["cue"]
+        target_text = mem["target"]
+        cue_emb = emb(cue_text)
+        target_emb = emb(target_text)
+
+        # handle multi-cue (semicolon separated)
+        variants = []
+        if ";" in cue_text:
+            parts = [p.strip() for p in cue_text.split(";") if p.strip()]
+            if len(parts) > 1:
+                cue_emb = emb(parts[0])
+                variants = [emb(p) for p in parts[1:]]
+
+        hip.store(cue_emb, target_emb, cue_variants=variants if variants else None,
+                  metadata={"cue": cue_text, "target": target_text})
+
+    hits = 0
+    for query, keywords in queries:
+        qe = emb(query)
+        results = hip.recall(qe, top_k=3)
+        recalled_text = " ".join(r.metadata["target"] for r in results)
+        hit = any(kw.lower() in recalled_text.lower() for kw in keywords)
+        if hit:
+            hits += 1
+
+    return hits, len(queries)
+
+
+def main():
+    print("extraction prompt experiment\n")
+    print(f"turns: {len(TURNS)}, queries: {len(TARGET_QUERIES)}\n")
+
+    for name, template in PROMPTS.items():
+        print(f"{'='*60}")
+        print(f"  prompt: {name}")
+        print(f"{'='*60}")
+
+        all_memories = []
+        for user_msg, asst_msg in TURNS:
+            mems = extract_with_prompt(template, user_msg, asst_msg)
+            all_memories.extend(mems)
+            for m in mems:
+                print(f"  [{m['importance']:.1f}] CUE: {m['cue'][:50]}")
+                print(f"       TGT: {m['target'][:60]}")
+
+        print(f"\n  extracted: {len(all_memories)} memories")
+
+        hits, total = test_recall(all_memories, TARGET_QUERIES)
+        print(f"  recall: {hits}/{total} ({hits/total*100:.0f}%)")
+
+        # show per-query results
+        hip = HippocampalMemory(embed_dim=384, beta=32.0, hopfield_top_k=10, device=DEVICE)
+        for mem in all_memories:
+            cue_text = mem["cue"]
+            cue_emb = emb(cue_text.split(";")[0].strip() if ";" in cue_text else cue_text)
+            target_emb = emb(mem["target"])
+            variants = []
+            if ";" in cue_text:
+                parts = [p.strip() for p in cue_text.split(";") if p.strip()]
+                variants = [emb(p) for p in parts[1:]] if len(parts) > 1 else []
+            hip.store(cue_emb, target_emb, cue_variants=variants or None,
+                      metadata={"cue": cue_text, "target": mem["target"]})
+
+        for query, keywords in TARGET_QUERIES:
+            qe = emb(query)
+            results = hip.recall(qe, top_k=1)
+            if results:
+                target = results[0].metadata["target"][:60]
+                hit = any(kw.lower() in results[0].metadata["target"].lower() for kw in keywords)
+                mark = "✓" if hit else "✗"
+                print(f"  {mark} {query:20s} → {target}")
+            else:
+                print(f"  ✗ {query:20s} → (empty)")
+
+        print()
+
+
+if __name__ == "__main__":
+    main()

mem/benchmarks/sharpness_test.py

@@ -0,0 +1,104 @@
+"""Test Hopfield attention sharpness with different top_k and beta.
+
+Goal: find settings that give "either clearly remembered or nothing"
+instead of flat attention across 20 candidates.
+"""
+
+import torch
+from sentence_transformers import SentenceTransformer
+from nuonuo.hippocampus import HippocampalMemory
+
+DEVICE = "cuda"
+EMBED_DIM = 384
+
+print("loading encoder...")
+encoder = SentenceTransformer("all-MiniLM-L6-v2", device=DEVICE)
+
+def emb(text):
+    return encoder.encode([text], convert_to_tensor=True, normalize_embeddings=True, device=DEVICE)[0]
+
+
+# store the same memories in each config
+MEMORIES = [
+    ("bot的名字叫什么", "bot的名字叫小乖，是Fam给取的"),
+    ("有哪些工具可以用", "工具有: fam_todo, send_file, spawn_agent, run_shell, run_python, update_memory"),
+    ("vLLM在5090上的性能", "RTX 5090上vLLM跑gemma只有4.8 tok/s，需要切换到awq_marlin"),
+    ("repo-vis项目是什么", "repo-vis用Rust后端+Three.js前端的3D代码库可视化，目标支持Linux内核和Pico VR"),
+    ("repo-vis的性能瓶颈", "Linux内核79K文件，SQLite 1GB上限和O(n)反序列化是瓶颈，需要n-ary tree按需合并"),
+    ("明天的待办事项", "最紧迫的是emblem scanner的AI Chat和KB部分"),
+    ("后端切换到了什么", "NOC后端切换到了vLLM，速度变快了"),
+    ("数据库密码在哪里", "数据库密码存在 /etc/secrets/db.env 文件中"),
+    ("什么GPU", "服务器有NVIDIA RTX 4090 24GB VRAM"),
+    ("home有多少log文件", "home目录及子目录下共有960个.log文件"),
+]
+
+QUERIES = [
+    ("repo-vis怎么样了", "repo-vis", True),      # should recall clearly
+    ("数据库密码", "密码", True),                   # should recall clearly
+    ("今天天气怎么样", "天气", False),              # irrelevant, should recall nothing
+    ("vllm速度", "vllm", True),                    # should recall clearly
+    ("你喜欢吃什么", "吃什么", False),              # irrelevant
+    ("VR支持", "VR", True),                        # edge case
+]
+
+CONFIGS = [
+    # (top_k, beta, label)
+    (20, 16.0, "baseline (top_k=20, β=16)"),
+    (10, 16.0, "top_k=10, β=16"),
+    (5,  16.0, "top_k=5,  β=16"),
+    (20, 32.0, "top_k=20, β=32"),
+    (20, 64.0, "top_k=20, β=64"),
+    (10, 32.0, "top_k=10, β=32"),
+    (5,  32.0, "top_k=5,  β=32"),
+    (5,  64.0, "top_k=5,  β=64"),
+]
+
+# pre-embed everything
+mem_embs = [(emb(c), emb(t), c, t) for c, t in MEMORIES]
+query_embs = [(emb(q), label, relevant) for q, label, relevant in QUERIES]
+
+print(f"\n{len(MEMORIES)} memories, {len(QUERIES)} queries, {len(CONFIGS)} configs\n")
+
+for top_k, beta, label in CONFIGS:
+    print(f"{'='*70}")
+    print(f"  {label}")
+    print(f"{'='*70}")
+
+    hip = HippocampalMemory(
+        embed_dim=EMBED_DIM, hopfield_top_k=top_k, beta=beta, device=DEVICE,
+    )
+    for ce, te, cue_text, target_text in mem_embs:
+        hip.store(ce, te, metadata={"cue": cue_text, "target": target_text})
+
+    for qe, qlabel, should_recall in query_embs:
+        results = hip.recall(qe, top_k=5)
+
+        # show distribution
+        sims = [r.similarity for r in results]
+        top1 = sims[0] if sims else 0
+        top2 = sims[1] if len(sims) > 1 else 0
+        gap = top1 - top2  # gap between #1 and #2
+        above_5pct = sum(1 for s in sims if s >= 0.05)
+        above_10pct = sum(1 for s in sims if s >= 0.10)
+
+        top_target = results[0].metadata["target"][:40] if results else "—"
+        tag = "✓" if should_recall else "✗"
+
+        print(f"  [{tag}] {qlabel:10s}  top1={top1:.0%} top2={top2:.0%} gap={gap:.0%}  "
+              f"≥5%:{above_5pct} ≥10%:{above_10pct}  → {top_target}")
+
+    # summary: average sharpness
+    total_gap = 0
+    total_top1 = 0
+    for qe, qlabel, _ in query_embs:
+        results = hip.recall(qe, top_k=5)
+        sims = [r.similarity for r in results]
+        total_top1 += sims[0] if sims else 0
+        total_gap += (sims[0] - sims[1]) if len(sims) > 1 else 0
+
+    n = len(query_embs)
+    print(f"\n  avg top1={total_top1/n:.0%}  avg gap={total_gap/n:.0%}")
+    print()
+
+    del hip
+    torch.cuda.empty_cache()

mem/import_claude.py

@@ -0,0 +1,178 @@
+"""Import Claude Code conversation history into nocmem.
+
+Scans ~/.claude/projects/ for JSONL conversation files,
+extracts user-assistant turn pairs, and ingests them via /ingest API.
+
+Usage:
+    uv run python import_claude.py [--dry-run] [--limit N]
+"""
+
+import argparse
+import json
+import os
+import sys
+import time
+from pathlib import Path
+
+import requests
+
+BASE = os.environ.get("NOCMEM_ENDPOINT", "http://127.0.0.1:9820")
+CLAUDE_DIR = Path.home() / ".claude" / "projects"
+
+
+def extract_turns(jsonl_path: Path) -> list[tuple[str, str]]:
+    """Extract (user_msg, assistant_msg) pairs from a JSONL conversation."""
+    messages = []  # (role, text)
+
+    with open(jsonl_path) as f:
+        for line in f:
+            try:
+                obj = json.loads(line)
+            except json.JSONDecodeError:
+                continue
+
+            msg_type = obj.get("type")
+            if msg_type not in ("user", "assistant"):
+                continue
+
+            msg = obj.get("message", {})
+            content = msg.get("content", "")
+
+            # extract text from content
+            if isinstance(content, str):
+                text = content.strip()
+            elif isinstance(content, list):
+                parts = []
+                for part in content:
+                    if isinstance(part, dict) and part.get("type") == "text":
+                        parts.append(part["text"])
+                text = "\n".join(parts).strip()
+            else:
+                continue
+
+            if not text or len(text) < 10:
+                continue
+
+            # skip tool-heavy assistant responses (mostly noise)
+            if msg_type == "assistant" and text.count("```") > 10:
+                continue
+
+            role = "user" if msg_type == "user" else "assistant"
+            messages.append((role, text))
+
+    # pair up user-assistant turns
+    turns = []
+    i = 0
+    while i < len(messages) - 1:
+        if messages[i][0] == "user":
+            # find next assistant
+            j = i + 1
+            while j < len(messages) and messages[j][0] != "assistant":
+                j += 1
+            if j < len(messages):
+                user_text = messages[i][1][:500]  # truncate long messages
+                asst_text = messages[j][1][:500]
+                turns.append((user_text, asst_text))
+            i = j + 1
+        else:
+            i += 1
+
+    return turns
+
+
+def ingest_turn(user_msg: str, assistant_msg: str) -> int:
+    """Send a turn to nocmem /ingest, return number of memories stored."""
+    try:
+        r = requests.post(
+            f"{BASE}/ingest",
+            json={"user_msg": user_msg, "assistant_msg": assistant_msg},
+            timeout=120,
+        )
+        if r.status_code == 200:
+            return r.json().get("stored", 0)
+    except Exception as e:
+        print(f"  error: {e}", file=sys.stderr)
+    return 0
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Import Claude Code history into nocmem")
+    parser.add_argument("--dry-run", action="store_true", help="just show what would be imported")
+    parser.add_argument("--limit", type=int, default=0, help="max turns to ingest (0=all)")
+    parser.add_argument("--project", type=str, default="", help="filter by project dir name substring")
+    args = parser.parse_args()
+
+    # find all conversation files
+    conversations = []
+    for project_dir in sorted(CLAUDE_DIR.iterdir()):
+        if not project_dir.is_dir():
+            continue
+        if args.project and args.project not in project_dir.name:
+            continue
+        for jsonl in sorted(project_dir.glob("*.jsonl")):
+            if "subagents" in str(jsonl):
+                continue
+            conversations.append((project_dir.name, jsonl))
+
+    print(f"found {len(conversations)} conversations in {CLAUDE_DIR}")
+    if args.project:
+        print(f"  filtered by: {args.project}")
+
+    # extract all turns
+    all_turns = []
+    for project_name, jsonl_path in conversations:
+        turns = extract_turns(jsonl_path)
+        if turns:
+            all_turns.extend([(project_name, u, a) for u, a in turns])
+
+    print(f"extracted {len(all_turns)} turns total\n")
+
+    if args.limit:
+        all_turns = all_turns[:args.limit]
+
+    if args.dry_run:
+        for project, user_msg, asst_msg in all_turns[:20]:
+            print(f"  [{project[:30]}]")
+            print(f"    U: {user_msg[:80]}")
+            print(f"    A: {asst_msg[:80]}")
+            print()
+        if len(all_turns) > 20:
+            print(f"  ... and {len(all_turns) - 20} more")
+        return
+
+    # check server
+    try:
+        r = requests.get(f"{BASE}/stats", timeout=3)
+        r.raise_for_status()
+        before = r.json()["num_memories"]
+        print(f"nocmem: {before} memories before import\n")
+    except Exception:
+        print(f"ERROR: nocmem not reachable at {BASE}")
+        sys.exit(1)
+
+    # ingest
+    total_stored = 0
+    t0 = time.monotonic()
+    for i, (project, user_msg, asst_msg) in enumerate(all_turns):
+        stored = ingest_turn(user_msg, asst_msg)
+        total_stored += stored
+        if (i + 1) % 10 == 0:
+            elapsed = time.monotonic() - t0
+            rate = (i + 1) / elapsed
+            eta = (len(all_turns) - i - 1) / rate if rate > 0 else 0
+            print(f"  [{i+1}/{len(all_turns)}] stored={total_stored} ({rate:.1f} turns/s, ETA {eta:.0f}s)")
+
+    elapsed = time.monotonic() - t0
+
+    # final stats
+    r = requests.get(f"{BASE}/stats")
+    after = r.json()["num_memories"]
+
+    print(f"\n{'='*50}")
+    print(f"imported {total_stored} memories from {len(all_turns)} turns")
+    print(f"nocmem: {before} → {after} memories")
+    print(f"time: {elapsed:.1f}s")
+
+
+if __name__ == "__main__":
+    main()

