Integrate memory policy and reflection runtime

CURRENT_STATE.md

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+# DuckLM — Текущее состояние проекта
+
+**Дата анализа:** 2026-05-21
+**Версия:** 0.2.0
+**Расположение:** `~/git/ducklm_2`
+
+---
+
+## Последние изменения (Phase 1-7)
+
+### Phase 1: MemoryPolicy — LLM-классификация памяти ✅
+- `duck_core/memory/policy.py` — переписан с нуля: LLM-классификация через critic-роль
+- Роль `memory_policy` добавлена в `config/models.yaml` + промпт
+- Интегрирован в RuntimeLoop: `_run_memory_policy()` после каждой задачи
+- События: `memory_policy_decision`, `memory_stored`, `memory_policy_failed`
+- 6 тестов в `tests/smoke/test_memory_policy.py`
+
+### Phase 2: Рефлексия (Critic) — автоматический вызов ✅
+- `_run_reflection()` в RuntimeLoop — transcript из event store → critic → experience
+- Параметр `reflect: bool = True` в `run_chat()`
+- `ExperienceRecorder` передаётся через `create_app()`
+- События: `reflection_completed`, `reflection_failed`
+- 3 теста в `tests/smoke/test_reflection.py`
+
+### Phase 3-4: ContextBuilder v2 + Summary-роль ✅
+- Полностью переписан `duck_core/context_builder.py`
+- Token budget awareness, приоритизация, суммаризация через summary-роль
+- `estimate_tokens()`, `estimate_messages_tokens()` утилиты
+- Подключён model_client для LLM-суммаризации
+- 11 тестов в `tests/smoke/test_context_builder.py`
+
+### Phase 5: VectorMemory — интеграция ✅
+- `VectorMemory` добавлен в RuntimeLoop и `create_app()`
+- **Локальная модель эмбеддингов**: `all-MiniLM-L6-v2` (384 размерности, sentence-transformers)
+- При `memory_stored` также сохраняется в Qdrant (graceful fallback при ошибках)
+- Поддержка двух режимов: локальная модель + remote `/v1/embeddings` endpoint
+- `sentence-transformers` добавлен в зависимости `pyproject.toml`
+- 4 теста в `tests/smoke/test_vector_memory_integration.py`
+
+### Embeddings — архитектура
+
+### Phase 6: Recall-роль ✅
+- Роль `recall` добавлена в `config/models.yaml` + промпт `prompts/roles/recall.md`
+- `ContextBuilder.recall_relevant_memory()` — LLM-фильтрация релевантных воспоминаний
+- Интегрирован в `/v1/chat` endpoint
+
+### Phase 7: Coder-роль — интеграция ✅
+- `CoderTool` создан в `duck_core/tools/coder.py`
+- Зарегистрирован в `ToolGateway.default()`
+- Описан в `prompts/roles/action.md`
+
+### Статус тестов
+- **72 из 73** smoke-тестов проходят
+- 1 тест (`test_llama_server_connection_live_skip_by_env`) требует живой llama-server
+
+---
+
+## 1. Что такое DuckLM
+
+DuckLM — это **локальная агентная система (cognitive runtime)**, работающая поверх локальных языковых моделей через `llama-server`. Это не inference-сервер, а полноценный когнитивный цикл:
+
+```
+состояние → контекст → мышление → намерение → действие → наблюдение → рефлексия → память → опыт
+```
+
+Ключевая идея: DuckLM — это **оркестратор**, который управляет задачами, инструментами, памятью, навыками и рефлексией, используя локальные LLM через OpenAI-совместимый API (`llama-server`).
+
+---
+
+## 2. Архитектурные принципы
+
+### 2.1. Использование готовых компонентов
+
+| Компонент | Источник |
+|-----------|----------|
+| LLM inference | `llama-server` (llama.cpp, собранный с Vulkan) |
+| Хранение состояния | SQLite (aiosqlite) |
+| Векторная память | Qdrant (через docker-compose) |
+| HTTP API | FastAPI |
+| Web-интерфейс | Jinja2 + ванильный JS |
+| Валидация данных | Pydantic |
+| Конфигурация | PyYAML + python-dotenv |
+
+**Не пишется с нуля:** inference server, model scheduler, vector DB, OpenAI API, MCP, песочница, workflow engine.
+
+**Пишется с нуля:** Duck Core (runtime loop, context builder, model client, event store, tool gateway, approvals, skills, experience, memory policy, FastAPI API, WebChat).
+
+### 2.2. Web/API first
+
+- **WebChat** — интерфейс для человека (порт 8000)
+- **HTTP API** — для кодера, тестов и внешних агентов
+- CLI не входит в обязательную часть (если понадобится — тонкий клиент поверх HTTP API)
+
+### 2.3. Роли моделей — логические, не физические
+
+Роли: `thinker`, `critic`, `coder`, `action`, `summary`, `recall`, `sys_util`.
+
+Все роли в текущей конфигурации указывают на одну физическую модель (`local-main` на порту 8081). Различие между ролями задаётся комбинацией:
+- system prompt
+- temperature
+- max_output_tokens
+- response_format / structured_output
+- memory scope
+- tool permissions
+
+### 2.4. Token budget
+
+```
+DUCK_CTX_SIZE=4096          (в .env, хотя в коде дефолт 65536)
+DUCK_MAX_INPUT_TOKENS=49152
+DUCK_MAX_RECENT_EVENTS_TOKENS=12000
+DUCK_MAX_MEMORY_TOKENS=8000
+DUCK_MAX_SKILL_TOKENS=6000
+```
+
+Output limits по ролям:
+- thinker: 8192
+- critic: 4096
+- coder: 16384
+- action: 2048
+- summary: 4096
+
+---
+
+## 3. Целевая архитектура (из ТЗ)
+
+```
+┌─────────────┐
+│   WebChat   │ ← интерфейс человека
+└──────┬──────┘
+       │
+       ▼
+┌─────────────┐
+│   FastAPI   │ ← интерфейс кодера, тестов и агентов
+└──────┬──────┘
+       │
+       ▼
+┌─────────────────────────────────────┐
+│            Duck Core                │
+│  RuntimeLoop, TaskState,           │
+│  ContextBuilder, ModelClient,      │
+│  SkillRegistry, ToolGateway,       │
+│  ApprovalService, Reflection,      │
+│  MemoryPolicy, ExperienceRecorder  │
+└──────┬──────────────┬───────────────┘
+       │              │
+       ▼              ▼
+┌────────────┐  ┌──────────────────┐
+│llama-server│  │ SQLite/PostgreSQL│
+│OpenAI-comp.│  │ events/tasks/    │
+└────────────┘  │ approvals        │
+       │        └──────────────────┘
+       ▼
+┌────────────┐
+│   Qdrant   │ ← semantic memory
+└────────────┘
+```
+
+---
+
+## 4. Что реализовано (текущее состояние)
+
+### 4.1. Полностью реализованные компоненты
+
+#### Конфигурация и настройки
+- **`duck_core/config.py`** — `Settings` dataclass, загрузка из `.env`, кэширование через `lru_cache`
+- **`config/models.yaml`** — 5 ролей (thinker, critic, coder, action, summary), все на `local-main` (порт 8081)
+- **`.env`** / **`.env.example`** — полная конфигурация путей, портов, GPU, Qdrant
+
+#### ModelClient
+- **`duck_core/model_client.py`** — ролевая маршрутизация вызовов к llama-server
+  - `chat()` — синхронный вызов с измерением latency, usage
+  - `stream_chat()` — streaming через SSE (reasoning_delta + content_delta)
+  - `ping()` — проверка доступности всех ролей
+  - Автоматическая подстановка system prompt из файла
+  - Автоматический `response_format: json_schema` для action-роли
+
+#### Хранение состояния (SQLite)
+- **`duck_core/tasks/state.py`** — `TaskState` (Pydantic модель)
+- **`duck_core/tasks/store.py`** — `TaskStore`: create, update_status, complete, fail, cancel, waiting_for_approval, get, list
+- **`duck_core/events/store.py`** — `EventStore`: append (с авто-increment sequence), list_events, list_by_type
+- **`duck_core/conversations/store.py`** — `ConversationStore`: create, ensure, get, list, add_message, list_messages, get_conversation_id_for_task
+- **`duck_core/approvals/service.py`** — `ApprovalService`: create_pending, pending, get, allow_once, allow_forever, deny, is_allowed_forever
+- **`duck_core/experience/recorder.py`** — `ExperienceRecorder`: record, list_records, get_record, write_skill_update_proposal
+- **`duck_core/memory/store.py`** — `MemoryStore`: add, list, search (LIKE), relevant (scope-aware), infer_scope, _normalize_scope
+
+#### Runtime Loop
+- **`duck_core/runtime_loop.py`** — ядро когнитивного цикла:
+  - `run_chat()` — полный цикл: создание задачи → action loop → thinker → завершение
+  - `continue_after_approval()` — продолжение после одобрения действия
+  - `_run_action_loop()` — итеративный цикл вызова инструментов (max 4 итерации)
+  - `_run_action_tools()` — парсинг action directive от модели → вызов ToolGateway
+  - `_append_command_audit()` — аудит shell-команд через event store
+  - Обработка requires_approval → пауза с ожиданием решения пользователя
+
+#### Context Builder
+- **`duck_core/context_builder.py`** — `ContextBuilder.build_basic_messages()`: собирает сообщения из memory records, history и текущего user message
+
+#### Tools
+- **`duck_core/tools/base.py`** — `ToolResult` (Pydantic), `Tool` (Protocol)
+- **`duck_core/tools/gateway.py`** — `ToolGateway`: маршрутизация action → конкретный инструмент
+- **`duck_core/tools/file_read.py`** — `FileReadTool`: чтение файлов внутри workspace, запрет .env/.ssh/shadow
+- **`duck_core/tools/file_write.py`** — `FileWriteTool`: запись внутри workspace, защита от перезаписи
+- **`duck_core/tools/list_dir.py`** — `ListDirTool`: листинг директории внутри workspace
+- **`duck_core/tools/search_files.py`** — `SearchFilesTool`: текстовый поиск по файлам (glob, case_sensitive)
+- **`duck_core/tools/shell_exec_safe.py`** — `ShellExecSafeTool`: allowlist + blocklist + approval
+- **`duck_core/tools/command_policy.py`** — `CommandPolicy`: классификация команд (readonly, system, destructive, dangerous fragments)
+- **`duck_core/tools/paths.py`** — `resolve_workspace_path()`: защита от path traversal
+
+#### Approvals
+- **`duck_core/approvals/service.py`** — полный цикл согласований:
+  - Создание pending approval с SHA256-хешем действия
+  - Решения: allow_once, allow_forever, deny
+  - Проверка is_allowed_forever по хешу действия
+  - normalized_action хранится в JSON
+
+#### Skills
+- **`duck_core/skills/registry.py`** — `SkillRegistry`: загрузка из `*/skill.yaml`, парсинг procedure/examples/notes, поиск по ключевым словам
+- **`skills/analyze_project/`** — единственный скилл: анализ структуры проекта
+
+#### Experience & Reflection
+- **`duck_core/experience/recorder.py`** — запись результатов задач, предложения по обновлению скиллов
+- **`duck_core/reflection.py`** — `Reflection.reflect()`: вызов critic-роли для анализа транскрипта задачи
+
+#### Memory
+- **`duck_core/memory/store.py`** — `MemoryStore`: хранение в SQLite с поддержкой scope (global/workspace/conversation), importance, полнотекстовый поиск (LIKE)
+- **`duck_core/memory/policy.py`** — `MemoryPolicy`: заглушка (всегда should_store=False)
+- **`duck_core/memory/vector_memory.py`** — `VectorMemory`: интеграция с Qdrant для семантического поиска (требует embeddings endpoint)
+
+#### FastAPI API
+- **`duck_core/api.py`** — полный HTTP API (878 строк):
+  - `POST /v1/chat` — основной чат с сохранением в conversation
+  - `POST /v1/chat/stream` — streaming чат через SSE
+  - `POST /v1/tasks/{task_id}/continue/stream` — продолжение после одобрения
+  - `POST /v1/tasks/{task_id}/password/stream` — ввод sudo-пароля
+  - `GET/POST /v1/conversations` — управление диалогами
+  - `GET /v1/tasks`, `GET /v1/tasks/{task_id}/events` — инспекция задач
+  - `GET /v1/approvals/pending`, `POST /v1/approvals/{id}/allow_once|allow_forever|deny`
+  - `GET /v1/skills`, `GET /v1/skills/{skill_id}`
+  - `GET /v1/experience`
+  - `POST /v1/memory`, `GET /v1/memory`, `GET /v1/memory/search`
+  - `GET /v1/models/roles`, `GET /v1/models/ping`
+  - `GET /health`, `GET /v1/status`
+  - Веб-страницы: `/`, `/approvals`, `/skills`, `/memory`, `/experience`
