feat(fabric): decommission Swapper from critical path, NCS = source of truth

- Node Worker: replace swapper_vision with ollama_vision (direct Ollama API)
- Node Worker: add NATS subjects for stt/tts/image (stubs ready)
- Node Worker: remove SWAPPER_URL dependency from config
- Router: vision calls go directly to Ollama /api/generate with images
- Router: local LLM calls go directly to Ollama /api/generate
- Router: add OLLAMA_URL and PREFER_NODE_WORKER=true feature flag
- Router: /v1/models now uses NCS global capabilities pool
- NCS: SWAPPER_URL="" -> skip Swapper probing (status=disabled)
- Swapper configs: remove all hardcoded model lists, keep only runtime
  URLs, timeouts, limits
- docker-compose.node1.yml: add OLLAMA_URL, PREFER_NODE_WORKER for router;
  SWAPPER_URL= for NCS; remove swapper-service from node-worker depends_on
- docker-compose.node2-sofiia.yml: same changes for NODA2

Swapper service still runs but is NOT in the critical inference path.
Source of truth for models is now NCS -> Ollama /api/tags.

Made-with: Cursor

docker-compose.node1.yml

@@ -48,9 +48,11 @@ services:
       - ROUTER_TOOL_MAX_ROUNDS=${ROUTER_TOOL_MAX_ROUNDS:-10}
       - AGROMATRIX_REVIEW_AUTH_MODE=${AGROMATRIX_REVIEW_AUTH_MODE:-bearer}
       - AGROMATRIX_REVIEW_BEARER_TOKENS=${AGROMATRIX_REVIEW_BEARER_TOKENS}
-      # ── Node Capabilities (multi-node model selection) ──
+      # ── Fabric Layer (NCS + Node Worker, Swapper being decommissioned) ──
       - NODE_CAPABILITIES_URL=http://node-capabilities:8099/capabilities
       - ENABLE_GLOBAL_CAPS_NATS=true
+      - OLLAMA_URL=http://172.18.0.1:11434
+      - PREFER_NODE_WORKER=true
     volumes:
       - ${DEPLOY_ROOT:-.}/services/router/router_config.yaml:/app/router_config.yaml:ro
       - ${DEPLOY_ROOT:-.}/services/router/router-config.yml:/app/router-config.yml:ro
@@ -498,8 +500,8 @@ services:
     container_name: node-capabilities-node1
     environment:
       - NODE_ID=noda1
-      - OLLAMA_BASE_URL=http://host.docker.internal:11434
+      - OLLAMA_BASE_URL=http://172.18.0.1:11434
-      - SWAPPER_URL=http://swapper-service:8890
+      - SWAPPER_URL=
       - CACHE_TTL_SEC=15
       - ENABLE_NATS_CAPS=true
       - NATS_URL=nats://nats:4222
@@ -527,15 +529,13 @@ services:
     environment:
       - NODE_ID=noda1
       - NATS_URL=nats://nats:4222
-      - OLLAMA_BASE_URL=http://host.docker.internal:11434
+      - OLLAMA_BASE_URL=http://172.18.0.1:11434
-      - SWAPPER_URL=http://swapper-service:8890
       - NODE_DEFAULT_LLM=qwen3.5:27b
-      - NODE_DEFAULT_VISION=qwen3-vl-8b
+      - NODE_DEFAULT_VISION=qwen3-vl:8b
       - NODE_WORKER_MAX_CONCURRENCY=2
       - NCS_REPORT_URL=http://node-capabilities:8099
     depends_on:
       - nats
-      - swapper-service
     networks:
       - dagi-network
     restart: unless-stopped

docker-compose.node2-sofiia.yml

@@ -25,9 +25,11 @@ services:
       - XAI_API_KEY=${XAI_API_KEY}
       - GROK_API_KEY=${XAI_API_KEY}
       - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY:-}
-      # ── Node Capabilities (multi-node model selection) ────────────────────
+      # ── Fabric Layer (NCS + Node Worker, no Swapper dependency) ──────────
       - NODE_CAPABILITIES_URL=http://node-capabilities:8099/capabilities
       - ENABLE_GLOBAL_CAPS_NATS=true
+      - OLLAMA_URL=http://host.docker.internal:11434
+      - PREFER_NODE_WORKER=true
       # ── Persistence backends ──────────────────────────────────────────────
       - ALERT_BACKEND=postgres
       - ALERT_DATABASE_URL=${ALERT_DATABASE_URL:-${DATABASE_URL}}
@@ -121,7 +123,7 @@ services:
     environment:
       - NODE_ID=NODA2
       - OLLAMA_BASE_URL=http://host.docker.internal:11434
-      - SWAPPER_URL=http://swapper-service:8890
+      - SWAPPER_URL=
       - LLAMA_SERVER_URL=http://host.docker.internal:11435
       - CACHE_TTL_SEC=15
       - ENABLE_NATS_CAPS=true
@@ -147,14 +149,12 @@ services:
       - NODE_ID=noda2
       - NATS_URL=nats://dagi-nats:4222
       - OLLAMA_BASE_URL=http://host.docker.internal:11434
-      - SWAPPER_URL=http://swapper-service:8890
       - NODE_DEFAULT_LLM=qwen3:14b
       - NODE_DEFAULT_VISION=llava:13b
       - NODE_WORKER_MAX_CONCURRENCY=2
       - NCS_REPORT_URL=http://node-capabilities:8099
     depends_on:
       - dagi-nats
-      - swapper-service
     networks:
       - dagi-network
     restart: unless-stopped

services/node-capabilities/main.py

@@ -20,7 +20,7 @@ app = FastAPI(title="Node Capabilities Service", version="1.0.0")
 
 NODE_ID = os.getenv("NODE_ID", "noda2")
 OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://host.docker.internal:11434")
-SWAPPER_URL = os.getenv("SWAPPER_URL", "http://swapper-service:8890")
+SWAPPER_URL = os.getenv("SWAPPER_URL", "")  # empty = skip Swapper probing
 LLAMA_SERVER_URL = os.getenv("LLAMA_SERVER_URL", "")
 