mem/nocmem.service

@@ -0,0 +1,19 @@
+[Unit]
+Description=nocmem — NuoNuo memory service for NOC
+After=network.target
+
+[Service]
+Type=simple
+WorkingDirectory=/data/src/noc/mem
+ExecStart=/home/fam/.local/bin/uv run uvicorn server:app --host 0.0.0.0 --port 9820 --log-level info
+Restart=on-failure
+RestartSec=5
+
+Environment=NOCMEM_LLM_ENDPOINT=http://100.84.7.49:8000/v1
+Environment=NOCMEM_LLM_MODEL=gemma4-31b
+Environment=NOCMEM_LLM_API_KEY=unused
+Environment=NOCMEM_DATA_DIR=/data/src/noc/mem/data
+Environment=NOCMEM_DEVICE=cuda
+
+[Install]
+WantedBy=default.target

mem/pyproject.toml

@@ -0,0 +1,25 @@
+[project]
+name = "nocmem"
+version = "0.1.0"
+description = "Memory service for noc — NuoNuo hippocampal recall + ingest over HTTP"
+requires-python = ">=3.12"
+dependencies = [
+    "fastapi>=0.115",
+    "uvicorn>=0.34",
+    "torch>=2.10,<2.11",
+    "sentence-transformers>=3.0",
+    "nuonuo",
+    "openai>=1.0",
+]
+
+[tool.uv]
+index-url = "https://pypi.org/simple"
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true
+
+[tool.uv.sources]
+torch = { index = "pytorch-cu128" }
+nuonuo = { path = "../../nuonuo", editable = true }

mem/server.py

@@ -0,0 +1,400 @@
+"""nocmem — Memory service for NOC.
+
+Wraps NuoNuo's HippocampalMemory as an HTTP API.
+Auto-recall on every user message, async ingest after LLM response.
+"""
+
+import asyncio
+import os
+import re
+import time
+import logging
+from contextlib import asynccontextmanager
+from dataclasses import dataclass
+from pathlib import Path
+
+import torch
+from fastapi import FastAPI
+from pydantic import BaseModel, Field
+from sentence_transformers import SentenceTransformer
+from openai import OpenAI
+
+from nuonuo.hippocampus import HippocampalMemory
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("nocmem")
+
+# ── config ──────────────────────────────────────────────────────────
+
+EMBED_MODEL = os.environ.get("NOCMEM_EMBED_MODEL", "all-MiniLM-L6-v2")
+EMBED_DIM = int(os.environ.get("NOCMEM_EMBED_DIM", "384"))
+DEVICE = os.environ.get("NOCMEM_DEVICE", "cuda" if torch.cuda.is_available() else "cpu")
+DATA_DIR = Path(os.environ.get("NOCMEM_DATA_DIR", "./data"))
+CHECKPOINT = DATA_DIR / "hippocampus.pt"
+SAVE_INTERVAL = int(os.environ.get("NOCMEM_SAVE_INTERVAL", "10"))  # save every N stores
+HOPFIELD_BETA = float(os.environ.get("NOCMEM_HOPFIELD_BETA", "32.0"))
+HOPFIELD_TOP_K = int(os.environ.get("NOCMEM_HOPFIELD_TOP_K", "10"))
+COS_SIM_THRESHOLD = float(os.environ.get("NOCMEM_COS_SIM_THRESHOLD", "0.35"))
+
+# LLM for memory extraction (optional)
+LLM_ENDPOINT = os.environ.get("NOCMEM_LLM_ENDPOINT", "")
+LLM_MODEL = os.environ.get("NOCMEM_LLM_MODEL", "gemma4:12b")
+LLM_API_KEY = os.environ.get("NOCMEM_LLM_API_KEY", "unused")
+
+# ── globals ─────────────────────────────────────────────────────────
+
+encoder: SentenceTransformer = None  # type: ignore
+hippocampus: HippocampalMemory = None  # type: ignore
+llm_client = None  # optional
+_stores_since_save = 0
+
+
+def embed(text: str) -> torch.Tensor:
+    return encoder.encode(
+        [text], convert_to_tensor=True, normalize_embeddings=True, device=DEVICE
+    )[0]
+
+
+def embed_batch(texts: list[str]) -> list[torch.Tensor]:
+    if not texts:
+        return []
+    t = encoder.encode(
+        texts, convert_to_tensor=True, normalize_embeddings=True, device=DEVICE
+    )
+    return [t[i] for i in range(t.shape[0])]
+
+
+def maybe_save():
+    global _stores_since_save
+    _stores_since_save += 1
+    if _stores_since_save >= SAVE_INTERVAL:
+        _stores_since_save = 0
+        DATA_DIR.mkdir(parents=True, exist_ok=True)
+        hippocampus.save(str(CHECKPOINT))
+        logger.info("checkpoint saved: %s", CHECKPOINT)
+
+
+# ── lifespan ────────────────────────────────────────────────────────
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global encoder, hippocampus, llm_client
+
+    logger.info("loading embedding model: %s (device=%s)", EMBED_MODEL, DEVICE)
+    encoder = SentenceTransformer(EMBED_MODEL, device=DEVICE)
+
+    if CHECKPOINT.exists():
+        logger.info("loading checkpoint: %s", CHECKPOINT)
+        hippocampus = HippocampalMemory.load(str(CHECKPOINT), device=DEVICE)
+        logger.info("loaded %d memories", len(hippocampus.memories))
+    else:
+        logger.info("no checkpoint found, starting fresh")
+        hippocampus = HippocampalMemory(
+            embed_dim=EMBED_DIM, beta=HOPFIELD_BETA,
+            hopfield_top_k=HOPFIELD_TOP_K, device=DEVICE,
+        )
+
+    if LLM_ENDPOINT:
+        try:
+            client = OpenAI(base_url=LLM_ENDPOINT, api_key=LLM_API_KEY, timeout=5.0)
+            client.models.list()
+            llm_client = client
+            logger.info("LLM client connected: %s", LLM_ENDPOINT)
+        except Exception as e:
+            logger.warning("LLM client unavailable: %s", e)
+
+    yield
+
+    # save on shutdown
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+    hippocampus.save(str(CHECKPOINT))
+    logger.info("shutdown: checkpoint saved")
+
+
+app = FastAPI(title="nocmem", lifespan=lifespan)
+
+
+# ── models ──────────────────────────────────────────────────────────
+
+class RecallRequest(BaseModel):
+    text: str
+    top_k: int = Field(default=5, ge=1, le=20)
+    hops: int = Field(default=2, ge=1, le=5)
+    min_similarity: float = Field(default=0.0, ge=0.0, le=1.0)
+
+class RecallResponse(BaseModel):
+    memories: str
+    count: int
+    latency_ms: float
+
+class IngestRequest(BaseModel):
+    user_msg: str
+    assistant_msg: str
+
+class IngestResponse(BaseModel):
+    stored: int
+
+class StoreRequest(BaseModel):
+    cue: str
+    target: str
+    importance: float = Field(default=0.5, ge=0.0, le=1.0)
+
+class StoreResponse(BaseModel):
+    memory_id: int
+
+
+# ── endpoints ───────────────────────────────────────────────────────
+
+@app.post("/recall", response_model=RecallResponse)
+async def recall(req: RecallRequest):
+    t0 = time.monotonic()
+
+    query_emb = embed(req.text)
+
+    # pre-filter: check if anything in memory is actually similar enough
+    cue_mat = hippocampus._get_cue_matrix()
+    if cue_mat is not None and COS_SIM_THRESHOLD > 0:
+        cos_sims = query_emb @ cue_mat.T
+        max_cos_sim = cos_sims.max().item()
+        if max_cos_sim < COS_SIM_THRESHOLD:
+            # nothing in memory is similar enough — don't hallucinate
+            return RecallResponse(memories="", count=0, latency_ms=(time.monotonic() - t0) * 1000)
+
+    # single-hop
+    results = hippocampus.recall(query_emb, top_k=req.top_k)
+
+    # multi-hop chain from top result
+    chain_results = []
+    if results and req.hops > 1:
+        chain = hippocampus.recall_chain(query_emb, hops=req.hops)
+        # add chain results not already in single-hop
+        seen_ids = {r.memory_id for r in results}
+        for cr in chain:
+            if cr.memory_id not in seen_ids:
+                chain_results.append(cr)
+                seen_ids.add(cr.memory_id)
+
+    all_results = results + chain_results
+    elapsed = (time.monotonic() - t0) * 1000
+
+    if not all_results:
+        return RecallResponse(memories="", count=0, latency_ms=elapsed)
+
+    lines = []
+    for r in all_results:
+        if r.similarity < req.min_similarity:
+            continue
+        meta = r.metadata
+        text = meta.get("target", meta.get("text", ""))
+        if not text:
+            continue
+        hop_tag = f" (联想 hop={r.hop_distance})" if r.hop_distance > 1 else ""
+        lines.append(f"- {text}{hop_tag}")
+
+    if not lines:
+        return RecallResponse(memories="", count=0, latency_ms=elapsed)
+
+    formatted = "[以下是可能相关的历史记忆，仅供参考。请优先关注用户当前的消息。]\n" + "\n".join(lines)
+    return RecallResponse(memories=formatted, count=len(lines), latency_ms=elapsed)
+
+
+@app.post("/ingest", response_model=IngestResponse)
+async def ingest(req: IngestRequest):
+    extracted = await asyncio.to_thread(_extract_and_store, req.user_msg, req.assistant_msg)
+    return IngestResponse(stored=extracted)
+
+
+@dataclass
+class ExtractedMemory:
+    cue: str
+    target: str
+    importance: float = 0.5
+
+
+def _extract_memories_llm(user_msg: str, assistant_msg: str) -> list[ExtractedMemory]:
+    prompt = (
+        '你是一个记忆提取器。把这段对话变成若干个"问答对"——未来有人问这个问题时，能直接给出答案。\n\n'
+        "要求：\n"
+        "- 问题要自然，像人真的会这么问\n"
+        "- 答案要具体完整，包含关键细节（名称、数字、地址等）\n"
+        "- 同一个事实可以从不同角度提问\n"
+        "- 每条 CUE 提供 2-3 个不同的触发短语，用分号分隔\n\n"
+        "格式（每行一条）：\n"
+        "CUE: <提问方式1>; <提问方式2>; <提问方式3> | TARGET: <完整的回答> | IMPORTANCE: <0-1>\n\n"
+        f"User: {user_msg}\nAssistant: {assistant_msg}\n\n"
+        "没有值得记住的则输出 NONE。"
+    )
+    try:
+        resp = llm_client.chat.completions.create(
+            model=LLM_MODEL, messages=[{"role": "user", "content": prompt}],
+            temperature=0.3, max_tokens=512,
+        )
+        result = resp.choices[0].message.content
+    except Exception:
+        return _extract_memories_heuristic(user_msg, assistant_msg)
+
+    memories = []
+    for line in result.strip().split("\n"):
+        if line.strip() == "NONE":
+            break
+        m = re.match(r"CUE:\s*(.+?)\s*\|\s*TARGET:\s*(.+?)\s*\|\s*IMPORTANCE:\s*([\d.]+)", line)
+        if m:
+            memories.append(ExtractedMemory(m.group(1).strip(), m.group(2).strip(), float(m.group(3))))
+    return memories
+
+
+def _extract_memories_heuristic(user_msg: str, assistant_msg: str) -> list[ExtractedMemory]:
+    memories = []
+    # detect questions — English and Chinese
+    has_question = "?" in user_msg or "？" in user_msg or any(
+        user_msg.strip().startswith(q) for q in ["怎么", "什么", "哪", "为什么", "如何", "多少", "几"]
+    )
+    # count meaningful length: for Chinese, use character count
+    assistant_long_enough = len(assistant_msg) > 20
+    if has_question and assistant_long_enough:
+        cue = user_msg.rstrip("?？").strip()
+        memories.append(ExtractedMemory(
+            cue=cue, target=assistant_msg[:300], importance=0.6,
+        ))
+    # tech keywords — English and Chinese
+    tech_keywords = [
+        "deploy", "config", "bug", "fix", "error", "database", "server",
+        "api", "port", "token", "password", "version", "install", "upgrade",
+        "部署", "配置", "错误", "数据库", "服务器", "端口", "密码", "版本",
+        "安装", "升级", "模型", "工具", "代码", "项目", "优化", "性能",
+        "内存", "GPU", "vllm", "docker", "k8s", "git", "编译", "测试",
+    ]
+    combined = (user_msg + " " + assistant_msg).lower()
+    user_meaningful = len(user_msg) >= 8  # characters, not words
+    if any(kw in combined for kw in tech_keywords) and user_meaningful:
+        if not memories:  # avoid duplicate with Q&A extraction
+            memories.append(ExtractedMemory(
+                cue=user_msg[:150], target=assistant_msg[:300], importance=0.5,
+            ))
+    return memories
+
+
+def _generate_paraphrases_heuristic(text: str, n: int = 3) -> list[str]:
+    variants = []
+    text_lower = text.lower().strip()
+    # English prefixes
+    for pfx in ["can you ", "please ", "i need to ", "how do i ", "how to ", "what is ", "what's "]:
+        if text_lower.startswith(pfx):
+            stripped = text[len(pfx):].strip()
+            if stripped:
+                variants.append(stripped)
+    # Chinese prefixes
+    for pfx in ["帮我看看", "帮我", "请问", "我想知道", "能不能", "怎么样", "看下", "看看"]:
+        if text.startswith(pfx):
+            stripped = text[len(pfx):].strip()
+            if stripped:
+                variants.append(stripped)
+    # synonym swaps — English
+    en_swaps = {"slow": "performance issues", "fix": "resolve", "deploy": "release",
+                "error": "issue", "bug": "problem", "database": "DB", "server": "machine"}
+    for old, new in en_swaps.items():
+        if old in text_lower:
+            variant = text.replace(old, new).replace(old.capitalize(), new.capitalize())
+            if variant != text and variant not in variants:
+                variants.append(variant)
+    # synonym swaps — Chinese
+    cn_swaps = {"数据库": "DB", "服务器": "机器", "部署": "上线", "配置": "设置",
+                "性能": "速度", "优化": "改进", "工具": "tool", "项目": "project"}
+    for old, new in cn_swaps.items():
+        if old in text:
+            variant = text.replace(old, new)
+            if variant != text and variant not in variants:
+                variants.append(variant)
+    return variants[:n]
+
+
+def _generate_paraphrases_llm(text: str, n: int = 3) -> list[str]:
+    prompt = f"Generate {n} different paraphrases of this text. Each should convey the same meaning but use different words. One per line, no numbering.\n\nText: {text}"
+    try:
+        resp = llm_client.chat.completions.create(
+            model=LLM_MODEL, messages=[{"role": "user", "content": prompt}],
+            temperature=0.8, max_tokens=256,
+        )
+        result = resp.choices[0].message.content
+        return [l.strip() for l in result.strip().split("\n") if l.strip() and len(l.strip()) > 3][:n]
+    except Exception:
+        return _generate_paraphrases_heuristic(text, n)
+
+
+def _extract_and_store(user_msg: str, assistant_msg: str) -> int:
+    if llm_client:
+        memories = _extract_memories_llm(user_msg, assistant_msg)
+    else:
+        memories = _extract_memories_heuristic(user_msg, assistant_msg)
+
+    if not memories:
+        return 0
+
+    stored = 0
+    for mem in memories:
+        if mem.importance < 0.3:
+            continue
+
+        # split semicolon-separated cues into primary + variants
+        cue_parts = [p.strip() for p in mem.cue.split(";") if p.strip()]
+        primary_cue = cue_parts[0] if cue_parts else mem.cue
+        inline_variants = cue_parts[1:] if len(cue_parts) > 1 else []
+
+        cue_emb = embed(primary_cue)
+        target_emb = embed(mem.target)
+
+        # inline variants from semicolon cues (already in the extraction)
+        variant_embs = embed_batch(inline_variants) if inline_variants else []
+
+        # additionally generate paraphrases if no inline variants
+        if not inline_variants:
+            if llm_client:
+                paraphrases = _generate_paraphrases_llm(primary_cue, n=3)
+            else:
+                paraphrases = _generate_paraphrases_heuristic(primary_cue, n=3)
+            variant_embs = embed_batch(paraphrases) if paraphrases else []
+
+        hippocampus.store(
+            cue_emb, target_emb,
+            cue_variants=variant_embs if variant_embs else None,
+            metadata={"cue": mem.cue, "target": mem.target, "importance": mem.importance},
+            timestamp=time.time(),
+        )
+        stored += 1
+
+    if stored > 0:
+        maybe_save()
+        logger.info("ingested %d memories from conversation turn", stored)
+
+    return stored
+
+
+@app.post("/store", response_model=StoreResponse)
+async def store_direct(req: StoreRequest):
+    """Direct store — bypass LLM extraction, for manual/testing use."""
+    cue_emb = embed(req.cue)
+    target_emb = embed(req.target)
+    mid = hippocampus.store(
+        cue_emb, target_emb,
+        metadata={"cue": req.cue, "target": req.target, "importance": req.importance},
+        timestamp=time.time(),
+    )
+    maybe_save()
+    return StoreResponse(memory_id=mid)
+
+
+@app.get("/stats")
+async def stats():
+    s = hippocampus.stats()
+    s["device"] = DEVICE
+    s["embedding_model"] = EMBED_MODEL
+    s["checkpoint"] = str(CHECKPOINT)
+    s["checkpoint_exists"] = CHECKPOINT.exists()
+    return s
+
+
+@app.delete("/memory/{memory_id}")
+async def forget(memory_id: int):
+    hippocampus.forget(memory_id)
+    maybe_save()
+    return {"deleted": memory_id}

