diff --git a/doc/examples/sample-config.yaml b/doc/examples/sample-config.yaml
index 9dbce09..49ad389 100644
--- a/doc/examples/sample-config.yaml
+++ b/doc/examples/sample-config.yaml
@@ -1,40 +1,32 @@
-# Sample NOMYO Router Configuration
-
-# Basic single endpoint configuration
+# config.yaml
+# Ollama endpoints
 endpoints:
-  - http://localhost:11434
+  - http://192.168.0.50:11434
+  - http://192.168.0.51:11434
+  - http://192.168.0.52:11434
+  # External OpenAI-compatible endpoints (will NOT be queried for /api/ps /api/ps_details)
+  - https://api.openai.com/v1
 
+# llama-server endpoints (OpenAI-compatible with /v1/models status info)
+# These endpoints will be queried for /api/tags, /api/ps, /api/ps_details
+# and included in the model selection pool for inference routing
+llama_server_endpoints:
+  - http://localhost:8080/v1
+  - http://localhost:8081/v1
+
+# Maximum concurrent connections *per endpoint‑model pair* (equals to OLLAMA_NUM_PARALLEL)
 max_concurrent_connections: 2
 
-# Optional router-level API key to secure the router and dashboard (leave blank to disable)
+# Optional router-level API key that gates router/API/web UI access (leave empty to disable)
 nomyo-router-api-key: ""
 
-# Multi-endpoint configuration with local Ollama instances
-# endpoints:
-#   - http://ollama-worker1:11434
-#   - http://ollama-worker2:11434
-#   - http://ollama-worker3:11434
-
-# Mixed configuration with Ollama and OpenAI endpoints
-# endpoints:
-#   - http://localhost:11434
-#   - https://api.openai.com/v1
-
-
 # API keys for remote endpoints
-# Use ${VAR_NAME} syntax to reference environment variables
+# Set an environment variable like OPENAI_KEY
+# Confirm endpoints are exactly as in endpoints block
 api_keys:
-  # Local Ollama instances typically don't require authentication
-  "http://localhost:11434": "ollama"
-
-  # Remote Ollama instances
-  # "http://remote-ollama:11434": "ollama"
-
-  # OpenAI API
-  # "https://api.openai.com/v1": "${OPENAI_KEY}"
-
-  # Anthropic API
-  # "https://api.anthropic.com/v1": "${ANTHROPIC_KEY}"
-
-  # Other OpenAI-compatible endpoints
-  # "https://api.mistral.ai/v1": "${MISTRAL_KEY}"
+  "http://192.168.0.50:11434": "ollama"
+  "http://192.168.0.51:11434": "ollama"
+  "http://192.168.0.52:11434": "ollama"
+  "https://api.openai.com/v1": "${OPENAI_KEY}"
+  "http://localhost:8080/v1": "llama-server"  # Optional API key for llama-server
+  "http://localhost:8081/v1": "llama-server"
\ No newline at end of file
diff --git a/router.py b/router.py
index 9fb0e86..51f5054 100644
--- a/router.py
+++ b/router.py
@@ -34,16 +34,18 @@ from PIL import Image
 # Successful results are cached for 300s
 _models_cache: dict[str, tuple[Set[str], float]] = {}
 _loaded_models_cache: dict[str, tuple[Set[str], float]] = {}
-# Transient errors are cached for 1s – the key stays until the
-# timeout expires, after which the endpoint will be queried again.
-_error_cache: dict[str, float] = {}
+# Transient errors are cached separately per concern so that a failure
+# in one path does not poison the other.
+_available_error_cache: dict[str, float] = {}
+_loaded_error_cache: dict[str, float] = {}
 
 # ------------------------------------------------------------------
 # Cache locks
 # ------------------------------------------------------------------
 _models_cache_lock = asyncio.Lock()
 _loaded_models_cache_lock = asyncio.Lock()
-_error_cache_lock = asyncio.Lock()
+_available_error_cache_lock = asyncio.Lock()
+_loaded_error_cache_lock = asyncio.Lock()
 
 # ------------------------------------------------------------------
 # In-flight request tracking (prevents cache stampede)
@@ -106,6 +108,8 @@ class Config(BaseSettings):
             "http://localhost:11434",
         ]
     )
+    # List of llama-server endpoints (OpenAI-compatible with /v1/models status info)
+    llama_server_endpoints: List[str] = Field(default_factory=list)
     # Max concurrent connections per endpoint‑model pair, see OLLAMA_NUM_PARALLEL
     max_concurrent_connections: int = 1
 
@@ -254,13 +258,19 @@ async def enforce_router_api_key(request: Request, call_next):
     # Strip the api_key query param from scope so access logs do not leak it
     _strip_api_key_from_scope(request)
     if provided_key is None:
-        response = await call_next(request)
-        # Add CORS headers for API key authentication requests
+        # No key provided but authentication is required - return 401
+        headers = {}
         if "/api/" in path and path != "/api/usage-stream":
-            response.headers["Access-Control-Allow-Origin"] = "*"
-            response.headers["Access-Control-Allow-Headers"] = "Authorization, Content-Type"
-            response.headers["Access-Control-Allow-Methods"] = "GET, POST, PUT, DELETE, OPTIONS"
-        return response
+            headers = {
+                "Access-Control-Allow-Origin": "*",
+                "Access-Control-Allow-Headers": "Authorization, Content-Type",
+                "Access-Control-Allow-Methods": "GET, POST, PUT, DELETE, OPTIONS",
+            }
+        return JSONResponse(
+            content={"detail": "Missing NOMYO Router API key"},
+            status_code=401,
+            headers=headers,
+        )
 
     if not secrets.compare_digest(str(provided_key), str(expected_key)):
         return JSONResponse(
@@ -339,7 +349,50 @@ def _format_connection_issue(url: str, error: Exception) -> str:
 
     return f"Error while contacting {url}: {error}"
 
+def _normalize_llama_model_name(name: str) -> str:
+    """Extract the model name from a huggingface-style identifier.
+    e.g. 'unsloth/gpt-oss-20b-GGUF:F16' -> 'gpt-oss-20b-GGUF'
+    """
+    if "/" in name:
+        name = name.rsplit("/", 1)[1]
+    if ":" in name:
+        name = name.split(":")[0]
+    return name
+
+def _extract_llama_quant(name: str) -> str:
+    """Extract the quantization level from a huggingface-style identifier.
+    e.g. 'unsloth/gpt-oss-20b-GGUF:Q8_0' -> 'Q8_0'
+    Returns empty string if no quant suffix is present.
+    """
+    if ":" in name:
+        return name.rsplit(":", 1)[1]
+    return ""
+
+def _is_llama_model_loaded(item: dict) -> bool:
+    """Return True if a llama-server /v1/models item has status 'loaded'.
+    Handles both dict format ({"value": "loaded"}) and plain string ("loaded")."""
+    status = item.get("status")
+    if isinstance(status, dict):
+        return status.get("value") == "loaded"
+    if isinstance(status, str):
+        return status == "loaded"
+    return False
+
 def is_ext_openai_endpoint(endpoint: str) -> bool:
+    """
+    Determine if an endpoint is an external OpenAI-compatible endpoint (not Ollama or llama-server).
+    
+    Returns True for:
+    - External services like OpenAI.com, Groq, etc.
+    
+    Returns False for:
+    - Ollama endpoints (without /v1, or with /v1 but default port 11434)
+    - llama-server endpoints (explicitly configured in llama_server_endpoints)
+    """
+    # Check if it's a llama-server endpoint (has /v1 and is in the configured list)
+    if endpoint in config.llama_server_endpoints:
+        return False
+    
     if "/v1" not in endpoint:
         return False
     