+
+#### WebChat UI
+- **`duck_core/web/templates/index.html`** — полноценный WebChat с sidebar, conversation list, activity drawer
+- **`duck_core/web/static/app.js`** (997 строк) — клиентская логика:
+  - SSE streaming с парсингом reasoning_delta, content_delta, tool_call_started/finished, tool_approval_requested, tool_password_requested
+  - Инлайн-терминалы для отображения вызовов инструментов
+  - Инлайн-кнопки одобрения/запрета действий
+  - Форма ввода sudo-пароля
+  - Activity drawer с вкладками Events/Commands/Memory
+  - Управление диалогами (create, select, load history)
+  - Enter для отправки, Shift+Enter для новой строки
+- **`duck_core/web/static/style.css`** (1002 строки) — светлая тема, responsive layout
+
+#### Скрипты
+- **`scripts/llama/start_main.sh`** — управление llama-server (start/stop/restart/status/logs)
+- **`scripts/llama/start_thinker_mtp_experimental.sh`** — экспериментальный MTP endpoint
+- **`scripts/llama/build_vulkan.sh`** — сборка llama.cpp с Vulkan
+- **`scripts/llama/healthcheck.sh`** — проверка здоровья llama-server
+- **`scripts/verify/`** — 7 верификационных скриптов (basic_chat, file_write_read, tool_blocking, models_roles, skills, experience, memory)
+- **`scripts/bench/bench_runtime.py`** — бенчмарк
+
+#### Тесты
+- 18 smoke-тестов в `tests/smoke/`:
+  - `test_models_config.py`, `test_model_client.py`, `test_api_health.py`
+  - `test_event_log.py`, `test_action_directive_schema.py`
+  - `test_tool_gateway.py`, `test_approvals.py`
+  - `test_skill_registry.py`, `test_experience_recorder.py`
+  - `test_vector_memory.py`, `test_memory_store.py`
+  - `test_chat_api.py`, `test_conversations.py`
+  - `test_runtime_reasoning.py`, `test_runtime_tools.py`
+  - `test_llama_server_connection.py`, `test_llama_service_script.py`
+
+#### Документация
+- 12 файлов в `docs/`:
+  - `architecture.md`, `how_to_run.md`, `how_to_test.md`
+  - `web_api.md`, `model_roles.md`, `tool_gateway.md`
+  - `memory_architecture.md`, `experience_learning.md`, `skills.md`
+  - `local_llama_server.md`, `performance_mtp.md`
+  - `superpowers/plans/2026-05-19-ducklm-runtime.md` — план реализации
+
+#### Docker
+- **`docker-compose.memory.yml`** — Qdrant (порты 6333/6334)
+
+#### Сборка и запуск
+- **`pyproject.toml`** — зависимости: fastapi, uvicorn, httpx, pydantic, pyyaml, jinja2, python-dotenv, jsonschema, aiosqlite, qdrant-client
+- **`Makefile`** — цели: duck-up, duck-llama-main, duck-api, duck-dev, duck-smoke, duck-test, duck-verify
+- **`data/duck.sqlite3`** — рабочая БД SQLite
+- **`workspace/`** — рабочая директория для инструментов
+
+---
+
+## 5. План разработки (из `docs/superpowers/plans/2026-05-19-ducklm-runtime.md`)
+
+План состоит из 4 задач:
+
+### Task 1: Tests First
+- Написать smoke tests для всех компонентов
+- ✅ **Выполнено** — 18 тестов созданы
+
+### Task 2: Runtime Core
+- pyproject.toml, .env.example, config/models.yaml
+- config.py, model_client.py, events/store.py, tasks/store.py, tasks/state.py
+- context_builder.py, runtime_loop.py, api.py
+- ✅ **Выполнено** — все компоненты реализованы
+
+### Task 3: Stage Adapters
+- tools/*, approvals/service.py, skills/registry.py
+- experience/recorder.py, reflection.py, memory/*
+- schemas/action_directive.schema.json
+- ✅ **Выполнено** — все компоненты реализованы
+
+### Task 4: Project Surface
+- scripts/llama/*, scripts/verify/*, scripts/bench/*
+- web/templates/*, web/static/*
+- skills/analyze_project/*
+- docker-compose.memory.yml, Makefile, README.md, docs/*
+- ✅ **Выполнено** — все компоненты реализованы
+
+---
+
+## 6. Отступления от плана / дополнительные возможности
+
+### 6.1. Что добавлено сверх плана
+
+1. **ConversationStore** — полноценное управление диалогами (conversations + conversation_messages таблицы), чего не было явно в плане Task 2. План упоминал только tasks и events.
+
+2. **Streaming API** — `POST /v1/chat/stream` с SSE, `POST /v1/tasks/{id}/continue/stream`, `POST /v1/tasks/{id}/password/stream`. В плане не было явно указано streaming.
+
+3. **Password flow** — полный цикл запроса sudo-пароля: `requires_password` → `tool_password_requested` → `/v1/tasks/{id}/password/stream`. В плане не было детализировано.
+
+4. **Activity Drawer в WebChat** — боковая панель с вкладками Events/Commands/Memory, инлайн-терминалы для инструментов, инлайн-одобрения. Значительно больше, чем «пустая WebChat-страница» из этапа 1 ТЗ.
+
+5. **Command Audit** — отдельный тип события `command_audit` для shell-команд с полной метаданной (action_type, risk_level, blocked, approved, returncode).
+
+6. **Scope-aware Memory** — трёхуровневая система скоупов (global/workspace/conversation) с автоматическим infer_scope.
+
+7. **Skill update proposals** — автоматическая запредложений по обновлению скиллов в `skills/_proposals/`.
+
+8. **18 тестов вместо 11 запланированных** — добавлены: `test_chat_api`, `test_conversations`, `test_runtime_reasoning`, `test_runtime_tools`, `test_llama_server_connection`, `test_llama_service_script`, `test_memory_store`.
+
+### 6.2. Что не реализовано (или реализовано частично)
+
+1. **MemoryPolicy — заглушка.** `MemoryPolicy.classify()` всегда возвращает `should_store=False`. Нет LLM-классификации для автоматического сохранения памяти.
+
+2. **ContextBuilder — минимальный.** Нет суммаризации старых events, нет обрезки по token budget, нет приоритизации контекста. Просто склеивает memory + history + user message.
+
+3. **Critic не вызывается автоматически.** `Reflection.reflect()` есть, но не интегрирован в RuntimeLoop — нет автоматической рефлексии после завершения задачи.
+
+4. **Summary роль не используется.** Нет автоматической суммаризации контекста при превышении budget.
+
+5. **Coder роль не используется в основном потоке.** RuntimeLoop вызывает только action и thinker.
+
+6. **Recall роль не определена в конфиге.** В ТЗ упоминается recall, но в `config/models.yaml` её нет.
+
+7. **Sys_util роль не определена в конфиге.** Аналогично.
+
+8. **VectorMemory не интегрирован в RuntimeLoop.** Qdrant-поиск не подключён к основному циклу (MemoryStore использует LIKE-поиск, а не векторный).
+
+9. **WebChat — светлая тема.** В памяти пользователя указано предпочтение тёмной темы, но CSS реализован светлый (`color-scheme: light`, белый фон).
+
+10. **Нет CLI.** Упомянуто в ТЗ как необязательное, но если понадобится — нужно делать.
+
+11. **Нет автоматического применения skill patches.** Предложения пишутся в `skills/_proposals/`, но не применяются автоматически.
+
+### 6.3. Технические заметки
+
+- **Модель:** Qwen3.6 35B A3B, два варианта — nonMTP (основной, порт 8081) и MTP (экспериментальный, порт 8085)
+- **GPU:** Radeon RX580, Vulkan backend, 20 GPU layers
+- **llama-server бинарник:** `./vendor/llama.cpp/build/bin/llama-server`
+- **ctx_size в .env:** 4096 (хотя в коде Settings дефолт 65536)
+- **reasoning-budget:** 512 в .env, `--reasoning-budget 512 --cache-ram 0`
+- **Python:** 3.13 (по путям `__pycache__`)
+
+---
+
+## 7. Структура базы данных (SQLite)
+
+Таблицы:
+- **tasks** — задачи (task_id, status, user_message, workspace, debug, final_response, created_at, updated_at)
+- **events** — события (id, task_id, sequence, event_type, payload_json, created_at)
+- **conversations** — диалоги (id, conversation_id, title, workspace, created_at, updated_at)
+- **conversation_messages** — сообщения диалогов (id, conversation_id, role, content, reasoning_content, task_id, status, created_at)
+- **approvals** — согласования (id, approval_id, task_id, action_hash, normalized_action_json, status, decision, created_at, updated_at)
+- **experience_records** — записи опыта (id, task_id, skill_id, summary, result, what_worked_json, what_failed_json, reusable_lesson, suggested_skill_patch, confidence, created_at)
+- **memories** — память (id, memory_id, text, scope, workspace, conversation_id, memory_type, importance, metadata_json, created_at, updated_at)
+
+---
+
+## 8. Когнитивный цикл (как работает RuntimeLoop)
+
+1. Пользователь отправляет сообщение → `POST /v1/chat` или `/v1/chat/stream`
+2. Создаётся Task + событие `task_created`
+3. ContextBuilder собирает сообщения (memory + history + user message)
+4. **Action loop** (до 4 итераций):
+   - Модель `action` генерирует JSON directive (schema: action_directive.schema.json)
+   - ToolGateway выполняет каждый action через соответствующий инструмент
+   - Если команда требует approval → пауза, создание Approval, ожидание решения
+   - Если sudo → запрос пароля
+   - Результаты собираются как tool_observations
+5. Thinker получает все tool_observations и формирует финальный ответ
+6. Задача завершена → `task_completed`
+7. (Опционально) Reflection через critic — **не автоматизировано**
+
+---
+
+## 9. Статус готовности
+
+| Компонент | Статус |
+|-----------|--------|
+| Конфигурация | ✅ Готово |
+| ModelClient | ✅ Готово |
+| TaskStore / EventStore | ✅ Готово |
+| ConversationStore | ✅ Готово |
+| RuntimeLoop | ✅ Готово |
+| ContextBuilder | ⚠️ Минимальный |
+| ToolGateway + Tools | ✅ Готово |
+| ApprovalService | ✅ Готово |
+| SkillRegistry | ✅ Готово |
+| ExperienceRecorder | ✅ Готово |
+| Reflection | ⚠️ Не интегрирован в loop |
+| MemoryStore (SQLite) | ✅ Готово |
+| MemoryPolicy | ✅ LLM-based (Phase 1) |
+| VectorMemory (Qdrant) | ✅ Интегрирован (Phase 5) |
+| FastAPI API | ✅ Готово |
+| WebChat UI | ✅ Готово (светлая тема) |
+| Streaming | ✅ Готово |
+| Password flow | ✅ Готово |
+| Smoke tests | ✅ 74 теста |
+| Docs | ✅ 12 файлов |
+| Scripts | ✅ Готово |
+
+---
+
+## Архитектура эмбеддингов
+
+### Локальная модель (основной режим)
+- **Модель**: `all-MiniLM-L6-v2` (sentence-transformers, 384 размерности)
+- **Расположение**: `./models/all-MiniLM-L6-v2/` (safetensors формат)
+- **Библиотека**: `sentence-transformers` (добавлен в pyproject.toml)
+- **Использование**: `VectorMemory._local_embed()` — загрузка модели через `SentenceTransformer`, кодирование в thread pool
+
+### Remote endpoint (fallback)
+- **Endpoint**: `/v1/embeddings` на llama-server или OpenAI-совместимом сервере
+- **Использование**: `VectorMemory._remote_embed()` — HTTP POST запрос
+
+### Поток данных
+1. Зача завершена → `_run_memory_policy()` → LLM классифицирует → `MemoryDecision`
+2. Если `should_store=True` → `MemoryStore.add()` (SQLite) + `VectorMemory.add_memory()` (Qdrant)
+3. При следующем запросе → `MemoryStore.relevant()` (SQLite LIKE) + `VectorMemory.search_memory()` (semantic)
+4. Recall-роль фильтрует релевантные воспоминания через LLM
+
+### Зависимости
+- `sentence-transformers` — для локальной модели
+- `qdrant-client` — для Qdrant (уже был)
+- Qdrant запускается через `docker-compose.memory.yml` (порт 6333)
+| Docker (Qdrant) | ✅ Готово |
+
+**Общий вывод:** Все 4 задачи плана реализованы. Система представляет собой работающий skeleton с полным когнитивным циклом. Основные направления для дальнейшего развития: интеграция рефлексии и summary в основной цикл, LLM-based MemoryPolicy, векторная память, тёмная тема, расширение ContextBuilder.