 _cache: Dict[str, Any] = {}
@@ -74,7 +74,10 @@ async def _collect_ollama() -> Dict[str, Any]:
 
 
 async def _collect_swapper() -> Dict[str, Any]:
-    runtime: Dict[str, Any] = {"base_url": SWAPPER_URL, "status": "unknown", "models": [], "vision_models": [], "active_model": None}
+    runtime: Dict[str, Any] = {"base_url": SWAPPER_URL or "n/a", "status": "unknown", "models": [], "vision_models": [], "active_model": None}
+    if not SWAPPER_URL:
+        runtime["status"] = "disabled"
+        return runtime
     try:
         async with httpx.AsyncClient(timeout=5) as c:
             h = await c.get(f"{SWAPPER_URL}/health")

services/node-worker/config.py

@@ -4,7 +4,6 @@ import os
 NODE_ID = os.getenv("NODE_ID", "noda2")
 NATS_URL = os.getenv("NATS_URL", "nats://dagi-nats:4222")
 OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://host.docker.internal:11434")
-SWAPPER_URL = os.getenv("SWAPPER_URL", "http://swapper-service:8890")
 DEFAULT_LLM = os.getenv("NODE_DEFAULT_LLM", "qwen3:14b")
 DEFAULT_VISION = os.getenv("NODE_DEFAULT_VISION", "llava:13b")
 MAX_CONCURRENCY = int(os.getenv("NODE_WORKER_MAX_CONCURRENCY", "2"))

services/node-worker/providers/ollama_vision.py

@@ -0,0 +1,49 @@
+"""Ollama vision provider — direct Ollama API with images, no Swapper dependency."""
+import logging
+from typing import Any, Dict, List, Optional
+
+import httpx
+
+from config import OLLAMA_BASE_URL, DEFAULT_VISION
+
+logger = logging.getLogger("provider.ollama_vision")
+
+
+async def infer(
+    images: Optional[List[str]] = None,
+    prompt: str = "",
+    model: str = "",
+    system: str = "",
+    max_tokens: int = 1024,
+    temperature: float = 0.2,
+    timeout_s: float = 60.0,
+) -> Dict[str, Any]:
+    model = model or DEFAULT_VISION
+
+    payload: Dict[str, Any] = {
+        "model": model,
+        "prompt": prompt or "Describe this image.",
+        "stream": False,
+        "options": {"num_predict": max_tokens, "temperature": temperature},
+    }
+    if images:
+        clean = []
+        for img in images:
+            if "," in img and img.startswith("data:"):
+                clean.append(img.split(",", 1)[1])
+            else:
+                clean.append(img)
+        payload["images"] = clean
+    if system:
+        payload["system"] = system
+
+    async with httpx.AsyncClient(timeout=timeout_s) as c:
+        resp = await c.post(f"{OLLAMA_BASE_URL}/api/generate", json=payload)
+        resp.raise_for_status()
+        data = resp.json()
+        return {
+            "text": data.get("response", ""),
+            "model": model,
+            "provider": "ollama_vision",
+            "eval_count": data.get("eval_count", 0),
+        }

services/node-worker/worker.py

@@ -9,7 +9,7 @@ from typing import Any, Dict
 import config
 from models import JobRequest, JobResponse, JobError
 from idempotency import IdempotencyStore
-from providers import ollama, swapper_vision
+from providers import ollama, ollama_vision
 import fabric_metrics as fm
 
 logger = logging.getLogger("node-worker")
@@ -27,9 +27,13 @@ async def start(nats_client):
     global _nats_client
     _nats_client = nats_client
 
+    nid = config.NODE_ID.lower()
     subjects = [
-        f"node.{config.NODE_ID.lower()}.llm.request",
+        f"node.{nid}.llm.request",
-        f"node.{config.NODE_ID.lower()}.vision.request",
+        f"node.{nid}.vision.request",
+        f"node.{nid}.stt.request",
+        f"node.{nid}.tts.request",
+        f"node.{nid}.image.request",
     ]
     for subj in subjects:
         await nats_client.subscribe(subj, cb=_handle_request)
@@ -160,7 +164,7 @@ async def _execute(job: JobRequest, remaining_ms: int) -> JobResponse:
             )
         elif job.required_type == "vision":
             result = await asyncio.wait_for(
-                swapper_vision.infer(
+                ollama_vision.infer(
                     images=payload.get("images"),
                     prompt=payload.get("prompt", ""),
                     model=model,
@@ -171,11 +175,20 @@ async def _execute(job: JobRequest, remaining_ms: int) -> JobResponse:
                 ),
                 timeout=timeout_s,
             )
+        elif job.required_type in ("stt", "tts", "image"):
+            return JobResponse(
+                job_id=job.job_id, trace_id=job.trace_id, node_id=config.NODE_ID,
+                status="error",
+                error=JobError(
+                    code="NOT_YET_IMPLEMENTED",
+                    message=f"{job.required_type} adapter coming soon; use direct runtime API for now",
+                ),
+            )
         else:
             return JobResponse(
                 job_id=job.job_id, trace_id=job.trace_id, node_id=config.NODE_ID,
                 status="error",
-                error=JobError(code="UNSUPPORTED_TYPE", message=f"{job.required_type} not implemented"),
+                error=JobError(code="UNSUPPORTED_TYPE", message=f"{job.required_type} not supported"),
             )
 