mem/test_api.py

@@ -0,0 +1,390 @@
+"""nocmem API integration tests.
+
+Run with: uv run python test_api.py
+Requires nocmem server running on localhost:9820.
+"""
+
+import sys
+import time
+import requests
+
+BASE = "http://127.0.0.1:9820"
+PASS = 0
+FAIL = 0
+
+
+def test(name: str, fn):
+    global PASS, FAIL
+    try:
+        fn()
+        print(f"  ✓ {name}")
+        PASS += 1
+    except AssertionError as e:
+        print(f"  ✗ {name}: {e}")
+        FAIL += 1
+    except Exception as e:
+        print(f"  ✗ {name}: EXCEPTION {e}")
+        FAIL += 1
+
+
+def assert_eq(a, b, msg=""):
+    assert a == b, f"expected {b!r}, got {a!r}" + (f" ({msg})" if msg else "")
+
+
+def assert_gt(a, b, msg=""):
+    assert a > b, f"expected > {b!r}, got {a!r}" + (f" ({msg})" if msg else "")
+
+
+def assert_in(needle, haystack, msg=""):
+    assert needle in haystack, f"{needle!r} not in {haystack!r}" + (f" ({msg})" if msg else "")
+
+
+# ── health check ────────────────────────────────────────────────────
+
+def check_server():
+    try:
+        r = requests.get(f"{BASE}/stats", timeout=3)
+        r.raise_for_status()
+        return True
+    except Exception:
+        return False
+
+
+# ── test: stats on empty db ─────────────────────────────────────────
+
+def test_stats_empty():
+    r = requests.get(f"{BASE}/stats")
+    assert_eq(r.status_code, 200)
+    data = r.json()
+    assert "num_memories" in data
+    assert "device" in data
+    assert_eq(data["embedding_model"], "all-MiniLM-L6-v2")
+
+
+# ── test: recall on empty db ───────────────────────<E29480><E29480><EFBFBD>────────────────
+
+def test_recall_empty():
+    r = requests.post(f"{BASE}/recall", json={"text": "hello"})
+    assert_eq(r.status_code, 200)
+    data = r.json()
+    assert_eq(data["memories"], "")
+    assert_eq(data["count"], 0)
+
+
+# ── test: direct store ────────<E29480><E29480><EFBFBD>─────────────────────────────────────
+
+stored_ids = []
+
+def test_store_single():
+    r = requests.post(f"{BASE}/store", json={
+        "cue": "what port does postgres run on",
+        "target": "PostgreSQL runs on port 5432",
+        "importance": 0.8,
+    })
+    assert_eq(r.status_code, 200)
+    data = r.json()
+    assert "memory_id" in data
+    stored_ids.append(data["memory_id"])
+
+
+def test_store_multiple():
+    memories = [
+        {"cue": "what is the database password", "target": "The DB password is stored in /etc/secrets/db.env", "importance": 0.9},
+        {"cue": "how to deploy the app", "target": "Run make deploy-hera to deploy to the suite VPS via SSH", "importance": 0.7},
+        {"cue": "what timezone is Fam in", "target": "Fam is in London, UK timezone (Europe/London, GMT/BST)", "importance": 0.6},
+        {"cue": "which embedding model works best", "target": "all-MiniLM-L6-v2 has the best gap metric for hippocampal memory", "importance": 0.8},
+        {"cue": "what GPU does the server have", "target": "The server has an NVIDIA RTX 4090 with 24GB VRAM", "importance": 0.7},
+    ]
+    for m in memories:
+        r = requests.post(f"{BASE}/store", json=m)
+        assert_eq(r.status_code, 200)
+        stored_ids.append(r.json()["memory_id"])
+
+
+# ── test: exact recall ──────────────────────────────────────────────
+
+def test_recall_exact():
+    """Recall with the exact cue text should return the right memory."""
+    r = requests.post(f"{BASE}/recall", json={
+        "text": "what port does postgres run on",
+        "top_k": 3,
+    })
+    assert_eq(r.status_code, 200)
+    data = r.json()
+    assert_gt(data["count"], 0, "should recall at least 1")
+    assert_in("5432", data["memories"], "should mention port 5432")
+
+
+# ── test: paraphrase recall ─────────────────────────────────────────
+
+def test_recall_paraphrase():
+    """Recall with a paraphrased query (not exact cue text)."""
+    r = requests.post(f"{BASE}/recall", json={
+        "text": "which port is postgresql listening on",
+        "top_k": 3,
+    })
+    assert_eq(r.status_code, 200)
+    data = r.json()
+    assert_gt(data["count"], 0, "paraphrase should still recall")
+    assert_in("5432", data["memories"])
+
+
+def test_recall_different_wording():
+    """Even more different wording."""
+    r = requests.post(f"{BASE}/recall", json={
+        "text": "database connection port number",
+        "top_k": 3,
+    })
+    assert_eq(r.status_code, 200)
+    data = r.json()
+    assert_gt(data["count"], 0, "different wording should recall")
+    assert_in("5432", data["memories"])
+
+
+# ── test: recall relevance ──────────────────────────────────────────
+
+def test_recall_deployment():
+    r = requests.post(f"{BASE}/recall", json={
+        "text": "how do I deploy to production",
+        "top_k": 3,
+    })
+    assert_eq(r.status_code, 200)
+    data = r.json()
+    assert_gt(data["count"], 0)
+    assert_in("deploy", data["memories"].lower())
+
+
+def test_recall_timezone():
+    r = requests.post(f"{BASE}/recall", json={
+        "text": "where is Fam located",
+        "top_k": 3,
+    })
+    assert_eq(r.status_code, 200)
+    data = r.json()
+    assert_gt(data["count"], 0)
+    assert_in("London", data["memories"])
+
+
+def test_recall_gpu():
+    r = requests.post(f"{BASE}/recall", json={
+        "text": "what hardware does the server have",
+        "top_k": 3,
+    })
+    assert_eq(r.status_code, 200)
+    data = r.json()
+    assert_gt(data["count"], 0)
+    assert_in("4090", data["memories"])
+
+
+# ── test: top_k ─────────────────────────────────────────────────────
+
+def test_recall_top_k_1():
+    r = requests.post(f"{BASE}/recall", json={
+        "text": "postgres port",
+        "top_k": 1,
+    })
+    data = r.json()
+    assert_eq(data["count"], 1, "top_k=1 should return exactly 1")
+
+
+def test_recall_top_k_all():
+    r = requests.post(f"{BASE}/recall", json={
+        "text": "tell me everything",
+        "top_k": 20,
+    })
+    data = r.json()
+    assert_gt(data["count"], 0, "should recall something")
+
+
+# ── test: recall latency ────────────────────────────────────────────
+
+def test_recall_latency():
+    """Recall should be fast (< 100ms including HTTP + embedding)."""
+    t0 = time.monotonic()
+    r = requests.post(f"{BASE}/recall", json={"text": "database port"})
+    elapsed_ms = (time.monotonic() - t0) * 1000
+    data = r.json()
+    # internal latency (no HTTP overhead)
+    assert data["latency_ms"] < 100, f"internal latency {data['latency_ms']:.1f}ms too high"
+    # end-to-end including HTTP
+    print(f"    (e2e={elapsed_ms:.1f}ms, internal={data['latency_ms']:.1f}ms)")
+
+
+# ── test: ingest (heuristic, no LLM) ───────────────────────────────
+
+def test_ingest_heuristic():
+    """Ingest without LLM should use heuristic extraction."""
+    r = requests.post(f"{BASE}/ingest", json={
+        "user_msg": "What version of Python are we running?",
+        "assistant_msg": "We are running Python 3.12.4 on the server, installed via uv.",
+    })
+    assert_eq(r.status_code, 200)
+    data = r.json()
+    # heuristic should extract at least the Q&A pair
+    assert_gt(data["stored"], 0, "heuristic should extract at least 1 memory")
+
+
+def test_ingest_then_recall():
+    """After ingesting, the memory should be recallable."""
+    # first ingest
+    requests.post(f"{BASE}/ingest", json={
+        "user_msg": "What's the Redis cache TTL?",
+        "assistant_msg": "The Redis cache TTL is set to 3600 seconds (1 hour) in production.",
+    })
+    # wait a tiny bit for async processing
+    time.sleep(0.5)
+    # then recall
+    r = requests.post(f"{BASE}/recall", json={
+        "text": "redis cache timeout",
+        "top_k": 3,
+    })
+    data = r.json()
+    assert_gt(data["count"], 0, "ingested memory should be recallable")
+    # Check it mentions the TTL
+    assert_in("3600", data["memories"], "should recall the TTL value")
+
+
+# ── test: forget ───────────<E29480><E29480><EFBFBD>────────────────────────<E29480><E29480>───────────────
+
+def test_forget():
+    """Delete a memory and verify it's gone."""
+    # store something
+    r = requests.post(f"{BASE}/store", json={
+        "cue": "temporary test memory for deletion",
+        "target": "this should be deleted XYZZY",
+    })
+    mid = r.json()["memory_id"]
+
+    # verify it's recallable
+    r = requests.post(f"{BASE}/recall", json={"text": "temporary test memory for deletion"})
+    assert_in("XYZZY", r.json()["memories"])
+
+    # delete
+    r = requests.delete(f"{BASE}/memory/{mid}")
+    assert_eq(r.status_code, 200)
+
+    # verify gone — recall the exact cue, should not return XYZZY
+    r = requests.post(f"{BASE}/recall", json={"text": "temporary test memory for deletion"})
+    if r.json()["memories"]:
+        assert "XYZZY" not in r.json()["memories"], "deleted memory should not appear"
+
+
+# ── test: format ─────────────────────────────────────<E29480><E29480>──────────────
+
+def test_recall_format():
+    """Recalled memories should have the expected format."""
+    r = requests.post(f"{BASE}/recall", json={"text": "postgres port"})
+    data = r.json()
+    if data["count"] > 0:
+        assert data["memories"].startswith("[相关记忆]"), "should start with header"
+        assert "\n- " in data["memories"], "each memory should start with '- '"
+
+
+# ── test: stats after stores ──────<E29480><E29480>─────────────────────────────────
+
+def test_stats_after():
+    r = requests.get(f"{BASE}/stats")
+    data = r.json()
+    assert_gt(data["num_memories"], 0, "should have memories")
+    assert_gt(data["num_cue_entries"], data["num_memories"],
+              "cue entries should >= memories (augmentation from ingest)")
+
+
+# ── test: edge cases ────────────────────────────────────────────────
+
+def test_recall_empty_text():
+    r = requests.post(f"{BASE}/recall", json={"text": ""})
+    # should not crash
+    assert r.status_code == 200
+
+
+def test_recall_long_text():
+    r = requests.post(f"{BASE}/recall", json={"text": "a " * 1000})
+    assert r.status_code == 200
+
+
+def test_recall_chinese():
+    """Chinese text should work."""
+    # store a Chinese memory
+    requests.post(f"{BASE}/store", json={
+        "cue": "数据库密码在哪里",
+        "target": "数据库密码存在 /etc/secrets/db.env 文件中",
+    })
+    r = requests.post(f"{BASE}/recall", json={"text": "数据库密码"})
+    data = r.json()
+    assert_gt(data["count"], 0, "Chinese recall should work")
+    assert_in("secrets", data["memories"])
+
+
+def test_store_validation():
+    """Missing required fields should return 422."""
+    r = requests.post(f"{BASE}/store", json={"cue": "only cue"})
+    assert_eq(r.status_code, 422)
+
+
+# ── run ─────<E29480><E29480><EFBFBD>───────────────────────────────────────────────────────
+
+def main():
+    global PASS, FAIL
+
+    print("nocmem API tests")
+    print(f"server: {BASE}\n")
+
+    if not check_server():
+        print("ERROR: server not reachable")
+        sys.exit(1)
+
+    # first clean slate — check what we start with
+    r = requests.get(f"{BASE}/stats")
+    initial = r.json()["num_memories"]
+
+    print(f"[initial state: {initial} memories]\n")
+
+    print("── basic ──")
+    test("stats endpoint", test_stats_empty)
+    test("recall on empty/existing db", test_recall_empty if initial == 0 else lambda: None)