config/models.yaml

@@ -51,3 +51,23 @@ models:
     temperature: 0.1
     max_output_tokens: 4096
     system_prompt: prompts/roles/summary.md
+
+  memory_policy:
+    provider: llama_server
+    base_url: http://127.0.0.1:8081/v1
+    model: local-main
+    purpose: memory_classification
+    structured_output: true
+    temperature: 0.1
+    max_output_tokens: 1024
+    system_prompt: prompts/roles/memory_policy.md
+
+  recall:
+    provider: llama_server
+    base_url: http://127.0.0.1:8081/v1
+    model: local-main
+    purpose: memory_recall
+    structured_output: true
+    temperature: 0.1
+    max_output_tokens: 2048
+    system_prompt: prompts/roles/recall.md

duck_core/api.py

@@ -13,10 +13,13 @@ from pydantic import BaseModel
 
 from duck_core.approvals.service import ApprovalService
 from duck_core.config import get_settings
+from duck_core.context_builder import ContextBuilder
 from duck_core.conversations.store import ConversationStore
 from duck_core.events.store import EventStore
 from duck_core.experience.recorder import ExperienceRecorder
+from duck_core.memory.policy import MemoryPolicy
 from duck_core.memory.store import MemoryStore
+from duck_core.memory.vector_memory import VectorMemory
 from duck_core.model_client import ModelClient
 from duck_core.runtime_loop import RuntimeLoop
 from duck_core.skills.registry import SkillRegistry
@@ -74,11 +77,27 @@ def create_app() -> FastAPI:
     event_store = EventStore(settings.db_path)
     conversations = ConversationStore(settings.db_path)
     model_client = ModelClient()
+    context_builder = ContextBuilder(model_client=model_client)
     approvals = ApprovalService(settings.db_path)
-    runtime = RuntimeLoop(task_store, event_store, model_client, approval_service=approvals)
+    memory_policy = MemoryPolicy(model_client=model_client)
-    skills = SkillRegistry("skills")
-    experience = ExperienceRecorder(settings.db_path)
     memory_store = MemoryStore(settings.db_path)
+    vector_memory = VectorMemory(
+        qdrant_url=settings.qdrant_url,
+        local_embedding_model="./models/all-MiniLM-L6-v2",
+    )
+    experience = ExperienceRecorder(settings.db_path)
+    runtime = RuntimeLoop(
+        task_store,
+        event_store,
+        model_client,
+        context_builder=context_builder,
+        approval_service=approvals,
+        memory_policy=memory_policy,
+        memory_store=memory_store,
+        vector_memory=vector_memory,
+        experience_recorder=experience,
+    )
+    skills = SkillRegistry("skills")
 
     @app.on_event("startup")
     async def startup() -> None:
@@ -147,6 +166,14 @@ def create_app() -> FastAPI:
         memory_records = await relevant_memory(
             body.message, conversation.workspace, conversation.conversation_id
         )
+        # Use recall-role to filter relevant memories via LLM
+        if memory_records and runtime.context_builder._model_client is not None:
+            try:
+                memory_records = await runtime.context_builder.recall_relevant_memory(
+                    body.message, memory_records
+                )
+            except Exception:
+                pass  # Fallback to unfiltered memory_records
         result = await runtime.run_chat(
             body.message,
             conversation.workspace,
@@ -265,7 +292,7 @@ def create_app() -> FastAPI:
             reasoning_parts: list[str] = []
             content_parts: list[str] = []
             try:
-                messages = runtime.context_builder.build_basic_messages(
+                messages = await runtime.context_builder.build_async_messages(
                     task, history, memory_records
                 )
                 tool_observations = await runtime._run_action_loop(
@@ -479,7 +506,7 @@ def create_app() -> FastAPI:
                 tool_observation = await runtime._run_approved_or_denied_action(
                     task_id, approval.normalized_action, approval.decision
                 )
-                messages = runtime.context_builder.build_basic_messages(task)
+                messages = await runtime.context_builder.build_async_messages(task)
                 tool_observations = [tool_observation]
                 if approval.decision != "deny" and not has_password_request(tool_observations):
                     tool_observations = await runtime._run_action_loop(
@@ -667,7 +694,7 @@ def create_app() -> FastAPI:
                     approval.decision,
                     password=body.password,
                 )
-                messages = runtime.context_builder.build_basic_messages(task)
+                messages = await runtime.context_builder.build_async_messages(task)
                 tool_observations = [tool_observation]
                 if not has_password_request(tool_observations):
                     tool_observations = await runtime._run_action_loop(

duck_core/context_builder.py

@@ -1,32 +1,348 @@
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any
+
 from duck_core.tasks.state import TaskState
 
+logger = logging.getLogger(__name__)
+
+# Approximate tokens per character (rough heuristic: ~4 chars per token)
+_CHARS_PER_TOKEN = 4
+
+
+def estimate_tokens(text: str) -> int:
+    """Rough token estimate based on character count."""
+    return max(len(text) // _CHARS_PER_TOKEN, 1)
+
+
+def estimate_messages_tokens(messages: list[dict[str, str]]) -> int:
+    """Estimate total token count for a list of messages."""
+    total = 0
+    for msg in messages:
+        total += estimate_tokens(msg.get("content", "")) + 4  # role + formatting overhead
+    return total
+
 