         logger.info(

services/router/main.py

@@ -877,12 +877,13 @@ app = FastAPI(title="DAARION Router", version="2.0.0")
 # Configuration
 NATS_URL = os.getenv("NATS_URL", "nats://nats:4222")
 SWAPPER_URL = os.getenv("SWAPPER_URL", "http://swapper-service:8890")
-# All multimodal services now through Swapper
+OLLAMA_URL = os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
-STT_URL = os.getenv("STT_URL", "http://swapper-service:8890")  # Swapper /stt endpoint
+PREFER_NODE_WORKER = os.getenv("PREFER_NODE_WORKER", "true").lower() in ("true", "1")
-TTS_URL = os.getenv("TTS_URL", "http://swapper-service:8890")  # Swapper /tts endpoint
+STT_URL = os.getenv("STT_URL", "http://swapper-service:8890")
-VISION_URL = os.getenv("VISION_URL", "http://172.18.0.1:11434")  # Host Ollama
+TTS_URL = os.getenv("TTS_URL", "http://swapper-service:8890")
-OCR_URL = os.getenv("OCR_URL", "http://swapper-service:8890")  # Swapper /ocr endpoint
+VISION_URL = os.getenv("VISION_URL", "http://host.docker.internal:11434")
-DOCUMENT_URL = os.getenv("DOCUMENT_URL", "http://swapper-service:8890")  # Swapper /document endpoint
+OCR_URL = os.getenv("OCR_URL", "http://swapper-service:8890")
+DOCUMENT_URL = os.getenv("DOCUMENT_URL", "http://swapper-service:8890")
 CITY_SERVICE_URL = os.getenv("CITY_SERVICE_URL", "http://daarion-city-service:7001")
 
 # CrewAI Routing Configuration
@@ -1083,8 +1084,8 @@ async def startup_event():
         runtime_guard_engine = None
     
     # Log backend URLs
-    logger.info(f"📡 Swapper URL: {SWAPPER_URL}")
+    logger.info(f"📡 Ollama URL: {OLLAMA_URL} (prefer_node_worker={PREFER_NODE_WORKER})")
-    logger.info(f"📡 STT URL: {STT_URL}")
+    logger.info(f"📡 Swapper URL: {SWAPPER_URL} (legacy, being decommissioned)")
     logger.info(f"📡 Vision URL: {VISION_URL}")
     logger.info(f"📡 OCR URL: {OCR_URL}")
     logger.info(f"📡 Neo4j URL: {NEO4J_URI}")
@@ -2388,33 +2389,39 @@ async def agent_infer(agent_id: str, request: InferRequest):
                 logger.warning(f"⚠️ Deterministic AgroMatrix plant flow failed, fallback to generic vision: {e}")
 
         try:
-            # Use Swapper's /vision endpoint (manages model loading)
+            vision_model = "qwen3-vl-8b"
-            vision_payload = {
+            vision_system = system_prompt or ""
-                "model": "qwen3-vl-8b",
+            if vision_system and memory_brief_text:
-                "prompt": request.prompt,
+                vision_system = f"{vision_system}\n\n[INTERNAL MEMORY - do NOT repeat to user]\n{memory_brief_text}"
-                "images": request.images,  # Swapper handles data URL conversion
+
-                "max_tokens": request.max_tokens or 1024,
+            clean_images = []
-                "temperature": request.temperature or 0.7
+            for img in (request.images or []):
-            }
+                if "," in img and img.startswith("data:"):
-            
+                    clean_images.append(img.split(",", 1)[1])
-            # Add system prompt if available
-            if system_prompt:
-                if memory_brief_text:
-                    vision_payload["system"] = f"{system_prompt}\n\n[INTERNAL MEMORY - do NOT repeat to user]\n{memory_brief_text}"
                 else:
-                    vision_payload["system"] = system_prompt
+                    clean_images.append(img)
-            
+
-            logger.info(f"🖼️ Sending to Swapper /vision: {SWAPPER_URL}/vision")
+            logger.info(f"🖼️ Vision inference: model={vision_model} images={len(clean_images)} prefer_nw={PREFER_NODE_WORKER}")
-            
+
             vision_resp = await http_client.post(
-                f"{SWAPPER_URL}/vision",
+                f"{OLLAMA_URL}/api/generate",
-                json=vision_payload,
+                json={
-                timeout=120.0
+                    "model": vision_model.replace("-", "-vl:").replace("qwen3-vl:", "qwen3-vl:") if ":" not in vision_model else vision_model,
+                    "prompt": request.prompt,
+                    "images": clean_images,
+                    "system": vision_system,
+                    "stream": False,
+                    "options": {
+                        "num_predict": request.max_tokens or 1024,
+                        "temperature": request.temperature or 0.7,
+                    },
+                },
+                timeout=120.0,
             )
             
             if vision_resp.status_code == 200:
                 vision_data = vision_resp.json()
-                raw_response = vision_data.get("text", "")
+                raw_response = vision_data.get("response", vision_data.get("text", ""))
                 full_response = _sanitize_vision_text_for_user(raw_response)
                 vision_web_query = ""
                 vision_sources: List[Dict[str, str]] = []
@@ -2519,14 +2526,23 @@ async def agent_infer(agent_id: str, request: InferRequest):
                             "Відповідай українською 2-4 реченнями, без службових фраз. "
                             f"Запит користувача: {request.prompt}"
                         )
+                        retry_ollama_payload = {
+                            "model": vision_model.replace("-", "-vl:").replace("qwen3-vl:", "qwen3-vl:") if ":" not in vision_model else vision_model,
+                            "prompt": retry_payload["prompt"],
+                            "images": clean_images,
+                            "stream": False,
+                            "options": {"num_predict": request.max_tokens or 1024, "temperature": 0.7},
+                        }
+                        if retry_payload.get("system"):
+                            retry_ollama_payload["system"] = retry_payload["system"]
                         retry_resp = await http_client.post(
-                            f"{SWAPPER_URL}/vision",
+                            f"{OLLAMA_URL}/api/generate",
-                            json=retry_payload,
+                            json=retry_ollama_payload,
-                            timeout=120.0
+                            timeout=120.0,
                         )
                         if retry_resp.status_code == 200:
                             retry_data = retry_resp.json()
-                            retry_raw = retry_data.get("text", "")
+                            retry_raw = retry_data.get("response", 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)
@@ -2541,7 +2557,7 @@ async def agent_infer(agent_id: str, request: InferRequest):
                 elif request_agent_id in DETERMINISTIC_PLANT_POLICY_AGENTS and _vision_response_is_blurry(full_response):
                     full_response = _build_image_fallback_response(request_agent_id, request.prompt)
 
