node1: add universal file tool, gateway document delivery, and sync runbook

docs/runbooks/NODE1_FILE_TOOL_SYNC_RUNBOOK.md

@@ -0,0 +1,124 @@
+# NODE1 File Tool Sync Runbook
+
+## Scope
+This runbook documents:
+- how NODE1 runtime drift was identified and synchronized,
+- how universal `file_tool` was introduced into the **actual** NODE1 router stack,
+- how to deploy and rollback safely.
+
+## Canonical Runtime (NODE1)
+- Host: `144.76.224.179`
+- Runtime repo: `/opt/microdao-daarion`
+- Compose: `/opt/microdao-daarion/docker-compose.node1.yml`
+- Router container: `dagi-router-node1`
+- Gateway container: `dagi-gateway-node1`
+- Router API contract: `POST /v1/agents/{agent_id}/infer` (not `/route`)
+
+## Drift Findings (before sync)
+The laptop had multiple repos; only this repo matches NODE1 architecture:
+- `/Users/apple/github-projects/microdao-daarion`
+
+Critical files were drifted against NODE1 runtime (15 files):
+- `docker-compose.node1.yml`
+- `gateway-bot/agent_registry.json`
+- `gateway-bot/http_api.py`
+- `gateway-bot/router_client.py`
+- `gateway-bot/senpai_prompt.txt`
+- `http_api.py`
+- `services/router/agent_tools_config.py`
+- `services/router/main.py`
+- `services/router/memory_retrieval.py`
+- `services/router/requirements.txt`
+- `services/router/router-config.yml`
+- `services/router/tool_manager.py`
+- `services/senpai-md-consumer/senpai/md_consumer/main.py`
+- `services/senpai-md-consumer/senpai/md_consumer/publisher.py`
+- `services/swapper-service/requirements.txt`
+
+## Sync Procedure (NODE1 -> Laptop)
+1. Snapshot and compare hashes:
+   - compute SHA256 for the critical file list on laptop and NODE1.
+2. Backup local copies:
+   - `rollback_backups/node1_sync_<timestamp>/...`
+3. Copy runtime files from NODE1 to laptop:
+   - `scp root@144.76.224.179:/opt/microdao-daarion/<file> ...`
+4. Verify 1:1 hash match.
+
+## File Tool Implementation
+Implemented in actual NODE1 stack (`services/router/*` + gateway):
+
+### Added actions
+- `csv_create`
+- `csv_update`
+- `json_export`
+- `yaml_export`
+- `zip_bundle`
+- `docx_create`
+- `docx_update`
+- `pdf_merge`
+- `pdf_split`
+- `pdf_fill`
+
+### Standard output contract
+For file-producing tool calls, router now propagates:
+- `file_base64`
+- `file_name`
+- `file_mime`
+- `message` (inside tool result payload)
+
+### Gateway behavior
+`gateway-bot/http_api.py` now sends Telegram `sendDocument` when file fields are present.
+
+## Changed Files
+- `services/router/requirements.txt`
+- `services/router/agent_tools_config.py`
+- `services/router/tool_manager.py`
+- `services/router/main.py`
+- `gateway-bot/router_client.py`
+- `gateway-bot/http_api.py`
+
+## Deployment Steps (NODE1)
+1. Backup target files on NODE1 before each step.
+2. Sync updated files to `/opt/microdao-daarion`.
+3. Compile checks:
+   - `python3 -m py_compile services/router/tool_manager.py services/router/main.py services/router/agent_tools_config.py gateway-bot/router_client.py gateway-bot/http_api.py`
+4. Rebuild/restart runtime services:
+   - `docker compose -f docker-compose.node1.yml up -d --build --no-deps router gateway`
+5. Health checks:
+   - `curl http://127.0.0.1:9102/health`
+   - `curl http://127.0.0.1:9300/health`
+
+## Smoke Tests
+Run inside `dagi-router-node1` to validate actions deterministically:
+- CSV create/update
+- JSON/YAML export
+- ZIP bundle
+- DOCX create/update
+- PDF merge/split/fill
+
+Also verify infer endpoint still works:
+- `POST http://127.0.0.1:9102/v1/agents/devtools/infer`
+
+## Backups Created During This Work
+- `rollback_backups/file_tool_step1_20260215_011637/...`
+- `rollback_backups/file_tool_step2_tool_manager.py.bak_20260215_012029`
+- `rollback_backups/file_tool_step3_tool_manager.py.bak_20260215_012200`
+- `rollback_backups/file_tool_step4_tool_manager.py.bak_20260215_012309`
+
+## Rollback (NODE1)
+```bash
+cd /opt/microdao-daarion
+cp rollback_backups/file_tool_step1_20260215_011637/services/router/requirements.txt services/router/requirements.txt
+cp rollback_backups/file_tool_step1_20260215_011637/services/router/agent_tools_config.py services/router/agent_tools_config.py
+cp rollback_backups/file_tool_step1_20260215_011637/services/router/tool_manager.py services/router/tool_manager.py
+cp rollback_backups/file_tool_step1_20260215_011637/services/router/main.py services/router/main.py
+cp rollback_backups/file_tool_step1_20260215_011637/gateway-bot/router_client.py gateway-bot/router_client.py
+cp rollback_backups/file_tool_step1_20260215_011637/gateway-bot/http_api.py gateway-bot/http_api.py
+
+docker compose -f docker-compose.node1.yml up -d --build --no-deps router gateway
+```
+
+## Notes
+- `docker-compose.node1.yml` may warn about deprecated `version` key; this is non-blocking.
+- Avoid `--remove-orphans` unless explicitly intended.
+- Use `--no-deps` for targeted router/gateway rollout to avoid unrelated service churn.

gateway-bot/http_api.py

@@ -57,6 +57,172 @@ LAST_PENDING_STATE: Dict[str, Dict[str, Any]] = {}
 PENDING_STATE_TTL = 1800  # 30 minutes
 
 
+# Per-user language preference cache (chat_id:user_id -> {lang, ts})
+USER_LANGUAGE_PREFS: Dict[str, Dict[str, Any]] = {}
+USER_LANGUAGE_PREF_TTL = 30 * 24 * 3600  # 30 days
+
+# Recent photo context for follow-up questions in chat (agent:chat:user -> {file_id, ts})
+RECENT_PHOTO_CONTEXT: Dict[str, Dict[str, Any]] = {}
+RECENT_PHOTO_TTL = 30 * 60  # 30 minutes
+
+
+def _cleanup_recent_photo_context() -> None:
+    now = time.time()
+    expired = [k for k, v in RECENT_PHOTO_CONTEXT.items() if now - float(v.get("ts", 0)) > RECENT_PHOTO_TTL]
+    for k in expired:
+        del RECENT_PHOTO_CONTEXT[k]
+
+
+def _set_recent_photo_context(agent_id: str, chat_id: str, user_id: str, file_id: str) -> None:
+    _cleanup_recent_photo_context()
+    key = f"{agent_id}:{chat_id}:{user_id}"
+    RECENT_PHOTO_CONTEXT[key] = {"file_id": file_id, "ts": time.time()}
+
+
+def _get_recent_photo_file_id(agent_id: str, chat_id: str, user_id: str) -> Optional[str]:
+    _cleanup_recent_photo_context()
+    key = f"{agent_id}:{chat_id}:{user_id}"
+    rec = RECENT_PHOTO_CONTEXT.get(key)
+    if not rec:
+        return None
+    return rec.get("file_id")
+
+
+def _looks_like_photo_followup(text: str) -> bool:
+    if not text:
+        return False
+    t = text.strip().lower()
+    markers = [
+        "що ти бачиш", "що на фото", "що на зображенні", "опиши фото", "подивись фото",
+        "what do you see", "what is in the image", "describe the photo",
+        "что ты видишь", "что на фото", "опиши фото", "посмотри фото",
+    ]
+    return any(m in t for m in markers)
+
+
+def _cleanup_user_language_prefs() -> None:
+    now = time.time()
+    expired = [k for k, v in USER_LANGUAGE_PREFS.items() if now - float(v.get("ts", 0)) > USER_LANGUAGE_PREF_TTL]
+    for k in expired:
+        del USER_LANGUAGE_PREFS[k]
+
+
+def _normalize_lang_code(raw: Optional[str]) -> Optional[str]:
+    if not raw:
+        return None
+    code = str(raw).strip().lower().replace("_", "-")
+    if code.startswith("uk"):
+        return "uk"
+    if code.startswith("ru"):
+        return "ru"
+    if code.startswith("en"):
+        return "en"
+    return None
+
+
+def _detect_language_from_text(text: str) -> Optional[str]:
+    if not text:
+        return None
+    t = text.lower()
+    letters = [ch for ch in t if ch.isalpha()]
+    if not letters:
+        return None
+
+    cyr = sum(1 for ch in letters if "а" <= ch <= "я" or ch in "іїєґё")
+    lat = sum(1 for ch in letters if "a" <= ch <= "z")
+
+    if cyr >= 3 and cyr >= lat:
+        # Ukrainian-specific letters strongly indicate Ukrainian.
+        if any(ch in t for ch in "іїєґ"):
+            return "uk"
+        # Russian-specific letters/symbols.
+        if any(ch in t for ch in "ёыэъ"):
+            return "ru"
+        # Soft lexical preference.
+        uk_hits = sum(1 for w in ("що", "який", "дякую", "будь", "будь ласка", "привіт") if w in t)
+        ru_hits = sum(1 for w in ("что", "какой", "спасибо", "пожалуйста", "привет") if w in t)
+        if uk_hits > ru_hits:
+            return "uk"
+        if ru_hits > uk_hits:
+            return "ru"
+        return "uk"
+
+    if lat >= 3 and lat > cyr:
+        return "en"
+
+    return None
+
+
+def resolve_preferred_language(chat_id: str, user_id: str, text: str, telegram_lang_code: Optional[str]) -> str:
+    _cleanup_user_language_prefs()
+    key = f"{chat_id}:{user_id}"
+
+    text_lang = _detect_language_from_text(text)
+    tg_lang = _normalize_lang_code(telegram_lang_code)
+    cached_lang = USER_LANGUAGE_PREFS.get(key, {}).get("lang")
+
+    preferred = text_lang or tg_lang or cached_lang or "uk"
+    USER_LANGUAGE_PREFS[key] = {"lang": preferred, "ts": time.time()}
+    return preferred
+
+
+def preferred_language_label(lang: str) -> str:
+    return {
+        "uk": "Ukrainian",
+        "ru": "Russian",
+        "en": "English",
+    }.get((lang or "").lower(), "Ukrainian")
+
+
+def _extract_preferred_language_from_profile_fact(fact: Optional[Dict[str, Any]]) -> Optional[str]:
+    if not isinstance(fact, dict):
+        return None
+    data = fact.get("fact_value_json")
+    if not isinstance(data, dict):
+        return None
+    preferred = _normalize_lang_code(data.get("preferred_language"))
+    if preferred:
+        return preferred
+    return _normalize_lang_code(data.get("language_code"))
+
+
+async def resolve_preferred_language_persistent(
+    chat_id: str,
+    user_id: str,
+    text: str,
+    telegram_lang_code: Optional[str],
+    team_id: Optional[str] = None,
+) -> str:
+    """Resolve language with memory-service fallback for post-restart continuity."""
+    _cleanup_user_language_prefs()
+    key = f"{chat_id}:{user_id}"
+
+    text_lang = _detect_language_from_text(text)
+    tg_lang = _normalize_lang_code(telegram_lang_code)
+    cached_lang = USER_LANGUAGE_PREFS.get(key, {}).get("lang")
+
+    if text_lang or tg_lang or cached_lang:
+        preferred = text_lang or tg_lang or cached_lang or "uk"
+        USER_LANGUAGE_PREFS[key] = {"lang": preferred, "ts": time.time()}
+        return preferred
+
+    try:
+        fact = await memory_client.get_fact(
+            user_id=f"tg:{user_id}",
+            fact_key="profile",
+            team_id=team_id,
+        )
+        fact_lang = _extract_preferred_language_from_profile_fact(fact)
+        if fact_lang:
+            USER_LANGUAGE_PREFS[key] = {"lang": fact_lang, "ts": time.time()}
+            return fact_lang
+    except Exception as e:
+        logger.debug(f"preferred language fact lookup failed: {e}")
+
+    USER_LANGUAGE_PREFS[key] = {"lang": "uk", "ts": time.time()}
+    return "uk"
+
+
 def _pending_state_cleanup():
     now = time.time()
     expired = [cid for cid, rec in LAST_PENDING_STATE.items() if now - rec.get('ts', 0) > PENDING_STATE_TTL]
@@ -483,9 +649,36 @@ async def agromatrix_telegram_webhook(update: TelegramUpdate):
     if user_id and user_id in op_ids:
         is_ops = True
 