@@ -353,6 +406,13 @@ def is_ext_openai_endpoint(endpoint: str) -> bool:
     
     return True  # It's an external OpenAI endpoint
 
+def is_openai_compatible(endpoint: str) -> bool:
+    """
+    Return True if the endpoint speaks the OpenAI API (not native Ollama).
+    This includes external OpenAI endpoints AND llama-server endpoints.
+    """
+    return "/v1" in endpoint or endpoint in config.llama_server_endpoints
+
 async def token_worker() -> None:
     try:
         while True:
@@ -508,7 +568,10 @@ class fetch:
         if api_key is not None:
             headers = {"Authorization": "Bearer " + api_key}
 
-        if "/v1" in endpoint:
+        if endpoint in config.llama_server_endpoints and "/v1" not in endpoint:
+            endpoint_url = f"{endpoint}/v1/models"
+            key = "data"
+        elif "/v1" in endpoint or endpoint in config.llama_server_endpoints:
             endpoint_url = f"{endpoint}/models"
             key = "data"
         else:
@@ -533,10 +596,21 @@ class fetch:
             message = _format_connection_issue(endpoint_url, e)
             print(f"[fetch.available_models] {message}")
             # Update error cache with lock protection
-            async with _error_cache_lock:
-                _error_cache[endpoint] = time.time()
+            async with _available_error_cache_lock:
+                _available_error_cache[endpoint] = time.time()
             return set()
 
+    async def _refresh_available_models(endpoint: str, api_key: Optional[str] = None) -> None:
+        """
+        Background task to refresh available models cache without blocking the caller.
+        Used for stale-while-revalidate pattern.
+        """
+        try:
+            await fetch._fetch_available_models_internal(endpoint, api_key)
+        except Exception as e:
+            # Silently fail - cache will remain stale but functional
+            print(f"[fetch._refresh_available_models] Background refresh failed for {endpoint}: {e}")
+
     async def available_models(endpoint: str, api_key: Optional[str] = None) -> Set[str]:
         """
         Query <endpoint>/api/tags and return a set of all model names that the
@@ -547,6 +621,10 @@ class fetch:
         Uses request coalescing to prevent cache stampede: if multiple requests
         arrive when cache is expired, only one actual HTTP request is made.
 
+        Uses stale-while-revalidate: when the cache is between 300-600s old,
+        the stale data is returned immediately while a background refresh runs.
+        This prevents model blackouts caused by transient timeouts.
+
         If the request fails (e.g. timeout, 5xx, or malformed response), an empty
         set is returned.
         """
@@ -554,19 +632,27 @@ class fetch:
         async with _models_cache_lock:
             if endpoint in _models_cache:
                 models, cached_at = _models_cache[endpoint]
+
+                # FRESH: < 300s old - return immediately
                 if _is_fresh(cached_at, 300):
                     return models
-                # Stale entry - remove it
+
+                # STALE: 300-600s old - return stale data and refresh in background
+                if _is_fresh(cached_at, 600):
+                    asyncio.create_task(fetch._refresh_available_models(endpoint, api_key))
+                    return models  # Return stale data immediately
+
+                # EXPIRED: > 600s old - too stale, must refresh synchronously
                 del _models_cache[endpoint]
 
         # Check error cache with lock protection
-        async with _error_cache_lock:
-            if endpoint in _error_cache:
-                if _is_fresh(_error_cache[endpoint], 10):
+        async with _available_error_cache_lock:
+            if endpoint in _available_error_cache:
+                if _is_fresh(_available_error_cache[endpoint], 30):
                     # Still within the short error TTL – pretend nothing is available
                     return set()
                 # Error expired – remove it
-                del _error_cache[endpoint]
+                del _available_error_cache[endpoint]
 
         # Request coalescing: check if another request is already fetching this endpoint
         async with _inflight_lock:
@@ -593,25 +679,56 @@ class fetch:
         """
         Internal function that performs the actual HTTP request to fetch loaded models.
         This is called by loaded_models() after checking caches and in-flight requests.
+        
+        For Ollama endpoints: queries /api/ps and returns model names
+        For llama-server endpoints: queries /v1/models and filters for status.value == "loaded"
         """
         client: aiohttp.ClientSession = app_state["session"]
-        try:
-            async with client.get(f"{endpoint}/api/ps") as resp:
-                await _ensure_success(resp)
-                data = await resp.json()
-            # The response format is:
-            #   {"models": [{"name": "model1"}, {"name": "model2"}]}
-            models = {m.get("name") for m in data.get("models", []) if m.get("name")}
+        
+        # Check if this is a llama-server endpoint
+        if endpoint in config.llama_server_endpoints:
+            # Query /v1/models for llama-server
+            try:
+                async with client.get(f"{endpoint}/models") as resp:
+                    await _ensure_success(resp)
+                    data = await resp.json()
+                
+                # Filter for loaded models only
+                items = data.get("data", [])
+                models = {
+                    item.get("id")
+                    for item in items
+                    if item.get("id") and _is_llama_model_loaded(item)
+                }
 
-            # Update cache with lock protection
-            async with _loaded_models_cache_lock:
-                _loaded_models_cache[endpoint] = (models, time.time())
-            return models
-        except Exception as e:
-            # If anything goes wrong we simply assume the endpoint has no models
-            message = _format_connection_issue(f"{endpoint}/api/ps", e)
-            print(f"[fetch.loaded_models] {message}")
-            return set()
+                # Update cache with lock protection
+                async with _loaded_models_cache_lock:
+                    _loaded_models_cache[endpoint] = (models, time.time())
+                return models
+            except Exception as e:
+                # If anything goes wrong we simply assume the endpoint has no models
+                message = _format_connection_issue(f"{endpoint}/models", e)
+                print(f"[fetch.loaded_models] {message}")
+                return set()
+        else:
+            # Original Ollama /api/ps logic
+            try:
+                async with client.get(f"{endpoint}/api/ps") as resp:
+                    await _ensure_success(resp)
+                    data = await resp.json()
+                # The response format is:
+                #   {"models": [{"name": "model1"}, {"name": "model2"}]}
+                models = {m.get("name") for m in data.get("models", []) if m.get("name")}
+
+                # Update cache with lock protection
+                async with _loaded_models_cache_lock:
+                    _loaded_models_cache[endpoint] = (models, time.time())
+                return models
+            except Exception as e:
+                # If anything goes wrong we simply assume the endpoint has no models
+                message = _format_connection_issue(f"{endpoint}/api/ps", e)
+                print(f"[fetch.loaded_models] {message}")
+                return set()
 
     async def _refresh_loaded_models(endpoint: str) -> None:
         """
@@ -642,7 +759,7 @@ class fetch:
                 models, cached_at = _loaded_models_cache[endpoint]
 