-                full_response = await _finalize_response_text(full_response, "swapper-vision")
+                full_response = await _finalize_response_text(full_response, "ollama-vision")
                 
                 # Store vision message in agent-specific memory
                 if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval and chat_id and user_id and full_response:
@@ -2567,10 +2583,10 @@ async def agent_infer(agent_id: str, request: InferRequest):
                     response=full_response,
                     model="qwen3-vl-8b",
                     tokens_used=None,
-                    backend="swapper-vision"
+                    backend="ollama-vision"
                 )
             else:
-                logger.error(f"❌ Swapper vision error: {vision_resp.status_code} - {vision_resp.text[:200]}")
+                logger.error(f"❌ Ollama vision error: {vision_resp.status_code} - {vision_resp.text[:200]}")
                 fallback_response = await _finalize_response_text(
                     _build_image_fallback_response(request_agent_id, request.prompt),
                     "swapper-vision-fallback",
@@ -2579,7 +2595,7 @@ async def agent_infer(agent_id: str, request: InferRequest):
                     response=fallback_response,
                     model="qwen3-vl-8b",
                     tokens_used=None,
-                    backend="swapper-vision-fallback"
+                    backend="vision-fallback"
                 )
                 
         except Exception as e:
@@ -2592,7 +2608,7 @@ async def agent_infer(agent_id: str, request: InferRequest):
                 response=fallback_response,
                 model="qwen3-vl-8b",
                 tokens_used=None,
-                backend="swapper-vision-fallback"
+                backend="vision-fallback"
             )
     
     # =========================================================================
@@ -3142,90 +3158,88 @@ async def agent_infer(agent_id: str, request: InferRequest):
         logger.warning(f"⚠️ No local model in config, using hardcoded fallback: {local_model}")
     
     try:
-        # Check if Swapper is available
+        ollama_model = local_model.replace("-", ":") if ":" not in local_model else local_model
-        health_resp = await http_client.get(f"{SWAPPER_URL}/health", timeout=5.0)
+        logger.info(f"📡 Calling Ollama direct: model={ollama_model}")
-        if health_resp.status_code == 200:
+
-            logger.info(f"📡 Calling Swapper with local model: {local_model}")
+        generate_resp = await http_client.post(
-            # Generate response via Swapper (which handles model loading)
+            f"{OLLAMA_URL}/api/generate",
-            generate_resp = await http_client.post(
+            json={
-                f"{SWAPPER_URL}/generate",
+                "model": ollama_model,
-                json={
+                "prompt": request.prompt,
-                    "model": local_model,
+                "system": system_prompt,
-                    "prompt": request.prompt,
+                "stream": False,
-                    "system": system_prompt,
+                "options": {
-                    "max_tokens": request.max_tokens,
+                    "num_predict": request.max_tokens or 2048,
-                    "temperature": request.temperature,
+                    "temperature": request.temperature or 0.7,
-                    "stream": False
                 },
-                timeout=300.0
+            },
-            )
+            timeout=300.0,
-            
+        )
-            if generate_resp.status_code == 200:
+        if generate_resp.status_code == 200:
-                data = generate_resp.json()
+            data = generate_resp.json()
-                local_response = _normalize_text_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):
-                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")
-                    logger.warning(f"⚠️ Empty-answer gate triggered for {request_agent_id}, retrying local generate once")
+                retry_prompt = (
-                    retry_prompt = (
+                    f"{request.prompt}\n\n"
-                        f"{request.prompt}\n\n"
+                    "Відповідай коротко і конкретно (2-5 речень), без службових або мета-фраз."
-                        "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."
-                    )
-                local_response = await _finalize_response_text(local_response, "swapper+ollama")
-                
-                # Store in agent-specific memory
-                if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval and chat_id and user_id and local_response:
-                    asyncio.create_task(
-                        memory_retrieval.store_message(
-                            agent_id=request_agent_id,
-                            user_id=user_id,
-                            username=username,
-                            message_text=request.prompt,
-                            response_text=local_response,
-                            chat_id=chat_id
-                        )
-                    )
-                
-                return InferResponse(
-                    response=local_response,
-                    model=local_model,
-                    tokens_used=data.get("eval_count", 0),
-                    backend="swapper+ollama"
                 )
-            else:
+                retry_resp = await http_client.post(
-                logger.error(f"❌ Swapper error: {generate_resp.status_code} - {generate_resp.text}")
+                    f"{OLLAMA_URL}/api/generate",
+                    json={
+                        "model": ollama_model,
+                        "prompt": retry_prompt,
+                        "system": system_prompt,
+                        "stream": False,
+                        "options": {
+                            "num_predict": request.max_tokens or 2048,
+                            "temperature": request.temperature or 0.7,
+                        },
+                    },
+                    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."
+                )
+            local_response = await _finalize_response_text(local_response, "ollama-direct")
+
+            if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval and chat_id and user_id and local_response:
+                asyncio.create_task(
+                    memory_retrieval.store_message(
+                        agent_id=request_agent_id,
+                        user_id=user_id,
+                        username=username,
+                        message_text=request.prompt,
+                        response_text=local_response,
+                        chat_id=chat_id
+                    )
+                )
+
+            return InferResponse(
+                response=local_response,
+                model=local_model,
+                tokens_used=data.get("eval_count", 0),
+                backend="ollama-direct"
+            )
+        else:
+            logger.error(f"❌ Ollama generate error: {generate_resp.status_code} - {generate_resp.text[:200]}")
     except Exception as e:
-        logger.error(f"❌ Swapper/Ollama error: {e}")
+        logger.error(f"❌ Ollama direct error: {e}")
-        # Fallback to direct Ollama if Swapper fails
         try:
-            logger.info(f"🔄 Falling back to direct Ollama connection")
+            logger.info(f"🔄 Falling back to Ollama with hardcoded model")
             generate_resp = await http_client.post(
-                f"{VISION_URL}/api/generate",
+                f"{OLLAMA_URL}/api/generate",
                 json={
-                    "model": "qwen3:8b",  # Use actual Ollama model name
+                    "model": "qwen3:8b",
                     "prompt": request.prompt,
                     "system": system_prompt,
                     "stream": False,
@@ -3526,42 +3540,38 @@ async def documents_versions(doc_id: str, agent_id: str, limit: int = 20):
 
