Stabilize live chat tool loop

.env.example

@@ -21,6 +21,7 @@ DUCK_MAX_INPUT_TOKENS=49152
 DUCK_MAX_RECENT_EVENTS_TOKENS=12000
 DUCK_MAX_MEMORY_TOKENS=8000
 DUCK_MAX_SKILL_TOKENS=6000
+DUCK_ENABLE_REFLECTION=0
 
 QDRANT_URL=http://127.0.0.1:6333
 

CURRENT_STATE.md

@@ -48,9 +48,11 @@ WebChat доступен через FastAPI на `http://127.0.0.1:8000/`.
 - MemoryStore в SQLite.
 - MemoryPolicy через LLM role `memory_policy` с fallback в безопасный no-store режим.
 - Structured JSON validation для `action` и `memory_policy`: невалидный JSON/schema violation не запускает tools и уходит в безопасный fallback.
+- Tool observations компактируются перед повторной подачей в model context; полные outputs остаются в event/audit log.
+- Duplicate tool actions в рамках одной задачи пропускаются, чтобы модель не выполняла одну и ту же команду повторно.
 - VectorMemory adapter для Qdrant с локальной embedding-моделью или remote embeddings endpoint.
 - Recall-фильтрация памяти через `recall` role.
-- Reflection через `critic` role.
+- Reflection через `critic` role доступна, но выключена по умолчанию в API (`DUCK_ENABLE_REFLECTION=0`), чтобы не забивать single-slot llama во время интерактивного чата.
 - ExperienceRecorder и skill update proposals.
 - Scripts для llama-server, verification и benchmark.
 - Docker compose для Qdrant.
@@ -69,6 +71,8 @@ WebChat доступен через FastAPI на `http://127.0.0.1:8000/`.
 - Skill candidate selection теперь используется в обычном и streaming chat.
 - `scripts/duck.sh status --probe` и `scripts/duck-mtp.sh status --probe` показывают live-состояние DuckLM runtime, model backend и vector memory.
 - Structured utility-outputs валидируются локально по JSON schema; это защищает tool loop и memory writes от мусора модели.
+- Live E2E выявил и исправил два runtime-дефекта: большие stdout больше не раздувают следующий planning prompt, повторяющиеся identical actions больше не исполняются повторно.
+- Critic reflection по умолчанию выключена для API/WebChat, потому что на одном `llama-server --parallel 1` она конкурировала с пользовательскими запросами и вызывала timeouts.
 
 ## Соответствие этапам из Ducklm.md
 
@@ -88,6 +92,7 @@ WebChat доступен через FastAPI на `http://127.0.0.1:8000/`.
 ## Остаточные ограничения
 
 - Qdrant и локальная embedding-модель должны быть доступны отдельно; при ошибках vector memory деградирует без падения runtime.
+- `DUCK_ENABLE_REFLECTION=1` включает critic reflection после задач, но для single-slot llama это может заметно тормозить последующие запросы.
 - Token speed считается приближённо по текущему estimator, а не по tokenizer конкретной модели.
 - Skill selection сейчас keyword-based. LLM selection можно добавить позже, если понадобится.
 - WebChat остаётся lightweight vanilla JS UI; это не production frontend framework.
@@ -151,5 +156,6 @@ bash scripts/duck-mtp.sh logs --follow
 1. Пройти live E2E checklist в WebChat на реальной модели.
 2. Вынести runtime/model role routing в явный конфиг с fallback-политикой, оставив Qwen основным backend для всех ролей.
 3. Расширить strict validation/fallback на `recall` и будущие structured utility-roles.
-4. При необходимости заменить keyword skill selection на LLM-based selection.
+4. Добавить WebChat runtime/status panel поверх `/v1/status?probe=true`.
-5. Позже мигрировать FastAPI startup на lifespan.
+5. При необходимости заменить keyword skill selection на LLM-based selection.
+6. Позже мигрировать FastAPI startup на lifespan.

docs/how_to_run.md

@@ -16,6 +16,10 @@ cp .env.example .env
 
 The default `DUCK_MAIN_MODEL_PATH` points to `./models/Qwen3.6/nonMTP/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf`.
 