-    # Operator NL or slash commands -> handle via Stepan handler
+    # Operator NL or operator slash commands -> handle via Stepan handler.
-    if is_slash or is_ops:
+    # Important: do NOT treat generic slash commands (/start, /agromatrix) as operator commands,
+    # otherwise regular users will see "Недостатньо прав" or Stepan errors.
+    operator_slash_cmds = {
+        "whoami",
+        "pending",
+        "pending_show",
+        "approve",
+        "reject",
+        "apply_dict",
+        "pending_stats",
+    }
+    slash_cmd = ""
+    if is_slash:
+        try:
+            slash_cmd = (msg_text.strip().split()[0].lstrip("/").strip().lower())
+        except Exception:
+            slash_cmd = ""
+    is_operator_slash = bool(slash_cmd) and slash_cmd in operator_slash_cmds
+
+    # Stepan handler currently depends on ChatOpenAI (OPENAI_API_KEY). If key is not configured,
+    # never route production traffic there (avoid "Помилка обробки..." and webhook 5xx).
+    stepan_enabled = bool(os.getenv("OPENAI_API_KEY", "").strip())
+    if stepan_enabled and (is_ops or is_operator_slash):
         return await handle_stepan_message(update, AGROMATRIX_CONFIG)
+    if (is_ops or is_operator_slash) and not stepan_enabled:
+        logger.warning(
+            "Stepan handler disabled (OPENAI_API_KEY missing); falling back to Router pipeline "
+            f"for chat_id={chat_id}, user_id={user_id}, slash_cmd={slash_cmd!r}"
+        )
 
     # General conversation -> standard Router pipeline (like all other agents)
     return await handle_telegram_webhook(AGROMATRIX_CONFIG, update)
@@ -611,14 +804,37 @@ def extract_bot_mentions(text: str) -> List[str]:
     return mentions
 
 
+def should_force_detailed_reply(text: str) -> bool:
+    """Soft signal: user explicitly asks for details/long format."""
+    if not text:
+        return False
+    lower = text.strip().lower()
+    detail_markers = [
+        "детально", "подробно", "розгорну", "розпиши", "по всіх пунктах",
+        "step by step", "покроково", "з прикладами", "глибоко", "deep dive",
+        "full", "повний розбір", "максимально детально",
+    ]
+    return any(m in lower for m in detail_markers)
+
+
 def should_force_concise_reply(text: str) -> bool:
-    """Якщо коротке або без питального знаку — просимо агента відповісти стисло."""
+    """Soft concise mode by default, unless user asks for detailed answer."""
     if not text:
         return True
+
     stripped = text.strip()
-    if len(stripped) <= 120 and "?" not in stripped:
+    if not stripped:
         return True
-    return False
+
+    if should_force_detailed_reply(stripped):
+        return False
+
+    # Very long user request usually means they expect context-aware answer.
+    if len(stripped) > 700:
+        return False
+
+    # For regular Q&A in chat keep first response concise by default.
+    return True
 
 
 COMPLEX_REASONING_KEYWORDS = [
@@ -808,7 +1024,9 @@ async def process_photo(
     user_id: str,
     username: str,
     dao_id: str,
-    photo: Dict[str, Any]
+    photo: Dict[str, Any],
+    caption_override: Optional[str] = None,
+    bypass_media_gate: bool = False,
 ) -> Dict[str, Any]:
     """
     Універсальна функція для обробки фото для будь-якого агента.
@@ -833,9 +1051,10 @@ async def process_photo(
         return {"ok": False, "error": "No file_id in photo"}
     
     logger.info(f"{agent_config.name}: Photo from {username} (tg:{user_id}), file_id: {file_id}")
+    _set_recent_photo_context(agent_config.agent_id, chat_id, user_id, file_id)
     
     # Get caption for media question check
-    caption = (update.message or {}).get("caption") or ""
+    caption = caption_override if caption_override is not None else ((update.message or {}).get("caption") or "")
     chat = (update.message or {}).get("chat", {})
     chat_type = chat.get("type", "private")
     is_private_chat = chat_type == "private"
@@ -843,7 +1062,7 @@ async def process_photo(
     
     # BEHAVIOR POLICY v1: Media-no-comment
     # Check if photo has a question/request in caption
-    if not is_private_chat and not is_training:
+    if not bypass_media_gate and not is_private_chat and not is_training:
         has_question = detect_media_question(caption)
         if not has_question:
             logger.info(f"🔇 MEDIA-NO-COMMENT: Photo without question. Agent {agent_config.agent_id} NOT responding.")
@@ -961,10 +1180,10 @@ async def process_photo(
             else:
                 await send_telegram_message(
                     chat_id,
-                    "Не вдалося отримати опис зображення.",
+                    "Не вдалося коректно обробити фото. Спробуйте інше фото або додайте короткий опис, що саме перевірити.",
                     telegram_token
                 )
-                return {"ok": False, "error": "No description in response"}
+                return {"ok": True, "handled": True, "reason": "vision_empty_response"}
         else:
             error_msg = response.get("error", "Unknown error") if isinstance(response, dict) else "Router error"
             logger.error(f"{agent_config.name}: Vision-8b error: {error_msg}")
@@ -1338,6 +1557,13 @@ async def handle_telegram_webhook(
     # Get DAO ID for this chat
     dao_id = get_dao_id(chat_id, "telegram", agent_id=agent_config.agent_id)
     
+    initial_preferred_lang = resolve_preferred_language(
+        chat_id=chat_id,
+        user_id=user_id,
+        text=update.message.get("text", ""),
+        telegram_lang_code=from_user.get("language_code"),
+    )
+
     # Оновлюємо факти про користувача/агента для побудови графу пам'яті
     asyncio.create_task(
         memory_client.upsert_fact(
@@ -1348,6 +1574,7 @@ async def handle_telegram_webhook(
                 "first_name": first_name,
                 "last_name": last_name,
                 "language_code": from_user.get("language_code"),
+                "preferred_language": initial_preferred_lang,
                 "is_bot": is_sender_bot,
             },
             team_id=dao_id,
@@ -1919,8 +2146,7 @@ async def handle_telegram_webhook(
         result = await process_photo(
             agent_config, update, chat_id, user_id, username, dao_id, photo
         )
-        if result.get("ok"):
+        return result
-            return result
     