 @app.get("/v1/models")
 async def list_available_models():
-    """List all available models across backends"""
+    """List all available models from NCS (global capabilities pool)."""
     models = []
-    
+
-    # Get Swapper models
     try:
-        resp = await http_client.get(f"{SWAPPER_URL}/models", timeout=5.0)
+        from global_capabilities_client import get_global_capabilities
-        if resp.status_code == 200:
+        pool = await get_global_capabilities()
-            data = resp.json()
+        for m in pool.get("served_models", []):
-            for m in data.get("models", []):
+            models.append({
-                models.append({
+                "id": m.get("name"),
-                    "id": m.get("name"),
+                "backend": m.get("runtime", "unknown"),
-                    "backend": "swapper",
+                "node": m.get("node", "?"),
-                    "size_gb": m.get("size_gb"),
+                "type": m.get("type", "llm"),
-                    "status": m.get("status", "available")
+                "size_gb": m.get("size_gb"),
-                })
+                "status": "served",
+            })
     except Exception as e:
-        logger.warning(f"Cannot get Swapper models: {e}")
+        logger.warning(f"Cannot get NCS global models: {e}")
-    
+
-    # Get Ollama models
+    if not models:
-    try:
+        try:
-        resp = await http_client.get(f"{VISION_URL}/api/tags", timeout=5.0)
+            resp = await http_client.get(f"{OLLAMA_URL}/api/tags", timeout=5.0)
-        if resp.status_code == 200:
+            if resp.status_code == 200:
-            data = resp.json()
+                for m in resp.json().get("models", []):
-            for m in data.get("models", []):
-                # Avoid duplicates
-                model_name = m.get("name")
-                if not any(x.get("id") == model_name for x in models):
                     models.append({
-                        "id": model_name,
+                        "id": m.get("name"),
                         "backend": "ollama",
                         "size_gb": round(m.get("size", 0) / 1e9, 1),
-                        "status": "loaded"
+                        "status": "loaded",
                     })
-    except Exception as e:
+        except Exception as e:
-        logger.warning(f"Cannot get Ollama models: {e}")
+            logger.warning(f"Cannot get Ollama models: {e}")
-    
+
     return {"models": models, "total": len(models)}
 
 

services/swapper-service/config/swapper_config.yaml

@@ -1,90 +1,35 @@
-# Swapper Configuration for Node #2 (Development Node)
+# Swapper Configuration — Default / Fallback
-# Single-active LLM scheduler
+#
-# MacBook Pro M4 Max - Apple Silicon (40-core GPU, 64GB RAM)
+# NOTE: Swapper is now a runtime gateway / executor only.
-# Auto-generated configuration with available Ollama models
+# Source of truth for models is NCS (Node Capabilities Service).
+# No hardcoded model lists — Swapper queries NCS or Ollama /api/tags at startup.
+#
+# Per-node overrides: swapper_config_node1.yaml, swapper_config_node2.yaml
 
-swapper:
+node_id: default
-  mode: single-active
+
-  max_concurrent_models: 1
+runtimes:
+  ollama:
+    url: http://localhost:11434
+    timeout: 300
+
+limits:
+  llm_concurrency: 2
+  vision_concurrency: 1
+  max_concurrent_models: 2
   model_swap_timeout: 300
-  gpu_enabled: true
-  metal_acceleration: true  # Apple Silicon GPU acceleration
-  # Модель для автоматичного завантаження при старті (опціонально)
-  # Якщо не вказано - моделі завантажуються тільки за запитом
-  # Рекомендовано: gpt-oss:latest (швидка модель) або phi3:latest (легка модель)
-  default_model: gpt-oss:latest  # Модель активується автоматично при старті
 
-models:
+timeouts:
-  # Fast LLM - GPT-OSS 20B (High Priority) - Main model for general tasks
+  llm_ms: 120000
-  gpt-oss-latest:
+  vision_ms: 180000
-    path: ollama:gpt-oss:latest
+  stt_ms: 60000
-    type: llm
+  tts_ms: 60000
-    size_gb: 13.0
+
-    priority: high
+gpu:
-    description: "Fast LLM for general tasks and conversations (20.9B params)"
+  enabled: false
-    
+  metal_acceleration: false
-  # Lightweight LLM - Phi3 3.8B (High Priority) - Fast responses
-  phi3-latest:
-    path: ollama:phi3:latest
-    type: llm
-    size_gb: 2.2
-    priority: high
-    description: "Lightweight LLM for fast responses (3.8B params)"
-    
-  # Code Specialist - StarCoder2 3B (Medium Priority) - Code engineering
-  starcoder2-3b:
-    path: ollama:starcoder2:3b
-    type: code
-    size_gb: 1.7
-    priority: medium
-    description: "Code specialist model for code engineering (3B params)"
-    
-  # Reasoning Model - Mistral Nemo 12.2B (High Priority) - Advanced reasoning
-  mistral-nemo-12b:
-    path: ollama:mistral-nemo:12b
-    type: llm
-    size_gb: 7.1
-    priority: high
-    description: "Advanced reasoning model for complex tasks (12.2B params)"
-    
-  # Reasoning Model - Gemma2 27B (Medium Priority) - Strategic reasoning
-  gemma2-27b:
-    path: ollama:gemma2:27b
-    type: llm
-    size_gb: 15.0
-    priority: medium
-    description: "Reasoning model for strategic tasks (27.2B params)"
-    
-  # Code Specialist - DeepSeek Coder 33B (High Priority) - Advanced code tasks
-  deepseek-coder-33b:
-    path: ollama:deepseek-coder:33b
-    type: code
-    size_gb: 18.0
-    priority: high
-    description: "Advanced code specialist model (33B params)"
-    
-  # Code Specialist - Qwen2.5 Coder 32B (High Priority) - Advanced code tasks
-  qwen2.5-coder-32b:
-    path: ollama:qwen2.5-coder:32b
-    type: code
-    size_gb: 19.0
-    priority: high
-    description: "Advanced code specialist model (32.8B params)"
-    
-  # Reasoning Model - DeepSeek R1 70B (High Priority) - Strategic reasoning (large model)
-  deepseek-r1-70b:
-    path: ollama:deepseek-r1:70b
-    type: llm
-    size_gb: 42.0
-    priority: high
-    description: "Strategic reasoning model (70.6B params, quantized)"
 