                 # FRESH: < 10s old - return immediately
-                if _is_fresh(cached_at, 10):
+                if _is_fresh(cached_at, 30):
                     return models
 
                 # STALE: 10-60s old - return stale data and refresh in background
@@ -655,12 +772,12 @@ class fetch:
                 del _loaded_models_cache[endpoint]
 
         # Check error cache with lock protection
-        async with _error_cache_lock:
-            if endpoint in _error_cache:
-                if _is_fresh(_error_cache[endpoint], 10):
+        async with _loaded_error_cache_lock:
+            if endpoint in _loaded_error_cache:
+                if _is_fresh(_loaded_error_cache[endpoint], 30):
                     return set()
                 # Error expired - remove it
-                del _error_cache[endpoint]
+                del _loaded_error_cache[endpoint]
 
         # Request coalescing: check if another request is already fetching this endpoint
         async with _inflight_lock:
@@ -682,16 +799,27 @@ class fetch:
                 if _inflight_loaded_models.get(endpoint) == task:
                     _inflight_loaded_models.pop(endpoint, None)
 
-    async def endpoint_details(endpoint: str, route: str, detail: str, api_key: Optional[str] = None) -> List[dict]:
+    async def endpoint_details(endpoint: str, route: str, detail: str, api_key: Optional[str] = None, skip_error_cache: bool = False) -> List[dict]:
         """
         Query <endpoint>/<route> to fetch <detail> and return a List of dicts with details
         for the corresponding Ollama endpoint. If the request fails we respond with "N/A" for detail.
+
+        When ``skip_error_cache`` is False (the default), the call is short-circuited
+        if the endpoint recently failed (recorded in ``_available_error_cache``).
+        Pass ``skip_error_cache=True`` from health-check routes that must always probe.
         """
+        # Fast-fail if the endpoint is known to be down (unless caller opts out)
+        if not skip_error_cache:
+            async with _available_error_cache_lock:
+                if endpoint in _available_error_cache:
+                    if _is_fresh(_available_error_cache[endpoint], 30):
+                        return []
+
         client: aiohttp.ClientSession = app_state["session"]
         headers = None
         if api_key is not None:
             headers = {"Authorization": "Bearer " + api_key}
-        
+
         request_url = f"{endpoint}{route}"
         try:
             async with client.get(request_url, headers=headers) as resp:
@@ -703,6 +831,9 @@ class fetch:
             # If anything goes wrong we cannot reply details
             message = _format_connection_issue(request_url, e)
             print(f"[fetch.endpoint_details] {message}")
+            # Record failure so subsequent calls skip this endpoint briefly
+            async with _available_error_cache_lock:
+                _available_error_cache[endpoint] = time.time()
             return []
 
 def ep2base(ep):
@@ -749,13 +880,13 @@ async def _make_chat_request(endpoint: str, model: str, messages: list, tools=No
     Helper function to make a chat request to a specific endpoint.
     Handles endpoint selection, client creation, usage tracking, and request execution.
     """
-    is_openai_endpoint = "/v1" in endpoint
-
-    if is_openai_endpoint:
+    use_openai = is_openai_compatible(endpoint)
+    if use_openai:
         if ":latest" in model:
             model = model.split(":latest")[0]
         if messages:
             messages = transform_images_to_data_urls(messages)
+            messages = transform_tool_calls_to_openai(messages)
         params = {
             "messages": messages,
             "model": model,
@@ -774,21 +905,23 @@ async def _make_chat_request(endpoint: str, model: str, messages: list, tools=No
             "response_format": {"type": "json_schema", "json_schema": format} if format is not None else None
         }
         params.update({k: v for k, v in optional_params.items() if v is not None})
-        oclient = openai.AsyncOpenAI(base_url=endpoint, default_headers=default_headers, api_key=config.api_keys[endpoint])
+        oclient = openai.AsyncOpenAI(base_url=ep2base(endpoint), default_headers=default_headers, api_key=config.api_keys.get(endpoint, "no-key"))
     else:
         client = ollama.AsyncClient(host=endpoint)
 
     await increment_usage(endpoint, model)
 
     try:
-        if is_openai_endpoint:
+        if use_openai:
             start_ts = time.perf_counter()
             response = await oclient.chat.completions.create(**params)
             if stream:
                 # For streaming, we need to collect all chunks
                 chunks = []
+                tc_acc = {}  # accumulate tool-call deltas
                 async for chunk in response:
                     chunks.append(chunk)
+                    _accumulate_openai_tc_delta(chunk, tc_acc)
                     if chunk.usage is not None:
                         prompt_tok = chunk.usage.prompt_tokens or 0
                         comp_tok = chunk.usage.completion_tokens or 0
@@ -797,6 +930,9 @@ async def _make_chat_request(endpoint: str, model: str, messages: list, tools=No
                 # Convert to Ollama format
                 if chunks:
                     response = rechunk.openai_chat_completion2ollama(chunks[-1], stream, start_ts)
+                    # Inject fully-accumulated tool calls into the final response
+                    if tc_acc and response.message:
+                        response.message.tool_calls = _build_ollama_tool_calls(tc_acc)
             else:
                 prompt_tok = response.usage.prompt_tokens or 0
                 comp_tok = response.usage.completion_tokens or 0
@@ -950,6 +1086,39 @@ def resize_image_if_needed(image_data):
         print(f"Error processing image: {e}")
         return None
 