     # Check if it's a voice message
     voice = update.message.get("voice")
@@ -1947,6 +2173,26 @@ async def handle_telegram_webhook(
     if not text:
         text = update.message.get("text", "")
         caption = update.message.get("caption", "")
+
+    # If user asks about a recently sent photo, run vision on cached photo file_id.
+    if text and _looks_like_photo_followup(text):
+        recent_file_id = _get_recent_photo_file_id(agent_config.agent_id, chat_id, user_id)
+        if recent_file_id:
+            logger.info(
+                f"{agent_config.name}: Detected follow-up photo question; using cached file_id={recent_file_id}"
+            )
+            followup_result = await process_photo(
+                agent_config=agent_config,
+                update=update,
+                chat_id=chat_id,
+                user_id=user_id,
+                username=username,
+                dao_id=dao_id,
+                photo={"file_id": recent_file_id},
+                caption_override=text,
+                bypass_media_gate=True,
+            )
+            return followup_result
         
         if not text and not caption:
             # Check for unsupported message types and silently ignore
@@ -2149,9 +2395,10 @@ async def handle_telegram_webhook(
         return {"ok": True, "ack": True, "reason": respond_reason}
     
     # FULL: proceed with LLM/Router call
-    # For prober requests, respond but don't send to Telegram
+    # For prober requests, skip LLM/Router entirely to save tokens
     if is_prober:
-        logger.info(f"\U0001f9ea PROBER: Agent {agent_config.agent_id} responding to prober request. Reason: {respond_reason}")
+        logger.info(f"\U0001f9ea PROBER: Agent {agent_config.agent_id} responding to prober (no LLM call). Reason: {respond_reason}")
+        return {"ok": True, "agent": agent_config.agent_id, "prober": True, "response_preview": "[prober-skip-llm]"}
     else:
         logger.info(f"\u2705 SOWA: Agent {agent_config.agent_id} WILL respond (FULL). Reason: {respond_reason}")
     
@@ -2183,6 +2430,15 @@ async def handle_telegram_webhook(
     else:
         message_with_context = f"{training_prefix}{text}"
     
+    preferred_lang = await resolve_preferred_language_persistent(
+        chat_id=chat_id,
+        user_id=user_id,
+        text=text or "",
+        telegram_lang_code=from_user.get("language_code"),
+        team_id=dao_id,
+    )
+    preferred_lang_label = preferred_language_label(preferred_lang)
+
     # Build request to Router
     system_prompt = agent_config.system_prompt
     logger.info(f"📝 {agent_config.name} system_prompt length: {len(system_prompt) if system_prompt else 0} chars")
@@ -2206,6 +2462,9 @@ async def handle_telegram_webhook(
             "mentioned_bots": mentioned_bots,
             "requires_complex_reasoning": needs_complex_reasoning,
             "is_reply_to_agent": is_reply_to_agent,
+            "is_training_group": is_training_group,
+            "preferred_response_language": preferred_lang,
+            "preferred_response_language_label": preferred_lang_label,
         },
         "context": {
             "agent_name": agent_config.name,
@@ -2218,17 +2477,30 @@ async def handle_telegram_webhook(
         },
     }
     
+    if should_force_detailed_reply(text):
+        router_request["metadata"]["force_detailed"] = True
+
     if should_force_concise_reply(text):
         # IMPORTANT: preserve conversation context! Only append concise instruction
+        router_request["metadata"]["force_concise"] = True
         router_request["message"] = (
             router_request["message"]
-            + "\n\n(Інструкція: дай максимально коротку відповідь, якщо не просили деталей "
+            + "\n\n(Інструкція: спочатку дай коротку відповідь по суті (1-3 абзаци), "
-            "і дочекайся додаткового питання.)"
+            "а якщо користувач попросить — розгорни детально.)"
+            + f"\n(Мова відповіді: {preferred_lang_label}.)"
+            + "\n(Не потрібно щоразу представлятися по імені або писати шаблонне: 'чим можу допомогти'.)"
         )
     
     if needs_complex_reasoning:
         router_request["metadata"]["provider"] = "cloud_deepseek"
         router_request["metadata"]["reason"] = "auto_complex"
+
+    if not should_force_concise_reply(text):
+        router_request["message"] = (
+            router_request["message"]
+            + f"\n\n(Мова відповіді: {preferred_lang_label}.)"
+            + "\n(Не потрібно щоразу представлятися по імені або писати шаблонне: 'чим можу допомогти'.)"
+        )
     
     # Send to Router
     logger.info(f"Sending to Router: agent={agent_config.agent_id}, dao={dao_id}, user=tg:{user_id}")
@@ -2238,6 +2510,9 @@ async def handle_telegram_webhook(
     if isinstance(response, dict) and response.get("ok"):
         answer_text = response.get("data", {}).get("text") or response.get("response", "")
         image_base64 = response.get("image_base64") or response.get("data", {}).get("image_base64")
+        file_base64 = response.get("file_base64") or response.get("data", {}).get("file_base64")
+        file_name = response.get("file_name") or response.get("data", {}).get("file_name") or "artifact.bin"
+        file_mime = response.get("file_mime") or response.get("data", {}).get("file_mime") or "application/octet-stream"
         
         # Debug logging
         logger.info(f"📦 Router response: {len(answer_text)} chars, model={response.get('model')}, backend={response.get('backend')}")
@@ -2246,7 +2521,9 @@ async def handle_telegram_webhook(
             logger.info(f"🖼️ Received image_base64: {len(image_base64)} chars")
         else:
             logger.debug("⚠️ No image_base64 in response")
-        
+        if file_base64:
+            logger.info(f"📄 Received file_base64: {len(file_base64)} chars ({file_name})")
+
         # Check for NO_OUTPUT (LLM decided not to respond)
         if is_no_output_response(answer_text):
             logger.info(f"🔇 NO_OUTPUT: Agent {agent_config.agent_id} returned empty/NO_OUTPUT. Not sending to Telegram.")
@@ -2305,8 +2582,27 @@ async def handle_telegram_webhook(
             logger.info(f"🧪 PROBER: Skipping Telegram send for prober request. Response: {answer_text[:100]}...")
             return {"ok": True, "agent": agent_config.agent_id, "prober": True, "response_preview": answer_text[:100]}
         
+        # Send file artifact if generated
+        if file_base64:
+            try:
+                file_bytes = base64.b64decode(file_base64)
+                token = telegram_token or os.getenv("TELEGRAM_BOT_TOKEN")
+                url = f"https://api.telegram.org/bot{token}/sendDocument"
+                caption = answer_text[:1024] if answer_text else ""
+                safe_name = str(file_name).split("/")[-1].split("\\")[-1] or "artifact.bin"
+                async with httpx.AsyncClient() as client:
+                    files = {"document": (safe_name, BytesIO(file_bytes), file_mime)}
+                    data = {"chat_id": chat_id}
+                    if caption:
+                        data["caption"] = caption
+                    response_doc = await client.post(url, files=files, data=data, timeout=45.0)
+                    response_doc.raise_for_status()
+                    logger.info(f"✅ Sent generated document to Telegram chat {chat_id}: {safe_name}")
+            except Exception as e:
+                logger.error(f"❌ Failed to send document to Telegram: {e}")
+                await send_telegram_message(chat_id, answer_text or "Файл згенеровано, але не вдалося надіслати документ.", telegram_token)
         # Send image if generated
-        if image_base64:
+        elif image_base64:
             try:
                 # Decode base64 image
                 image_bytes = base64.b64decode(image_base64)
@@ -2344,6 +2640,7 @@ async def handle_telegram_webhook(
             agent_metadata={
                 "mentioned_bots": mentioned_bots,
                 "requires_complex_reasoning": needs_complex_reasoning,
+                "preferred_language": preferred_lang,
             },
             username=username,
         )

gateway-bot/router_client.py

@@ -20,6 +20,46 @@ except ImportError:
 # Router configuration from environment
 ROUTER_BASE_URL = os.getenv("ROUTER_URL", "http://127.0.0.1:9102")
 ROUTER_TIMEOUT = float(os.getenv("ROUTER_TIMEOUT", "180.0"))
+GATEWAY_MAX_TOKENS_DEFAULT = int(os.getenv("GATEWAY_MAX_TOKENS_DEFAULT", "700"))
+GATEWAY_MAX_TOKENS_CONCISE = int(os.getenv("GATEWAY_MAX_TOKENS_CONCISE", "220"))
+GATEWAY_MAX_TOKENS_TRAINING = int(os.getenv("GATEWAY_MAX_TOKENS_TRAINING", "900"))
+GATEWAY_TEMPERATURE_DEFAULT = float(os.getenv("GATEWAY_TEMPERATURE_DEFAULT", "0.4"))
+GATEWAY_MAX_TOKENS_SENPAI_DEFAULT = int(os.getenv("GATEWAY_MAX_TOKENS_SENPAI_DEFAULT", "320"))
+GATEWAY_MAX_TOKENS_DETAILED = int(os.getenv("GATEWAY_MAX_TOKENS_DETAILED", "900"))
+
+
+def _apply_runtime_communication_guardrails(system_prompt: str, metadata: Dict[str, Any]) -> str:
+    """Apply global communication constraints for all agents in Telegram flows."""
+    if not system_prompt:
+        return system_prompt
+
+    lang_label = (metadata or {}).get("preferred_response_language_label") or "user language"
+    guardrail = (
+        "\n\n[GLOBAL COMMUNICATION POLICY]\n"
+        "1) Do not introduce yourself by name in every message.\n"
+        "2) Do not add repetitive generic closers like 'how can I help' unless user explicitly asks.\n"
+        "3) Continue the dialog naturally from context.\n"
+        f"4) Respond in {lang_label}, matching the user's latest language.\n"
+    )
+    return system_prompt + guardrail
+
+
+def _apply_agent_style_guardrails(agent_id: str, system_prompt: str) -> str:
+    """Apply lightweight runtime style constraints for specific agents."""
+    if not system_prompt:
+        return system_prompt
+
+    if agent_id == "nutra":
+        nutra_guardrail = (
+            "\n\n[STYLE LOCK - NUTRA]\n"
+            "Always write in first-person singular and feminine form.\n"
+            "Use feminine wording in Ukrainian/Russian (e.g., 'я підготувала', 'я готова', "
+            "'я зрозуміла').\n"
+            "Never switch to masculine forms (e.g., 'понял', 'готов').\n"
+        )
+        return system_prompt + nutra_guardrail
+
+    return system_prompt
 
