feat: add LLM Router component using OpenRouter specs

src/backend/base/langflow/components/processing/__init__.py

@@ -4,6 +4,7 @@
 from .extract_key import ExtractDataKeyComponent
 from .filter_data_values import DataFilterComponent
 from .json_cleaner import JSONCleaner
+from .llm_router import LLMRouterComponent
 from .merge_data import MergeDataComponent
 from .message_to_data import MessageToDataComponent
 from .parse_data import ParseDataComponent
@@ -19,6 +20,7 @@
     "DataFilterComponent",
     "ExtractDataKeyComponent",
     "JSONCleaner",
+    "LLMRouterComponent",
     "MergeDataComponent",
     "MessageToDataComponent",
     "ParseDataComponent",

src/backend/base/langflow/components/processing/llm_router.py

@@ -0,0 +1,178 @@
+import json
+
+import requests
+
+from langflow.base.models.chat_result import get_chat_result
+from langflow.base.models.model_utils import get_model_name
+from langflow.custom import Component
+from langflow.io import DropdownInput, HandleInput, Output
+from langflow.schema.message import Message
+
+
+class LLMRouterComponent(Component):
+    display_name = "LLM Router"
+    description = "Routes the input to the most appropriate LLM based on OpenRouter model specifications"
+    icon = "git-branch"
+
+    inputs = [
+        HandleInput(
+            name="models",
+            display_name="Language Models",
+            input_types=["LanguageModel"],
+            required=True,
+            is_list=True,
+            info="List of LLMs to route between",
+        ),
+        HandleInput(
+            name="input_value",
+            display_name="Input",
+            input_types=["Message"],
+            info="The input message to be routed",
+        ),
+        HandleInput(
+            name="judge_llm",
+            display_name="Judge LLM",
+            input_types=["LanguageModel"],
+            info="LLM that will evaluate and select the most appropriate model",
+        ),
+        DropdownInput(
+            name="optimization",
+            display_name="Optimization",
+            options=["quality", "speed", "cost", "balanced"],
+            value="balanced",
+            info="Optimization preference for model selection",
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Output", name="output", method="route_to_model"),
+        Output(
+            display_name="Selected Model",
+            name="selected_model",
+            method="get_selected_model",
+            required_inputs=["output"],
+        ),
+    ]
+
+    _selected_model_name: str | None = None
+
+    def get_selected_model(self) -> str:
+        return self._selected_model_name or ""
+
+    def _get_model_specs(self, model_name: str) -> str:
+        """Fetch specific model information from OpenRouter API."""
+        http_success = 200
+        base_info = f"Model: {model_name}\n"
+
+        # Remove any special characters and spaces, keep only alphanumeric
+        clean_name = "".join(c.lower() for c in model_name if c.isalnum())
+        url = f"https://openrouter.ai/api/v1/models/{clean_name}/endpoints"
+
+        try:
+            response = requests.get(url, timeout=10)
+        except requests.exceptions.RequestException as e:
+            return base_info + f"Error fetching specs: {e!s}"
+
+        if response.status_code != http_success:
+            return base_info + "No specifications available"
+
+        try:
+            data = response.json().get("data", {})
+        except (json.JSONDecodeError, requests.exceptions.JSONDecodeError):
+            return base_info + "Error parsing response data"
+
+        # Extract relevant information
+        context_length = data.get("context_length", "Unknown")
+        max_completion_tokens = data.get("max_completion_tokens", "Unknown")
+        architecture = data.get("architecture", {})
+        tokenizer = architecture.get("tokenizer", "Unknown")
+        instruct_type = architecture.get("instruct_type", "Unknown")
+
+        pricing = data.get("pricing", {})
+        prompt_price = pricing.get("prompt", "Unknown")
+        completion_price = pricing.get("completion", "Unknown")
+
+        description = data.get("description", "No description available")
+        created = data.get("created", "Unknown")
+
+        return f"""
+Model: {model_name}
+Description: {description}
+Context Length: {context_length} tokens
+Max Completion Tokens: {max_completion_tokens}
+Tokenizer: {tokenizer}
+Instruct Type: {instruct_type}
+Pricing: ${prompt_price}/1k tokens (prompt), ${completion_price}/1k tokens (completion)
+Created: {created}
+"""
+
+    MISSING_INPUTS_MSG = "Missing required inputs: models, input_value, or judge_llm"
+
+    async def route_to_model(self) -> Message:
+        if not self.models or not self.input_value or not self.judge_llm:
+            raise ValueError(self.MISSING_INPUTS_MSG)
+
+        system_prompt = {
+            "role": "system",
+            "content": (
+                "You are a model selection expert. Analyze the input and select the most "
+                "appropriate model based on:\n"
+                "1. Task complexity and requirements\n"
+                "2. Context length needed\n"
+                "3. Model capabilities\n"
+                "4. Cost considerations\n"
+                "5. Speed requirements\n\n"
+                "Consider the detailed model specifications provided and the user's "
+                "optimization preference. Return only the index number (0-based) of the best model."
+            ),
+        }
+
+        # Create list of available models with their detailed specs
+        models_info = []
+        for i, model in enumerate(self.models):
+            model_name = get_model_name(model)
+            model_specs = self._get_model_specs(model_name)
+            models_info.append(f"=== Model {i} ===\n{model_specs}")
+
+        models_str = "\n\n".join(models_info)
+
+        user_message = {
+            "role": "user",
+            "content": f"""Available Models with Specifications:\n{models_str}\n
+            Optimization Preference: {self.optimization}\n
+            Input Query: "{self.input_value.text}"\n
+            Based on the model specifications and optimization preference,
+            select the most appropriate model (return only the index number):""",
+        }
+
+        try:
+            # Get judge's decision
+            response = await self.judge_llm.ainvoke([system_prompt, user_message])
+
+            try:
+                selected_index = int(response.content.strip())
+                if 0 <= selected_index < len(self.models):
+                    chosen_model = self.models[selected_index]
+                    self._selected_model_name = get_model_name(chosen_model)
+                else:
+                    chosen_model = self.models[0]
+                    self._selected_model_name = get_model_name(chosen_model)
+            except ValueError:
+                chosen_model = self.models[0]
+                self._selected_model_name = get_model_name(chosen_model)
+
+            # Get response from chosen model
+            return get_chat_result(
+                runnable=chosen_model,
+                input_value=self.input_value,
+            )
+
+        except (RuntimeError, ValueError) as e:
+            self.status = f"Error: {e!s}"
+            # Fallback to first model
+            chosen_model = self.models[0]
+            self._selected_model_name = get_model_name(chosen_model)
+            return get_chat_result(
+                runnable=chosen_model,
+                input_value=self.input_value,
+            )