+def transform_tool_calls_to_openai(message_list):
+    """
+    Ensure tool_calls in assistant messages conform to the OpenAI format:
+    - Each tool call must have "type": "function"
+    - Each tool call must have an "id"
+    - arguments must be a JSON string, not a dict
+    Also ensure tool-role messages have a tool_call_id.
+    """
+    # Track generated IDs so tool-role messages can reference them
+    last_tool_call_ids = {}
+    for msg in message_list:
+        role = msg.get("role")
+        if role == "assistant" and "tool_calls" in msg:
+            for tc in msg["tool_calls"]:
+                if "type" not in tc:
+                    tc["type"] = "function"
+                if "id" not in tc:
+                    tc["id"] = f"call_{secrets.token_hex(16)}"
+                func = tc.get("function", {})
+                if isinstance(func.get("arguments"), dict):
+                    func["arguments"] = orjson.dumps(func["arguments"]).decode("utf-8")
+                # Remember the id for the following tool-role message
+                name = func.get("name")
+                if name:
+                    last_tool_call_ids[name] = tc["id"]
+        elif role == "tool":
+            if "tool_call_id" not in msg:
+                # Try to match by name from a preceding assistant tool_call
+                name = msg.get("name") or msg.get("tool_name")
+                if name and name in last_tool_call_ids:
+                    msg["tool_call_id"] = last_tool_call_ids.pop(name)
+    return message_list
+
 def transform_images_to_data_urls(message_list):
     for message in message_list:
         if "images" in message:
@@ -977,6 +1146,51 @@ def transform_images_to_data_urls(message_list):
 
     return message_list
 
+def _accumulate_openai_tc_delta(chunk, accumulator: dict) -> None:
+    """Accumulate tool_call deltas from a single OpenAI streaming chunk.
+
+    ``accumulator`` is a dict mapping tool-call *index* to
+    ``{"id": str, "name": str, "arguments": str}`` where ``arguments``
+    is the concatenation of all JSON fragments seen so far.
+    """
+    if not chunk.choices:
+        return
+    delta = chunk.choices[0].delta
+    tc_deltas = getattr(delta, "tool_calls", None)
+    if not tc_deltas:
+        return
+    for tc in tc_deltas:
+        idx = tc.index
+        if idx not in accumulator:
+            accumulator[idx] = {
+                "id": getattr(tc, "id", None) or f"call_{secrets.token_hex(16)}",
+                "name": tc.function.name if tc.function else None,
+                "arguments": "",
+            }
+        else:
+            if getattr(tc, "id", None):
+                accumulator[idx]["id"] = tc.id
+            if tc.function and tc.function.name:
+                accumulator[idx]["name"] = tc.function.name
+        if tc.function and tc.function.arguments:
+            accumulator[idx]["arguments"] += tc.function.arguments
+
+def _build_ollama_tool_calls(accumulator: dict) -> list | None:
+    """Convert accumulated tool-call data into Ollama-format tool_calls list."""
+    if not accumulator:
+        return None
+    result = []
+    for idx in sorted(accumulator.keys()):
+        tc = accumulator[idx]
+        try:
+            args = orjson.loads(tc["arguments"]) if tc["arguments"] else {}
+        except (orjson.JSONDecodeError, TypeError):
+            args = {}
+        result.append(ollama.Message.ToolCall(
+            function=ollama.Message.ToolCall.Function(name=tc["name"], arguments=args)
+        ))
+    return result
+
 class rechunk:
     def openai_chat_completion2ollama(chunk: dict, stream: bool, start_ts: float) -> ollama.ChatResponse:
         now = time.perf_counter()
@@ -987,29 +1201,46 @@ class rechunk:
                 done=True,
                 done_reason='stop',
                 total_duration=int((now - start_ts) * 1_000_000_000),
-                load_duration=100000, 
+                load_duration=100000,
                 prompt_eval_count=int(chunk.usage.prompt_tokens),
-                prompt_eval_duration=int((now - start_ts) * 1_000_000_000 * (chunk.usage.prompt_tokens / chunk.usage.completion_tokens / 100)), 
+                prompt_eval_duration=int((now - start_ts) * 1_000_000_000 * (chunk.usage.prompt_tokens / chunk.usage.completion_tokens / 100)),
                 eval_count=int(chunk.usage.completion_tokens),
                 eval_duration=int((now - start_ts) * 1_000_000_000),
-                message={"role": "assistant"}
+                message=ollama.Message(role="assistant", content=""),
                 )
         with_thinking = chunk.choices[0] if chunk.choices[0] else None
         if stream == True:
-            thinking = getattr(with_thinking.delta, "reasoning", None) if with_thinking else None
+            thinking = (getattr(with_thinking.delta, "reasoning_content", None) or getattr(with_thinking.delta, "reasoning", None)) if with_thinking else None
             role = chunk.choices[0].delta.role or "assistant"
             content = chunk.choices[0].delta.content or ''
         else:
-            thinking = getattr(with_thinking, "reasoning", None) if with_thinking else None
+            thinking = (getattr(with_thinking.message, "reasoning_content", None) or getattr(with_thinking.message, "reasoning", None)) if with_thinking else None
             role = chunk.choices[0].message.role or "assistant"
             content = chunk.choices[0].message.content or ''
+        # Convert OpenAI tool_calls to Ollama format
+        # In streaming mode, tool_calls arrive as partial deltas across multiple chunks
+        # (name only in first delta, arguments as incremental JSON fragments).
+        # Callers must accumulate deltas and inject the final result; skip here.
+        ollama_tool_calls = None
+        if not stream:
+            raw_tool_calls = getattr(with_thinking.message, "tool_calls", None) if with_thinking else None
+            if raw_tool_calls:
+                ollama_tool_calls = []
+                for tc in raw_tool_calls:
+                    try:
+                        args = orjson.loads(tc.function.arguments) if isinstance(tc.function.arguments, str) else (tc.function.arguments or {})
+                    except (orjson.JSONDecodeError, TypeError):
+                        args = {}
+                    ollama_tool_calls.append(ollama.Message.ToolCall(
+                        function=ollama.Message.ToolCall.Function(name=tc.function.name, arguments=args)
+                    ))
         assistant_msg = ollama.Message(
             role=role,
             content=content,
             thinking=thinking,
             images=None,
             tool_name=None,
-            tool_calls=None)
+            tool_calls=ollama_tool_calls)
         rechunk = ollama.ChatResponse(
             model=chunk.model, 
             created_at=iso8601_ns(),
@@ -1134,35 +1365,40 @@ async def choose_endpoint(model: str) -> str:
     6️⃣  If no endpoint advertises the model at all, raise an error.
     """
     # 1️⃣  Gather advertised‑model sets for all endpoints concurrently
-    tag_tasks = [fetch.available_models(ep) for ep in config.endpoints if "/v1" not in ep]
-    tag_tasks += [fetch.available_models(ep, config.api_keys[ep]) for ep in config.endpoints if "/v1" in ep]
+    #     Include both config.endpoints and config.llama_server_endpoints
+    llama_eps_extra = [ep for ep in config.llama_server_endpoints if ep not in config.endpoints]
+    all_endpoints = config.endpoints + llama_eps_extra
+
+    tag_tasks = [fetch.available_models(ep) for ep in config.endpoints if not is_openai_compatible(ep)]
+    tag_tasks += [fetch.available_models(ep, config.api_keys.get(ep)) for ep in config.endpoints if is_openai_compatible(ep)]
+    tag_tasks += [fetch.available_models(ep, config.api_keys.get(ep)) for ep in llama_eps_extra]
     advertised_sets = await asyncio.gather(*tag_tasks)
 