 
 async def send_to_router(body: Dict[str, Any]) -> Dict[str, Any]:
@@ -32,6 +72,8 @@ async def send_to_router(body: Dict[str, Any]) -> Dict[str, Any]:
     context = body.get("context", {})
     
     system_prompt = body.get("system_prompt") or context.get("system_prompt")
+    system_prompt = _apply_agent_style_guardrails(agent_id, system_prompt)
+    system_prompt = _apply_runtime_communication_guardrails(system_prompt, metadata)
     
     if system_prompt:
         logger.info(f"Using system prompt ({len(system_prompt)} chars) for agent {agent_id}")
@@ -39,10 +81,28 @@ async def send_to_router(body: Dict[str, Any]) -> Dict[str, Any]:
     infer_url = f"{ROUTER_BASE_URL}/v1/agents/{agent_id}/infer"
     metadata["agent_id"] = agent_id
     
+    # Keep defaults moderate to avoid overly long replies while preserving flexibility.
+    max_tokens = GATEWAY_MAX_TOKENS_DEFAULT
+
+    # Senpai tends to over-verbose responses in Telegram; use lower default unless user asked details.
+    if agent_id == "senpai":
+        max_tokens = GATEWAY_MAX_TOKENS_SENPAI_DEFAULT
+
+    if metadata.get("is_training_group"):
+        max_tokens = GATEWAY_MAX_TOKENS_TRAINING
+
+    if metadata.get("force_detailed"):
+        max_tokens = max(max_tokens, GATEWAY_MAX_TOKENS_DETAILED)
+
+    if metadata.get("force_concise"):
+        max_tokens = min(max_tokens, GATEWAY_MAX_TOKENS_CONCISE)
+    
     infer_body = {
         "prompt": message,
         "system_prompt": system_prompt,
-        "metadata": metadata
+        "metadata": metadata,
+        "max_tokens": max_tokens,
+        "temperature": float(metadata.get("temperature_override", GATEWAY_TEMPERATURE_DEFAULT)),
     }
     
     images = context.get("images", [])
@@ -54,7 +114,10 @@ async def send_to_router(body: Dict[str, Any]) -> Dict[str, Any]:
         infer_body["provider_override"] = metadata["provider"]
     
     prov = metadata.get("provider", "default")
-    logger.info(f"Sending to Router ({infer_url}): agent={agent_id}, provider={prov}, has_images={bool(images)}, prompt_len={len(message)}")
+    logger.info(
+        f"Sending to Router ({infer_url}): agent={agent_id}, provider={prov}, "
+        f"has_images={bool(images)}, prompt_len={len(message)}, max_tokens={max_tokens}"
+    )
     
     try:
         async with httpx.AsyncClient(timeout=ROUTER_TIMEOUT) as client:
@@ -74,12 +137,18 @@ async def send_to_router(body: Dict[str, Any]) -> Dict[str, Any]:
                 "ok": True,
                 "data": {
                     "text": result.get("response", result.get("text", "")),
-                    "image_base64": result.get("image_base64")
+                    "image_base64": result.get("image_base64"),
+                    "file_base64": result.get("file_base64"),
+                    "file_name": result.get("file_name"),
+                    "file_mime": result.get("file_mime"),
                 },
                 "response": result.get("response", result.get("text", "")),
                 "model": result.get("model"),
                 "backend": result.get("backend"),
-                "image_base64": result.get("image_base64")
+                "image_base64": result.get("image_base64"),
+                "file_base64": result.get("file_base64"),
+                "file_name": result.get("file_name"),
+                "file_mime": result.get("file_mime"),
             }
     
     except httpx.TimeoutException as e:

services/router/agent_tools_config.py

@@ -26,45 +26,64 @@ FULL_STANDARD_STACK = [
     "presentation_create",
     "presentation_status",
     "presentation_download",
+
+    # File artifacts
+    "file_tool",
 ]
 
 # Specialized tools per agent (on top of standard stack)
 AGENT_SPECIALIZED_TOOLS = {
     # Helion - Energy platform
     # Specialized: energy calculations, solar/wind analysis
-    "helion": [],
+    "helion": ['comfy_generate_image', 'comfy_generate_video'],
     
     # Alateya - R&D Lab OS
     # Specialized: experiment tracking, hypothesis testing
-    "alateya": [],
+    "alateya": ['comfy_generate_image', 'comfy_generate_video'],
     
     # Nutra - Health & Nutrition
     # Specialized: nutrition calculations, supplement analysis
-    "nutra": [],
+    "nutra": ['comfy_generate_image', 'comfy_generate_video'],
     
     # AgroMatrix - Agriculture
     # Specialized: crop analysis, weather integration, field mapping
-    "agromatrix": [],
+    "agromatrix": ['comfy_generate_image', 'comfy_generate_video'],
     
     # GreenFood - Food & Eco
     # Specialized: recipe analysis, eco-scoring
-    "greenfood": [],
+    "greenfood": ['comfy_generate_image', 'comfy_generate_video'],
     
     # Druid - Knowledge Search
     # Specialized: deep RAG, document comparison
-    "druid": [],
+    "druid": ['comfy_generate_image', 'comfy_generate_video'],
     
     # DaarWizz - DAO Coordination
     # Specialized: governance tools, voting, treasury
-    "daarwizz": [],
+    "daarwizz": ['comfy_generate_image', 'comfy_generate_video'],
     
     # Clan - Community
     # Specialized: event management, polls, member tracking
-    "clan": [],
+    "clan": ['comfy_generate_image', 'comfy_generate_video'],
     
     # Eonarch - Philosophy & Evolution
     # Specialized: concept mapping, timeline analysis
-    "eonarch": [],
+    "eonarch": ['comfy_generate_image', 'comfy_generate_video'],
+    
+    # SenpAI (Gordon Senpai) - Trading & Markets
+    # Specialized: real-time market data, features, signals
+    "senpai": ['market_data', 'comfy_generate_image', 'comfy_generate_video'],
+    
+    # Soul / Athena - Spiritual Mentor
+    "soul": ['comfy_generate_image', 'comfy_generate_video'],
+    
+    # Yaromir - Tech Lead
+    "yaromir": ['comfy_generate_image', 'comfy_generate_video'],
+    
+    # Sofiia - Chief AI Architect
+    "sofiia": ['comfy_generate_image', 'comfy_generate_video'],
+    
+    # Daarion - Media Generation
+    "daarion": ['comfy_generate_image', 'comfy_generate_video'],
 }
 
 # CrewAI team structure per agent (future implementation)

services/router/main.py

@@ -9,6 +9,7 @@ import re
 import yaml
 import httpx
 import logging
+import hashlib
 import time  # For latency metrics
 
 # CrewAI Integration
@@ -40,6 +41,30 @@ except ImportError:
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+TRUSTED_DOMAINS_CONFIG_PATH = os.getenv("TRUSTED_DOMAINS_CONFIG_PATH", "./trusted_domains.yml")
+_trusted_domains_cache: Dict[str, Any] = {"mtime": None, "data": {}}
+
+
+def _load_trusted_domains_overrides() -> Dict[str, Any]:
+    """Load optional trusted domains overrides editable by mentors."""
+    global _trusted_domains_cache
+    try:
+        if not os.path.exists(TRUSTED_DOMAINS_CONFIG_PATH):
+            return {}
+        mtime = os.path.getmtime(TRUSTED_DOMAINS_CONFIG_PATH)
+        if _trusted_domains_cache.get("mtime") == mtime:
+            return _trusted_domains_cache.get("data") or {}
+
+        with open(TRUSTED_DOMAINS_CONFIG_PATH, "r", encoding="utf-8") as f:
+            raw = yaml.safe_load(f) or {}
+        if not isinstance(raw, dict):
+            raw = {}
+        _trusted_domains_cache = {"mtime": mtime, "data": raw}
+        return raw
+    except Exception as e:
+        logger.warning(f"⚠️ Failed to load trusted domains overrides: {e}")
+        return {}
+
 
 def _strip_dsml_keep_text_before(text: str) -> str:
     """If response contains DSML, return only the part before the first DSML-like tag. Otherwise return empty (caller will use fallback)."""
@@ -69,6 +94,499 @@ def _strip_dsml_keep_text_before(text: str) -> str:
     return prefix if len(prefix) > 30 else ""
 