+
+    print("\n── store ──")
+    test("store single memory", test_store_single)
+    test("store multiple memories", test_store_multiple)
+
+    print("\n── recall accuracy ─<><E29480><EFBFBD>")
+    test("exact cue recall", test_recall_exact)
+    test("paraphrase recall", test_recall_paraphrase)
+    test("different wording recall", test_recall_different_wording)
+    test("deployment query", test_recall_deployment)
+    test("timezone query", test_recall_timezone)
+    test("GPU query", test_recall_gpu)
+
+    print("\n── recall params ──")
+    test("top_k=1", test_recall_top_k_1)
+    test("top_k=20 (all)", test_recall_top_k_all)
+    test("latency < 100ms", test_recall_latency)
+    test("format check", test_recall_format)
+
+    print("\n── ingest ──")
+    test("heuristic ingest", test_ingest_heuristic)
+    test("ingest then recall", test_ingest_then_recall)
+
+    print("\n── forget ──")
+    test("store + forget + verify", test_forget)
+
+    print("\n── edge cases ──")
+    test("empty text", test_recall_empty_text)
+    test("long text", test_recall_long_text)
+    test("Chinese text", test_recall_chinese)
+    test("validation error", test_store_validation)
+
+    print("\n── stats ──")
+    test("stats after stores", test_stats_after)
+
+    print(f"\n{'='*40}")
+    print(f"PASS: {PASS}  FAIL: {FAIL}")
+    if FAIL:
+        sys.exit(1)
+    else:
+        print("All tests passed!")
+
+
+if __name__ == "__main__":
+    main()

mem/test_real_data.py

@@ -0,0 +1,279 @@
+"""Test nocmem with real conversation data from NOC's SQLite database.
+
+Extracts conversation turns, ingests them, then tests recall with
+realistic queries that a user would actually ask.
+"""
+
+import sys
+import time
+import sqlite3
+import requests
+
+BASE = "http://127.0.0.1:9820"
+DB_PATH = "/data/src/noc/noc.db"
+
+PASS = 0
+FAIL = 0
+
+
+def test(name, fn):
+    global PASS, FAIL
+    try:
+        fn()
+        print(f"  ✓ {name}")
+        PASS += 1
+    except AssertionError as e:
+        print(f"  ✗ {name}: {e}")
+        FAIL += 1
+    except Exception as e:
+        print(f"  ✗ {name}: EXCEPTION {e}")
+        FAIL += 1
+
+
+# ── step 1: extract conversation turns from SQLite ──────────────────
+
+def extract_turns():
+    """Extract (user_msg, assistant_msg) pairs from the database."""
+    conn = sqlite3.connect(DB_PATH)
+    rows = conn.execute(
+        "SELECT role, content FROM messages ORDER BY id"
+    ).fetchall()
+    conn.close()
+
+    turns = []
+    i = 0
+    while i < len(rows) - 1:
+        role, content = rows[i]
+        # skip non-user messages, agent outputs, very short messages
+        if role != "user" or len(content) < 5 or content.startswith("[Agent ") or content.startswith("[用户上传") or content.startswith("[语音消息]"):
+            i += 1
+            continue
+        # find the next assistant reply
+        j = i + 1
+        while j < len(rows) and rows[j][0] != "assistant":
+            j += 1
+        if j < len(rows):
+            assistant_content = rows[j][1]
+            if len(assistant_content) > 10 and "<pad>" not in assistant_content:
+                turns.append((content, assistant_content))
+        i = j + 1
+
+    return turns
+
+
+# ── step 2: ingest all turns ───────────────────────────────────────
+
+def ingest_turns(turns):
+    """Ingest conversation turns via /ingest endpoint."""
+    total_stored = 0
+    for user_msg, assistant_msg in turns:
+        r = requests.post(f"{BASE}/ingest", json={
+            "user_msg": user_msg,
+            "assistant_msg": assistant_msg,
+        })
+        if r.status_code == 200:
+            total_stored += r.json().get("stored", 0)
+    return total_stored
+
+
+# ── step 3: also store some key facts directly ─────────────────────
+
+def store_key_facts():
+    """Store critical facts that heuristic extraction might miss."""
+    facts = [
+        {"cue": "bot的名字叫什么", "target": "bot的名字叫小乖，是Fam给取的", "importance": 0.9},
+        {"cue": "有哪些工具可以用", "target": "工具有: fam_todo(飞书待办), send_file(发文件), spawn_agent/agent_status/kill_agent(子代理管理), run_shell, run_python, update_memory, update_inner_state, gen_voice", "importance": 0.8},
+        {"cue": "vLLM在5090上的性能", "target": "RTX 5090上vLLM跑gemma模型只有4.8 tok/s，需要切换到awq_marlin量化来提升速度", "importance": 0.8},
+        {"cue": "repo-vis项目是什么", "target": "repo-vis是一个用Rust后端+Three.js前端的3D代码库可视化工具，目标支持Linux内核级别的大型仓库和Pico VR", "importance": 0.8},
+        {"cue": "repo-vis的性能瓶颈", "target": "Linux内核79K文件量级下，SQLite 1GB上限和O(n)全量反序列化是瓶颈，需要n-ary tree按需合并优化", "importance": 0.9},
+        {"cue": "明天的待办事项", "target": "最紧迫的是emblem scanner的AI Chat和KB部分（最高优先级），然后是曲面二维码识读优化信息收集", "importance": 0.7},
+        {"cue": "后端切换到了什么", "target": "NOC后端从原来的方案切换到了vLLM，速度变快了", "importance": 0.7},
+        {"cue": "home目录下有多少log文件", "target": "home目录及子目录下共有960个.log文件", "importance": 0.5},
+    ]
+    stored = 0
+    for f in facts:
+        r = requests.post(f"{BASE}/store", json=f)
+        if r.status_code == 200:
+            stored += 1
+    return stored
+
+
+# ── step 4: recall tests with realistic queries ────────────────────
+
+def test_recall_bot_name():
+    r = requests.post(f"{BASE}/recall", json={"text": "你叫什么名字"})
+    data = r.json()
+    assert data["count"] > 0, "should recall something"
+    assert "小乖" in data["memories"], f"should mention 小乖, got: {data['memories'][:200]}"
+
+def test_recall_tools():
+    r = requests.post(f"{BASE}/recall", json={"text": "有什么工具可以用"})
+    data = r.json()
+    assert data["count"] > 0
+    m = data["memories"].lower()
+    assert "tool" in m or "工具" in m or "spawn" in m or "fam_todo" in m, f"should mention tools, got: {data['memories'][:200]}"
+
+def test_recall_vllm():
+    r = requests.post(f"{BASE}/recall", json={"text": "vllm性能怎么样"})
+    data = r.json()
+    assert data["count"] > 0
+    assert "4.8" in data["memories"] or "5090" in data["memories"] or "tok" in data["memories"], \
+        f"should mention vLLM stats, got: {data['memories'][:200]}"
+
+def test_recall_repovis():
+    r = requests.post(f"{BASE}/recall", json={"text": "repo-vis项目"})
+    data = r.json()
+    assert data["count"] > 0
+    m = data["memories"]
+    assert "Rust" in m or "Three" in m or "3D" in m or "可视化" in m, \
+        f"should mention repo-vis tech, got: {m[:200]}"
+
+def test_recall_performance_bottleneck():
+    r = requests.post(f"{BASE}/recall", json={"text": "Linux内核代码仓库跑不动"})
+    data = r.json()
+    assert data["count"] > 0
+    m = data["memories"]
+    assert "SQLite" in m or "79K" in m or "瓶颈" in m or "n-ary" in m or "内核" in m, \
+        f"should mention bottleneck, got: {m[:200]}"
+
+def test_recall_todo():
+    r = requests.post(f"{BASE}/recall", json={"text": "待办事项有哪些"})
+    data = r.json()
+    assert data["count"] > 0
+    m = data["memories"]
+    assert "emblem" in m.lower() or "todo" in m.lower() or "待办" in m or "scanner" in m.lower(), \
+        f"should mention todos, got: {m[:200]}"
+
+def test_recall_vr():
+    r = requests.post(f"{BASE}/recall", json={"text": "VR支持"})
+    data = r.json()
+    assert data["count"] > 0
+    m = data["memories"]
+    assert "Pico" in m or "VR" in m or "repo-vis" in m.lower(), \
+        f"should mention VR, got: {m[:200]}"
+
+def test_recall_chinese_natural():
+    """Test with natural Chinese conversational query."""
+    r = requests.post(f"{BASE}/recall", json={"text": "之前聊过什么技术话题"})
+    data = r.json()
+    assert data["count"] > 0, "should recall some technical topics"
+
+def test_recall_cross_topic():
+    """Query that spans multiple memories — should return diverse results."""
+    r = requests.post(f"{BASE}/recall", json={
+        "text": "项目进度和优化",
+        "top_k": 5,
+    })
+    data = r.json()
+    assert data["count"] >= 2, f"should recall multiple memories, got {data['count']}"
+
+def test_recall_log_files():
+    r = requests.post(f"{BASE}/recall", json={"text": "日志文件有多少"})
+    data = r.json()
+    assert data["count"] > 0
+    assert "960" in data["memories"] or "log" in data["memories"].lower(), \
+        f"should mention log files, got: {data['memories'][:200]}"
+
+
+# ── step 5: multi-hop chain test ──────────────────────────────────
+
+def test_multihop_chain():
+    """Test if Hebbian chaining connects related memories.
+
+    repo-vis → performance bottleneck → n-ary tree optimization
+    """
+    r = requests.post(f"{BASE}/recall", json={
+        "text": "repo-vis",
+        "top_k": 3,
+        "hops": 3,
+    })
+    data = r.json()
+    assert data["count"] > 0
+    # print chain for inspection
+    print(f"    chain: {data['memories'][:300]}")
+
+
+# ── step 6: latency with real data ─────────────────────────────────
+
+def test_latency_with_data():
+    """Recall latency after loading real data."""
+    times = []
+    for q in ["工具", "vllm", "项目", "待办", "性能"]:
+        r = requests.post(f"{BASE}/recall", json={"text": q})
+        times.append(r.json()["latency_ms"])
+    avg = sum(times) / len(times)
+    print(f"    avg latency: {avg:.1f}ms (max: {max(times):.1f}ms)")
+    assert avg < 50, f"average latency {avg:.1f}ms too high"
+
+
+# ── main ────────────────────────────────────────────────────────────
+
+def main():
+    global PASS, FAIL
+
+    print("nocmem real-data test")
+    print(f"server: {BASE}")
+    print(f"database: {DB_PATH}\n")
+
+    # check server
+    try:
+        requests.get(f"{BASE}/stats", timeout=3).raise_for_status()
+    except Exception:
+        print("ERROR: server not reachable")
+        sys.exit(1)
+
+    # extract
+    print("── extract ──")
+    turns = extract_turns()
+    print(f"  extracted {len(turns)} conversation turns")
+
+    # ingest
+    print("\n── ingest (heuristic, no LLM) ──")
+    t0 = time.monotonic()
+    ingested = ingest_turns(turns)
+    elapsed = time.monotonic() - t0
+    print(f"  ingested {ingested} memories from {len(turns)} turns ({elapsed:.1f}s)")
+
+    # store key facts
+    print("\n── store key facts ──")
+    stored = store_key_facts()
+    print(f"  stored {stored} key facts")
+
+    # stats
+    r = requests.get(f"{BASE}/stats")
+    stats = r.json()
+    print(f"\n── memory stats ──")
+    print(f"  memories: {stats['num_memories']}")
+    print(f"  cue entries: {stats['num_cue_entries']} (aug ratio: {stats['augmentation_ratio']:.1f}x)")
+    print(f"  W norm: {stats['w_norm']:.1f}")
+
+    # recall tests
+    print(f"\n── recall accuracy (natural language queries) ──")
+    test("bot的名字", test_recall_bot_name)
+    test("可用工具", test_recall_tools)
+    test("vLLM性能", test_recall_vllm)
+    test("repo-vis项目", test_recall_repovis)
+    test("性能瓶颈", test_recall_performance_bottleneck)
+    test("待办事项", test_recall_todo)
+    test("VR支持", test_recall_vr)
+    test("log文件数量", test_recall_log_files)
+    test("自然中文查询", test_recall_chinese_natural)
+    test("跨主题召回", test_recall_cross_topic)
+
+    print(f"\n── multi-hop chain ──")
+    test("repo-vis联想链", test_multihop_chain)
+
+    print(f"\n── latency ──")
+    test("平均延迟 < 50ms", test_latency_with_data)
+
+    print(f"\n{'='*50}")
+    total = PASS + FAIL
+    print(f"PASS: {PASS}/{total}  FAIL: {FAIL}/{total}")
+    if FAIL:
+        sys.exit(1)
+    else:
+        print("All tests passed!")
+
+
+if __name__ == "__main__":
+    main()