 storage:
   models_dir: /app/models
   cache_dir: /app/cache
   swap_dir: /app/swap
-
-ollama:
-  url: http://localhost:11434  # Native Ollama on MacBook (via Pieces OS or brew)
-  timeout: 300
-

services/swapper-service/config/swapper_config_node1.yaml

@@ -1,186 +1,37 @@
 # Swapper Configuration for Node #1 (Production Server)
-# Optimized Multimodal Stack: LLM + Vision + OCR + Document + Audio
 # Hetzner GEX44 - NVIDIA RTX 4000 SFF Ada (20GB VRAM)
 #
-# ВАЖЛИВО: Ембедінги через зовнішні API:
+# NOTE: Swapper is now a runtime gateway / executor only.
-#   - Text: Cohere API (embed-multilingual-v3.0, 1024 dim)
+# Source of truth for models is NCS (Node Capabilities Service).
-#   - Image: Vision Encoder (OpenCLIP ViT-L/14, 768 dim)
+# No hardcoded model lists — Swapper queries NCS or Ollama /api/tags at startup.
-#   НЕ використовуємо локальні embedding моделі!
 
-swapper:
+node_id: noda1
-  mode: multi-active
+
-  max_concurrent_models: 4  # LLM + OCR + STT + TTS (до 15GB)
+runtimes:
+  ollama:
+    url: http://172.18.0.1:11434
+    timeout: 300
+  # comfyui:
+  #   url: http://127.0.0.1:8188
+
+limits:
+  llm_concurrency: 2
+  vision_concurrency: 1
+  max_concurrent_models: 4
   model_swap_timeout: 300
-  gpu_enabled: true
+
+timeouts:
+  llm_ms: 120000
+  vision_ms: 180000
+  stt_ms: 60000
+  tts_ms: 60000
+  image_gen_ms: 300000
+
+gpu:
+  enabled: true
   metal_acceleration: false
-  default_model: qwen3-8b
-  lazy_load_ocr: true
-  lazy_load_audio: true
-  # Автоматичне вивантаження при нестачі VRAM
   auto_unload_on_oom: true
-  vram_threshold_gb: 18  # Починати вивантажувати при 18GB
+  vram_threshold_gb: 18
-
-models:
-  # ============================================
-  # LLM MODELS (Ollama) - тільки qwen3
-  # ============================================
-  
-  # Primary LLM - Qwen3 8B (includes math, coding, reasoning)
-  qwen3-8b:
-    path: ollama:qwen3:8b
-    type: llm
-    size_gb: 5.2
-    priority: high
-    description: "Qwen3 8B - primary LLM with math, coding, reasoning capabilities"
-    capabilities:
-      - chat
-      - math
-      - coding
-      - reasoning
-      - multilingual
-
-  # ============================================
-  # VISION MODELS (Ollama)
-  # ============================================
-  
-  # Vision Model - Qwen3-VL 8B
-  qwen3-vl-8b:
-    path: ollama:qwen3-vl:8b
-    type: vision
-    size_gb: 6.1
-    priority: high
-    description: "Qwen3-VL 8B for image understanding and visual reasoning"
-    capabilities:
-      - image_understanding
-      - visual_qa
-      - diagram_analysis
-      - ocr_basic
-
-  # ============================================
-  # OCR/DOCUMENT MODELS (HuggingFace)
-  # ============================================
-  
-  # GOT-OCR2.0 - Best for documents, tables, formulas
-  got-ocr2:
-    path: huggingface:stepfun-ai/GOT-OCR2_0
-    type: ocr
-    size_gb: 7.0
-    priority: high
-    description: "Best OCR for documents, tables, formulas, handwriting"
-    capabilities:
-      - documents
-      - tables
-      - formulas
-      - handwriting
-      - multilingual
-    
-  # Donut - Document Understanding (no external OCR, 91% CORD)
-  donut-base:
-    path: huggingface:naver-clova-ix/donut-base
-    type: ocr
-    size_gb: 3.0
-    priority: high
-    description: "Document parsing without OCR engine (91% CORD accuracy)"
-    capabilities:
-      - document_parsing
-      - receipts
-      - forms
-      - invoices
-    
-  # Donut fine-tuned for receipts/invoices (CORD dataset)
-  donut-cord:
-    path: huggingface:naver-clova-ix/donut-base-finetuned-cord-v2
-    type: ocr
-    size_gb: 3.0
-    priority: medium
-    description: "Donut fine-tuned for receipts extraction"
-    capabilities:
-      - receipts
-      - invoices
-      - structured_extraction
-
-  # IBM Granite Docling - Document conversion with structure preservation
-  granite-docling:
-    path: huggingface:ds4sd/docling-ibm-granite-vision-1b
-    type: document
-    size_gb: 2.5
-    priority: high
-    description: "IBM Granite Docling for PDF/document structure extraction"
-    capabilities:
-      - pdf_conversion
-      - table_extraction
-      - formula_extraction
-      - layout_preservation
-      - doctags_format
-
-  # ============================================
-  # AUDIO MODELS - STT (Speech-to-Text)
-  # ============================================
-  
-  # Faster Whisper Large-v3 - Best STT quality
-  faster-whisper-large:
-    path: huggingface:Systran/faster-whisper-large-v3
-    type: stt
-    size_gb: 3.0
-    priority: high
-    description: "Faster Whisper Large-v3 - best quality, 99 languages"
-    capabilities:
-      - speech_recognition
-      - transcription
-      - multilingual
-      - timestamps
-      - ukrainian
-    
-  # Whisper Small - Fast/lightweight for quick transcription
-  whisper-small:
-    path: huggingface:openai/whisper-small
-    type: stt
-    size_gb: 0.5
-    priority: medium
-    description: "Whisper Small for fast transcription"
-    capabilities:
-      - speech_recognition
-      - transcription
-
-  # ============================================
-  # AUDIO MODELS - TTS (Text-to-Speech)
-  # ============================================
-  
-  # Coqui XTTS-v2 - Best multilingual TTS with Ukrainian support
-  xtts-v2:
-    path: huggingface:coqui/XTTS-v2
-    type: tts
-    size_gb: 2.0
-    priority: high
-    description: "XTTS-v2 multilingual TTS with voice cloning, Ukrainian support"
-    capabilities:
-      - text_to_speech
-      - voice_cloning
-      - multilingual
-      - ukrainian
-      - 17_languages
-
-  # ============================================
-  # IMAGE GENERATION MODELS (HuggingFace/Diffusers)
-  # ============================================
-  
-  # FLUX.2 Klein 4B - High quality image generation with lazy loading
-  flux-klein-4b:
-    path: huggingface:black-forest-labs/FLUX.2-klein-base-4B
-    type: image_generation
-    size_gb: 15.4
-    priority: medium
-    description: "FLUX.2 Klein 4B - high quality image generation, lazy loaded on demand"
-    capabilities:
-      - text_to_image
-      - high_quality
-      - 1024x1024
-      - artistic
-    default_params:
-      num_inference_steps: 50
-      guidance_scale: 4.0
-      width: 1024
-      height: 1024
 