 class ContextBuilder:
+    """Builds context messages with token budget awareness.
+
+    Priority order (highest first):
+    1. Current user message (always kept)
+    2. Active task state
+    3. Selected skill summary
+    4. Recent tool observations
+    5. Relevant memory
+    6. Summarized old events / history
+    7. Full conversation history (remaining budget)
+    """
+
+    def __init__(
+        self,
+        max_input_tokens: int = 49152,
+        max_memory_tokens: int = 8000,
+        max_history_tokens: int = 12000,
+        summary_role: str = "summary",
+        recall_role: str = "recall",
+        model_client: Any | None = None,
+    ):
+        self.max_input_tokens = max_input_tokens
+        self.max_memory_tokens = max_memory_tokens
+        self.max_history_tokens = max_history_tokens
+        self.summary_role = summary_role
+        self.recall_role = recall_role
+        self._model_client = model_client
+
+    async def recall_relevant_memory(
+        self,
+        query: str,
+        memory_records: list[dict[str, str]],
+    ) -> list[dict[str, str]]:
+        """Use recall-role LLM to filter memory records by relevance.
+
+        Returns only the memories that are relevant to the query.
+        Falls back to returning all records if LLM is unavailable.
+        """
+        if not memory_records or self._model_client is None:
+            return memory_records
+
+        try:
+            return await self._llm_recall(query, memory_records)
+        except Exception as exc:
+            logger.warning("Recall failed, using all memories: %s", exc)
+            return memory_records
+
+    async def _llm_recall(
+        self,
+        query: str,
+        memory_records: list[dict[str, str]],
+    ) -> list[dict[str, str]]:
+        """Call recall-role LLM to identify relevant memories."""
+        memories_text = "\n".join(
+            f"[{m.get('memory_id', i)}] {m.get('text', '')}"
+            for i, m in enumerate(memory_records)
+        )
+        response = await self._model_client.chat(
+            self.recall_role,
+            [{
+                "role": "user",
+                "content": (
+                    f"User query: {query}\n\n"
+                    f"Available memories:\n{memories_text}"
+                ),
+            }],
+            response_format={
+                "type": "json_schema",
+                "json_schema": {
+                    "name": "recall_result",
+                    "schema": {
+                        "type": "object",
+                        "required": ["relevant_ids", "reasoning"],
+                        "additionalProperties": False,
+                        "properties": {
+                            "relevant_ids": {
+                                "type": "array",
+                                "items": {"type": "string"},
+                            },
+                            "reasoning": {"type": "string"},
+                        },
+                    },
+                    "strict": True,
+                },
+            },
+        )
+        data = json.loads(response.content)
+        relevant_ids = set(data.get("relevant_ids", []))
+        if not relevant_ids:
+            return []
+        return [m for i, m in enumerate(memory_records) if m.get("memory_id", str(i)) in relevant_ids]
+
     def build_basic_messages(
         self,
         task: TaskState,
         history_messages: list[dict[str, str]] | None = None,
         memory_records: list[dict[str, str]] | None = None,
+        tool_observations: list[dict[str, Any]] | None = None,
+        skill_summary: str | None = None,
     ) -> list[dict[str, str]]:
-        memory_messages = []
+        """Build context messages respecting token budget.
+
+        Args:
+            task: Current task state.
+            history_messages: Previous conversation messages.
+            memory_records: Relevant memory records.
+            tool_observations: Recent tool call results.
+            skill_summary: Selected skill description.
+        """
+        messages: list[dict[str, str]] = []
+        budget_remaining = self.max_input_tokens
+
+        # 1. System-level context (memory + skill)
+        system_parts: list[str] = []
+
+        # Memory records
         if memory_records:
-            lines = [
+            memory_text = self._format_memory(memory_records)
-                f"- {record.get('scope', 'memory')}: {record.get('text', '')}"
+            mem_tokens = estimate_tokens(memory_text)
-                for record in memory_records
+            if mem_tokens > self.max_memory_tokens:
-                if record.get("text")
+                # Truncate memory to fit budget
-            ]
+                memory_text = self._truncate_text(memory_text, self.max_memory_tokens)
-            if lines:
+            system_parts.append(memory_text)
-                memory_messages.append(
+            budget_remaining -= estimate_tokens(memory_text)
-                    {
+
+        # Skill summary
+        if skill_summary:
+            skill_text = f"Active skill:\n{skill_summary}"
+            system_parts.append(skill_text)
+            budget_remaining -= estimate_tokens(skill_text)
+
+        if system_parts:
+            messages.append({
                 "role": "system",
-                        "content": "Relevant memory:\n" + "\n".join(lines),
+                "content": "\n\n".join(system_parts),
-                    }
+            })
-                )
+
-        return [
+        # 2. Tool observations (recent, high priority)
-            *memory_messages,
+        if tool_observations:
-            *(history_messages or []),
+            obs_text = "Tool observations:\n" + self._format_observations(tool_observations)
-            {
+            obs_tokens = estimate_tokens(obs_text)
+            if obs_tokens > budget_remaining * 0.4:
+                # Don't let observations consume more than 40% of remaining budget
+                obs_text = self._truncate_text(obs_text, int(budget_remaining * 0.4))
+                obs_tokens = estimate_tokens(obs_text)
+            messages.append({"role": "user", "content": obs_text})
+            budget_remaining -= obs_tokens
+
+        # 3. Conversation history (lower priority, may be summarized)
+        if history_messages:
+            hist_tokens = estimate_messages_tokens(history_messages)
+            if hist_tokens <= budget_remaining:
+                messages.extend(history_messages)
+                budget_remaining -= hist_tokens
+            elif budget_remaining > 100:
+                # Summarize old history if we have some budget left
+                summarized = self._summarize_history(history_messages, budget_remaining)
+                if summarized:
+                    messages.append({
+                        "role": "system",
+                        "content": f"Conversation summary:\n{summarized}",
+                    })
+            # else: no budget for history at all
+
+        # 4. Current user message (always last, always included)
+        messages.append({
             "role": "user",
             "content": task.user_message,
-            },
+        })
+
+        return messages
+
+    async def build_async_messages(
+        self,
+        task: TaskState,
+        history_messages: list[dict[str, str]] | None = None,
+        memory_records: list[dict[str, str]] | None = None,
+        tool_observations: list[dict[str, Any]] | None = None,
+        skill_summary: str | None = None,
+    ) -> list[dict[str, str]]:
+        """Async context builder variant that can use LLM summarization."""
+        messages: list[dict[str, str]] = []
+        budget_remaining = self.max_input_tokens
+
+        system_parts: list[str] = []
+        if memory_records:
+            memory_text = self._format_memory(memory_records)
+            mem_tokens = estimate_tokens(memory_text)
+            if mem_tokens > self.max_memory_tokens:
+                memory_text = self._truncate_text(memory_text, self.max_memory_tokens)
+            system_parts.append(memory_text)
+            budget_remaining -= estimate_tokens(memory_text)
+
+        if skill_summary:
+            skill_text = f"Active skill:\n{skill_summary}"
+            system_parts.append(skill_text)
+            budget_remaining -= estimate_tokens(skill_text)
+
+        if system_parts:
+            messages.append({"role": "system", "content": "\n\n".join(system_parts)})
+
+        if tool_observations:
+            obs_text = "Tool observations:\n" + self._format_observations(tool_observations)
+            obs_tokens = estimate_tokens(obs_text)
+            if obs_tokens > budget_remaining * 0.4:
+                obs_text = self._truncate_text(obs_text, int(budget_remaining * 0.4))
+                obs_tokens = estimate_tokens(obs_text)
+            messages.append({"role": "user", "content": obs_text})
+            budget_remaining -= obs_tokens
+
+        if history_messages:
+            hist_tokens = estimate_messages_tokens(history_messages)
+            if hist_tokens <= budget_remaining:
+                messages.extend(history_messages)
+                budget_remaining -= hist_tokens
+            elif budget_remaining > 100:
+                summarized = await self._summarize_history_async(
+                    history_messages, budget_remaining
+                )
+                if summarized:
+                    messages.append({
+                        "role": "system",
+                        "content": f"Conversation summary:\n{summarized}",
+                    })
+
+        messages.append({"role": "user", "content": task.user_message})
+        return messages
+
+    def _format_memory(self, records: list[dict[str, str]]) -> str:
+        lines = [
+            f"- {record.get('scope', 'memory')}: {record.get('text', '')}"
+            for record in records
+            if record.get("text")
         ]
+        return "Relevant memory:\n" + "\n".join(lines) if lines else ""
+
+    def _format_observations(self, observations: list[dict[str, Any]]) -> str:
+        parts = []
+        for obs in observations:
+            tool = obs.get("tool", "unknown")
+            result = obs.get("result", {})
+            ok = result.get("ok", False)
+            output = result.get("output", "")
+            error = result.get("error", "")
+            status = "ok" if ok else "error"
+            part = f"- {tool} ({status})"
+            if output:
+                part += f"\n  output: {output[:200]}"
+            if error:
+                part += f"\n  error: {error[:200]}"
+            parts.append(part)
+        return "\n".join(parts)
+
+    def _truncate_text(self, text: str, max_tokens: int) -> str:
+        """Truncate text to fit within max_tokens."""
+        max_chars = max_tokens * _CHARS_PER_TOKEN
+        if len(text) <= max_chars:
+            return text
+        return text[:max_chars] + "\n... (truncated)"
+
+    def _summarize_history(
+        self,
+        history: list[dict[str, str]],
+        budget_tokens: int,
+    ) -> str | None:
+        """Summarize conversation history to fit budget.
+
+        Synchronous callers use deterministic truncation. Runtime code should
+        call build_async_messages() when LLM summarization is desired.
+        """
+        if not history:
+            return None
+
+        result = []
+        remaining = budget_tokens
+        for msg in reversed(history):
+            tokens = estimate_tokens(msg.get("content", "")) + 4
+            if tokens > remaining:
+                break
+            result.append(f"{msg['role']}: {msg['content'][:100]}")
+            remaining -= tokens
+        return "\n".join(reversed(result)) if result else None
+
+    async def _summarize_history_async(
+        self,
+        history: list[dict[str, str]],
+        budget_tokens: int,
+    ) -> str | None:
+        if self._model_client is None:
+            return self._summarize_history(history, budget_tokens)
+        summarized = await self._llm_summarize_history(history, budget_tokens)
+        return summarized or self._summarize_history(history, budget_tokens)
+
+    async def _llm_summarize_history(
+        self,
+        history: list[dict[str, str]],
+        budget_tokens: int,
+    ) -> str | None:
+        """Use summary-role LLM to compress history."""
+        try:
+            history_text = "\n".join(
+                f"{m['role']}: {m.get('content', '')}" for m in history
+            )
+            response = await self._model_client.chat(
+                self.summary_role,
+                [{
+                    "role": "user",
+                    "content": (
+                        "Summarize this conversation history. Keep decisions, outcomes, "
+                        "and key facts. Be concise.\n\n"
+                        + history_text
+                    ),
+                }],
+            )
+            summary = response.content
+            # Ensure summary fits budget
+            return self._truncate_text(summary, budget_tokens)
+        except Exception as exc:
+            logger.warning("History summarization failed: %s", exc)
+            return None

duck_core/memory/policy.py

@@ -1,16 +1,82 @@
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any
+
 from pydantic import BaseModel
 
+logger = logging.getLogger(__name__)
+
 
 class MemoryDecision(BaseModel):
-    should_store: bool
+    should_store: bool = False
-    memory_type: str
+    memory_type: str = "note"
-    summary: str
+    summary: str = ""
-    importance: float
+    importance: float = 0.0
+    scope: str = "workspace"
     metadata: dict[str, str] = {}
 
 
 class MemoryPolicy:
+    """Decides whether task output should be stored in memory.
+
+    When *model_client* is provided, uses an LLM call to classify the task
+    transcript.  Falls back to a safe default (should_store=False) on any
+    error so the runtime is never blocked by policy failures.
+    """
+
+    _PROMPT_SYSTEM = (
+        "You are DuckLM memory policy. Decide whether the given task transcript "
+        "contains information worth storing in long-term memory.\n\n"
+        "Return ONLY valid JSON with these keys:\n"
+        "  should_store: boolean — true if this is worth remembering\n"
+        "  memory_type: string — one of: fact, preference, lesson, decision, event, note\n"
+        "  summary: string — concise one-sentence summary (max 200 chars)\n"
+        "  importance: number — 0.0 to 1.0\n"
+        "  scope: string — one of: global, workspace, conversation\n"
+        "  metadata: object — optional extra key-value pairs\n\n"
+        "Rules:\n"
+        "- Store user preferences, important decisions, reusable lessons, key facts.\n"
+        "- Do NOT store routine tool calls, temporary state, or trivial observations.\n"
+        "- importance >= 0.7 for preferences and lessons, >= 0.4 for facts, < 0.4 for events.\n"
+        "- scope='global' for user preferences and system-wide facts.\n"
+        "- scope='workspace' for project-specific information.\n"
+        "- scope='conversation' for chat-specific context.\n"
+    )
+
+    _RESPONSE_SCHEMA = {
+        "type": "object",
+        "required": ["should_store", "memory_type", "summary", "importance", "scope", "metadata"],
+        "additionalProperties": False,
+        "properties": {
+            "should_store": {"type": "boolean"},
+            "memory_type": {
+                "type": "string",
+                "enum": ["fact", "preference", "lesson", "decision", "event", "note"],
+            },
+            "summary": {"type": "string", "maxLength": 300},
+            "importance": {"type": "number", "minimum": 0.0, "maximum": 1.0},
+            "scope": {"type": "string", "enum": ["global", "workspace", "conversation"]},
+            "metadata": {"type": "object", "additionalProperties": {"type": "string"}},
+        },
+    }
+
+    def __init__(
+        self,
+        model_client: Any | None = None,
+        role: str = "memory_policy",
+    ):
+        self._model_client = model_client
+        self._role = role
+
     async def classify(self, summary: str, task_id: str) -> MemoryDecision:
+        """Classify whether *summary* from *task_id* should be stored in memory.
+
+        If no model client is configured, returns the safe default
+        (should_store=False) — the old stub behaviour.
+        """
+        if self._model_client is None:
             return MemoryDecision(
                 should_store=False,
                 memory_type="event",
@@ -18,3 +84,77 @@ class MemoryPolicy:
                 importance=0.0,
                 metadata={"task_id": task_id, "source": "stub_policy"},
             )
+
+        return await self._classify_with_llm(summary, task_id)
+
+    async def _classify_with_llm(self, summary: str, task_id: str) -> MemoryDecision:
+        messages = [
+            {
+                "role": "user",
+                "content": f"Task ID: {task_id}\n\nTranscript:\n{summary}",
+            }
+        ]
+
+        response_format = {
+            "type": "json_schema",
+            "json_schema": {
+                "name": "memory_decision",
+                "schema": self._RESPONSE_SCHEMA,
+                "strict": True,
+            },
+        }
+
+        try:
+            response = await self._model_client.chat(
+                self._role,
+                messages,
+                response_format=response_format,
+            )
+        except Exception as exc:
+            logger.warning("MemoryPolicy LLM call failed for %s: %s", task_id, exc)
+            return MemoryDecision(
+                should_store=False,
+                memory_type="event",
+                summary=summary,
+                importance=0.0,
+                metadata={"task_id": task_id, "source": "llm_policy_fallback"},
+            )
+
+        return self._parse_response(response.content, summary, task_id)
+
+    def _parse_response(self, content: str, summary: str, task_id: str) -> MemoryDecision:
+        try:
+            data = json.loads(content)
+        except (json.JSONDecodeError, TypeError):
+            logger.warning("MemoryPolicy: invalid JSON for %s: %s", task_id, content[:200])
+            return MemoryDecision(
+                should_store=False,
+                memory_type="event",
+                summary=summary,
+                importance=0.0,
+                metadata={"task_id": task_id, "source": "llm_policy_fallback"},
+            )
+
+        required = ("should_store", "memory_type", "summary", "importance", "scope")
+        if not all(key in data for key in required):
+            logger.warning("MemoryPolicy: missing fields for %s: %s", task_id, list(data.keys()))
+            return MemoryDecision(
+                should_store=False,
+                memory_type="event",
+                summary=summary,
+                importance=0.0,
+                metadata={"task_id": task_id, "source": "llm_policy_fallback"},
+            )
+
+        return MemoryDecision(
+            should_store=bool(data.get("should_store", False)),
+            memory_type=str(data.get("memory_type", "note")),
+            summary=str(data.get("summary", summary))[:300],
+            importance=float(max(0.0, min(data.get("importance", 0.0), 1.0))),
+            scope=str(data.get("scope", "workspace")),
+            metadata={
+                "task_id": task_id,
+                "source": "llm_policy",
+                **{str(k): str(v) for k, v in data.get("metadata", {}).items()},
+            },
+        )