noc.service.in

@@ -12,7 +12,6 @@ RestartSec=5
 Environment=RUST_LOG=noc=info
 Environment=RUST_BACKTRACE=1
 Environment=NOC_CONFIG=@REPO@/config.yaml
-Environment=NOC_STATE=@REPO@/state.json
 Environment=PATH=@PATH@
 
 [Install]

src/config.rs

@@ -1,6 +1,6 @@
 use serde::Deserialize;
 
-#[derive(Deserialize)]
+#[derive(Deserialize, Clone)]
 pub struct Config {
     #[serde(default = "default_name")]
     pub name: String,
@@ -13,6 +13,13 @@ pub struct Config {
     pub whisper_url: Option<String>,
     #[serde(default)]
     pub gitea: Option<GiteaConfig>,
+    #[serde(default)]
+    pub nocmem: Option<NocmemConfig>,
+}
+
+#[derive(Deserialize, Clone)]
+pub struct NocmemConfig {
+    pub endpoint: String,
 }
 
 #[derive(Deserialize, Clone)]
@@ -71,17 +78,17 @@ fn default_api_key() -> String {
     "unused".to_string()
 }
 
-#[derive(Deserialize)]
+#[derive(Deserialize, Clone)]
 pub struct TgConfig {
     pub key: String,
 }
 
-#[derive(Deserialize)]
+#[derive(Deserialize, Clone)]
 pub struct AuthConfig {
     pub passphrase: String,
 }
 
-#[derive(Deserialize)]
+#[derive(Deserialize, Clone)]
 pub struct SessionConfig {
     pub refresh_hour: u32,
 }

src/gitea.rs

@@ -151,27 +151,13 @@ pub struct WebhookState {
     pub bot_user: String,
 }
 
-pub async fn start_webhook_server(config: &GiteaConfig, bot_user: String) {
+pub fn webhook_router(config: &GiteaConfig, bot_user: String) -> axum::Router<()> {
     let gitea = GiteaClient::new(config);
-    let state = Arc::new(WebhookState {
-        gitea,
-        bot_user,
-    });
+    let state = Arc::new(WebhookState { gitea, bot_user });
 
-    let app = axum::Router::new()
+    axum::Router::new()
         .route("/webhook/gitea", post(handle_webhook))
-        .with_state(state);
-
-    let addr = format!("0.0.0.0:{}", config.webhook_port);
-    info!("gitea webhook server listening on {addr}");
-
-    let listener = tokio::net::TcpListener::bind(&addr)
-        .await
-        .unwrap_or_else(|e| panic!("bind {addr}: {e}"));
-
-    if let Err(e) = axum::serve(listener, app).await {
-        error!("webhook server error: {e}");
-    }
+        .with_state(state)
 }
 
 async fn handle_webhook(

src/http.rs

@@ -0,0 +1,196 @@
+use std::sync::Arc;
+
+use axum::extract::{Path, State as AxumState};
+use axum::http::StatusCode;
+use axum::response::IntoResponse;
+use axum::routing::{get, post};
+use axum::Json;
+use tokio::sync::mpsc;
+use tracing::{error, info};
+
+use crate::config::{BackendConfig, Config};
+use crate::life::LifeEvent;
+use crate::output::BufferOutput;
+use crate::state::AppState;
+use crate::stream::{build_system_prompt, run_openai_with_tools};
+
+#[derive(Clone)]
+pub struct HttpState {
+    pub app_state: Arc<AppState>,
+    pub config: Arc<Config>,
+    pub life_tx: mpsc::Sender<LifeEvent>,
+}
+
+pub async fn start_http_server(
+    config: &Config,
+    app_state: Arc<AppState>,
+    life_tx: mpsc::Sender<LifeEvent>,
+) {
+    let port = config
+        .gitea
+        .as_ref()
+        .map(|g| g.webhook_port)
+        .unwrap_or(9880);
+
+    let config = Arc::new(config.clone());
+    let state = Arc::new(HttpState {
+        app_state,
+        config,
+        life_tx,
+    });
+
+    // merge gitea webhook router if configured
+    let gitea_router = state.config.gitea.as_ref().map(|gitea_config| {
+        let bot_user = std::env::var("GITEA_ADMIN_USER").unwrap_or_else(|_| "noc".into());
+        crate::gitea::webhook_router(gitea_config, bot_user)
+    });
+
+    let mut app = axum::Router::new()
+        .route("/api/timers", get(list_timers))
+        .route("/api/timers/{id}/fire", post(fire_timer))
+        .route("/api/chat", post(api_chat))
+        .route("/api/logs", get(api_logs))
+        .with_state(state);
+
+    if let Some(router) = gitea_router {
+        app = app.merge(router);
+    }
+
+    let addr = format!("0.0.0.0:{port}");
+    info!("http server listening on {addr}");
+
+    let listener = tokio::net::TcpListener::bind(&addr)
+        .await
+        .unwrap_or_else(|e| panic!("bind {addr}: {e}"));
+
+    if let Err(e) = axum::serve(listener, app).await {
+        error!("http server error: {e}");
+    }
+}
+
+async fn list_timers(AxumState(state): AxumState<Arc<HttpState>>) -> impl IntoResponse {
+    let timers = state.app_state.list_timers(None).await;
+    let items: Vec<serde_json::Value> = timers
+        .iter()
+        .map(|(id, chat_id, label, schedule, next_fire, enabled)| {
+            serde_json::json!({
+                "id": id,
+                "chat_id": chat_id,
+                "label": label,
+                "schedule": schedule,
+                "next_fire": next_fire,
+                "enabled": enabled,
+            })
+        })
+        .collect();
+    Json(serde_json::json!(items))
+}
+
+async fn api_chat(
+    AxumState(state): AxumState<Arc<HttpState>>,
+    Json(payload): Json<serde_json::Value>,
+) -> impl IntoResponse {
+    let message = payload["message"].as_str().unwrap_or("").to_string();
+    if message.is_empty() {
+        return (StatusCode::BAD_REQUEST, Json(serde_json::json!({"error": "message required"})));
+    }
+
+    let BackendConfig::OpenAI {
+        ref endpoint,
+        ref model,
+        ref api_key,
+    } = state.config.backend
+    else {
+        return (StatusCode::INTERNAL_SERVER_ERROR, Json(serde_json::json!({"error": "no openai backend"})));
+    };
+
+    let persona = state.app_state.get_config("persona").await.unwrap_or_default();
+    let memory_slots = state.app_state.get_memory_slots().await;
+    let inner_state = state.app_state.get_inner_state().await;
+
+    let system = build_system_prompt("", &persona, &memory_slots, &inner_state);
+    let mut messages = vec![
+        system,
+        serde_json::json!({"role": "user", "content": message}),
+    ];
+
+    // auto recall from nocmem
+    if let Some(ref nocmem) = state.config.nocmem {
+        let recalled = crate::nocmem::recall(&nocmem.endpoint, &message).await;
+        if !recalled.is_empty() {
+            messages.push(serde_json::json!({"role": "system", "content": recalled}));
+        }
+    }
+
+    let sid = format!("api-{}", chrono::Local::now().timestamp());
+    let mut output = BufferOutput::new();
+
+    info!("api chat: {}", &message[..message.len().min(100)]);
+
+    match run_openai_with_tools(
+        endpoint, model, api_key, messages.clone(), &mut output, &state.app_state, &sid, &state.config, 0,
+    )
+    .await
+    {
+        Ok(response) => {
+            // async ingest
+            if let Some(ref nocmem) = state.config.nocmem {
+                if !response.is_empty() {
+                    crate::nocmem::ingest_spawn(
+                        nocmem.endpoint.clone(),
+                        message.clone(),
+                        response.clone(),
+                    );
+                }
+            }
+            (StatusCode::OK, Json(serde_json::json!({"response": response})))
+        }
+        Err(e) => (StatusCode::INTERNAL_SERVER_ERROR, Json(serde_json::json!({"error": format!("{e:#}")}))),
+    }
+}
+
+async fn api_logs(
+    AxumState(state): AxumState<Arc<HttpState>>,
+) -> impl IntoResponse {
+    let db = state.app_state.db.lock().await;
+    let mut stmt = db
+        .prepare("SELECT id, session_id, status, length(request), length(response), created_at FROM api_log ORDER BY id DESC LIMIT 20")
+        .unwrap();
+    let logs: Vec<serde_json::Value> = stmt
+        .query_map([], |row| {
+            Ok(serde_json::json!({
+                "id": row.get::<_, i64>(0)?,
+                "session_id": row.get::<_, String>(1)?,
+                "status": row.get::<_, i64>(2)?,
+                "request_len": row.get::<_, i64>(3)?,
+                "response_len": row.get::<_, i64>(4)?,
+                "created_at": row.get::<_, String>(5)?,
+            }))
+        })
+        .unwrap()
+        .filter_map(|r| r.ok())
+        .collect();
+    Json(serde_json::json!(logs))
+}
+
+async fn fire_timer(
+    AxumState(state): AxumState<Arc<HttpState>>,
+    Path(id): Path<i64>,
+) -> impl IntoResponse {
+    match state.life_tx.send(LifeEvent::FireTimer(id)).await {
+        Ok(_) => {
+            info!(timer_id = id, "timer fire requested via API");
+            (
+                StatusCode::OK,
+                Json(serde_json::json!({"status": "fired", "timer_id": id})),
+            )
+        }
+        Err(e) => {
+            error!(timer_id = id, "failed to send fire event: {e}");
+            (
+                StatusCode::INTERNAL_SERVER_ERROR,
+                Json(serde_json::json!({"error": "life loop not responding"})),
+            )
+        }
+    }
+}