+`DUCK_ENABLE_REFLECTION=0` is the recommended default for the local single-slot
+stack. Set it to `1` only when you explicitly want critic reflection after each
+chat and accept that it can slow down the next request.
+
 3. Start DuckLM:
 
 ```bash

docs/web_api.md

@@ -51,6 +51,9 @@ Structured utility roles are validated locally before side effects:
 
 - `action` output must match `duck_core/schemas/action_directive.schema.json`; invalid directives are logged as `action_directive_failed` and no tool runs.
 - `memory_policy` output must match its JSON schema; invalid decisions fall back to `should_store=false`.
+- Large tool outputs are compacted before being sent back to the model; full outputs remain in task events and command audit.
+- Duplicate identical tool actions in one task are skipped and logged as `tool_call_skipped`.
+- Critic reflection is controlled by `DUCK_ENABLE_REFLECTION`; the default is off for responsive single-slot local chat.
 
 Chat requests accept optional `reasoning`:
 

duck_core/api.py

@@ -261,6 +261,7 @@ def create_app() -> FastAPI:
             memory_records=memory_records,
             skill_summary=await selected_skill_summary(body.message),
             reasoning=body.reasoning,
+            reflect=bool(settings.enable_reflection),
         )
         await conversations.add_message(
             conversation.conversation_id,
@@ -454,8 +455,7 @@ def create_app() -> FastAPI:
                         *messages,
                         {
                             "role": "user",
-                            "content": "tool_observations:\n"
+                            "content": runtime.format_tool_observations_for_model(tool_observations),
-                            + json.dumps(tool_observations, ensure_ascii=False, indent=2),
                         },
                     ]
                 yield runtime_status(
@@ -531,7 +531,13 @@ def create_app() -> FastAPI:
                         "generation_stats": generation_stats.summary(),
                     },
                 )
-                asyncio.create_task(runtime.complete_postprocessing(task.task_id, content))
+                asyncio.create_task(
+                    runtime.complete_postprocessing(
+                        task.task_id,
+                        content,
+                        reflect=bool(settings.enable_reflection),
+                    )
+                )
                 yield sse(
                     "done",
                     {
@@ -707,8 +713,7 @@ def create_app() -> FastAPI:
                     *messages,
                     {
                         "role": "user",
-                        "content": "tool_observations:\n"
+                        "content": runtime.format_tool_observations_for_model(tool_observations),
-                        + json.dumps(tool_observations, ensure_ascii=False, indent=2),
                     },
                 ]
                 yield runtime_status(task_id, "answering", "Формирую ответ...")
@@ -763,7 +768,13 @@ def create_app() -> FastAPI:
                         "generation_stats": generation_stats.summary(),
                     },
                 )
-                asyncio.create_task(runtime.complete_postprocessing(task_id, content))
+                asyncio.create_task(
+                    runtime.complete_postprocessing(
+                        task_id,
+                        content,
+                        reflect=bool(settings.enable_reflection),
+                    )
+                )
                 yield sse(
                     "done",
                     {
@@ -878,8 +889,7 @@ def create_app() -> FastAPI:
                     *messages,
                     {
                         "role": "user",
-                        "content": "tool_observations:\n"
+                        "content": runtime.format_tool_observations_for_model(tool_observations),
-                        + json.dumps(tool_observations, ensure_ascii=False, indent=2),
                     },
                 ]
                 yield runtime_status(task_id, "answering", "Формирую ответ...")
@@ -934,7 +944,13 @@ def create_app() -> FastAPI:
                         "generation_stats": generation_stats.summary(),
                     },
                 )
-                asyncio.create_task(runtime.complete_postprocessing(task_id, content))
+                asyncio.create_task(
+                    runtime.complete_postprocessing(
+                        task_id,
+                        content,
+                        reflect=bool(settings.enable_reflection),
+                    )
+                )
                 yield sse(
                     "done",
                     {

duck_core/config.py

@@ -23,6 +23,7 @@ class Settings:
     max_memory_tokens: int = 8000
     max_skill_tokens: int = 6000
     qdrant_url: str = "http://127.0.0.1:6333"
+    enable_reflection: int = 0
     skip_live_llm_tests: int = 0
 
     @property
@@ -52,5 +53,6 @@ def get_settings() -> Settings:
         max_memory_tokens=int(os.getenv("DUCK_MAX_MEMORY_TOKENS", "8000")),
         max_skill_tokens=int(os.getenv("DUCK_MAX_SKILL_TOKENS", "6000")),
         qdrant_url=os.getenv("QDRANT_URL", "http://127.0.0.1:6333"),
+        enable_reflection=int(os.getenv("DUCK_ENABLE_REFLECTION", "0")),