 
+
+
+
+def _vision_prompt_wants_web(prompt: str) -> bool:
+    if not prompt:
+        return False
+    p = prompt.lower()
+    markers = [
+        "знайди", "пошукай", "пошук", "в інтернет", "в інтернеті", "у відкритих джерелах",
+        "що це", "що на фото", "який це", "яка це", "identify", "find online", "search web",
+        "назва", "бренд", "виробник", "інструкція", "дозування", "регламент", "де купити", "ціна",
+    ]
+    return any(m in p for m in markers)
+
+
+def _vision_answer_uncertain(answer: str) -> bool:
+    if not answer:
+        return True
+    a = answer.lower()
+    uncertain_markers = [
+        "ймовірно", "можливо", "схоже", "не впевнений", "не можу визначити", "важко сказати",
+        "probably", "maybe", "looks like", "not sure", "cannot identify"
+    ]
+    return any(m in a for m in uncertain_markers)
+
+
+EMPTY_ANSWER_GUARD_AGENTS = {"devtools", "monitor"}
+
+
+def _normalize_text_response(text: str) -> str:
+    return re.sub(r"\s+", " ", str(text or "")).strip()
+
+
+def _needs_empty_answer_recovery(text: str) -> bool:
+    normalized = _normalize_text_response(text)
+    if not normalized:
+        return True
+    low = normalized.lower()
+    if len(normalized) < 8:
+        return True
+    meta_markers = (
+        "the user", "user asked", "i need", "let me", "analysis", "thinking",
+        "користувач", "потрібно", "спочатку", "сначала"
+    )
+    if any(m in low for m in meta_markers) and len(normalized) < 80:
+        return True
+    if normalized in {"...", "ok", "done"}:
+        return True
+    return False
+
+
+def _image_response_needs_retry(text: str) -> bool:
+    normalized = _normalize_text_response(text)
+    if _needs_empty_answer_recovery(normalized):
+        return True
+    low = normalized.lower()
+    blocked_markers = (
+        "не можу бачити", "не можу аналізувати зображення", "опишіть фото словами",
+        "cannot view images", "cannot analyze image", "as a text model"
+    )
+    if any(m in low for m in blocked_markers):
+        return True
+    return len(normalized) < 24
+
+
+def _vision_response_is_blurry(text: str) -> bool:
+    low = _normalize_text_response(text).lower()
+    if not low:
+        return False
+    blurry_markers = (
+        "розмит", "нечітк", "не дуже чітк", "blur", "blurry", "out of focus", "low quality"
+    )
+    return any(m in low for m in blurry_markers)
+
+
+def _build_image_fallback_response(agent_id: str, prompt: str = "") -> str:
+    if (agent_id or "").lower() == "agromatrix":
+        return (
+            "Фото поки занадто нечітке, тому діагноз неточний. "
+            "Надішли, будь ласка, 2-3 чіткі фото: загальний вигляд рослини, крупний план проблемної ділянки "
+            "і (для листка) нижній бік. Якщо можеш, додай культуру та стадію росту."
+        )
+    return "Я поки не бачу достатньо деталей на фото. Надішли, будь ласка, чіткіше фото або крупний план об'єкта."
+
+
+
+def _sanitize_vision_text_for_user(text: str) -> str:
+    if not text:
+        return ""
+    normalized = re.sub(r"\s+", " ", str(text)).strip()
+    if not normalized:
+        return ""
+
+    sentences = [seg.strip() for seg in re.split(r"(?<=[.!?])\s+", normalized) if seg.strip()]
+    meta_markers = (
+        "okay", "the user", "user sent", "they want", "i need", "let me", "i will",
+        "first, look at the image", "look at the image", "first, analyze",
+        "first, looking at the image", "looking at the image",
+        "хорошо", "користувач", "пользователь", "потрібно", "нужно", "спочатку", "сначала"
+    )
+    cleaned = [sent for sent in sentences if not any(m in sent.lower() for m in meta_markers)]
+    if cleaned:
+        out = " ".join(cleaned[:3]).strip()
+    else:
+        # If text is only meta-reasoning, prefer empty over leaking service text to user.
+        if any(m in normalized.lower() for m in meta_markers):
+            return ""
+        out = " ".join(sentences[:3]).strip()
+
+    if len(out) > 700:
+        out = out[:700].rsplit(" ", 1)[0] + "..."
+    return out
+
+
+def _extract_vision_search_facts(text: str, max_chars: int = 220) -> str:
+    fact = _sanitize_vision_text_for_user(text)
+
+    # If sanitizer dropped everything (meta-only), try to recover object phrase.
+    if not fact and text:
+        raw = re.sub(r"\s+", " ", str(text)).strip()
+        raw = re.sub(r"(?i)^okay,?\s*", "", raw)
+        raw = re.sub(r"(?i)^let\'s\s+see\.?\s*", "", raw)
+        raw = re.sub(r"(?i)^the user sent (an image|a photo|a picture) of\s+", "", raw)
+        raw = re.sub(r"(?i)^user sent (an image|a photo|a picture) of\s+", "", raw)
+        raw = re.sub(r"(?i)^an image of\s+", "", raw)
+        raw = re.sub(r"(?i)they want.*$", "", raw).strip(" .")
+        fact = raw
+
+    if not fact:
+        return ""
+
+    fact = re.sub(r"(?i)джерела\s*:\s*.*$", "", fact).strip()
+    fact = re.sub(r"[*_`#\[\]()]", "", fact)
+    fact = re.sub(r"\s{2,}", " ", fact).strip(" .,")
+    if len(fact) > max_chars:
+        fact = fact[:max_chars].rsplit(" ", 1)[0]
+    return fact
+
+
+def _build_vision_web_query(prompt: str, vision_text: str) -> str:
+    # Keep query compact and deterministic for web_search tool.
+    source_intent = any(k in (prompt or "").lower() for k in ("джерел", "підтвердж", "source", "reference"))
+    prompt_part = (prompt or "").strip()
+    # Remove generic question wrappers that pollute search quality.
+    prompt_part = re.sub(r"(?i)що\s*це\s*на\s*фото\??", "", prompt_part).strip(" .")
+    prompt_part = re.sub(r"(?i)дай\s*2-?3\s*джерела", "", prompt_part).strip(" .")
+    prompt_part = re.sub(r"(?i)дай\s*\d+\s*джерел[а-я]*\s*для", "", prompt_part).strip(" .")
+    prompt_part = re.sub(r"(?i)дай\s*\d+\s*джерел[а-я]*", "", prompt_part).strip(" .")
+    prompt_part = re.sub(r"(?i)знайди\s*в\s*інтернеті", "", prompt_part).strip(" .")
+    prompt_part = re.sub(r"(?i)знайди\s*в\s*інтернеті\s*схожі\s*джерела", "", prompt_part).strip(" .")
+    prompt_part = re.sub(r"(?i)підтвердження", "", prompt_part).strip(" .")
+    prompt_part = re.sub(r"(?i)якщо\s*не\s*впевнений.*$", "", prompt_part).strip(" .")
+    prompt_part = re.sub(r"(?i)пошукай.*$", "", prompt_part).strip(" .")
+    prompt_part = re.sub(r"(?iu)\bі\b\.?$", "", prompt_part).strip(" .")
+
+    vision_part = _extract_vision_search_facts(vision_text)
+    if vision_part:
+        tokens = re.findall(r"[a-zA-Zа-яА-ЯіїєІЇЄ0-9]{3,}", vision_part.lower())
+        generic_tokens = {
+            "first", "look", "image", "photo", "picture", "context", "the", "and",
+            "спочатку", "подивись", "зображення", "фото", "картинка", "контекст",
+        }
+        if len(tokens) < 3 or len(vision_part) < 18 or all(t in generic_tokens for t in tokens):
+            # Too vague entity extraction (e.g., single word "rex") -> skip web enrichment.
+            return ""
+    if vision_part:
+        if prompt_part:
+            q = f"{vision_part}. контекст: {prompt_part}".strip(" .")
+        else:
+            q = vision_part
+        if source_intent:
+            q = f"{q} wikipedia encyclopedia"
+        return q.strip()
+    return ""
+
+
+def _compact_web_search_result(raw: str, query: str = "", agent_id: str = "", max_chars: int = 900) -> str:
+    if not raw:
+        return ""
+    text = str(raw).strip()
+    if not text:
+        return ""
+
+    def _extract_domain(url: str) -> str:
+        if not url:
+            return ""
+        d = url.lower().strip()
+        d = d.replace("https://", "").replace("http://", "")
+        d = d.split("/")[0]
+        if d.startswith("www."):
+            d = d[4:]
+        return d
+
+    low_signal_tokens = (
+        "grid maker", "converter", "convert", "download", "wallpaper", "stock photo",
+        "instagram", "pinterest", "tiktok", "youtube", "facebook", "generator", "meme",
+    )
+    low_signal_domains = (
+        "pinterest.com", "instagram.com", "tiktok.com", "youtube.com",
+        "facebook.com", "vk.com", "yandex.", "stackexchange.com",
+        "zhihu.com", "baidu.com",
+    )
+    trusted_common_domains = (
+        "wikipedia.org", "wikidata.org", "britannica.com",
+        "who.int", "fao.org", "oecd.org", "worldbank.org", "un.org", "europa.eu",
+        "nature.com", "science.org", "sciencedirect.com", "springer.com",
+    )
+    trusted_agro_domains = (
+        "fao.org", "europa.eu", "ec.europa.eu", "usda.gov", "nass.usda.gov",
+        "ukragroconsult.com", "minagro.gov.ua", "rada.gov.ua", "kmu.gov.ua",
+        "agroportal.ua", "latifundist.com", "kurkul.com",
+    )
+    trusted_by_agent = {
+        "agromatrix": trusted_agro_domains,
+        "alateya": (
+            "europa.eu", "un.org", "worldbank.org", "oecd.org",
+        ),
+        "clan": (
+            "europa.eu", "un.org", "wikipedia.org",
+        ),
+        "daarwizz": (
+            "openai.com", "anthropic.com", "mistral.ai", "huggingface.co",
+            "python.org", "github.com",
+        ),
+        "devtools": (
+            "github.com", "docs.python.org", "pypi.org", "docker.com",
+            "kubernetes.io", "fastapi.tiangolo.com", "postgresql.org",
+        ),
+        "druid": (
+            "who.int", "nih.gov", "ncbi.nlm.nih.gov", "wikipedia.org",
+        ),
+        "eonarch": (
+            "iea.org", "irena.org", "entsoe.eu", "europa.eu", "worldbank.org",
+        ),
+        "greenfood": (
+            "fao.org", "who.int", "efsa.europa.eu", "usda.gov", "ec.europa.eu",
+        ),
+        "senpai": (