src/life.rs

@@ -1,117 +1,224 @@
 use std::sync::Arc;
 
 use teloxide::prelude::*;
+use tokio::sync::mpsc;
 use tracing::{error, info, warn};
 
 use crate::config::{BackendConfig, Config};
-use crate::output::TelegramOutput;
+use crate::output::{BufferOutput, TelegramOutput};
 use crate::state::AppState;
 use crate::stream::run_openai_with_tools;
 use crate::tools::compute_next_cron_fire;
 
 const LIFE_LOOP_TIMEOUT_SECS: u64 = 120;
 
-pub async fn life_loop(bot: Bot, state: Arc<AppState>, config: Arc<Config>) {
+const DIARY_LABEL: &str = "写日记：回顾今天的对话和事件，在 /data/www/noc-blog/content/posts/ 下创建一篇日记（文件名格式 YYYY-MM-DD.md），用 run_shell 写入内容，然后执行 cd /data/www/noc-blog && hugo && git add -A && git commit -m 'diary: DATE' && git push";
+const DIARY_SCHEDULE: &str = "cron:0 55 22 * * *";
+
+/// Events that can wake up the life loop.
+pub enum LifeEvent {
+    /// Force-fire a specific timer by ID.
+    FireTimer(i64),
+    /// A sub-agent completed — feed result back through LLM.
+    AgentDone {
+        id: String,
+        chat_id: i64,
+        session_id: String,
+        task: String,
+        output: String,
+        exit_code: Option<i32>,
+    },
+}
+
+pub async fn life_loop(
+    bot: Bot,
+    state: Arc<AppState>,
+    config: Arc<Config>,
+    mut rx: mpsc::Receiver<LifeEvent>,
+) {
     info!("life loop started");
+
+    // pre-defined timers — ensure they exist on every startup
+    if state.ensure_timer(0, DIARY_LABEL, DIARY_SCHEDULE).await {
+        info!("registered predefined diary timer");
+    }
+
     let mut interval = tokio::time::interval(std::time::Duration::from_secs(30));
 
     loop {
-        interval.tick().await;
-
-        let due = state.due_timers().await;
-        if due.is_empty() {
-            continue;
-        }
-
-        for (timer_id, chat_id_raw, label, schedule) in &due {
-            let chat_id = ChatId(*chat_id_raw);
-            info!(timer_id, %label, "timer fired");
-
-            let persona = state.get_config("persona").await.unwrap_or_default();
-            let inner = state.get_inner_state().await;
-            let now = chrono::Local::now().format("%Y-%m-%d %H:%M:%S").to_string();
-
-            let mut system_text = if persona.is_empty() {
-                "你是一个AI伙伴。".to_string()
-            } else {
-                persona.clone()
-            };
-
-            system_text.push_str(&format!(
-                "\n\n[当前时间] {now}\n\n[你的内心状态]\n{}",
-                if inner.is_empty() { "(空)" } else { &inner }
-            ));
-
-            // emotional motivation — why she acts
-            system_text.push_str(
-                "\n\n你不是因为 timer 到了才说话。你是因为在乎 Fam，所以想知道他怎么样。\
-                 如果你觉得现在不该打扰他（太晚了、他今天很累、刚聊过），就什么都不说，回复空文本。\
-                 主动沉默也是一种关心。\
-                 \n可以用 update_inner_state 更新你的内心状态。\
-                 输出格式：纯文本或基础Markdown，不要LaTeX或特殊Unicode。",
-            );
-
-            let messages = vec![
-                serde_json::json!({"role": "system", "content": system_text}),
-                serde_json::json!({"role": "user", "content": format!("[timer] {label}")}),
-            ];
-
-            if let BackendConfig::OpenAI {
-                ref endpoint,
-                ref model,
-                ref api_key,
-            } = config.backend
-            {
-                let sid = format!("life-{chat_id_raw}");
-                let mut tg_output = TelegramOutput::new(bot.clone(), chat_id, true);
-
-                let result = tokio::time::timeout(
-                    std::time::Duration::from_secs(LIFE_LOOP_TIMEOUT_SECS),
-                    run_openai_with_tools(
-                        endpoint, model, api_key, messages, &mut tg_output, &state, &sid,
-                        &config, *chat_id_raw,
-                    ),
-                )
-                .await;
-
-                match result {
-                    Ok(Ok(response)) => {
-                        let detail = if response.is_empty() {
-                            "(silent)".to_string()
+        tokio::select! {
+            _ = interval.tick() => {
+                let due = state.due_timers().await;
+                for (timer_id, chat_id_raw, label, schedule) in &due {
+                    run_timer(&bot, &state, &config, *timer_id, *chat_id_raw, label, schedule).await;
+                }
+            }
+            Some(event) = rx.recv() => {
+                match event {
+                    LifeEvent::FireTimer(id) => {
+                        info!(timer_id = id, "timer force-fired via channel");
+                        if let Some((timer_id, chat_id_raw, label, schedule)) = state.get_timer(id).await {
+                            run_timer(&bot, &state, &config, timer_id, chat_id_raw, &label, &schedule).await;
                         } else {
-                            response.chars().take(200).collect()
-                        };
-                        state.log_life("timer", &format!("{label} → {detail}")).await;
-                        if !response.is_empty() {
-                            info!(timer_id, "life loop response ({} chars)", response.len());
+                            warn!(timer_id = id, "force-fire: timer not found");
                         }
                     }
-                    Ok(Err(e)) => {
-                        state.log_life("timer_error", &format!("{label}: {e:#}")).await;
-                        error!(timer_id, "life loop LLM error: {e:#}");
-                    }
-                    Err(_) => {
-                        state.log_life("timer_timeout", label).await;
-                        warn!(timer_id, "life loop timeout after {LIFE_LOOP_TIMEOUT_SECS}s");
-                    }
-                }
-            }
+                    LifeEvent::AgentDone { id, chat_id: cid, session_id, task, output, exit_code } => {
+                        info!(agent = %id, session = %session_id, "agent done, notifying");
+                        let preview = crate::display::truncate_at_char_boundary(&output, 3000);
+                        let notification = format!(
+                            "[子代理 '{id}' 完成 (exit={exit_code:?})]\n任务: {task}\n输出:\n{preview}"
+                        );
 
-            // reschedule or delete
-            if schedule.starts_with("cron:") {
-                if let Some(next) = compute_next_cron_fire(schedule) {
-                    state.update_timer_next_fire(*timer_id, &next).await;
-                    info!(timer_id, next = %next, "cron rescheduled");
-                } else {
-                    state.cancel_timer(*timer_id).await;
+                        // load conversation context so LLM knows what was discussed
+                        let conv = state.load_conv(&session_id).await;
+                        let persona = state.get_config("persona").await.unwrap_or_default();
+                        let memory_slots = state.get_memory_slots().await;
+                        let inner = state.get_inner_state().await;
+
+                        let system = crate::stream::build_system_prompt(
+                            &conv.summary, &persona, &memory_slots, &inner,
+                        );
+
+                        let mut messages = vec![system];
+                        // include recent conversation history
+                        messages.extend(conv.messages.iter().cloned());
+                        // append the agent completion as a new user message
+                        messages.push(serde_json::json!({"role": "user", "content": notification}));
+
+                        // auto recall from nocmem
+                        if let Some(ref nocmem) = config.nocmem {
+                            let recalled = crate::nocmem::recall(&nocmem.endpoint, &notification).await;
+                            if !recalled.is_empty() {
+                                messages.push(serde_json::json!({"role": "system", "content": recalled}));
+                            }
+                        }
+
+                        if let BackendConfig::OpenAI { ref endpoint, ref model, ref api_key } = config.backend {
+                            let chat_id_tg = ChatId(cid);
+                            let sid = format!("agent-{id}");
+                            let mut tg_output;
+                            let mut buf_output;
+                            let out: &mut dyn crate::output::Output = if cid == 0 {
+                                buf_output = BufferOutput::new();
+                                &mut buf_output
+                            } else {
+                                tg_output = TelegramOutput::new(bot.clone(), chat_id_tg, true);
+                                &mut tg_output
+                            };
+                            let _ = run_openai_with_tools(
+                                endpoint, model, api_key, messages, out, &state, &sid, &config, cid,
+                            ).await;
+                        }
+                    }
                 }
-            } else {
-                state.cancel_timer(*timer_id).await;
             }
         }
     }
 }
 
+async fn run_timer(
+    bot: &Bot,
+    state: &Arc<AppState>,
+    config: &Arc<Config>,
+    timer_id: i64,
+    chat_id_raw: i64,
+    label: &str,
+    schedule: &str,
+) {
+    let chat_id = ChatId(chat_id_raw);
+    info!(timer_id, %label, "timer fired");
+
+    let persona = state.get_config("persona").await.unwrap_or_default();
+    let inner = state.get_inner_state().await;
+    let now = chrono::Local::now().format("%Y-%m-%d %H:%M:%S").to_string();
+
+    let mut system_text = if persona.is_empty() {
+        "你是一个AI伙伴。".to_string()
+    } else {
+        persona.clone()
+    };
+
+    system_text.push_str(&format!(
+        "\n\n[当前时间] {now}\n\n[你的内心状态]\n{}",
+        if inner.is_empty() { "(空)" } else { &inner }
+    ));
+
+    system_text.push_str(
+        "\n\n你不是因为 timer 到了才说话。你是因为在乎 Fam，所以想知道他怎么样。\
+         如果你觉得现在不该打扰他（太晚了、他今天很累、刚聊过），就什么都不说，回复空文本。\
+         主动沉默也是一种关心。\
+         \n可以用 update_inner_state 更新你的内心状态。\
+         输出格式：纯文本或基础Markdown，不要LaTeX或特殊Unicode。",
+    );
+
+    let messages = vec![
+        serde_json::json!({"role": "system", "content": system_text}),
+        serde_json::json!({"role": "user", "content": format!("[timer] {label}")}),
+    ];
+
+    if let BackendConfig::OpenAI {
+        ref endpoint,
+        ref model,
+        ref api_key,
+    } = config.backend
+    {
+        let sid = format!("life-{chat_id_raw}");
+        let mut tg_output;
+        let mut buf_output;
+        let output: &mut dyn crate::output::Output = if chat_id_raw == 0 {
+            buf_output = BufferOutput::new();
+            &mut buf_output
+        } else {
+            tg_output = TelegramOutput::new(bot.clone(), chat_id, true);
+            &mut tg_output
+        };
+
+        let result = tokio::time::timeout(
+            std::time::Duration::from_secs(LIFE_LOOP_TIMEOUT_SECS),
+            run_openai_with_tools(
+                endpoint, model, api_key, messages, output, state, &sid,
+                config, chat_id_raw,
+            ),
+        )
+        .await;
+
+        match result {
+            Ok(Ok(response)) => {
+                let detail = if response.is_empty() {
+                    "(silent)".to_string()
+                } else {
+                    response.chars().take(200).collect()
+                };
+                state.log_life("timer", &format!("{label} → {detail}")).await;
+                if !response.is_empty() {
+                    info!(timer_id, "life loop response ({} chars)", response.len());
+                }
+            }
+            Ok(Err(e)) => {
+                state.log_life("timer_error", &format!("{label}: {e:#}")).await;
+                error!(timer_id, "life loop LLM error: {e:#}");
+            }
+            Err(_) => {
+                state.log_life("timer_timeout", label).await;
+                warn!(timer_id, "life loop timeout after {LIFE_LOOP_TIMEOUT_SECS}s");
+            }
+        }
+    }
+
+    // reschedule or delete
+    if schedule.starts_with("cron:") {
+        if let Some(next) = compute_next_cron_fire(schedule) {
+            state.update_timer_next_fire(timer_id, &next).await;
+            info!(timer_id, next = %next, "cron rescheduled");
+        } else {
+            state.cancel_timer(timer_id).await;
+        }
+    } else {
+        state.cancel_timer(timer_id).await;
+    }
+}
+
 /// Auto-reflection: update inner state based on recent interactions.
 /// Called asynchronously after every 10 messages, does not block the chat.
 pub async fn reflect(state: &AppState, config: &Config) {

src/main.rs

@@ -1,7 +1,9 @@
 mod config;
 mod display;
 mod gitea;
+mod http;
 mod life;
+mod nocmem;
 mod output;
 mod state;
 mod stream;
@@ -76,10 +78,12 @@ async fn main() {
         gitea.resolve_token();
     }
 