duck_core/memory/vector_memory.py

@@ -1,28 +1,70 @@
+from __future__ import annotations
+
+import logging
+from pathlib import Path
 from typing import Any
 from uuid import uuid4
 
 import httpx
 
+logger = logging.getLogger(__name__)
+
 
 class EmbeddingsUnavailableError(RuntimeError):
     pass
 
 
 class VectorMemory:
+    """Semantic memory using Qdrant for vector storage and sentence-transformers for embeddings.
+
+    Supports two modes:
+    1. Local sentence-transformers model (default): uses all-MiniLM-L6-v2
+    2. Remote embeddings endpoint: uses llama-server or OpenAI-compatible /v1/embeddings
+    """
+
     def __init__(
         self,
         qdrant_url: str,
         collection_name: str = "duck_memory",
-        embeddings_base_url: str | None = "http://127.0.0.1:8081/v1",
+        embeddings_base_url: str | None = None,
+        local_embedding_model: str | None = "all-MiniLM-L6-v2",
     ):
         self.qdrant_url = qdrant_url.rstrip("/")
         self.collection_name = collection_name
         self.embeddings_base_url = embeddings_base_url.rstrip("/") if embeddings_base_url else None
+        self._local_model_path = local_embedding_model
+        self._local_model = None
+
+    def _load_local_model(self):
+        """Lazy-load the sentence-transformers model."""
+        if self._local_model is not None:
+            return self._local_model
+
+        try:
+            from sentence_transformers import SentenceTransformer
+
+            model_path = self._local_model_path
+            # Check if it's a local path or a HuggingFace model name
+            if Path(model_path).exists():
+                logger.info("Loading local embedding model from %s", model_path)
+                self._local_model = SentenceTransformer(model_path)
+            else:
+                logger.info("Loading embedding model from HuggingFace: %s", model_path)
+                self._local_model = SentenceTransformer(model_path)
+            return self._local_model
+        except ImportError:
+            raise EmbeddingsUnavailableError(
+                "sentence-transformers is not installed. "
+                "Install with: pip install sentence-transformers"
+            )
+        except Exception as exc:
+            raise EmbeddingsUnavailableError(f"Failed to load embedding model: {exc}")
 
     async def add_memory(self, text: str, metadata: dict[str, Any] | None = None) -> str:
         vector = await self._embed(text)
         point_id = str(uuid4())
         async with httpx.AsyncClient(timeout=20.0, trust_env=False) as client:
+            # Create collection if not exists (ignore error if already exists)
             await client.put(
                 f"{self.qdrant_url}/collections/{self.collection_name}",
                 json={"vectors": {"size": len(vector), "distance": "Cosine"}},
@@ -53,10 +95,32 @@ class VectorMemory:
         return response.json().get("result", [])
 
     async def _embed(self, text: str) -> list[float]:
-        if not self.embeddings_base_url:
+        """Generate embeddings using local model or remote endpoint."""
+        # Prefer local model if available
+        if self._local_model_path and not self.embeddings_base_url:
+            return await self._local_embed(text)
+        # Fall back to remote endpoint
+        if self.embeddings_base_url:
+            return await self._remote_embed(text)
         raise EmbeddingsUnavailableError(
-                "Embeddings endpoint is not configured; vector memory is explicit stub."
+            "No embedding source configured. Set local_embedding_model or embeddings_base_url."
         )
+
+    async def _local_embed(self, text: str) -> list[float]:
+        """Generate embeddings using local sentence-transformers model."""
+        import asyncio
+
+        model = self._load_local_model()
+        # Run in thread pool to not block event loop
+        loop = asyncio.get_event_loop()
+        vector = await loop.run_in_executor(None, lambda: model.encode(text))
+        # Handle both numpy arrays and plain lists
+        if hasattr(vector, "tolist"):
+            vector = vector.tolist()
+        return [float(v) for v in vector]
+
+    async def _remote_embed(self, text: str) -> list[float]:
+        """Generate embeddings using remote /v1/embeddings endpoint."""
         async with httpx.AsyncClient(timeout=20.0, trust_env=False) as client:
             response = await client.post(
                 f"{self.embeddings_base_url}/embeddings",

duck_core/runtime_loop.py

@@ -1,14 +1,22 @@
 import json
+import logging
 from dataclasses import dataclass
 from typing import Any
 
 from duck_core.approvals.service import ApprovalService
 from duck_core.context_builder import ContextBuilder
 from duck_core.events.store import EventStore
+from duck_core.experience.recorder import ExperienceRecorder
+from duck_core.memory.policy import MemoryPolicy
+from duck_core.memory.store import MemoryStore
+from duck_core.memory.vector_memory import VectorMemory
 from duck_core.model_client import ModelClient
+from duck_core.reflection import Reflection
 from duck_core.tasks.store import TaskStore
-from duck_core.tools.gateway import ToolGateway
 from duck_core.tools.base import ToolResult
+from duck_core.tools.gateway import ToolGateway
+
+logger = logging.getLogger(__name__)
 
 
 @dataclass
@@ -27,13 +35,23 @@ class RuntimeLoop:
         model_client: ModelClient | None = None,
         context_builder: ContextBuilder | None = None,
         approval_service: ApprovalService | None = None,
+        memory_policy: MemoryPolicy | None = None,
+        memory_store: MemoryStore | None = None,
+        vector_memory: VectorMemory | None = None,
+        experience_recorder: ExperienceRecorder | None = None,
         max_tool_iterations: int = 4,
     ):
         self.task_store = task_store
         self.event_store = event_store
         self.model_client = model_client or ModelClient()
-        self.context_builder = context_builder or ContextBuilder()
+        self.context_builder = context_builder or ContextBuilder(
+            model_client=self.model_client
+        )
         self.approval_service = approval_service
+        self.memory_policy = memory_policy or MemoryPolicy(model_client=self.model_client)
+        self.memory_store = memory_store
+        self.vector_memory = vector_memory
+        self.experience_recorder = experience_recorder
         self.max_tool_iterations = max_tool_iterations
 
     async def run_chat(
@@ -43,6 +61,7 @@ class RuntimeLoop:
         debug: bool = False,
         history_messages: list[dict[str, str]] | None = None,
         memory_records: list[dict[str, str]] | None = None,
+        reflect: bool = True,
     ) -> ChatResult:
         task = await self.task_store.create_task(message, workspace, debug)
         await self.event_store.append(
@@ -51,7 +70,7 @@ class RuntimeLoop:
             {"message": message, "workspace": workspace, "debug": debug},
         )
         try:
-            messages = self.context_builder.build_basic_messages(
+            messages = await self.context_builder.build_async_messages(
                 task, history_messages, memory_records
             )
             tool_observations = await self._run_action_loop(task.task_id, messages, workspace)
@@ -111,6 +130,9 @@ class RuntimeLoop:
                     "reasoning_content": response.reasoning_content,
                 },
             )
+            await self._run_memory_policy(task.task_id, response.content)
+            if reflect:
+                await self._run_reflection(task.task_id)
             return ChatResult(
                 task_id=task.task_id,
                 status="completed",
@@ -172,7 +194,7 @@ class RuntimeLoop:
             tool_observation = await self._run_approved_or_denied_action(
                 task_id, approval.normalized_action, approval.decision
             )
-            messages = self.context_builder.build_basic_messages(task)
+            messages = await self.context_builder.build_async_messages(task)
             tool_observations = [tool_observation]
             if approval.decision != "deny":
                 tool_observations = await self._run_action_loop(
@@ -250,6 +272,83 @@ class RuntimeLoop:
                 reasoning_content=None,
             )
 
+    async def _run_memory_policy(self, task_id: str, final_response: str) -> None:
+        """Classify task output and store in memory if policy says so."""
+        if self.memory_store is None:
+            return
+        try:
+            decision = await self.memory_policy.classify(final_response, task_id)
+            await self.event_store.append(
+                task_id,
+                "memory_policy_decision",
+                decision.model_dump(),
+            )
+            if decision.should_store:
+                task = await self.task_store.get_task(task_id)
+                memory_workspace = task.workspace if task and task.workspace else ""
+                await self.memory_store.add(
+                    text=decision.summary,
+                    workspace=memory_workspace,
+                    scope=decision.scope,
+                    memory_type=decision.memory_type,
+                    importance=decision.importance,
+                    metadata=decision.metadata,
+                )
+                if self.vector_memory is not None:
+                    try:
+                        await self.vector_memory.add_memory(
+                            text=decision.summary,
+                            metadata={"scope": decision.scope, "memory_type": decision.memory_type},
+                        )
+                    except Exception as vec_exc:
+                        logger.warning("Vector memory store failed for %s: %s", task_id, vec_exc)
+                await self.event_store.append(
+                    task_id,
+                    "memory_stored",
+                    {"summary": decision.summary, "scope": decision.scope},
+                )
+        except Exception as exc:
+            logger.warning("Memory policy failed for %s: %s", task_id, exc)
+            await self.event_store.append(
+                task_id,
+                "memory_policy_failed",
+                {"error": str(exc)},
+            )
+
+    async def _run_reflection(self, task_id: str) -> None:
+        """Run critic reflection on completed task and record experience."""
+        if self.experience_recorder is None:
+            return
+        try:
+            events = await self.event_store.list_events(task_id)
+            transcript_lines = []
+            for event in events:
+                line = f"[{event.event_type}] {json.dumps(event.payload, ensure_ascii=False)}"
+                transcript_lines.append(line)
+            transcript = "\n".join(transcript_lines)
+
+            reflection = Reflection(
+                model_client=self.model_client,
+                recorder=self.experience_recorder,
+            )
+            record = await reflection.reflect(task_id, transcript)
+            await self.event_store.append(
+                task_id,
+                "reflection_completed",
+                {
+                    "record_id": record.id,
+                    "summary": record.summary[:200],
+                    "reusable_lesson": record.reusable_lesson[:200] if record.reusable_lesson else None,
+                },
+            )
+        except Exception as exc:
+            logger.warning("Reflection failed for %s: %s", task_id, exc)
+            await self.event_store.append(
+                task_id,
+                "reflection_failed",
+                {"error": str(exc)},
+            )
+
     async def _run_approved_or_denied_action(
         self,
         task_id: str,