     # 2️⃣  Filter endpoints that advertise the requested model
     candidate_endpoints = [
-        ep for ep, models in zip(config.endpoints, advertised_sets)
+        ep for ep, models in zip(all_endpoints, advertised_sets)
         if model in models
     ]
-    
+
     # 6️⃣
     if not candidate_endpoints:
-        if ":latest" in model:  #ollama naming convention not applicable to openai
+        if ":latest" in model:  #ollama naming convention not applicable to openai/llama-server
             model_without_latest = model.split(":latest")[0]
             candidate_endpoints = [
-                ep for ep, models in zip(config.endpoints, advertised_sets)
-                if model_without_latest in models and is_ext_openai_endpoint(ep)
+                ep for ep, models in zip(all_endpoints, advertised_sets)
+                if model_without_latest in models and (is_ext_openai_endpoint(ep) or ep in config.llama_server_endpoints)
             ]
         if not candidate_endpoints:
             # Only add :latest suffix if model doesn't already have a version suffix
             if ":" not in model:
                 model = model + ":latest"
             candidate_endpoints = [
-                ep for ep, models in zip(config.endpoints, advertised_sets)
+                ep for ep, models in zip(all_endpoints, advertised_sets)
                 if model in models
             ]
         if not candidate_endpoints:
             raise RuntimeError(
-                f"None of the configured endpoints ({', '.join(config.endpoints)}) "
+                f"None of the configured endpoints ({', '.join(all_endpoints)}) "
                 f"advertise the model '{model}'."
             )
     # 3️⃣  Among the candidates, find those that have the model *loaded*
@@ -1210,7 +1446,7 @@ async def choose_endpoint(model: str) -> str:
             # Then by total endpoint usage (ascending) to balance idle endpoints
             endpoints_with_free_slot.sort(
                 key=lambda ep: (
-                    -usage_counts.get(ep, {}).get(model, 0),  # Primary: per-model usage (descending - prefer endpoints with connections)
+                    #-usage_counts.get(ep, {}).get(model, 0),  # Primary: per-model usage (descending - prefer endpoints with connections)
                     sum(usage_counts.get(ep, {}).values())    # Secondary: total endpoint usage (ascending - prefer idle endpoints)
                 )
             )
@@ -1266,14 +1502,13 @@ async def proxy(request: Request):
 
     
     endpoint = await choose_endpoint(model)
-    is_openai_endpoint = "/v1" in endpoint
-
-    if is_openai_endpoint:
+    use_openai = is_openai_compatible(endpoint)
+    if use_openai:
         if ":latest" in model:
             model = model.split(":latest")
             model = model[0]
         params = {
-            "prompt": prompt, 
+            "prompt": prompt,
             "model": model,
         }
 
@@ -1289,7 +1524,7 @@ async def proxy(request: Request):
             "suffix": suffix,
             }
         params.update({k: v for k, v in optional_params.items() if v is not None})
-        oclient = openai.AsyncOpenAI(base_url=endpoint, default_headers=default_headers, api_key=config.api_keys[endpoint])
+        oclient = openai.AsyncOpenAI(base_url=ep2base(endpoint), default_headers=default_headers, api_key=config.api_keys.get(endpoint, "no-key"))
     else:
         client = ollama.AsyncClient(host=endpoint)
     await increment_usage(endpoint, model)
@@ -1297,14 +1532,14 @@ async def proxy(request: Request):
     # 4. Async generator that streams data and decrements the counter
     async def stream_generate_response():
         try:
-            if is_openai_endpoint:
+            if use_openai:
                 start_ts = time.perf_counter()
                 async_gen = await oclient.completions.create(**params)
             else:
                 async_gen = await client.generate(model=model, prompt=prompt, suffix=suffix, system=system, template=template, context=context, stream=stream, think=think, raw=raw, format=_format, images=images, options=options, keep_alive=keep_alive)
             if stream == True:
                 async for chunk in async_gen:
-                    if is_openai_endpoint:
+                    if use_openai:
                         chunk = rechunk.openai_completion2ollama(chunk, stream, start_ts)
                     prompt_tok = chunk.prompt_eval_count or 0
                     comp_tok   = chunk.eval_count or 0
@@ -1316,7 +1551,7 @@ async def proxy(request: Request):
                         json_line = orjson.dumps(chunk)
                     yield json_line.encode("utf-8") + b"\n"
             else:
-                if is_openai_endpoint:
+                if use_openai:
                     response = rechunk.openai_completion2ollama(async_gen, stream, start_ts)
                     response = response.model_dump_json()
                 else:
@@ -1386,16 +1621,16 @@ async def chat_proxy(request: Request):
     else:
         opt = False
     endpoint = await choose_endpoint(model)
-    is_openai_endpoint = "/v1" in endpoint
-
-    if is_openai_endpoint:
+    use_openai = is_openai_compatible(endpoint)
+    if use_openai:
         if ":latest" in model:
             model = model.split(":latest")
             model = model[0]
         if messages:
             messages = transform_images_to_data_urls(messages)
+            messages = transform_tool_calls_to_openai(messages)
         params = {
-            "messages": messages, 
+            "messages": messages,
             "model": model,
             }
         optional_params = {
@@ -1412,7 +1647,7 @@ async def chat_proxy(request: Request):
             "response_format": {"type": "json_schema", "json_schema": _format} if _format is not None else None
             }
         params.update({k: v for k, v in optional_params.items() if v is not None})
-        oclient = openai.AsyncOpenAI(base_url=endpoint, default_headers=default_headers, api_key=config.api_keys[endpoint])
+        oclient = openai.AsyncOpenAI(base_url=ep2base(endpoint), default_headers=default_headers, api_key=config.api_keys.get(endpoint, "no-key"))
     else:
         client = ollama.AsyncClient(host=endpoint)
     await increment_usage(endpoint, model)
@@ -1420,7 +1655,7 @@ async def chat_proxy(request: Request):
     async def stream_chat_response():
         try:
             # The chat method returns a generator of dicts (or GenerateResponse)
-            if is_openai_endpoint:
+            if use_openai:
                 start_ts = time.perf_counter()
                 async_gen = await oclient.chat.completions.create(**params)
             else:
@@ -1430,9 +1665,14 @@ async def chat_proxy(request: Request):
                 else:
                     async_gen = await client.chat(model=model, messages=messages, tools=tools, stream=stream, think=think, format=_format, options=options, keep_alive=keep_alive)
             if stream == True:
+                tc_acc = {}  # accumulate OpenAI tool-call deltas across chunks
                 async for chunk in async_gen:
-                    if is_openai_endpoint:
+                    if use_openai:
+                        _accumulate_openai_tc_delta(chunk, tc_acc)
                         chunk = rechunk.openai_chat_completion2ollama(chunk, stream, start_ts)
+                        # Inject fully-accumulated tool calls only into the final chunk
+                        if chunk.done and tc_acc and chunk.message:
+                            chunk.message.tool_calls = _build_ollama_tool_calls(tc_acc)
                     # `chunk` can be a dict or a pydantic model – dump to JSON safely
                     prompt_tok = chunk.prompt_eval_count or 0
                     comp_tok   = chunk.eval_count or 0
@@ -1444,7 +1684,7 @@ async def chat_proxy(request: Request):
                         json_line = orjson.dumps(chunk)
                     yield json_line.encode("utf-8") + b"\n"
             else:
-                if is_openai_endpoint:
+                if use_openai:
                     response = rechunk.openai_chat_completion2ollama(async_gen, stream, start_ts)
                     response = response.model_dump_json()
                 else:
@@ -1503,13 +1743,12 @@ async def embedding_proxy(request: Request):
 