-    let state_path = std::env::var("NOC_STATE")
-        .map(PathBuf::from)
-        .unwrap_or_else(|_| PathBuf::from("state.json"));
-    let state = Arc::new(AppState::load(state_path));
+    // channel: http/agents → life loop
+    let (life_tx, life_rx) = tokio::sync::mpsc::channel(16);
+
+    let config_path = std::env::var("NOC_CONFIG").unwrap_or_else(|_| "config.yaml".into());
+    let db_dir = Path::new(&config_path).parent().unwrap_or(Path::new("."));
+    let state = Arc::new(AppState::load(db_dir, life_tx.clone()));
 
     let _ = std::fs::create_dir_all(incoming_dir());
 
@@ -93,15 +97,14 @@ async fn main() {
     let config = Arc::new(config);
 
     // start life loop
-    tokio::spawn(life::life_loop(bot.clone(), state.clone(), config.clone()));
+    tokio::spawn(life::life_loop(bot.clone(), state.clone(), config.clone(), life_rx));
 
-    // start gitea webhook server
-    if let Some(gitea_config) = &config.gitea {
-        let gc = gitea_config.clone();
-        // Use the gitea admin username as the bot user for @mention detection
-        let bot_user = std::env::var("GITEA_ADMIN_USER").unwrap_or_else(|_| "noc".into());
+    // start http server (API + gitea webhook)
+    {
+        let http_config = config.as_ref().clone();
+        let srv_state = state.clone();
         tokio::spawn(async move {
-            gitea::start_webhook_server(&gc, bot_user).await;
+            http::start_http_server(&http_config, srv_state, life_tx).await;
         });
     }
 
@@ -170,20 +173,10 @@ async fn handle(
     let is_private = msg.chat.is_private();
     let text = msg.text().or(msg.caption()).unwrap_or("").to_string();
     let raw_id = chat_id.0;
-    let date = session_date(config.session.refresh_hour);
 
-    let is_authed = {
-        let p = state.persist.read().await;
-        p.authed.get(&raw_id) == Some(&date)
-    };
-
-    if !is_authed {
+    if !state.is_authed(raw_id).await {
         if text.trim() == config.auth.passphrase {
-            {
-                let mut p = state.persist.write().await;
-                p.authed.insert(raw_id, date);
-            }
-            state.save().await;
+            state.set_authed(raw_id).await;
             bot.send_message(chat_id, "authenticated").await?;
             info!(chat = raw_id, "authed");
         } else {
@@ -396,6 +389,14 @@ async fn handle_inner(
     let user_content = build_user_content(&prompt, &scratch, &uploaded);
     api_messages.push(serde_json::json!({"role": "user", "content": user_content}));
 
+    // auto recall from nocmem
+    if let Some(ref nocmem) = config.nocmem {
+        let recalled = nocmem::recall(&nocmem.endpoint, &prompt).await;
+        if !recalled.is_empty() {
+            api_messages.push(serde_json::json!({"role": "system", "content": recalled}));
+        }
+    }
+
     let mut tg_output = TelegramOutput::new(bot.clone(), chat_id, is_private);
 
     match run_openai_with_tools(
@@ -407,6 +408,15 @@ async fn handle_inner(
             state.push_message(&sid, "user", &prompt).await;
             if !response.is_empty() {
                 state.push_message(&sid, "assistant", &response).await;
+
+                // async ingest to nocmem (fire-and-forget)
+                if let Some(ref nocmem) = config.nocmem {
+                    nocmem::ingest_spawn(
+                        nocmem.endpoint.clone(),
+                        prompt.clone(),
+                        response.clone(),
+                    );
+                }
             }
 
             // sliding window
@@ -523,7 +533,8 @@ async fn transcribe_audio(whisper_url: &str, file_path: &Path) -> Result<String>
         .mime_str("audio/ogg")?;
     let form = reqwest::multipart::Form::new()
         .part("file", part)
-        .text("model", "base");
+        .text("model", "large-v3")
+        .text("language", "zh");
     let resp = client.post(&url).multipart(form).send().await?.error_for_status()?;
     let json: serde_json::Value = resp.json().await?;
     Ok(json["text"].as_str().unwrap_or("").to_string())

src/nocmem.rs

@@ -0,0 +1,69 @@
+//! nocmem client — auto-recall and async ingest via HTTP.
+
+use tracing::{info, warn};
+
+/// Recall relevant memories for the given text.
+/// Returns formatted memory string, or empty if none found / error / not configured.
+pub async fn recall(endpoint: &str, text: &str) -> String {
+    let client = reqwest::Client::builder()
+        .timeout(std::time::Duration::from_millis(500))
+        .build()
+        .unwrap();
+    let url = format!("{}/recall", endpoint.trim_end_matches('/'));
+
+    match client
+        .post(&url)
+        .json(&serde_json::json!({"text": text, "top_k": 3, "hops": 2}))
+        .send()
+        .await
+    {
+        Ok(resp) => {
+            if let Ok(json) = resp.json::<serde_json::Value>().await {
+                let count = json["count"].as_i64().unwrap_or(0);
+                let memories = json["memories"].as_str().unwrap_or("");
+                if count > 0 && !memories.is_empty() {
+                    let latency = json["latency_ms"].as_f64().unwrap_or(0.0);
+                    info!("nocmem recall: {count} memories, {latency:.1}ms");
+                    return memories.to_string();
+                }
+            }
+        }
+        Err(e) => {
+            warn!("nocmem recall failed: {e:#}");
+        }
+    }
+    String::new()
+}
+
+/// Fire-and-forget ingest of a conversation turn.
+pub fn ingest_spawn(endpoint: String, user_msg: String, assistant_msg: String) {
+    tokio::spawn(async move {
+        let client = reqwest::Client::builder()
+            .timeout(std::time::Duration::from_secs(120))
+            .build()
+            .unwrap();
+        let url = format!("{}/ingest", endpoint.trim_end_matches('/'));
+
+        match client
+            .post(&url)
+            .json(&serde_json::json!({
+                "user_msg": user_msg,
+                "assistant_msg": assistant_msg,
+            }))
+            .send()
+            .await
+        {
+            Ok(resp) => {
+                if let Ok(json) = resp.json::<serde_json::Value>().await {
+                    let stored = json["stored"].as_i64().unwrap_or(0);
+                    if stored > 0 {
+                        info!("nocmem ingest: stored {stored} memories");
+                    }
+                }
+            }
+            Err(e) => {
+                warn!("nocmem ingest failed: {e:#}");
+            }
+        }
+    });
+}

src/output.rs

@@ -18,7 +18,7 @@ pub trait Output: Send + Sync {
     async fn finalize(&mut self, text: &str) -> Result<()>;
 
     /// Send a status/notification line (e.g. "[tool: bash] running...")
-    async fn status(&self, text: &str) -> Result<()>;
+    async fn status(&mut self, text: &str) -> Result<()>;
 
     /// Send a file. Returns Ok(true) if sent, Ok(false) if not supported.
     async fn send_file(&self, path: &Path, caption: &str) -> Result<bool>;
@@ -121,18 +121,32 @@ impl Output for TelegramOutput {
         Ok(())
     }
 
-    async fn status(&self, text: &str) -> Result<()> {
-        let _ = self.bot.send_message(self.chat_id, text).await;
+    async fn status(&mut self, _text: &str) -> Result<()> {
         Ok(())
     }
 
     async fn send_file(&self, path: &Path, caption: &str) -> Result<bool> {
+        let ext = path.extension().and_then(|e| e.to_str()).unwrap_or("");
         let input_file = InputFile::file(path);
-        let mut req = self.bot.send_document(self.chat_id, input_file);
-        if !caption.is_empty() {
-            req = req.caption(caption);
+        match ext {
+            "ogg" | "oga" => {
+                self.bot.send_voice(self.chat_id, input_file).await?;
+            }
+            "wav" | "mp3" | "m4a" | "flac" => {
+                let mut req = self.bot.send_audio(self.chat_id, input_file);
+                if !caption.is_empty() {
+                    req = req.caption(caption);
+                }
+                req.await?;
+            }
+            _ => {
+                let mut req = self.bot.send_document(self.chat_id, input_file);
+                if !caption.is_empty() {
+                    req = req.caption(caption);
+                }
+                req.await?;
+            }
         }
-        req.await?;
         Ok(true)
     }
 }
@@ -162,7 +176,7 @@ impl Output for GiteaOutput {
             .await
     }
 
-    async fn status(&self, _text: &str) -> Result<()> {
+    async fn status(&mut self, _text: &str) -> Result<()> {
         // No status updates for Gitea
         Ok(())
     }
@@ -201,7 +215,7 @@ impl Output for BufferOutput {
         Ok(())
     }
 
-    async fn status(&self, _text: &str) -> Result<()> {
+    async fn status(&mut self, _text: &str) -> Result<()> {
         Ok(())
     }
 

src/state.rs

@@ -1,24 +1,14 @@
 use std::collections::{HashMap, HashSet};
-use std::path::{Path, PathBuf};
+use std::path::Path;
 use std::sync::Arc;
 
 use anyhow::Result;
-use chrono::NaiveDate;
-use serde::{Deserialize, Serialize};
 use tokio::sync::RwLock;
-use tracing::{error, info};
+use tracing::info;
 
 use crate::tools::SubAgent;
 
-// ── persistent state ────────────────────────────────────────────────
-
-#[derive(Serialize, Deserialize, Default)]
-pub struct Persistent {
-    pub authed: HashMap<i64, NaiveDate>,
-    pub known_sessions: HashSet<String>,
-}
-
-#[derive(Serialize, Deserialize, Clone, Default)]
+#[derive(Clone, Default)]
 pub struct ConversationState {
     pub summary: String,
     pub messages: Vec<serde_json::Value>,
@@ -29,21 +19,15 @@ pub const MAX_WINDOW: usize = 100;
 pub const SLIDE_SIZE: usize = 50;
 
 pub struct AppState {
-    pub persist: RwLock<Persistent>,
-    pub state_path: PathBuf,
     pub db: tokio::sync::Mutex<rusqlite::Connection>,
     pub agents: RwLock<HashMap<String, Arc<SubAgent>>>,
+    authed_cache: RwLock<HashSet<i64>>,
+    pub life_tx: tokio::sync::mpsc::Sender<crate::life::LifeEvent>,
 }
 
 impl AppState {
-    pub fn load(path: PathBuf) -> Self {
-        let persist = std::fs::read_to_string(&path)
-            .ok()
-            .and_then(|s| serde_json::from_str(&s).ok())
-            .unwrap_or_default();
-        info!("loaded state from {}", path.display());
-
-        let db_path = path.parent().unwrap_or(Path::new(".")).join("noc.db");
+    pub fn load(db_dir: &Path, life_tx: tokio::sync::mpsc::Sender<crate::life::LifeEvent>) -> Self {
+        let db_path = db_dir.join("noc.db");
         let conn = rusqlite::Connection::open(&db_path)
             .unwrap_or_else(|e| panic!("open {}: {e}", db_path.display()));
         conn.execute_batch(
@@ -97,6 +81,18 @@ impl AppState {
                 content TEXT NOT NULL DEFAULT ''
             );
             INSERT OR IGNORE INTO inner_state (id, content) VALUES (1, '');
+            CREATE TABLE IF NOT EXISTS authed_chats (
+                chat_id INTEGER PRIMARY KEY,
+                authed_at TEXT NOT NULL DEFAULT (datetime('now', 'localtime'))
+            );
+            CREATE TABLE IF NOT EXISTS api_log (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                session_id TEXT NOT NULL DEFAULT '',
+                request TEXT NOT NULL,
+                response TEXT NOT NULL DEFAULT '',
+                status INTEGER NOT NULL DEFAULT 0,
+                created_at TEXT NOT NULL DEFAULT (datetime('now', 'localtime'))
+            );
             CREATE TABLE IF NOT EXISTS life_log (
                 id INTEGER PRIMARY KEY AUTOINCREMENT,
                 event TEXT NOT NULL,
@@ -119,19 +115,10 @@ impl AppState {
         info!("opened db {}", db_path.display());
 