+            "binance.com", "bybit.com", "coinbase.com", "kraken.com",
+            "coindesk.com", "cointelegraph.com", "tradingview.com",
+            "cftc.gov", "sec.gov", "esma.europa.eu",
+        ),
+        "sofiia": (
+            "who.int", "nih.gov", "ncbi.nlm.nih.gov", "ema.europa.eu",
+            "fda.gov", "mayoclinic.org", "nhs.uk",
+        ),
+        "helion": (
+            "iea.org", "irena.org", "entsoe.eu", "europa.eu", "worldbank.org",
+        ),
+        "nutra": (
+            "fao.org", "who.int", "efsa.europa.eu", "fda.gov",
+        ),
+        "microdao_orchestrator": (
+            "openai.com", "anthropic.com", "mistral.ai", "github.com",
+            "europa.eu", "un.org", "worldbank.org",
+        ),
+        "monitor": (
+            "grafana.com", "prometheus.io", "elastic.co", "datadoghq.com",
+            "opentelemetry.io",
+        ),
+        "soul": (
+            "who.int", "nih.gov", "ncbi.nlm.nih.gov", "wikipedia.org",
+        ),
+        "yaromir": (
+            "europa.eu", "un.org", "worldbank.org", "wikipedia.org",
+        ),
+    }
+    def _norm_domain_entry(value: Any) -> str:
+        if isinstance(value, dict):
+            value = value.get("url") or value.get("domain") or ""
+        value = str(value or "").strip().lower()
+        if not value:
+            return ""
+        value = value.replace("https://", "").replace("http://", "")
+        value = value.split("/")[0]
+        if value.startswith("www."):
+            value = value[4:]
+        return value
+
+    def _norm_domain_list(values: Any) -> List[str]:
+        out: List[str] = []
+        if not isinstance(values, list):
+            return out
+        for v in values:
+            d = _norm_domain_entry(v)
+            if d:
+                out.append(d)
+        return out
+
+    overrides = _load_trusted_domains_overrides()
+    extra_low_signal = _norm_domain_list(overrides.get("low_signal_domains"))
+    if extra_low_signal:
+        low_signal_domains = tuple(dict.fromkeys([*low_signal_domains, *extra_low_signal]))
+    extra_common = _norm_domain_list(overrides.get("common_domains"))
+    if extra_common:
+        trusted_common_domains = tuple(dict.fromkeys([*trusted_common_domains, *extra_common]))
+    agents_overrides = overrides.get("agents") if isinstance(overrides.get("agents"), dict) else {}
+    for a, cfg in agents_overrides.items():
+        if not isinstance(cfg, dict):
+            continue
+        doms = _norm_domain_list(cfg.get("domains"))
+        if doms:
+            base = trusted_by_agent.get(str(a).lower(), ())
+            merged = tuple(dict.fromkeys([*base, *doms]))
+            trusted_by_agent[str(a).lower()] = merged
+    agro_query_terms = {
+        "агро", "agro", "crop", "crops", "fertilizer", "fertilizers",
+        "field", "soil", "harvest", "yield", "pesticide", "herbicide",
+        "farm", "farming", "tractor", "зерно", "пшениц", "кукурудз",
+        "соняшник", "ріпак", "врожай", "ґрунт", "поле", "добрив",
+        "насіння", "ззр", "фермер",
+    }
+    query_terms = {t for t in re.findall(r"[a-zA-Zа-яА-ЯіїєІЇЄ0-9]{3,}", (query or "").lower())}
+    agro_mode = any(any(k in term for k in agro_query_terms) for term in query_terms)
+    agent_trusted_domains = trusted_by_agent.get((agent_id or "").lower(), ())
+
+    # Parse bullet blocks from tool output.
+    chunks = []
+    current = []
+    for line in text.splitlines():
+        ln = line.rstrip()
+        if ln.startswith("- ") and current:
+            chunks.append("\n".join(current))
+            current = [ln]
+        else:
+            current.append(ln)
+    if current:
+        chunks.append("\n".join(current))
+
+    scored = []
+    for chunk in chunks:
+        lines = [ln.strip() for ln in chunk.splitlines() if ln.strip()]
+        title = lines[0][2:].strip() if lines and lines[0].startswith("- ") else (lines[0] if lines else "")
+        url_line = next((ln for ln in lines if ln.lower().startswith("url:")), "")
+        url = url_line.split(":", 1)[1].strip() if ":" in url_line else ""
+        domain = _extract_domain(url)
+        text_blob = " ".join(lines).lower()
+
+        if any(x in domain for x in low_signal_domains):
+            continue
+
+        score = 0
+        for t in query_terms:
+            if t in text_blob:
+                score += 2
+        if any(tok in text_blob for tok in low_signal_tokens):
+            score -= 3
+        if domain.endswith(".gov") or domain.endswith(".gov.ua") or domain.endswith(".edu"):
+            score += 2
+        if any(domain == d or domain.endswith("." + d) for d in trusted_common_domains):
+            score += 2
+        if any(domain == d or domain.endswith("." + d) for d in agent_trusted_domains):
+            score += 2
+        if any(domain.endswith(d) for d in ("wikipedia.org", "wikidata.org", "fao.org", "europa.eu")):
+            score += 2
+        if agro_mode:
+            if any(domain == d or domain.endswith("." + d) for d in trusted_agro_domains):
+                score += 3
+            else:
+                score -= 1
+        if not url:
+            score -= 1
+        if len(title) < 6:
+            score -= 1
+
+        scored.append((score, domain, chunk))
+
+    def _is_trusted_agro(domain: str) -> bool:
+        if not domain:
+            return False
+        if any(domain == d or domain.endswith("." + d) for d in trusted_common_domains):
+            return True
+        return any(domain == d or domain.endswith("." + d) for d in trusted_agro_domains)
+
+    scored.sort(key=lambda x: x[0], reverse=True)
+    kept = []
+    seen_domains = set()
+    if agro_mode:
+        for s, domain, chunk in scored:
+            if s < 1 or not _is_trusted_agro(domain):
+                continue
+            if domain and domain in seen_domains:
+                continue
+            if domain:
+                seen_domains.add(domain)
+            kept.append(chunk)
+            if len(kept) >= 3:
+                break
+
+    if kept:
+        compact = "\n\n".join(kept).strip()
+        if len(compact) > max_chars:
+            compact = compact[:max_chars].rstrip() + "..."
+        return compact
+
+    for s, domain, chunk in scored:
+        if s < 2:
+            continue
+        if domain and domain in seen_domains:
+            continue
+        if domain:
+            seen_domains.add(domain)
+        kept.append(chunk)
+        if len(kept) >= 3:
+            break
+    if not kept:
+        return ""
+
+    compact = "\n\n".join(kept).strip()
+    if len(compact) > max_chars:
+        compact = compact[:max_chars].rstrip() + "..."
+    return compact
+
+
+def _extract_sources_from_compact(compact: str, max_items: int = 3) -> List[Dict[str, str]]:
+    if not compact:
+        return []
+    items: List[Dict[str, str]] = []
+    chunks = [c for c in compact.split("\n\n") if c.strip()]
+    for chunk in chunks:
+        lines = [ln.strip() for ln in chunk.splitlines() if ln.strip()]
+        if not lines:
+            continue
+        title = lines[0][2:].strip() if lines[0].startswith("- ") else lines[0]
+        url_line = next((ln for ln in lines if ln.lower().startswith("url:")), "")
+        url = url_line.split(":", 1)[1].strip() if ":" in url_line else ""
+        if not url:
+            continue
+        items.append({"title": title[:180], "url": url[:500]})
+        if len(items) >= max_items:
+            break
+    return items
+
+def _condition_matches(cond: Dict[str, Any], agent_id: str, metadata: Dict[str, Any]) -> bool:
+    """Minimal matcher for router-config `when` conditions."""
+    if not isinstance(cond, dict):
+        return True
+
+    meta = metadata or {}
+
+    if "agent" in cond and cond.get("agent") != agent_id:
+        return False
+
+    if "mode" in cond and meta.get("mode") != cond.get("mode"):
+        return False
+
+    if "metadata_has" in cond:
+        key = cond.get("metadata_has")
+        if key not in meta:
+            return False
+
+    if "metadata_equals" in cond:
+        eq = cond.get("metadata_equals") or {}
+        for k, v in eq.items():
+            if meta.get(k) != v:
+                return False
+
+    if "task_type" in cond:
+        expected = cond.get("task_type")
+        actual = meta.get("task_type")
+        if isinstance(expected, list):
+            if actual not in expected:
+                return False
+        elif actual != expected:
+            return False
+
+    if "api_key_available" in cond:
+        env_name = cond.get("api_key_available")
+        if not (isinstance(env_name, str) and os.getenv(env_name)):
+            return False
+
+    if "and" in cond:
+        clauses = cond.get("and") or []
+        if not isinstance(clauses, list):
+            return False
+        for clause in clauses:
+            if not _condition_matches(clause, agent_id, meta):
+                return False
+
+    return True
+
+
+def _select_default_llm(agent_id: str, metadata: Dict[str, Any], base_llm: str, routing_rules: List[Dict[str, Any]]) -> str:
+    """Select LLM by first matching routing rule with `use_llm`."""
+    for rule in routing_rules:
+        when = rule.get("when", {})
+        if _condition_matches(when, agent_id, metadata):
+            use_llm = rule.get("use_llm")
+            if use_llm:
+                logger.info(f"🎯 Agent {agent_id} routing rule {rule.get('id', '<no-id>')} -> {use_llm}")
+                return use_llm
+    return base_llm
+
 app = FastAPI(title="DAARION Router", version="2.0.0")
 
