diff --git a/src/codegate/providers/openai/provider.py b/src/codegate/providers/openai/provider.py
index 6e936cf4..f4d3e8ed 100644
--- a/src/codegate/providers/openai/provider.py
+++ b/src/codegate/providers/openai/provider.py
@@ -18,8 +18,9 @@ class OpenAIProvider(BaseProvider):
     def __init__(
         self,
         pipeline_factory: PipelineFactory,
+        # Enable receiving other completion handlers from childs, i.e. OpenRouter and LM Studio
+        completion_handler: LiteLLmShim = LiteLLmShim(stream_generator=sse_stream_generator),
     ):
-        completion_handler = LiteLLmShim(stream_generator=sse_stream_generator)
         super().__init__(
             OpenAIInputNormalizer(),
             OpenAIOutputNormalizer(),
diff --git a/src/codegate/providers/openrouter/provider.py b/src/codegate/providers/openrouter/provider.py
index de65662d..dd934161 100644
--- a/src/codegate/providers/openrouter/provider.py
+++ b/src/codegate/providers/openrouter/provider.py
@@ -2,12 +2,14 @@
 from typing import Dict
 
 from fastapi import Header, HTTPException, Request
+from litellm import atext_completion
 from litellm.types.llms.openai import ChatCompletionRequest
 
 from codegate.clients.clients import ClientType
 from codegate.clients.detector import DetectClient
 from codegate.pipeline.factory import PipelineFactory
 from codegate.providers.fim_analyzer import FIMAnalyzer
+from codegate.providers.litellmshim import LiteLLmShim, sse_stream_generator
 from codegate.providers.normalizer.completion import CompletionNormalizer
 from codegate.providers.openai import OpenAIProvider
 
@@ -20,15 +22,45 @@ def normalize(self, data: Dict) -> ChatCompletionRequest:
         return super().normalize(data)
 
     def denormalize(self, data: ChatCompletionRequest) -> Dict:
-        if data.get("had_prompt_before", False):
-            del data["had_prompt_before"]
-
-        return data
+        """
+        Denormalize a FIM OpenRouter request. Force it to be an accepted atext_completion format.
+        """
+        denormalized_data = super().denormalize(data)
+        # We are forcing atext_completion which expects to have a "prompt" key in the data
+        # Forcing it in case is not present
+        if "prompt" in data:
+            return denormalized_data
+        custom_prompt = ""
+        for msg_dict in denormalized_data.get("messages", []):
+            content_obj = msg_dict.get("content")
+            if not content_obj:
+                continue
+            if isinstance(content_obj, list):
+                for content_dict in content_obj:
+                    custom_prompt += (
+                        content_dict.get("text", "") if isinstance(content_dict, dict) else ""
+                    )
+            elif isinstance(content_obj, str):
+                custom_prompt += content_obj
+
+        # Erase the original "messages" key. Replace it by "prompt"
+        del denormalized_data["messages"]
+        denormalized_data["prompt"] = custom_prompt
+
+        return denormalized_data
 
 
 class OpenRouterProvider(OpenAIProvider):
     def __init__(self, pipeline_factory: PipelineFactory):
-        super().__init__(pipeline_factory)
+        super().__init__(
+            pipeline_factory,
+            # We get FIM requests in /completions. LiteLLM is forcing /chat/completions
+            # which returns "choices":[{"delta":{"content":"some text"}}]
+            # instead of "choices":[{"text":"some text"}] expected by the client (Continue)
+            completion_handler=LiteLLmShim(
+                stream_generator=sse_stream_generator, fim_completion_func=atext_completion
+            ),
+        )
         self._fim_normalizer = OpenRouterNormalizer()
 
     @property