 storage:
   models_dir: /app/models
@@ -188,33 +39,8 @@ storage:
   swap_dir: /app/swap
   huggingface_cache: /root/.cache/huggingface
 
-ollama:
-  url: http://172.18.0.1:11434
-  timeout: 300
-
 huggingface:
   device: cuda
   torch_dtype: float16
   trust_remote_code: true
   low_cpu_mem_usage: true
-
-# ============================================
-# EMBEDDING SERVICES (External APIs)
-# НЕ через Swapper - окремі сервіси!
-# ============================================
-# 
-# Text Embeddings:
-#   Service: Memory Service → Cohere API
-#   Model: embed-multilingual-v3.0
-#   Dimension: 1024
-#   Endpoint: Memory Service handles internally
-#
-# Image/Multimodal Embeddings:
-#   Service: Vision Encoder (port 8001)
-#   Model: OpenCLIP ViT-L/14
-#   Dimension: 768
-#   Endpoint: http://vision-encoder:8001/embed
-#
-# Vector Storage:
-#   Qdrant (port 6333) - separate collections for text vs image embeddings
-#   ВАЖЛИВО: НЕ змішувати embedding spaces в одній колекції!

services/swapper-service/config/swapper_config_node2.yaml

@@ -1,126 +1,40 @@
 # Swapper Configuration for Node #2 (Development Node)
-# Single-active LLM scheduler
 # MacBook Pro M4 Max - Apple Silicon (40-core GPU, 64GB RAM)
-# Auto-generated configuration with available Ollama models
+#
+# NOTE: Swapper is now a runtime gateway / executor only.
+# Source of truth for models is NCS (Node Capabilities Service).
+# No hardcoded model lists — Swapper queries NCS or Ollama /api/tags at startup.
 
-swapper:
+node_id: noda2
-  mode: single-active
+
+runtimes:
+  ollama:
+    url: http://host.docker.internal:11434
+    timeout: 300
+  # mlx:
+  #   stt_model: whisper-large-v3-turbo
+  #   tts_model: kokoro-82m
+  # comfyui:
+  #   url: http://127.0.0.1:8188
+
+limits:
+  llm_concurrency: 1
+  vision_concurrency: 1
   max_concurrent_models: 1
   model_swap_timeout: 300
-  gpu_enabled: true
-  metal_acceleration: true  # Apple Silicon GPU acceleration
-  # Модель для автоматичного завантаження при старті (опціонально)
-  # Якщо не вказано - моделі завантажуються тільки за запитом
-  # Рекомендовано: gpt-oss:latest (швидка модель) або phi3:latest (легка модель)
-  # Стартова модель має бути реально встановлена в Ollama на NODA2
-  default_model: qwen3:14b  # Модель активується автоматично при старті
 
-models:
+timeouts:
-  # Fast LLM - GPT-OSS 20B (High Priority) - Main model for general tasks
+  llm_ms: 120000
-  gpt-oss-latest:
+  vision_ms: 180000
-    path: ollama:gpt-oss:latest
+  stt_ms: 60000
-    type: llm
+  tts_ms: 60000
-    size_gb: 13.0
+  image_gen_ms: 300000
-    priority: high
-    description: "Fast LLM for general tasks and conversations (20.9B params)"
-    
-  # Lightweight LLM - Phi3 3.8B (High Priority) - Fast responses
-  phi3-latest:
-    path: ollama:phi3:latest
-    type: llm
-    size_gb: 2.2
-    priority: high
-    description: "Lightweight LLM for fast responses (3.8B params)"
-    
-  # General Reasoning - Qwen3 14B (High Priority)
-  qwen3-14b:
-    path: ollama:qwen3:14b
-    type: llm
-    size_gb: 9.3
-    priority: high
-    description: "Balanced local model for Sofiia and router fallback"
 