     # 2. Endpoint logic
     endpoint = await choose_endpoint(model)
-    is_openai_endpoint = "/v1" in endpoint
-
-    if is_openai_endpoint:
+    use_openai = is_openai_compatible(endpoint)
+    if use_openai:
         if ":latest" in model:
             model = model.split(":latest")
             model = model[0]
-        client = openai.AsyncOpenAI(base_url=endpoint, api_key=config.api_keys[endpoint])
+        client = openai.AsyncOpenAI(base_url=ep2base(endpoint), api_key=config.api_keys.get(endpoint, "no-key"))
     else:
         client = ollama.AsyncClient(host=endpoint)
     await increment_usage(endpoint, model)
@@ -1517,7 +1756,7 @@ async def embedding_proxy(request: Request):
     async def stream_embedding_response():
         try:
             # The chat method returns a generator of dicts (or GenerateResponse)
-            if is_openai_endpoint:
+            if use_openai:
                 async_gen = await client.embeddings.create(input=prompt, model=model)
                 async_gen = rechunk.openai_embeddings2ollama(async_gen)
             else:
@@ -1570,13 +1809,12 @@ async def embed_proxy(request: Request):
 
     # 2. Endpoint logic
     endpoint = await choose_endpoint(model)
-    is_openai_endpoint = is_ext_openai_endpoint(endpoint) #"/v1" in endpoint
-
-    if is_openai_endpoint:
+    use_openai = is_openai_compatible(endpoint)
+    if use_openai:
         if ":latest" in model:
             model = model.split(":latest")
             model = model[0]
-        client = openai.AsyncOpenAI(base_url=endpoint, api_key=config.api_keys[endpoint])
+        client = openai.AsyncOpenAI(base_url=ep2base(endpoint), api_key=config.api_keys.get(endpoint, "no-key"))
     else:
         client = ollama.AsyncClient(host=endpoint)
     await increment_usage(endpoint, model)
@@ -1584,7 +1822,7 @@ async def embed_proxy(request: Request):
     async def stream_embedding_response():
         try:
             # The chat method returns a generator of dicts (or GenerateResponse)
-            if is_openai_endpoint:
+            if use_openai:
                 async_gen = await client.embeddings.create(input=_input, model=model)
                 async_gen = rechunk.openai_embed2ollama(async_gen, model)
             else:
@@ -1983,8 +2221,11 @@ async def tags_proxy(request: Request):
     # 1. Query all endpoints for models
     tasks = [fetch.endpoint_details(ep, "/api/tags", "models") for ep in config.endpoints if "/v1" not in ep]
     tasks += [fetch.endpoint_details(ep, "/models", "data", config.api_keys[ep]) for ep in config.endpoints if "/v1" in ep]
+    # Also query llama-server endpoints not already covered by config.endpoints
+    llama_eps_for_tags = [ep for ep in config.llama_server_endpoints if ep not in config.endpoints]
+    tasks += [fetch.endpoint_details(ep, "/models", "data", config.api_keys.get(ep)) for ep in llama_eps_for_tags]
     all_models = await asyncio.gather(*tasks)
-    
+
     models = {'models': []}
     for modellist in all_models:
         for model in modellist:
@@ -2010,18 +2251,47 @@ async def tags_proxy(request: Request):
 @app.get("/api/ps")
 async def ps_proxy(request: Request):
     """
-    Proxy a ps request to all Ollama endpoints and reply a unique list of all running models.
+    Proxy a ps request to all Ollama and llama-server endpoints and reply a unique list of all running models.
 
+    For Ollama endpoints: queries /api/ps
+    For llama-server endpoints: queries /v1/models with status.value == "loaded"
     """
-    # 1. Query all endpoints for running models
-    tasks = [fetch.endpoint_details(ep, "/api/ps", "models") for ep in config.endpoints if "/v1" not in ep]
-    loaded_models = await asyncio.gather(*tasks)
+    # 1. Query Ollama endpoints for running models via /api/ps
+    ollama_tasks = [fetch.endpoint_details(ep, "/api/ps", "models") for ep in config.endpoints if "/v1" not in ep]
+    # 2. Query llama-server endpoints for loaded models via /v1/models
+    # Also query endpoints from llama_server_endpoints that may not be in config.endpoints
+    all_llama_endpoints = set(config.llama_server_endpoints) | set(ep for ep in config.endpoints if ep in config.llama_server_endpoints)
+    llama_tasks = [
+        fetch.endpoint_details(ep, "/models", "data", config.api_keys.get(ep))
+        for ep in all_llama_endpoints
+    ]
+    
+    ollama_loaded = await asyncio.gather(*ollama_tasks) if ollama_tasks else []
+    llama_loaded = await asyncio.gather(*llama_tasks) if llama_tasks else []
 
     models = {'models': []}
-    for modellist in loaded_models:
-        models['models'] += modellist
+    # Add Ollama models (if any)
+    if ollama_loaded:
+        for modellist in ollama_loaded:
+            models['models'] += modellist
+    # Add llama-server models (filter for loaded only, if any)
+    if llama_loaded:
+        for modellist in llama_loaded:
+            loaded_models = [item for item in modellist if _is_llama_model_loaded(item)]
+            # Convert llama-server format to Ollama-like format for consistency
+            for item in loaded_models:
+                raw_id = item.get("id", "")
+                normalized = _normalize_llama_model_name(raw_id)
+                quant = _extract_llama_quant(raw_id)
+                models['models'].append({
+                    "name": normalized,
+                    "id": normalized,
+                    "digest": "",
+                    "status": item.get("status"),
+                    "details": {"quantization_level": quant} if quant else {}
+                })
     