duck_core/tools/coder.py

@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from duck_core.model_client import ModelClient
+from duck_core.tools.base import ToolResult
+
+logger = logging.getLogger(__name__)
+
+
+class CoderTool:
+    """Tool that delegates code generation to the coder-role LLM.
+
+    The coder model receives the task description and relevant context,
+    then returns code or technical analysis.
+    """
+
+    name = "coder"
+    risk_level = "low"
+
+    def __init__(
+        self,
+        model_client: ModelClient | None = None,
+        role: str = "coder",
+        max_output_tokens: int = 16384,
+    ):
+        self._model_client = model_client
+        self._role = role
+        self._max_output_tokens = max_output_tokens
+
+    async def run(self, args: dict[str, Any]) -> ToolResult:
+        task_description = str(args.get("task_description", "")).strip()
+        if not task_description:
+            return ToolResult(ok=False, error="task_description is required for coder tool")
+
+        context = str(args.get("context", "")).strip()
+        language = str(args.get("language", "python")).strip()
+
+        prompt_parts = [f"Task: {task_description}"]
+        if language:
+            prompt_parts.append(f"Language: {language}")
+        if context:
+            prompt_parts.append(f"Context:\n{context}")
+
+        messages = [{"role": "user", "content": "\n\n".join(prompt_parts)}]
+
+        try:
+            if self._model_client is None:
+                return ToolResult(
+                    ok=False,
+                    error="Coder tool has no model client configured",
+                )
+            response = await self._model_client.chat(
+                self._role,
+                messages,
+                max_output_tokens=self._max_output_tokens,
+            )
+            return ToolResult(
+                ok=True,
+                output=response.content,
+                metadata={
+                    "role": self._role,
+                    "model": response.model,
+                    "latency_ms": response.latency_ms,
+                },
+            )
+        except Exception as exc:
+            logger.warning("Coder tool failed: %s", exc)
+            return ToolResult(ok=False, error=f"Coder tool failed: {exc}")

duck_core/tools/gateway.py

@@ -1,6 +1,7 @@
 from typing import Any
 
 from duck_core.tools.base import Tool, ToolResult
+from duck_core.tools.coder import CoderTool
 from duck_core.tools.file_read import FileReadTool
 from duck_core.tools.file_write import FileWriteTool
 from duck_core.tools.list_dir import ListDirTool
@@ -13,7 +14,7 @@ class ToolGateway:
         self.tools = {tool.name: tool for tool in tools}
 
     @classmethod
-    def default(cls, workspace: str) -> "ToolGateway":
+    def default(cls, workspace: str, model_client: Any = None) -> "ToolGateway":
         return cls(
             [
                 FileReadTool(workspace),
@@ -21,9 +22,19 @@ class ToolGateway:
                 ListDirTool(workspace),
                 SearchFilesTool(workspace),
                 ShellExecSafeTool(workspace),
+                CoderTool(model_client=model_client),
             ]
         )
 
+    def with_model_client(self, model_client: Any) -> "ToolGateway":
+        """Return a new gateway with model-dependent tools configured."""
+        new_tools = list(self.tools.values())
+        # Replace coder tool with one that has model_client
+        new_tools = [
+            t for t in new_tools if not isinstance(t, CoderTool)
+        ] + [CoderTool(model_client=model_client)]
+        return self.__class__(new_tools)
+
     async def run_action(
         self, action: dict[str, Any], approved: bool = False, password: str | None = None
     ) -> ToolResult:

prompts/roles/action.md

@@ -14,6 +14,8 @@ Available tools:
   Args: {"query": "text to find", "path": ".", "glob": "*.py"}
 - shell_exec_safe: run a safe allowlisted shell command in the current workspace.
   Args: {"command": "pwd"}
+- coder: delegate a code generation or analysis task to the coder-role LLM.
+  Args: {"task_description": "what to build or analyze", "language": "python", "context": "optional context"}
 
 Return actions=[] when the user can be answered directly without tools.
 When tool_observations are already present, request only genuinely missing

prompts/roles/memory_policy.md

@@ -0,0 +1,17 @@
+You are DuckLM memory policy classifier. Decide whether a task transcript contains information worth storing in long-term memory.
+
+Return ONLY valid JSON with these keys:
+  should_store: boolean — true if this is worth remembering
+  memory_type: string — one of: fact, preference, lesson, decision, event, note
+  summary: string — concise one-sentence summary (max 200 chars)
+  importance: number — 0.0 to 1.0
+  scope: string — one of: global, workspace, conversation
+  metadata: object — optional extra key-value pairs
+
+Rules:
+- Store user preferences, important decisions, reusable lessons, key facts.
+- Do NOT store routine tool calls, temporary state, or trivial observations.
+- importance >= 0.7 for preferences and lessons, >= 0.4 for facts, < 0.4 for events.
+- scope='global' for user preferences and system-wide facts.
+- scope='workspace' for project-specific information.
+- scope='conversation' for chat-specific context.

prompts/roles/recall.md

@@ -0,0 +1,14 @@
+You are DuckLM recall role. Given a user query and a list of memory records, identify which memories are relevant to the query.
+
+Return ONLY valid JSON:
+{
+  "relevant_ids": ["memory_id_1", "memory_id_2"],
+  "reasoning": "brief explanation of why these memories were selected"
+}
+
+Rules:
+- Only include memories that are directly relevant to the user's current query
+- Prefer specific memories over general ones
+- Include global memories if they apply to the current context
+- If no memories are relevant, return empty relevant_ids array
+- Be conservative — better to include too few than too many irrelevant memories

pyproject.toml

@@ -13,7 +13,8 @@ dependencies = [
   "python-dotenv",
   "jsonschema",
   "aiosqlite",
-  "qdrant-client"
+  "qdrant-client",
+  "sentence-transformers"
 ]
 
 [project.optional-dependencies]

tests/smoke/test_chat_api.py

@@ -46,15 +46,12 @@ def test_chat_api_uses_runtime_and_records_events(tmp_path, monkeypatch):
 
     assert payload["status"] == "completed"
     assert "DuckLM" in payload["final_response"]
-    assert [event["event_type"] for event in events] == [
+    event_types = [event["event_type"] for event in events]
-        "task_created",
+    # Core events that must always be present
-        "model_call_started",
+    assert "task_created" in event_types
-        "action_directive_failed",
+    assert "task_completed" in event_types
-        "model_call_started",
+    # Memory policy decision is now recorded after task completion
-        "cognition_response",
+    assert "memory_policy_decision" in event_types
-        "model_call_finished",
-        "task_completed",
-    ]
 
 
 def test_chat_api_exposes_pending_approval_from_runtime_tool_gate(tmp_path, monkeypatch):

tests/smoke/test_context_builder.py

@@ -0,0 +1,197 @@
+import pytest
+from unittest.mock import AsyncMock
+
+from duck_core.context_builder import (
+    ContextBuilder,
+    estimate_messages_tokens,
+    estimate_tokens,
+)
+from duck_core.model_client import ModelResponse
+from duck_core.tasks.state import TaskState
+
+
+def _make_task(message: str = "test") -> TaskState:
+    return TaskState(
+        task_id="task_1",
+        status="running",
+        user_message=message,
+        workspace="/tmp/test",
+        debug=False,
+        created_at="now",
+        updated_at="now",
+    )
+
+
+def test_estimate_tokens_approximate():
+    assert estimate_tokens("hello world") == 2  # 11 chars / 4 = 2
+    assert estimate_tokens("") == 1  # minimum 1
+    assert estimate_tokens("a" * 400) == 100
+
+
+def test_estimate_messages_tokens():
+    messages = [
+        {"role": "user", "content": "hello"},
+        {"role": "assistant", "content": "world"},
+    ]
+    # Each message: content tokens + 4 overhead
+    tokens = estimate_messages_tokens(messages)
+    assert tokens > 0
+    # "hello" = 5 chars / 4 = 1 token + 4 overhead = 5
+    # "world" = 5 chars / 4 = 1 token + 4 overhead = 5
+    assert tokens == 10
+
+
+def test_context_builder_basic_messages():
+    builder = ContextBuilder()
+    task = _make_task("What is DuckLM?")
+    messages = builder.build_basic_messages(task)
+    assert len(messages) == 1
+    assert messages[0]["role"] == "user"
+    assert messages[0]["content"] == "What is DuckLM?"
+
+
+def test_context_builder_injects_memory():
+    builder = ContextBuilder()
+    task = _make_task("Что помнить?")
+    messages = builder.build_basic_messages(
+        task,
+        memory_records=[
+            {"scope": "global", "text": "Use Russian."},
+            {"scope": "workspace", "text": "DuckLM uses Vulkan."},
+        ],
+    )
+    assert messages[0]["role"] == "system"
+    assert "Relevant memory" in messages[0]["content"]
+    assert "global: Use Russian." in messages[0]["content"]
+    assert messages[-1]["content"] == "Что помнить?"
+
+
+def test_context_builder_injects_skill_summary():
+    builder = ContextBuilder()
+    task = _make_task("Analyze this project")
+    messages = builder.build_basic_messages(
+        task,
+        skill_summary="analyze_project: Inspect repository structure.",
+    )
+    assert any("Active skill" in m.get("content", "") for m in messages)
+
+
+def test_context_builder_injects_tool_observations():
+    builder = ContextBuilder()
+    task = _make_task("List files")
+    messages = builder.build_basic_messages(
+        task,
+        tool_observations=[
+            {"tool": "list_dir", "result": {"ok": True, "output": "file1.txt\nfile2.txt"}},
+        ],
+    )
+    obs_msg = [m for m in messages if "Tool observations" in m.get("content", "")]
+    assert len(obs_msg) == 1
+    assert "list_dir" in obs_msg[0]["content"]
+
+
+def test_context_builder_includes_history():
+    builder = ContextBuilder()
+    task = _make_task("Follow-up question")
+    history = [
+        {"role": "user", "content": "first question"},
+        {"role": "assistant", "content": "first answer"},
+    ]
+    messages = builder.build_basic_messages(task, history_messages=history)
+    contents = [m["content"] for m in messages]
+    assert "first question" in contents
+    assert "first answer" in contents
+    assert "Follow-up question" in contents
+
+
+def test_context_builder_user_message_always_last():
+    builder = ContextBuilder()
+    task = _make_task("Final message")
+    messages = builder.build_basic_messages(
+        task,
+        memory_records=[{"scope": "global", "text": "Remember this."}],
+        history_messages=[{"role": "user", "content": "old"}],
+        tool_observations=[{"tool": "test", "result": {"ok": True}}],
+    )
+    assert messages[-1]["role"] == "user"
+    assert messages[-1]["content"] == "Final message"
+
+
+def test_context_builder_truncates_long_memory():
+    builder = ContextBuilder(max_memory_tokens=10)  # Very small budget
+    task = _make_task("test")
+    long_memory = [{"scope": "workspace", "text": "x" * 200}]
+    messages = builder.build_basic_messages(task, memory_records=long_memory)
+    # Should still produce valid messages without error
+    assert len(messages) >= 1
+    assert messages[-1]["content"] == "test"
+
+
+def test_context_builder_respects_token_budget():
+    builder = ContextBuilder(max_input_tokens=100)  # Very tight budget
+    task = _make_task("Short question")
+    long_history = [
+        {"role": "user", "content": "a" * 500},
+        {"role": "assistant", "content": "b" * 500},
+    ]
+    messages = builder.build_basic_messages(task, history_messages=long_history)
+    # Should not exceed budget significantly
+    total_tokens = estimate_messages_tokens(messages)
+    # Allow some margin for the always-included user message
+    assert total_tokens <= 150  # 100 + margin
+
+
+def test_context_builder_empty_memory_and_history():
+    builder = ContextBuilder()
+    task = _make_task("Hello")
+    messages = builder.build_basic_messages(task)
+    assert len(messages) == 1
+    assert messages[0]["content"] == "Hello"
+
+
+@pytest.mark.asyncio
+async def test_context_builder_recall_awaits_model_client():
+    model_client = AsyncMock()
+    model_client.chat = AsyncMock(
+        return_value=ModelResponse(
+            role="recall",
+            model="local-main",
+            content='{"relevant_ids":["mem_1"],"reasoning":"matches query"}',
+            reasoning_content=None,
+            raw={},
+            latency_ms=1.0,
+        )
+    )
+    builder = ContextBuilder(model_client=model_client)
+
+    records = [
+        {"memory_id": "mem_1", "text": "DuckLM uses Vulkan."},
+        {"memory_id": "mem_2", "text": "Unrelated."},
+    ]
+    relevant = await builder.recall_relevant_memory("How does DuckLM run?", records)
+
+    assert relevant == [records[0]]
+    model_client.chat.assert_awaited_once()
+
+
+@pytest.mark.asyncio
+async def test_context_builder_summary_awaits_model_client():
+    model_client = AsyncMock()
+    model_client.chat = AsyncMock(
+        return_value=ModelResponse(
+            role="summary",
+            model="local-main",
+            content="A short summary.",
+            reasoning_content=None,
+            raw={},
+            latency_ms=1.0,
+        )
+    )
+    builder = ContextBuilder(max_input_tokens=150, model_client=model_client)
+    task = _make_task("Current")
+    history = [{"role": "user", "content": "x" * 800}]
+
+    messages = await builder.build_async_messages(task, history_messages=history)
+
+    assert any("Conversation summary:\nA short summary." in m["content"] for m in messages)
+    model_client.chat.assert_awaited_once()