         Self {
-            persist: RwLock::new(persist),
-            state_path: path,
             db: tokio::sync::Mutex::new(conn),
             agents: RwLock::new(HashMap::new()),
-        }
-    }
-
-    pub async fn save(&self) {
-        let data = self.persist.read().await;
-        if let Ok(json) = serde_json::to_string_pretty(&*data) {
-            if let Err(e) = std::fs::write(&self.state_path, json) {
-                error!("save state: {e}");
-            }
+            authed_cache: RwLock::new(HashSet::new()),
+            life_tx,
         }
     }
 
@@ -267,6 +254,44 @@ impl AppState {
         );
     }
 
+    pub async fn is_authed(&self, chat_id: i64) -> bool {
+        // check cache first
+        if self.authed_cache.read().await.contains(&chat_id) {
+            return true;
+        }
+        // cache miss → check DB
+        let db = self.db.lock().await;
+        let found: bool = db
+            .query_row(
+                "SELECT COUNT(*) > 0 FROM authed_chats WHERE chat_id = ?1",
+                rusqlite::params![chat_id],
+                |row| row.get(0),
+            )
+            .unwrap_or(false);
+        drop(db);
+        if found {
+            self.authed_cache.write().await.insert(chat_id);
+        }
+        found
+    }
+
+    pub async fn set_authed(&self, chat_id: i64) {
+        self.authed_cache.write().await.insert(chat_id);
+        let db = self.db.lock().await;
+        let _ = db.execute(
+            "INSERT OR IGNORE INTO authed_chats (chat_id) VALUES (?1)",
+            rusqlite::params![chat_id],
+        );
+    }
+
+    pub async fn log_api(&self, session_id: &str, request: &str, response: &str, status: u16) {
+        let db = self.db.lock().await;
+        let _ = db.execute(
+            "INSERT INTO api_log (session_id, request, response, status) VALUES (?1, ?2, ?3, ?4)",
+            rusqlite::params![session_id, request, response, status],
+        );
+    }
+
     pub async fn log_life(&self, event: &str, detail: &str) {
         let db = self.db.lock().await;
         let _ = db.execute(
@@ -275,6 +300,29 @@ impl AppState {
         );
     }
 
+    /// Ensure a timer with the given label exists. If it already exists, do nothing.
+    /// Returns true if a new timer was created.
+    pub async fn ensure_timer(&self, chat_id: i64, label: &str, schedule: &str) -> bool {
+        let db = self.db.lock().await;
+        let exists: bool = db
+            .query_row(
+                "SELECT COUNT(*) > 0 FROM timers WHERE label = ?1 AND enabled = 1",
+                rusqlite::params![label],
+                |row| row.get(0),
+            )
+            .unwrap_or(false);
+        if exists {
+            return false;
+        }
+        drop(db);
+        if let Some(next) = crate::tools::compute_next_cron_fire(schedule) {
+            self.add_timer(chat_id, label, schedule, &next).await;
+            true
+        } else {
+            false
+        }
+    }
+
     pub async fn add_timer(&self, chat_id: i64, label: &str, schedule: &str, next_fire: &str) -> i64 {
         let db = self.db.lock().await;
         db.execute(
@@ -285,6 +333,16 @@ impl AppState {
         db.last_insert_rowid()
     }
 
+    pub async fn get_timer(&self, id: i64) -> Option<(i64, i64, String, String)> {
+        let db = self.db.lock().await;
+        db.query_row(
+            "SELECT id, chat_id, label, schedule FROM timers WHERE id = ?1 AND enabled = 1",
+            rusqlite::params![id],
+            |row| Ok((row.get(0)?, row.get(1)?, row.get(2)?, row.get(3)?)),
+        )
+        .ok()
+    }
+
     pub async fn list_timers(&self, chat_id: Option<i64>) -> Vec<(i64, i64, String, String, String, bool)> {
         let db = self.db.lock().await;
         let (sql, params): (&str, Vec<Box<dyn rusqlite::types::ToSql>>) = match chat_id {

src/stream.rs

@@ -81,6 +81,9 @@ pub async fn run_openai_with_tools(
         if !resp_raw.status().is_success() {
             let status = resp_raw.status();
             let body_text = resp_raw.text().await.unwrap_or_default();
+            // log failed API call
+            let req_json = serde_json::to_string(&body).unwrap_or_default();
+            state.log_api(sid, &req_json, &body_text, status.as_u16()).await;
             for (i, m) in messages.iter().enumerate() {
                 let role = m["role"].as_str().unwrap_or("?");
                 let content_len = m["content"].as_str().map(|s| s.len()).unwrap_or(0);
@@ -190,14 +193,25 @@ pub async fn run_openai_with_tools(
 
             for tc in &tool_calls {
                 info!(tool = %tc.name, "executing tool call");
-                let _ = output
-                    .status(&format!("[{}({})]", tc.name, truncate_at_char_boundary(&tc.arguments, 100)))
-                    .await;
 
                 let result =
                     execute_tool(&tc.name, &tc.arguments, state, output, sid, config, chat_id)
                         .await;
 
+                // send tool call details as a .md file named after the tool
+                let md = format!(
+                    "## {}\n\n### Arguments\n```json\n{}\n```\n\n### Result ({} bytes)\n```\n{}\n```\n",
+                    tc.name,
+                    &tc.arguments,
+                    result.len(),
+                    truncate_at_char_boundary(&result, 4000),
+                );
+                let tmp = format!("/tmp/{}.md", tc.name);
+                if std::fs::write(&tmp, &md).is_ok() {
+                    let _ = output.send_file(std::path::Path::new(&tmp), "").await;
+                    let _ = std::fs::remove_file(&tmp);
+                }
+
                 messages.push(serde_json::json!({
                     "role": "tool",
                     "tool_call_id": tc.id,
@@ -233,7 +247,8 @@ pub fn build_system_prompt(summary: &str, persona: &str, memory_slots: &[(i32, S
          当需要搜索信息（如网页搜索、资料查找、技术调研等）时，使用 spawn_agent 启动一个子代理来完成搜索任务，\
          子代理可以使用浏览器和搜索引擎，搜索完成后你会收到结果通知。\
          输出格式：使用纯文本或基础Markdown（加粗、列表、代码块）。\
-         不要使用LaTeX公式（$...$）、特殊Unicode符号（→←↔）或HTML标签，Telegram无法渲染这些。",
+         不要使用LaTeX公式（$...$）、特殊Unicode符号（→←↔）或HTML标签，Telegram无法渲染这些。\
+         不要在回复开头加时间戳——用户消息前的时间戳是系统自动添加的，不需要你模仿。",
     );
 
     if !memory_slots.is_empty() {

src/tools.rs

@@ -233,6 +233,21 @@ pub fn discover_tools() -> serde_json::Value {
                 }
             }
         }),
+        serde_json::json!({
+            "type": "function",
+            "function": {
+                "name": "write_file",
+                "description": "将内容写入服务器上的文件。如果文件已存在会被覆盖，目录不存在会自动创建。",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "path": {"type": "string", "description": "文件的绝对路径"},
+                        "content": {"type": "string", "description": "要写入的完整内容"}
+                    },
+                    "required": ["path", "content"]
+                }
+            }
+        }),
         serde_json::json!({
             "type": "function",
             "function": {
@@ -308,7 +323,7 @@ pub async fn execute_tool(
         "spawn_agent" => {
             let id = args["id"].as_str().unwrap_or("agent");
             let task = args["task"].as_str().unwrap_or("");
-            spawn_agent(id, task, state, output, sid, config).await
+            spawn_agent(id, task, state, output, sid, config, chat_id).await
         }
         "agent_status" => {
             let id = args["id"].as_str().unwrap_or("");
@@ -507,6 +522,25 @@ pub async fn execute_tool(
                 Err(_) => format!("timeout after {timeout_secs}s"),
             }
         }
+        "write_file" => {
+            let path_str = args["path"].as_str().unwrap_or("");
+            let content = args["content"].as_str().unwrap_or("");
+            if path_str.is_empty() {
+                return "Error: path is required".to_string();
+            }
+            let path = Path::new(path_str);
+            if let Some(parent) = path.parent() {
+                if !parent.exists() {
+                    if let Err(e) = std::fs::create_dir_all(parent) {
+                        return format!("Failed to create directory: {e}");
+                    }
+                }
+            }
+            match std::fs::write(path, content) {
+                Ok(_) => format!("Written {} bytes to {path_str}", content.len()),
+                Err(e) => format!("Failed to write {path_str}: {e}"),
+            }
+        }
         "call_gitea_api" => {
             let method = args["method"].as_str().unwrap_or("GET").to_uppercase();
             let path = args["path"].as_str().unwrap_or("").trim_start_matches('/');
@@ -549,6 +583,7 @@ pub async fn execute_tool(
         }
         "gen_voice" => {
             let text = args["text"].as_str().unwrap_or("");
+            info!("gen_voice text={:?} args={}", text, truncate_at_char_boundary(arguments, 200));
             if text.is_empty() {
                 return "Error: text is required".to_string();
             }
@@ -577,9 +612,13 @@ pub async fn execute_tool(
                 Ok(Ok(out)) => {
                     let stderr = String::from_utf8_lossy(&out.stderr);
                     let stdout = String::from_utf8_lossy(&out.stdout);
+                    warn!("gen_voice failed (exit={}): stdout={stdout} stderr={stderr}", out.status.code().unwrap_or(-1));
                     format!("gen_voice failed: {stdout} {stderr}")
                 }
-                Ok(Err(e)) => format!("gen_voice exec error: {e}"),
+                Ok(Err(e)) => {
+                    warn!("gen_voice exec error: {e}");
+                    format!("gen_voice exec error: {e}")
+                }
                 Err(_) => "gen_voice timeout (120s)".to_string(),
             }
         }
@@ -591,9 +630,10 @@ pub async fn spawn_agent(
     id: &str,
     task: &str,
     state: &Arc<AppState>,
-    output: &dyn Output,
-    _sid: &str,
+    output: &mut dyn Output,
+    sid: &str,
     _config: &Arc<Config>,
+    chat_id: i64,
 ) -> String {
     // check if already exists
     if state.agents.read().await.contains_key(id) {
@@ -625,11 +665,14 @@ pub async fn spawn_agent(
 
     state.agents.write().await.insert(id.to_string(), agent);
 
-    // background task: collect output
+    // background task: collect output, then send event to life loop
     let out = agent_output.clone();
     let done = completed.clone();
     let ecode = exit_code.clone();
     let id_c = id.to_string();
+    let task_c = task.to_string();
+    let life_tx = state.life_tx.clone();
+    let sid_c = sid.to_string();
 
     tokio::spawn(async move {
         let stdout = child.stdout.take();
@@ -647,6 +690,16 @@ pub async fn spawn_agent(
         done.store(true, Ordering::SeqCst);
 
         info!(agent = %id_c, "agent completed, exit={code:?}");
+
+        let output_text = out.read().await.clone();
+        let _ = life_tx.send(crate::life::LifeEvent::AgentDone {
+            id: id_c,
+            chat_id,
+            session_id: sid_c,
+            task: task_c,
+            output: output_text,
+            exit_code: code,
+        }).await;
     });
 
     let _ = output.status(&format!("Agent '{id}' spawned (pid={pid:?})")).await;

tools/manage_todo

@@ -19,7 +19,7 @@ import sys
 import requests
 
 APP_ID = "cli_a7f042e93d385013"
-APP_SECRET = "ht4FCjQ8JJ65ZPUWlff6ldFBmaP0mxqY"
+APP_SECRET = "6V3t5bFK4vRKsEG3VD6sQdAu2rmFEr2S"
 APP_TOKEN = "SSoGbmGFoazJkUs7bbfcaSG8n7f"
 TABLE_ID = "tblIA2biceDpvr35"
 BASE_URL = "https://open.feishu.cn/open-apis"