-    # 2. Return a JSONResponse with deduplicated currently deployed models
+    # 3. Return a JSONResponse with deduplicated currently deployed models
     return JSONResponse(
         content={"models": dedupe_on_keys(models['models'], ['digest'])},
         status_code=200,
@@ -2033,19 +2303,63 @@ async def ps_proxy(request: Request):
 @app.get("/api/ps_details")
 async def ps_details_proxy(request: Request):
     """
-    Proxy a ps request to all Ollama endpoints and reply with per-endpoint instances.
+    Proxy a ps request to all Ollama and llama-server endpoints and reply with per-endpoint instances.
     This keeps /api/ps backward compatible while providing richer data.
+    
+    For Ollama endpoints: queries /api/ps
+    For llama-server endpoints: queries /v1/models with status info
     """
-    tasks = [(ep, fetch.endpoint_details(ep, "/api/ps", "models")) for ep in config.endpoints if "/v1" not in ep]
-    loaded_models = await asyncio.gather(*[task for _, task in tasks])
+    # 1. Query Ollama endpoints via /api/ps
+    ollama_tasks = [(ep, fetch.endpoint_details(ep, "/api/ps", "models")) for ep in config.endpoints if "/v1" not in ep]
+    # 2. Query llama-server endpoints via /v1/models
+    # Also query endpoints from llama_server_endpoints that may not be in config.endpoints
+    all_llama_endpoints = set(config.llama_server_endpoints) | set(ep for ep in config.endpoints if ep in config.llama_server_endpoints)
+    llama_tasks = [
+        (ep, fetch.endpoint_details(ep, "/models", "data", config.api_keys.get(ep)))
+        for ep in all_llama_endpoints
+    ]
+    
+    ollama_loaded = await asyncio.gather(*[task for _, task in ollama_tasks]) if ollama_tasks else []
+    llama_loaded = await asyncio.gather(*[task for _, task in llama_tasks]) if llama_tasks else []
 
     models: list[dict] = []
-    for (endpoint, modellist) in zip([ep for ep, _ in tasks], loaded_models):
-        for model in modellist:
-            if isinstance(model, dict):
-                model_with_endpoint = dict(model)
-                model_with_endpoint["endpoint"] = endpoint
-                models.append(model_with_endpoint)
+    
+    # Add Ollama models with endpoint info (if any)
+    if ollama_loaded:
+        for (endpoint, modellist) in zip([ep for ep, _ in ollama_tasks], ollama_loaded):
+            for model in modellist:
+                if isinstance(model, dict):
+                    model_with_endpoint = dict(model)
+                    model_with_endpoint["endpoint"] = endpoint
+                    models.append(model_with_endpoint)
+    
+    # Add llama-server models with endpoint info and full status metadata (if any)
+    if llama_loaded:
+        for (endpoint, modellist) in zip([ep for ep, _ in llama_tasks], llama_loaded):
+            # Filter for loaded models only
+            loaded_models = [item for item in modellist if _is_llama_model_loaded(item)]
+            for item in loaded_models:
+                if isinstance(item, dict) and item.get("id"):
+                    raw_id = item["id"]
+                    normalized = _normalize_llama_model_name(raw_id)
+                    quant = _extract_llama_quant(raw_id)
+                    model_with_endpoint = {
+                        "name": normalized,
+                        "id": normalized,
+                        "original_name": raw_id,
+                        "digest": "",
+                        "details": {"quantization_level": quant} if quant else {},
+                        "endpoint": endpoint,
+                        "status": item.get("status"),
+                        "created": item.get("created"),
+                        "owned_by": item.get("owned_by")
+                    }
+                    # Include full llama-server status details (args, preset)
+                    status_info = item.get("status", {})
+                    if isinstance(status_info, dict):
+                        model_with_endpoint["llama_status_args"] = status_info.get("args")
+                        model_with_endpoint["llama_status_preset"] = status_info.get("preset")
+                    models.append(model_with_endpoint)
 
     return JSONResponse(content={"models": models}, status_code=200)
 
@@ -2068,7 +2382,7 @@ async def usage_proxy(request: Request):
 async def config_proxy(request: Request):
     """
     Return a simple JSON object that contains the configured
-    Ollama endpoints. The front‑end uses this to display
+    Ollama endpoints and llama_server_endpoints. The front‑end uses this to display
     which endpoints are being proxied.
     """
     async def check_endpoint(url: str):
@@ -2092,9 +2406,17 @@ async def config_proxy(request: Request):
             detail = _format_connection_issue(target_url, e)
             return {"url": url, "status": "error", "detail": detail}
 
-    results = await asyncio.gather(*[check_endpoint(ep) for ep in config.endpoints])
+    # Check Ollama endpoints
+    ollama_results = await asyncio.gather(*[check_endpoint(ep) for ep in config.endpoints])
+    
+    # Check llama-server endpoints
+    llama_results = []
+    if config.llama_server_endpoints:
+        llama_results = await asyncio.gather(*[check_endpoint(ep) for ep in config.llama_server_endpoints])
+    
     return {
-        "endpoints": results,
+        "endpoints": ollama_results,
+        "llama_server_endpoints": llama_results,
         "require_router_api_key": bool(config.router_api_key),
     }
 
@@ -2129,8 +2451,8 @@ async def openai_embedding_proxy(request: Request):
     # 2. Endpoint logic
     endpoint = await choose_endpoint(model)
     await increment_usage(endpoint, model)
-    if "/v1" in endpoint: # and is_ext_openai_endpoint(endpoint):
-        api_key = config.api_keys[endpoint]
+    if is_openai_compatible(endpoint):
+        api_key = config.api_keys.get(endpoint, "no-key")
     else:
         api_key = "ollama"
     base_url = ep2base(endpoint)
@@ -2215,8 +2537,7 @@ async def openai_chat_completions_proxy(request: Request):
     endpoint = await choose_endpoint(model)
     await increment_usage(endpoint, model)
     base_url = ep2base(endpoint)
-
-    oclient = openai.AsyncOpenAI(base_url=base_url, default_headers=default_headers, api_key=config.api_keys[endpoint])
+    oclient = openai.AsyncOpenAI(base_url=base_url, default_headers=default_headers, api_key=config.api_keys.get(endpoint, "no-key"))
     # 3. Async generator that streams completions data and decrements the counter
     async def stream_ochat_response():
         try:
@@ -2341,8 +2662,7 @@ async def openai_completions_proxy(request: Request):
     endpoint = await choose_endpoint(model)
     await increment_usage(endpoint, model)
     base_url = ep2base(endpoint)
-
-    oclient = openai.AsyncOpenAI(base_url=base_url, default_headers=default_headers, api_key=config.api_keys[endpoint])
+    oclient = openai.AsyncOpenAI(base_url=base_url, default_headers=default_headers, api_key=config.api_keys.get(endpoint, "no-key"))
 