         skip_live_llm_tests=int(os.getenv("DUCK_SKIP_LIVE_LLM_TESTS", "0")),
     )

duck_core/runtime_loop.py

@@ -19,6 +19,7 @@ from duck_core.tools.gateway import ToolGateway
 
 logger = logging.getLogger(__name__)
 ACTION_DIRECTIVE_SCHEMA = load_json_schema("duck_core/schemas/action_directive.schema.json")
+MAX_TOOL_OBSERVATION_TEXT_CHARS = 2000
 
 
 @dataclass
@@ -96,8 +97,7 @@ class RuntimeLoop:
                     *messages,
                     {
                         "role": "user",
-                        "content": "tool_observations:\n"
+                        "content": self.format_tool_observations_for_model(tool_observations),
-                        + json.dumps(tool_observations, ensure_ascii=False, indent=2),
                     },
                 ]
             await self.event_store.append(
@@ -232,8 +232,7 @@ class RuntimeLoop:
                 *messages,
                 {
                     "role": "user",
-                    "content": "tool_observations:\n"
+                    "content": self.format_tool_observations_for_model(tool_observations),
-                    + json.dumps(tool_observations, ensure_ascii=False, indent=2),
                 },
             ]
             await self.event_store.append(task_id, "model_call_started", {"role": "thinker"})
@@ -394,9 +393,13 @@ class RuntimeLoop:
             "tool": tool_name,
             "reason": action.get("reason"),
             "decision": decision,
+            "action": action,
             "result": result_payload,
         }
 
+    def _action_key(self, action: dict[str, Any]) -> str:
+        return json.dumps(action, ensure_ascii=False, sort_keys=True, separators=(",", ":"))
+
     async def _approval_action_index(self, task_id: str, action: dict[str, Any]) -> int:
         events = await self.event_store.list_events(task_id)
         for event in reversed(events):
@@ -413,6 +416,7 @@ class RuntimeLoop:
         messages: list[dict[str, str]],
         workspace: str | None,
         start_index: int = 1,
+        seen_action_keys: set[str] | None = None,
     ) -> list[dict[str, Any]]:
         try:
             await self.event_store.append(task_id, "model_call_started", {"role": "action"})
@@ -443,6 +447,21 @@ class RuntimeLoop:
                     {"index": index, "ok": False, "error": "Action must be an object"}
                 )
                 continue
+            action_key = self._action_key(action)
+            if seen_action_keys is not None and action_key in seen_action_keys:
+                await self.event_store.append(
+                    task_id,
+                    "tool_call_skipped",
+                    {
+                        "index": index,
+                        "tool": str(action.get("tool", "")),
+                        "reason": "duplicate_action",
+                        "action": action,
+                    },
+                )
+                continue
+            if seen_action_keys is not None:
+                seen_action_keys.add(action_key)
             tool_name = str(action.get("tool", ""))
             await self.event_store.append(
                 task_id,
@@ -478,6 +497,7 @@ class RuntimeLoop:
                         "index": index,
                         "tool": tool_name,
                         "reason": action.get("reason"),
+                        "action": action,
                         "requires_approval": True,
                         "approval_id": approval.approval_id if approval else None,
                         "result": result_payload,
@@ -494,11 +514,39 @@ class RuntimeLoop:
                     "index": index,
                     "tool": tool_name,
                     "reason": action.get("reason"),
+                    "action": action,
                     "result": result_payload,
                 }
             )
         return observations
 
+    def format_tool_observations_for_model(self, observations: list[dict[str, Any]]) -> str:
+        return "tool_observations:\n" + json.dumps(
+            self.compact_tool_observations_for_model(observations),
+            ensure_ascii=False,
+            indent=2,
+        )
+
+    def compact_tool_observations_for_model(
+        self, observations: list[dict[str, Any]]
+    ) -> list[dict[str, Any]]:
+        return [self._compact_observation_for_model(observation) for observation in observations]
+
+    def _compact_observation_for_model(self, value: Any) -> Any:
+        if isinstance(value, dict):
+            return {key: self._compact_observation_for_model(item) for key, item in value.items()}
+        if isinstance(value, list):
+            return [self._compact_observation_for_model(item) for item in value]
+        if isinstance(value, str) and len(value) > MAX_TOOL_OBSERVATION_TEXT_CHARS:
+            half = MAX_TOOL_OBSERVATION_TEXT_CHARS // 2
+            omitted = len(value) - MAX_TOOL_OBSERVATION_TEXT_CHARS
+            return (
+                value[:half]
+                + f"\n... [truncated {omitted} chars for model context] ...\n"
+                + value[-half:]
+            )
+        return value
+
     async def _append_command_audit(
         self,
         task_id: str,
@@ -538,14 +586,18 @@ class RuntimeLoop:
         initial_observations: list[dict[str, Any]] | None = None,
     ) -> list[dict[str, Any]]:
         all_observations = list(initial_observations or [])
+        seen_action_keys = {
+            self._action_key(observation["action"])
+            for observation in all_observations
+            if isinstance(observation.get("action"), dict)
+        }
         current_messages = messages
         if all_observations:
             current_messages = [
                 *messages,
                 {
                     "role": "user",
-                    "content": "tool_observations:\n"
+                    "content": self.format_tool_observations_for_model(all_observations),
-                    + json.dumps(all_observations, ensure_ascii=False, indent=2),
                 },
             ]
         for _ in range(self.max_tool_iterations):
@@ -554,6 +606,7 @@ class RuntimeLoop:
                 current_messages,
                 workspace,
                 start_index=len(all_observations) + 1,
+                seen_action_keys=seen_action_keys,
             )
             if not observations:
                 return all_observations
@@ -564,8 +617,7 @@ class RuntimeLoop:
                 *messages,
                 {
                     "role": "user",
-                    "content": "tool_observations:\n"
+                    "content": self.format_tool_observations_for_model(all_observations),
-                    + json.dumps(all_observations, ensure_ascii=False, indent=2),
                 },
             ]
         await self.event_store.append(

tests/smoke/test_api_stream_chat.py

@@ -113,6 +113,7 @@ def test_stream_chat_forwards_reasoning_toggle_to_thinker(tmp_path, monkeypatch)
 
 def test_stream_chat_runs_memory_policy_and_reflection_after_completion(tmp_path, monkeypatch):
     monkeypatch.setenv("DUCK_DB_PATH", str(tmp_path / "duck.sqlite3"))
+    monkeypatch.setenv("DUCK_ENABLE_REFLECTION", "1")
 
     async def fake_chat(self, role, messages, temperature=None, max_output_tokens=None, response_format=None):
         if role == "action":

tests/smoke/test_runtime_tools.py

@@ -176,6 +176,41 @@ class FakeMalformedActionModelClient:
         )
 
 
+class FakeRepeatingActionModelClient:
+    async def chat(self, role, messages):
+        if role == "action":
+            return ModelResponse(
+                role=role,
+                model="local-main",
+                content=json.dumps(
+                    {
+                        "kind": "action_directive",
+                        "intent": "repeat same action",
+                        "risk_level": "low",
+                        "actions": [
+                            {
+                                "tool": "file_read",
+                                "args": {"path": "note.txt"},
+                                "reason": "Read requested file",
+                            }
+                        ],
+                    }
+                ),
+                reasoning_content=None,
+                raw={},
+                latency_ms=5.0,
+            )
+        assert role == "thinker"
+        return ModelResponse(
+            role=role,
+            model="local-main",
+            content="Final answer from first observation.",
+            reasoning_content=None,
+            raw={},
+            latency_ms=12.0,
+        )
+
+
 @pytest.mark.asyncio
 async def test_runtime_executes_action_directive_tool_and_finishes_with_observation(tmp_path):
     (tmp_path / "note.txt").write_text("hello from tool")
@@ -253,6 +288,48 @@ async def test_runtime_rejects_malformed_action_directive_before_tools(tmp_path)
     assert not any(event.event_type == "tool_call_started" for event in events)
 
 
+def test_runtime_compacts_large_tool_observations_for_model_context(tmp_path):
+    db_path = str(tmp_path / "duck.sqlite3")
+    task_store = TaskStore(db_path)
+    event_store = EventStore(db_path)
+    loop = RuntimeLoop(task_store, event_store, FakeToolModelClient())
+
+    compact = loop.format_tool_observations_for_model([
+        {
+            "tool": "shell_exec_safe",
+            "result": {
+                "ok": True,
+                "output": "A" * 2500 + "KEEP_TAIL",
+                "metadata": {"command": "ls /tmp"},
+            },
+        }
+    ])
+
+    assert "tool_observations" in compact
+    assert "truncated" in compact
+    assert "KEEP_TAIL" in compact
+    assert len(compact) < 2300
+
+
+@pytest.mark.asyncio
+async def test_runtime_skips_duplicate_action_within_same_task(tmp_path):
+    (tmp_path / "note.txt").write_text("hello once")
+    db_path = str(tmp_path / "duck.sqlite3")
+    task_store = TaskStore(db_path)
+    event_store = EventStore(db_path)
+    loop = RuntimeLoop(task_store, event_store, FakeRepeatingActionModelClient())
+
+    result = await loop.run_chat("read note.txt", str(tmp_path), debug=True)
+    events = await event_store.list_events(result.task_id)
+    finished_tools = [event for event in events if event.event_type == "tool_call_finished"]
+    skipped_tools = [event for event in events if event.event_type == "tool_call_skipped"]
+
+    assert result.status == "completed"
+    assert len(finished_tools) == 1
+    assert len(skipped_tools) == 1
+    assert skipped_tools[0].payload["reason"] == "duplicate_action"
+
+
 class FakeApprovalModelClient:
     async def chat(self, role, messages):
         if role == "action":