 # Configuration
@@ -404,6 +922,9 @@ class InferResponse(BaseModel):
     tokens_used: Optional[int] = None
     backend: str
     image_base64: Optional[str] = None  # Generated image in base64 format
+    file_base64: Optional[str] = None
+    file_name: Optional[str] = None
+    file_mime: Optional[str] = None
 
 
 
@@ -675,13 +1196,14 @@ async def agent_infer(agent_id: str, request: InferRequest):
     # Get system prompt from database or config
     system_prompt = request.system_prompt
     
-    # Debug logging for system prompt
+    system_prompt_source = "request"
     if system_prompt:
         logger.info(f"📝 Received system_prompt from request: {len(system_prompt)} chars")
         logger.debug(f"System prompt preview: {system_prompt[:200]}...")
     else:
-        logger.warning(f"⚠️ No system_prompt in request for agent {agent_id}, trying to load...")
+        system_prompt_source = "city_service"
-    
+        logger.info(f"ℹ️ No system_prompt in request for agent {agent_id}, loading from configured sources")
+
     if not system_prompt:
         try:
             from prompt_builder import get_agent_system_prompt
@@ -694,8 +1216,26 @@ async def agent_infer(agent_id: str, request: InferRequest):
         except Exception as e:
             logger.warning(f"⚠️ Could not load prompt from database: {e}")
             # Fallback to config
+            system_prompt_source = "router_config"
             agent_config = router_config.get("agents", {}).get(agent_id, {})
             system_prompt = agent_config.get("system_prompt")
+
+    if not system_prompt:
+        system_prompt_source = "empty"
+        logger.warning(f"⚠️ System prompt unavailable for {agent_id}; continuing with provider defaults")
+
+    system_prompt_hash = hashlib.sha256((system_prompt or "").encode("utf-8")).hexdigest()[:12]
+    effective_metadata = dict(metadata)
+    effective_metadata["system_prompt_hash"] = system_prompt_hash
+    effective_metadata["system_prompt_source"] = system_prompt_source
+    effective_metadata["system_prompt_version"] = (
+        metadata.get("system_prompt_version")
+        or f"{agent_id}:{system_prompt_hash}"
+    )
+    logger.info(
+        f"🧩 Prompt meta for {agent_id}: source={system_prompt_source}, "
+        f"version={effective_metadata['system_prompt_version']}, hash={system_prompt_hash}"
+    )
     
     # Determine which backend to use
     # Use router config to get default model for agent, fallback to qwen3:8b
@@ -713,8 +1253,8 @@ async def agent_infer(agent_id: str, request: InferRequest):
                 agent_id=agent_id,
                 prompt=request.prompt,
                 agent_config=agent_config,
-                force_crewai=request.metadata.get("force_crewai", False) if request.metadata else False,
+                metadata=effective_metadata,
-                
+                force_crewai=effective_metadata.get("force_crewai", False),
             )
             
             logger.info(f"🎭 CrewAI decision for {agent_id}: {use_crewai} ({crewai_reason})")
@@ -727,7 +1267,12 @@ async def agent_infer(agent_id: str, request: InferRequest):
                     context={
                         "memory_brief": memory_brief_text,
                         "system_prompt": system_prompt,
-                        "metadata": metadata,
+                        "system_prompt_meta": {
+                            "source": system_prompt_source,
+                            "version": effective_metadata.get("system_prompt_version"),
+                            "hash": system_prompt_hash,
+                        },
+                        "metadata": effective_metadata,
                     },
                     team=crewai_cfg.get("team")
                 )
@@ -755,9 +1300,8 @@ async def agent_infer(agent_id: str, request: InferRequest):
                     return InferResponse(
                         response=crew_result["result"],
                         model="crewai-" + agent_id,
-                        provider="crewai",
+                        backend="crewai",
-                        tokens_used=0,
+                        tokens_used=0
-                        latency_ms=int(latency * 1000)
                     )
                 else:
                     logger.warning(f"⚠️ CrewAI failed, falling back to direct LLM")
@@ -765,15 +1309,9 @@ async def agent_infer(agent_id: str, request: InferRequest):
             logger.exception(f"❌ CrewAI error: {e}, falling back to direct LLM")
 
     default_llm = agent_config.get("default_llm", "qwen3:8b")
-    
+
-    # Check if there's a routing rule for this agent
     routing_rules = router_config.get("routing", [])
-    for rule in routing_rules:
+    default_llm = _select_default_llm(agent_id, metadata, default_llm, routing_rules)
-        if rule.get("when", {}).get("agent") == agent_id:
-            if "use_llm" in rule:
-                default_llm = rule.get("use_llm")
-                logger.info(f"🎯 Agent {agent_id} routing to: {default_llm}")
-                break
     
     # Get LLM profile configuration
     llm_profiles = router_config.get("llm_profiles", {})
@@ -819,15 +1357,114 @@ async def agent_infer(agent_id: str, request: InferRequest):
             
             if vision_resp.status_code == 200:
                 vision_data = vision_resp.json()
-                full_response = vision_data.get("text", "")
+                raw_response = vision_data.get("text", "")
-                
+                full_response = _sanitize_vision_text_for_user(raw_response)
+                vision_web_query = ""
+                vision_sources: List[Dict[str, str]] = []
+
                 # Debug: log full response structure
-                logger.info(f"✅ Vision response: {len(full_response)} chars, success={vision_data.get('success')}, keys={list(vision_data.keys())}")
+                logger.info(
+                    f"✅ Vision response: raw={len(raw_response)} chars, sanitized={len(full_response)} chars, "
+                    f"success={vision_data.get('success')}, keys={list(vision_data.keys())}"
+                )
+                if raw_response and not full_response:
+                    full_response = _extract_vision_search_facts(raw_response, max_chars=280)
                 if not full_response:
                     logger.warning(f"⚠️ Empty vision response! Full data: {str(vision_data)[:500]}")
+
+                # Optional vision -> web enrichment (soft policy):
+                # if prompt explicitly asks to search online OR vision answer is uncertain.
+                if (full_response or raw_response) and TOOL_MANAGER_AVAILABLE and tool_manager:
+                    try:
+                        wants_web = _vision_prompt_wants_web(request.prompt)
+                        uncertain = _vision_answer_uncertain(full_response or raw_response)
+                        if wants_web or uncertain:
+                            query = _build_vision_web_query(request.prompt, full_response or raw_response)
+                            if not query:
+                                logger.info("🔎 Vision web enrich skipped: query not actionable")
+                            else:
+                                vision_web_query = query
+                                search_result = await tool_manager.execute_tool(
+                                    "web_search",
+                                    {"query": query, "max_results": 3},
+                                    agent_id=request_agent_id,
+                                    chat_id=chat_id,
+                                    user_id=user_id,
+                                )
+                                if search_result and search_result.success and search_result.result:
+
+                                    compact_search = _compact_web_search_result(
+                                        search_result.result,
+                                        query=query,
+                                        agent_id=request_agent_id,
+                                    )
+
+                                    if compact_search and "Нічого не знайдено" not in compact_search:
+                                        vision_sources = _extract_sources_from_compact(compact_search)
+
+                                        base_text = full_response or "Не вдалося надійно ідентифікувати об'єкт на фото."
+
+                                        full_response = (
+
+                                            f"{base_text}\n\n"
+
+                                            f"Додатково знайшов у відкритих джерелах:\n{compact_search}"
+
+                                        )
+
+                                    logger.info(
+                                        "🌐 Vision web enrichment applied "
+                                        f"for agent={request_agent_id}, wants_web={wants_web}, uncertain={uncertain}, "
+                                        f"sources={len(vision_sources)}"
+                                    )
+                    except Exception as e:
+                        logger.warning(f"⚠️ Vision web enrichment failed: {e}")
+
+                if vision_web_query:
+                    logger.info(
+                        f"🗂️ Vision enrichment metadata: agent={request_agent_id}, "
+                        f"query='{vision_web_query[:180]}', sources={len(vision_sources)}"
+                    )
+
+                # Image quality gate: one soft retry if response looks empty/meta.
+                if _image_response_needs_retry(full_response):
+                    try:
+                        logger.warning(f"⚠️ Vision quality gate triggered for agent={request_agent_id}, retrying once")
+                        retry_payload = dict(vision_payload)
+                        retry_payload["prompt"] = (
+                            "Опиши зображення по суті: що зображено, ключові деталі, можливий контекст. "
+                            "Відповідай українською 2-4 реченнями, без службових фраз. "
+                            f"Запит користувача: {request.prompt}"
+                        )
+                        retry_resp = await http_client.post(
+                            f"{SWAPPER_URL}/vision",
+                            json=retry_payload,
+                            timeout=120.0
+                        )
+                        if retry_resp.status_code == 200:
+                            retry_data = retry_resp.json()
+                            retry_raw = retry_data.get("text", "")
+                            retry_text = _sanitize_vision_text_for_user(retry_raw)
+                            if retry_raw and not retry_text:
+                                retry_text = _extract_vision_search_facts(retry_raw, max_chars=280)
+                            if retry_text and not _image_response_needs_retry(retry_text):
+                                full_response = retry_text
+                                logger.info(f"✅ Vision quality retry improved response for agent={request_agent_id}")
+                    except Exception as e:
+                        logger.warning(f"⚠️ Vision quality retry failed: {e}")
+
+                if _image_response_needs_retry(full_response):
+                    full_response = _build_image_fallback_response(request_agent_id, request.prompt)
+                elif request_agent_id == "agromatrix" and _vision_response_is_blurry(full_response):
+                    full_response = _build_image_fallback_response(request_agent_id, request.prompt)
                 