-  # Reasoning Model - Qwen3.5 35B A3B (High Priority)
+gpu:
-  qwen3.5-35b-a3b:
+  enabled: true
-    path: ollama:qwen3.5:35b-a3b
+  metal_acceleration: true
-    type: llm
-    size_gb: 22.0
-    priority: high
-    description: "Large reasoning model for complex Sofiia requests"
-    
-  # Reasoning Model - GLM 4.7 Flash (High Priority) - Fast general model
-  glm-4.7-flash:
-    path: ollama:glm-4.7-flash:32k
-    type: llm
-    size_gb: 19.0
-    priority: high
-    description: "Multi-purpose reasoning model (fast context)"
-    
-  # Reasoning Model - Gemma2 27B (Medium Priority) - Strategic reasoning
-  gemma2-27b:
-    path: ollama:gemma2:27b
-    type: llm
-    size_gb: 15.0
-    priority: medium
-    description: "Reasoning model for strategic tasks (27.2B params)"
-    
-  # Code Specialist - DeepSeek Coder 33B (High Priority) - Advanced code tasks
-  deepseek-coder-33b:
-    path: ollama:deepseek-coder:33b
-    type: code
-    size_gb: 18.0
-    priority: high
-    description: "Advanced code specialist model (33B params)"
-    
-  # Code Specialist - Qwen2.5 Coder 32B (High Priority) - Advanced code tasks
-  qwen2.5-coder-32b:
-    path: ollama:qwen2.5-coder:32b
-    type: code
-    size_gb: 19.0
-    priority: high
-    description: "Advanced code specialist model (32.8B params)"
-    
-  # Reasoning Model - DeepSeek R1 70B (High Priority) - Strategic reasoning (large model)
-  deepseek-r1-70b:
-    path: ollama:deepseek-r1:70b
-    type: llm
-    size_gb: 42.0
-    priority: high
-    description: "Strategic reasoning model (70.6B params, quantized)"
-
-  # Vision Model - LLaVA 13B (P0 Fix: NODA2 fallback vision)
-  # Available in Ollama on NODA2 — used until qwen3-vl:8b is installed
-  llava-13b:
-    path: ollama:llava:13b
-    type: vision
-    size_gb: 8.0
-    priority: high
-    description: "LLaVA 13B vision model (multimodal CLIP+LLM). P0 fallback until qwen3-vl:8b."
-    vision: true
-    ollama_model: "llava:13b"
-
-  # Vision Model - Qwen3-VL 8B (RECOMMENDED: install with: ollama pull qwen3-vl:8b)
-  # Better quality than llava:13b. Enable once installed.
-  # qwen3-vl-8b:
-  #   path: ollama:qwen3-vl:8b
-  #   type: vision
-  #   size_gb: 5.5
-  #   priority: high
-  #   description: "Qwen3-VL 8B — modern vision-language model (recommended)"
-  #   vision: true
-  #   ollama_model: "qwen3-vl:8b"
 
 storage:
   models_dir: /app/models
   cache_dir: /app/cache
   swap_dir: /app/swap
-
-ollama:
-  url: http://host.docker.internal:11434  # host.docker.internal → native Ollama on MacBook (NODA2 P1 fix)
-  timeout: 300
-
-# Vision endpoint configuration
-# /vision/models returns all models where vision: true
-vision:
-  default_model: llava-13b
-  ollama_base_url: http://host.docker.internal:11434

services/swapper-service/config/swapper_config_node3.yaml

@@ -1,63 +1,37 @@
 # Swapper Configuration for Node #3 (AI/ML Workstation)
-# Single-active LLM scheduler
+# Threadripper PRO + RTX 3090 24GB — GPU-intensive workloads
-# Threadripper PRO + RTX 3090 24GB - GPU-intensive workloads
+#
+# NOTE: Swapper is now a runtime gateway / executor only.
+# Source of truth for models is NCS (Node Capabilities Service).
+# No hardcoded model lists.
 
-swapper:
+node_id: noda3
-  mode: single-active
+
-  max_concurrent_models: 1
+runtimes:
+  ollama:
+    url: http://localhost:11434
+    timeout: 300
+  comfyui:
+    url: http://127.0.0.1:8188
+
+limits:
+  llm_concurrency: 2
+  vision_concurrency: 1
+  max_concurrent_models: 2
   model_swap_timeout: 300
-  gpu_enabled: true
-  metal_acceleration: false  # NVIDIA GPU, not Apple Silicon
-  # Модель для автоматичного завантаження при старті
-  # qwen3-8b - основна модель (4.87 GB), швидка відповідь на перший запит
-  default_model: qwen3-8b
 
-models:
+timeouts:
-  # Primary LLM - Qwen3 8B (High Priority) - Main model from INFRASTRUCTURE.md
+  llm_ms: 120000
-  qwen3-8b:
+  vision_ms: 180000
-    path: ollama:qwen3:8b
+  image_gen_ms: 600000
-    type: llm
+
-    size_gb: 4.87
+gpu:
-    priority: high
+  enabled: true
-    description: "Primary LLM for general tasks and conversations"
+  metal_acceleration: false
-    
+  auto_unload_on_oom: true
-  # Vision Model - Qwen3-VL 8B (High Priority) - For image processing
+  vram_threshold_gb: 22
-  qwen3-vl-8b:
-    path: ollama:qwen3-vl:8b
-    type: vision
-    size_gb: 5.72
-    priority: high
-    description: "Vision model for image understanding and processing"
-    
-  # Qwen2.5 7B Instruct (High Priority)
-  qwen2.5-7b-instruct:
-    path: ollama:qwen2.5:7b-instruct-q4_K_M
-    type: llm
-    size_gb: 4.36
-    priority: high
-    description: "Qwen2.5 7B Instruct model"
-    
-  # Lightweight LLM - Qwen2.5 3B Instruct (Medium Priority)
-  qwen2.5-3b-instruct:
-    path: ollama:qwen2.5:3b-instruct-q4_K_M
-    type: llm
-    size_gb: 1.80
-    priority: medium
-    description: "Lightweight LLM for faster responses"
-    
-  # Math Specialist - Qwen2 Math 7B (High Priority)
-  qwen2-math-7b:
-    path: ollama:qwen2-math:7b
-    type: math
-    size_gb: 4.13
-    priority: high
-    description: "Specialized model for mathematical tasks"
 
 storage:
   models_dir: /app/models
   cache_dir: /app/cache
   swap_dir: /app/swap
-
-ollama:
-  url: http://ollama:11434  # From Docker container to Ollama service
-  timeout: 300