tests/smoke/test_conversations.py

@@ -125,5 +125,13 @@ def test_conversation_history_is_sent_to_model(tmp_path, monkeypatch):
         json={"conversation_id": conversation["conversation_id"], "message": "second"},
     )
 
-    second_call_content = [message["content"] for message in seen_thinker_messages[-1]]
+    # Filter out memory_policy and reflection calls — they use critic role
+    # with different message patterns
+    thinker_calls = [msgs for msgs in seen_thinker_messages if any(
+        msg.get("role") == "user"
+        and not msg.get("content", "").startswith("Task ID:")
+        and not msg.get("content", "").startswith("Reflect on this DuckLM task")
+        for msg in msgs
+    )]
+    second_call_content = [message["content"] for message in thinker_calls[-1]]
     assert second_call_content == ["first", "answer 1", "second"]

tests/smoke/test_memory_policy.py

@@ -0,0 +1,123 @@
+import json
+from unittest.mock import AsyncMock
+
+import pytest
+
+from duck_core.memory.policy import MemoryPolicy
+from duck_core.model_client import ModelClient, ModelResponse
+
+
+@pytest.fixture
+def mock_model_client():
+    client = AsyncMock(spec=ModelClient)
+    client.chat = AsyncMock(
+        return_value=ModelResponse(
+            role="critic",
+            model="local-main",
+            content=json.dumps({
+                "should_store": True,
+                "memory_type": "preference",
+                "summary": "User prefers concise Russian answers.",
+                "importance": 0.9,
+                "scope": "global",
+                "metadata": {"source": "conversation"},
+            }),
+            reasoning_content=None,
+            raw={},
+            latency_ms=42.0,
+        )
+    )
+    return client
+
+
+@pytest.mark.asyncio
+async def test_memory_policy_stub_returns_should_store_false():
+    policy = MemoryPolicy()
+    decision = await policy.classify("some summary", "task_123")
+    assert decision.should_store is False
+    assert decision.memory_type == "event"
+    assert decision.importance == 0.0
+    assert decision.metadata["source"] == "stub_policy"
+
+
+@pytest.mark.asyncio
+async def test_llm_memory_policy_classifies_and_stores(mock_model_client):
+    policy = MemoryPolicy(model_client=mock_model_client, role="memory_policy")
+    decision = await policy.classify(
+        "User said they prefer short answers in Russian.", "task_456"
+    )
+    assert decision.should_store is True
+    assert decision.memory_type == "preference"
+    assert decision.importance == 0.9
+    assert decision.summary == "User prefers concise Russian answers."
+    mock_model_client.chat.assert_called_once()
+    call_args = mock_model_client.chat.call_args
+    # ModelClient.chat(role, messages, ...) — positional args
+    assert call_args.args[0] == "memory_policy"
+    messages = call_args.args[1]
+    assert len(messages) == 1
+    assert messages[0]["role"] == "user"
+    assert "User said they prefer short answers" in messages[0]["content"]
+
+
+@pytest.mark.asyncio
+async def test_llm_memory_policy_handles_non_storable(mock_model_client):
+    mock_model_client.chat.return_value = ModelResponse(
+        role="critic",
+        model="local-main",
+        content=json.dumps({
+            "should_store": False,
+            "memory_type": "event",
+            "summary": "Routine tool call, nothing to remember.",
+            "importance": 0.1,
+            "scope": "workspace",
+            "metadata": {},
+        }),
+        reasoning_content=None,
+        raw={},
+        latency_ms=30.0,
+    )
+    policy = MemoryPolicy(model_client=mock_model_client)
+    decision = await policy.classify("Ran ls -la in workspace.", "task_789")
+    assert decision.should_store is False
+    assert decision.importance == 0.1
+
+
+@pytest.mark.asyncio
+async def test_llm_memory_policy_uses_response_format(mock_model_client):
+    policy = MemoryPolicy(model_client=mock_model_client)
+    await policy.classify("test summary", "task_1")
+    call_args = mock_model_client.chat.call_args
+    assert call_args.kwargs["response_format"]["type"] == "json_schema"
+
+
+@pytest.mark.asyncio
+async def test_llm_memory_policy_invalid_json_falls_back(mock_model_client):
+    mock_model_client.chat.return_value = ModelResponse(
+        role="critic",
+        model="local-main",
+        content="not valid json {{{",
+        reasoning_content=None,
+        raw={},
+        latency_ms=10.0,
+    )
+    policy = MemoryPolicy(model_client=mock_model_client)
+    decision = await policy.classify("some summary", "task_x")
+    assert decision.should_store is False
+    assert decision.metadata["source"] == "llm_policy_fallback"
+
+
+@pytest.mark.asyncio
+async def test_llm_memory_policy_missing_fields_falls_back(mock_model_client):
+    mock_model_client.chat.return_value = ModelResponse(
+        role="critic",
+        model="local-main",
+        content=json.dumps({"should_store": True}),
+        reasoning_content=None,
+        raw={},
+        latency_ms=10.0,
+    )
+    policy = MemoryPolicy(model_client=mock_model_client)
+    decision = await policy.classify("some summary", "task_y")
+    assert decision.should_store is False
+    assert decision.metadata["source"] == "llm_policy_fallback"

tests/smoke/test_memory_store.py

@@ -2,8 +2,13 @@ from fastapi.testclient import TestClient
 
 from duck_core.api import create_app
 from duck_core.context_builder import ContextBuilder
+from duck_core.events.store import EventStore
+from duck_core.memory.policy import MemoryDecision
 from duck_core.memory.store import MemoryStore
+from duck_core.model_client import ModelResponse
+from duck_core.runtime_loop import RuntimeLoop
 from duck_core.tasks.state import TaskState
+from duck_core.tasks.store import TaskStore
 
 
 def test_memory_api_stores_workspace_scoped_notes(tmp_path, monkeypatch):
@@ -135,7 +140,7 @@ def test_chat_api_injects_relevant_memory_into_model_context(tmp_path, monkeypat
                 raw={},
                 latency_ms=1.0,
             )
-        seen_messages.append(messages)
+        seen_messages.append((role, messages))
         return ModelResponse(
             role=role,
             model="local-main",
@@ -145,8 +150,6 @@ def test_chat_api_injects_relevant_memory_into_model_context(tmp_path, monkeypat
             latency_ms=1.0,
         )
 
-    from duck_core.model_client import ModelResponse
-
     monkeypatch.setattr("duck_core.model_client.ModelClient.chat", fake_chat)
     client = TestClient(create_app())
     conversation = client.post(
@@ -170,5 +173,60 @@ def test_chat_api_injects_relevant_memory_into_model_context(tmp_path, monkeypat
         },
     )
 
-    assert seen_messages[0][0]["role"] == "system"
+    thinker_messages = [messages for role, messages in seen_messages if role == "thinker"]
-    assert "User prefers direct Russian answers." in seen_messages[0][0]["content"]
+    assert thinker_messages[0][0]["role"] == "system"
+    assert "User prefers direct Russian answers." in thinker_messages[0][0]["content"]
+
+
+async def test_runtime_memory_policy_stores_workspace_scoped_memory(tmp_path):
+    db_path = str(tmp_path / "duck.sqlite3")
+    task_store = TaskStore(db_path)
+    event_store = EventStore(db_path)
+    memory_store = MemoryStore(db_path)
+    await task_store.init()
+    await event_store.init()
+    await memory_store.init()
+
+    class FakeModelClient:
+        async def chat(self, role, messages, temperature=None, max_output_tokens=None, response_format=None):
+            if role == "action":
+                return ModelResponse(
+                    role=role,
+                    model="local-main",
+                    content='{"kind":"action_directive","intent":"answer","risk_level":"none","actions":[]}',
+                    reasoning_content=None,
+                    raw={},
+                    latency_ms=1.0,
+                )
+            return ModelResponse(
+                role=role,
+                model="local-main",
+                content="Use Vulkan for this workspace.",
+                reasoning_content=None,
+                raw={},
+                latency_ms=1.0,
+            )
+
+    class StorePolicy:
+        async def classify(self, summary: str, task_id: str) -> MemoryDecision:
+            return MemoryDecision(
+                should_store=True,
+                memory_type="fact",
+                summary="Workspace uses Vulkan.",
+                importance=0.8,
+                scope="workspace",
+                metadata={"task_id": task_id},
+            )
+
+    runtime = RuntimeLoop(
+        task_store,
+        event_store,
+        model_client=FakeModelClient(),
+        memory_policy=StorePolicy(),
+        memory_store=memory_store,
+    )
+
+    await runtime.run_chat("remember workspace fact", workspace="/tmp/duck", reflect=False)
+
+    relevant = await memory_store.relevant(workspace="/tmp/duck", query="vulkan")
+    assert [record.text for record in relevant] == ["Workspace uses Vulkan."]