                 # Store vision message in agent-specific memory
                 if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval and chat_id and user_id and full_response:
+                    vision_meta: Dict[str, Any] = {}
+                    if vision_web_query:
+                        vision_meta["vision_search_query"] = vision_web_query[:500]
+                    if vision_sources:
+                        vision_meta["vision_sources"] = vision_sources
                     asyncio.create_task(
                         memory_retrieval.store_message(
                             agent_id=request_agent_id,
@@ -836,7 +1473,8 @@ async def agent_infer(agent_id: str, request: InferRequest):
                             message_text=f"[Image] {request.prompt}",
                             response_text=full_response,
                             chat_id=chat_id,
-                            message_type="vision"
+                            message_type="vision",
+                            metadata=vision_meta if vision_meta else None,
                         )
                     )
                 
@@ -848,11 +1486,21 @@ async def agent_infer(agent_id: str, request: InferRequest):
                 )
             else:
                 logger.error(f"❌ Swapper vision error: {vision_resp.status_code} - {vision_resp.text[:200]}")
-                # Fall through to text processing
+                return InferResponse(
+                    response=_build_image_fallback_response(request_agent_id, request.prompt),
+                    model="qwen3-vl-8b",
+                    tokens_used=None,
+                    backend="swapper-vision-fallback"
+                )
                 
         except Exception as e:
             logger.error(f"❌ Vision processing failed: {e}", exc_info=True)
-            # Fall through to text processing
+            return InferResponse(
+                response=_build_image_fallback_response(request_agent_id, request.prompt),
+                model="qwen3-vl-8b",
+                tokens_used=None,
+                backend="swapper-vision-fallback"
+            )
     
     # =========================================================================
     # SMART LLM ROUTER WITH AUTO-FALLBACK
@@ -881,6 +1529,10 @@ async def agent_infer(agent_id: str, request: InferRequest):
     max_tokens = request.max_tokens or llm_profile.get("max_tokens", 2048)
     temperature = request.temperature or llm_profile.get("temperature", 0.2)
     
+    cloud_provider_names = {"deepseek", "mistral", "grok", "openai", "anthropic"}
+    allow_cloud = provider in cloud_provider_names
+    if not allow_cloud:
+        logger.info(f"☁️ Cloud providers disabled for agent {agent_id}: provider={provider}")
     # Define cloud providers with fallback order
     cloud_providers = [
         {
@@ -905,7 +1557,10 @@ async def agent_infer(agent_id: str, request: InferRequest):
             "timeout": 60
         }
     ]
-    
+
+    if not allow_cloud:
+        cloud_providers = []
+
     # If specific provider requested, try it first
     if provider in ["deepseek", "mistral", "grok"]:
         # Reorder to put requested provider first
@@ -916,7 +1571,7 @@ async def agent_infer(agent_id: str, request: InferRequest):
     # Get tool definitions if Tool Manager is available
     tools_payload = None
     if TOOL_MANAGER_AVAILABLE and tool_manager:
-        tools_payload = tool_manager.get_tool_definitions()
+        tools_payload = tool_manager.get_tool_definitions(request_agent_id)
         logger.debug(f"🔧 {len(tools_payload)} tools available for function calling")
     
     for cloud in cloud_providers:
@@ -1034,14 +1689,23 @@ async def agent_infer(agent_id: str, request: InferRequest):
                         except:
                             tool_args = {}
                         
-                        result = await tool_manager.execute_tool(tool_name, tool_args, agent_id=request_agent_id)
+                        result = await tool_manager.execute_tool(
+                            tool_name,
+                            tool_args,
+                            agent_id=request_agent_id,
+                            chat_id=chat_id,
+                            user_id=user_id,
+                        )
                         tool_result_dict = {
                             "tool_call_id": tc.get("id", ""),
                             "name": tool_name,
                             "success": result.success,
                             "result": result.result,
                             "error": result.error,
-                            "image_base64": result.image_base64  # Store image if generated
+                            "image_base64": result.image_base64,  # Store image if generated
+                            "file_base64": result.file_base64,
+                            "file_name": result.file_name,
+                            "file_mime": result.file_mime,
                         }
                         if result.image_base64:
                             logger.info(f"🖼️ Tool {tool_name} generated image: {len(result.image_base64)} chars")
@@ -1149,14 +1813,22 @@ async def agent_infer(agent_id: str, request: InferRequest):
                     
                     # Check if any tool generated an image
                     generated_image = None
+                    generated_file_base64 = None
+                    generated_file_name = None
+                    generated_file_mime = None
                     logger.debug(f"🔍 Checking {len(tool_results)} tool results for images...")
                     for tr in tool_results:
                         img_b64 = tr.get("image_base64")
                         if img_b64:
                             generated_image = img_b64
                             logger.info(f"🖼️ Image generated by tool: {tr['name']} ({len(img_b64)} chars)")
-                            break
+                        file_b64 = tr.get("file_base64")
-                        else:
+                        if file_b64 and not generated_file_base64:
+                            generated_file_base64 = file_b64
+                            generated_file_name = tr.get("file_name")
+                            generated_file_mime = tr.get("file_mime")
+                            logger.info(f"📄 File generated by tool: {tr['name']} ({len(file_b64)} chars)")
+                        if not img_b64:
                             logger.debug(f"  Tool {tr['name']}: no image_base64")
                     
                     logger.info(f"✅ {cloud['name'].upper()} response received, {tokens_used} tokens")
@@ -1179,7 +1851,10 @@ async def agent_infer(agent_id: str, request: InferRequest):
                         model=cloud["model"],
                         tokens_used=tokens_used,
                         backend=f"{cloud['name']}-cloud",
-                        image_base64=generated_image
+                        image_base64=generated_image,
+                        file_base64=generated_file_base64,
+                        file_name=generated_file_name,
+                        file_mime=generated_file_mime,
                     )
                 else:
                     logger.warning(f"⚠️ {cloud['name'].upper()} returned empty response, trying next provider")
@@ -1253,7 +1928,38 @@ async def agent_infer(agent_id: str, request: InferRequest):
             
             if generate_resp.status_code == 200:
                 data = generate_resp.json()
-                local_response = data.get("response", "")
+                local_response = _normalize_text_response(data.get("response", ""))
+
+                # Empty-answer gate for selected local top-level agents.
+                if request_agent_id in EMPTY_ANSWER_GUARD_AGENTS and _needs_empty_answer_recovery(local_response):
+                    logger.warning(f"⚠️ Empty-answer gate triggered for {request_agent_id}, retrying local generate once")
+                    retry_prompt = (
+                        f"{request.prompt}\n\n"
+                        "Відповідай коротко і конкретно (2-5 речень), без службових або мета-фраз."
+                    )
+                    retry_resp = await http_client.post(
+                        f"{SWAPPER_URL}/generate",
+                        json={
+                            "model": local_model,
+                            "prompt": retry_prompt,
+                            "system": system_prompt,
+                            "max_tokens": request.max_tokens,
+                            "temperature": request.temperature,
+                            "stream": False
+                        },
+                        timeout=300.0
+                    )
+                    if retry_resp.status_code == 200:
+                        retry_data = retry_resp.json()
+                        retry_text = _normalize_text_response(retry_data.get("response", ""))
+                        if retry_text and not _needs_empty_answer_recovery(retry_text):
+                            local_response = retry_text
+
+                if _needs_empty_answer_recovery(local_response):
+                    local_response = (
+                        "Я не отримав корисну відповідь з першої спроби. "
+                        "Сформулюй запит коротко ще раз, і я відповім конкретно."
+                    )
                 
                 # Store in agent-specific memory
                 if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval and chat_id and user_id and local_response:
@@ -1649,4 +2355,3 @@ async def shutdown_event():
     if nc:
         await nc.close()
         logger.info("🔌 NATS connection closed")
-

services/router/requirements.txt

@@ -5,6 +5,9 @@ nats-py==2.6.0
 PyYAML==6.0.1
 httpx>=0.25.0
 neo4j>=5.14.0
+openpyxl>=3.1.2
+python-docx>=1.1.2
+pypdf>=5.1.0
 
 # Memory Retrieval v3.0
 asyncpg>=0.29.0