     # 3. Async generator that streams completions data and decrements the counter
     async def stream_ocompletions_response(model=model):
@@ -2398,22 +2718,46 @@ async def openai_completions_proxy(request: Request):
 @app.get("/v1/models")
 async def openai_models_proxy(request: Request):
     """
-    Proxy an OpenAI API models request to Ollama endpoints and reply with a unique list of all models.
-
+    Proxy an OpenAI API models request to Ollama and llama-server endpoints and reply with a unique list of models.
+    
+    For Ollama endpoints: queries /api/tags (all models)
+    For llama-server endpoints: queries /v1/models and filters for status.value == "loaded"
     """
-    # 1. Query all endpoints for models
-    tasks = [fetch.endpoint_details(ep, "/api/tags", "models") for ep in config.endpoints if "/v1" not in ep]
-    tasks += [fetch.endpoint_details(ep, "/models", "data", config.api_keys[ep]) for ep in config.endpoints if "/v1" in ep]
-    all_models = await asyncio.gather(*tasks)
+    # 1. Query Ollama endpoints for all models via /api/tags
+    ollama_tasks = [fetch.endpoint_details(ep, "/api/tags", "models") for ep in config.endpoints if "/v1" not in ep]
+    # 2. Query llama-server endpoints for loaded models via /v1/models
+    # Also query endpoints from llama_server_endpoints that may not be in config.endpoints
+    all_llama_endpoints = set(config.llama_server_endpoints) | set(ep for ep in config.endpoints if ep in config.llama_server_endpoints)
+    llama_tasks = [
+        fetch.endpoint_details(ep, "/models", "data", config.api_keys.get(ep))
+        for ep in all_llama_endpoints
+    ]
+    
+    ollama_models = await asyncio.gather(*ollama_tasks) if ollama_tasks else []
+    llama_models = await asyncio.gather(*llama_tasks) if llama_tasks else []
     
     models = {'data': []}
-    for modellist in all_models:
-        for model in modellist:
-            if not "id" in model.keys():  # Relable Ollama models with OpenAI Model.id from Model.name
-                model['id'] = model['name']
-            else:
-                model['name'] = model['id']
-        models['data'] += modellist
+    
+    # Add Ollama models (if any)
+    if ollama_models:
+        for modellist in ollama_models:
+            for model in modellist:
+                if not "id" in model.keys():  # Relable Ollama models with OpenAI Model.id from Model.name
+                    model['id'] = model.get('name', model.get('id', ''))
+                else:
+                    model['name'] = model['id']
+                models['data'].append(model)
+    
+    # Add llama-server models (filter for loaded only, if any)
+    if llama_models:
+        for modellist in llama_models:
+            loaded_models = [item for item in modellist if _is_llama_model_loaded(item)]
+            for model in loaded_models:
+                if not "id" in model.keys():
+                    model['id'] = model.get('name', model.get('id', ''))
+                else:
+                    model['name'] = model['id']
+                models['data'].append(model)
     
     # 2. Return a JSONResponse with a deduplicated list of unique models for inference
     return JSONResponse(
@@ -2459,7 +2803,7 @@ async def health_proxy(request: Request):
     * The HTTP status code is 200 when everything is healthy, 503 otherwise.
     """
     # Run all health checks in parallel
-    tasks = [fetch.endpoint_details(ep, "/api/version", "version") for ep in config.endpoints] # if not is_ext_openai_endpoint(ep)]
+    tasks = [fetch.endpoint_details(ep, "/api/version", "version", skip_error_cache=True) for ep in config.endpoints] # if not is_ext_openai_endpoint(ep)]
 
     results = await asyncio.gather(*tasks, return_exceptions=True)
 
@@ -2524,6 +2868,7 @@ async def startup_event() -> None:
         print(
             f"Loaded configuration from {config_path}:\n"
             f" endpoints={config.endpoints},\n"
+            f" llama_server_endpoints={config.llama_server_endpoints},\n"
             f" max_concurrent_connections={config.max_concurrent_connections}"
         )
     else:
diff --git a/static/index.html b/static/index.html
index 09cc6fe..3d4d364 100644
--- a/static/index.html
+++ b/static/index.html
@@ -379,7 +379,7 @@
                             <th>Quant</th>
                             <th>Ctx</th>
                             <th>Size</th>
-                            <th>Until</th>
+                            <th>Unload</th>
                             <th>Digest</th>
                             <th>Tokens</th>
                         </tr>
@@ -683,7 +683,12 @@ function renderTimeSeriesChart(timeSeriesData, chart, minutes) {
                         showApiKeyModal("Enter the NOMYO Router API key to load the dashboard.");
                     }
                     const body = document.getElementById("endpoints-body");
-                    body.innerHTML = data.endpoints
+                    
+                    // Build HTML for both endpoints and llama_server_endpoints
+                    let html = "";
+                    
+                    // Add Ollama endpoints
+                    html += data.endpoints
                         .map((e) => {
                             const statusClass =
                                 e.status === "ok"
@@ -698,6 +703,27 @@ function renderTimeSeriesChart(timeSeriesData, chart, minutes) {
         </tr>`;
                         })
                         .join("");
+                    
+                    // Add llama-server endpoints
+                    if (data.llama_server_endpoints && data.llama_server_endpoints.length > 0) {
+                        html += data.llama_server_endpoints
+                            .map((e) => {
+                                const statusClass =
+                                    e.status === "ok"
+                                        ? "status-ok"
+                                        : "status-error";
+                                const version = e.version || "N/A";
+                                return `
+        <tr>
+          <td class="endpoint">${e.url}</td>
+          <td class="status ${statusClass}">${e.status}</td>
+          <td class="version">${version}</td>
+        </tr>`;
+                            })
+                            .join("");
+                    }
+                    
+                    body.innerHTML = html;
                 } catch (e) {
                     console.error(e);
                     const body = document.getElementById("endpoints-body");
@@ -837,7 +863,7 @@ function renderTimeSeriesChart(timeSeriesData, chart, minutes) {
 
                     const formatUntil = (value) => {
                         if (value === null || value === undefined || value === "") {
-                            return "Forever";
+                            return "∞";
                         }
 
                         let targetTime;
@@ -898,10 +924,11 @@ function renderTimeSeriesChart(timeSeriesData, chart, minutes) {
                             const params = modelInstances[0]?.details?.parameter_size ?? "";
                             const quant = modelInstances[0]?.details?.quantization_level ?? "";
                             const ctx = modelInstances[0]?.context_length ?? "";
+                            const originalName = modelInstances[0]?.original_name || modelName;
                             const uniqueEndpoints = Array.from(new Set(endpoints));
                             const endpointsData = encodeURIComponent(JSON.stringify(uniqueEndpoints));
                             return `<tr data-model="${modelName}" data-endpoints="${endpointsData}">
-                                <td class="model">${modelName} <a href="#" class="stats-link" data-model="${modelName}">stats</a></td>
+                                <td class="model">${modelName} <a href="#" class="stats-link" data-model="${originalName}">stats</a></td>
                                 <td>${renderInstanceList(endpoints)}</td>
                                 <td>${params}</td>
                                 <td>${quant}</td>