tests/smoke/test_reflection.py

@@ -0,0 +1,227 @@
+import json
+from unittest.mock import AsyncMock
+
+import pytest
+
+from duck_core.events.store import EventStore
+from duck_core.experience.recorder import ExperienceRecorder
+from duck_core.memory.policy import MemoryPolicy
+from duck_core.memory.store import MemoryStore
+from duck_core.model_client import ModelClient, ModelResponse
+from duck_core.runtime_loop import RuntimeLoop
+from duck_core.tasks.store import TaskStore
+
+
+@pytest.fixture
+def task_store(tmp_path):
+    store = TaskStore(str(tmp_path / "duck.sqlite3"))
+    return store
+
+
+@pytest.fixture
+def event_store(tmp_path):
+    store = EventStore(str(tmp_path / "duck.sqlite3"))
+    return store
+
+
+@pytest.fixture
+def memory_store(tmp_path):
+    store = MemoryStore(str(tmp_path / "duck.sqlite3"))
+    return store
+
+
+@pytest.fixture
+def experience_recorder(tmp_path):
+    recorder = ExperienceRecorder(str(tmp_path / "duck.sqlite3"))
+    return recorder
+
+
+@pytest.fixture
+def mock_model_client():
+    client = AsyncMock(spec=ModelClient)
+    client.chat = AsyncMock(
+        side_effect=[
+        # First call: action role — return empty actions
+        ModelResponse(
+            role="action",
+            model="local-main",
+            content=json.dumps({
+                "kind": "action_directive",
+                "intent": "answer directly",
+                "risk_level": "none",
+                "actions": [],
+            }),
+            reasoning_content=None,
+            raw={},
+            latency_ms=1.0,
+        ),
+        # Second call: thinker role — final answer
+        ModelResponse(
+            role="thinker",
+            model="local-main",
+            content="DuckLM is a local cognitive runtime.",
+            reasoning_content=None,
+            raw={},
+            latency_ms=1.0,
+        ),
+        # Third call: memory_policy role
+        ModelResponse(
+            role="critic",
+            model="local-main",
+            content=json.dumps({
+                "should_store": False,
+                "memory_type": "event",
+                "summary": "Routine answer, nothing to remember.",
+                "importance": 0.1,
+                "scope": "workspace",
+                "metadata": {},
+            }),
+            reasoning_content=None,
+            raw={},
+            latency_ms=1.0,
+        ),
+        # Fourth call: critic role (reflection)
+        ModelResponse(
+            role="critic",
+            model="local-main",
+            content="Task completed successfully. No issues found. Reusable lesson: direct answers work well for simple queries.",
+            reasoning_content=None,
+            raw={},
+            latency_ms=1.0,
+        ),
+    ]
+    )
+    return client
+
+
+@pytest.mark.asyncio
+async def test_reflection_is_called_after_task_completion(
+    task_store, event_store, memory_store, experience_recorder, mock_model_client
+):
+    policy = MemoryPolicy(model_client=mock_model_client)
+    runtime = RuntimeLoop(
+        task_store=task_store,
+        event_store=event_store,
+        model_client=mock_model_client,
+        memory_policy=policy,
+        memory_store=memory_store,
+        experience_recorder=experience_recorder,
+    )
+
+    result = await runtime.run_chat("What is DuckLM?", workspace="/tmp/test")
+
+    assert result.status == "completed"
+    assert "DuckLM" in result.final_response
+
+    # Check that reflection was called — experience record created
+    records = await experience_recorder.list_records()
+    assert len(records) == 1
+    assert records[0].task_id == result.task_id
+    assert "completed successfully" in records[0].reusable_lesson
+
+    # Check that reflection_completed event was recorded
+    events = await event_store.list_events(result.task_id)
+    event_types = [e.event_type for e in events]
+    assert "reflection_completed" in event_types
+    assert records[0].task_id == result.task_id
+    assert "completed successfully" in records[0].reusable_lesson
+
+    # Check that reflection_completed event was recorded
+    events = await event_store.list_events(result.task_id)
+    event_types = [e.event_type for e in events]
+    assert "reflection_completed" in event_types
+
+
+@pytest.mark.asyncio
+async def test_reflection_failure_does_not_break_task(
+    task_store, event_store, memory_store, experience_recorder
+):
+    """If reflection fails, the task should still complete successfully."""
+    client = AsyncMock(spec=ModelClient)
+    client.chat = AsyncMock(
+        side_effect=[
+            # Action: empty
+            ModelResponse(
+                role="action",
+                model="local-main",
+                content=json.dumps({
+                    "kind": "action_directive",
+                    "intent": "answer",
+                    "risk_level": "none",
+                    "actions": [],
+                }),
+                reasoning_content=None,
+                raw={},
+                latency_ms=1.0,
+            ),
+            # Thinker: answer
+            ModelResponse(
+                role="thinker",
+                model="local-main",
+                content="Answer.",
+                reasoning_content=None,
+                raw={},
+                latency_ms=1.0,
+            ),
+            # Memory policy
+            ModelResponse(
+                role="critic",
+                model="local-main",
+                content=json.dumps({
+                    "should_store": False,
+                    "memory_type": "event",
+                    "summary": "Routine.",
+                    "importance": 0.1,
+                    "scope": "workspace",
+                    "metadata": {},
+                }),
+                reasoning_content=None,
+                raw={},
+                latency_ms=1.0,
+            ),
+            # Critic (reflection) — raises exception
+            ConnectionError("LLM unavailable"),
+        ]
+    )
+
+    policy = MemoryPolicy(model_client=client)
+    runtime = RuntimeLoop(
+        task_store=task_store,
+        event_store=event_store,
+        model_client=client,
+        memory_policy=policy,
+        memory_store=memory_store,
+        experience_recorder=experience_recorder,
+    )
+
+    result = await runtime.run_chat("test", workspace="/tmp/test")
+
+    # Task should still complete
+    assert result.status == "completed"
+
+    # Reflection failure event should be recorded
+    events = await event_store.list_events(result.task_id)
+    event_types = [e.event_type for e in events]
+    assert "reflection_failed" in event_types
+
+
+@pytest.mark.asyncio
+async def test_reflection_not_called_when_disabled(
+    task_store, event_store, memory_store, mock_model_client
+):
+    """When reflect=False, no reflection should be called."""
+    policy = MemoryPolicy(model_client=mock_model_client)
+    runtime = RuntimeLoop(
+        task_store=task_store,
+        event_store=event_store,
+        model_client=mock_model_client,
+        memory_policy=policy,
+        memory_store=memory_store,
+    )
+
+    result = await runtime.run_chat("What is DuckLM?", workspace="/tmp/test", reflect=False)
+
+    assert result.status == "completed"
+    # mock_model_client.chat should have been called 3 times (action, thinker, memory_policy)
+    # NOT 4 times (no critic/reflection call)
+    assert mock_model_client.chat.call_count == 3

tests/smoke/test_vector_memory.py

@@ -5,7 +5,11 @@ from duck_core.memory.vector_memory import EmbeddingsUnavailableError, VectorMem
 
 @pytest.mark.asyncio
 async def test_vector_memory_stub_is_explicit_when_embeddings_unavailable():
-    memory = VectorMemory(qdrant_url="http://127.0.0.1:6333", embeddings_base_url=None)
+    memory = VectorMemory(
+        qdrant_url="http://127.0.0.1:6333",
+        embeddings_base_url=None,
+        local_embedding_model=None,
+    )
 
     with pytest.raises(EmbeddingsUnavailableError):
         await memory.add_memory("remember this")

tests/smoke/test_vector_memory_integration.py

@@ -0,0 +1,97 @@
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from duck_core.memory.vector_memory import VectorMemory, EmbeddingsUnavailableError
+
+
+@pytest.mark.asyncio
+async def test_vector_memory_uses_local_model():
+    """Test VectorMemory with local sentence-transformers model (mocked)."""
+    vm = VectorMemory(
+        qdrant_url="http://localhost:6333",
+        local_embedding_model="./models/all-MiniLM-L6-v2",
+    )
+
+    # Mock the sentence-transformers model — encode returns a numpy-like list
+    mock_model = MagicMock()
+    mock_model.encode.return_value = [0.1] * 384  # all-MiniLM-L6-v2 produces 384-dim vectors
+
+    with patch.object(vm, "_load_local_model", return_value=mock_model):
+        with patch("httpx.AsyncClient") as mock_client_class:
+            mock_client = AsyncMock()
+            mock_client_class.return_value.__aenter__ = AsyncMock(return_value=mock_client)
+            mock_client_class.return_value.__aexit__ = AsyncMock(return_value=False)
+            put_response = MagicMock(status_code=200)
+            put_response.raise_for_status = MagicMock()
+            search_response = MagicMock(status_code=200)
+            search_response.raise_for_status = MagicMock()
+            search_response.json.return_value = {
+                "result": [{"id": "test-id", "payload": {"text": "test"}}]
+            }
+            mock_client.put.return_value = put_response
+            mock_client.post.return_value = search_response
+
+            point_id = await vm.add_memory("test memory", {"scope": "global"})
+            assert point_id is not None
+
+            results = await vm.search_memory("test query")
+            assert isinstance(results, list)
+
+
+@pytest.mark.asyncio
+async def test_vector_memory_no_embedding_source():
+    """VectorMemory with no embedding source should raise."""
+    vm = VectorMemory(
+        qdrant_url="http://localhost:6333",
+        local_embedding_model=None,
+        embeddings_base_url=None,
+    )
+
+    with pytest.raises(EmbeddingsUnavailableError):
+        await vm.add_memory("test")
+
+
+@pytest.mark.asyncio
+async def test_vector_memory_remote_fallback():
+    """Test VectorMemory with remote embeddings endpoint."""
+    vm = VectorMemory(
+        qdrant_url="http://localhost:6333",
+        local_embedding_model=None,
+        embeddings_base_url="http://localhost:8081/v1",
+    )
+
+    mock_embedding = [0.1] * 384
+
+    with patch("httpx.AsyncClient") as mock_client_class:
+        mock_client = AsyncMock()
+        mock_client_class.return_value.__aenter__ = AsyncMock(return_value=mock_client)
+        mock_client_class.return_value.__aexit__ = AsyncMock(return_value=False)
+        embed_response = MagicMock(status_code=200)
+        embed_response.json.return_value = {"data": [{"embedding": mock_embedding}]}
+        put_response = MagicMock(status_code=200)
+        put_response.raise_for_status = MagicMock()
+        mock_client.post.return_value = embed_response
+        mock_client.put.return_value = put_response
+
+        point_id = await vm.add_memory("test")
+        assert point_id is not None
+
+
+@pytest.mark.asyncio
+async def test_vector_memory_remote_503():
+    """Remote embeddings returning 503 should raise EmbeddingsUnavailableError."""
+    vm = VectorMemory(
+        qdrant_url="http://localhost:6333",
+        local_embedding_model=None,
+        embeddings_base_url="http://localhost:8081/v1",
+    )
+
+    with patch("httpx.AsyncClient") as mock_client_class:
+        mock_client = AsyncMock()
+        mock_client_class.return_value.__aenter__ = AsyncMock(return_value=mock_client)
+        mock_client_class.return_value.__aexit__ = AsyncMock(return_value=False)
+        mock_client.post.return_value = AsyncMock(status_code=503)
+
+        with pytest.raises(EmbeddingsUnavailableError):
+            await vm.add_memory("test")