diff --git a/api/Dockerfile b/api/Dockerfile
index d8afae0..91078f9 100644
--- a/api/Dockerfile
+++ b/api/Dockerfile
@@ -25,7 +25,7 @@ RUN pip install --user --no-cache-dir -r requirements.txt && \
 
 # Copy and install pipecat from local submodule
 COPY pipecat /tmp/pipecat
-RUN pip install --user --no-cache-dir '/tmp/pipecat[cartesia,deepgram,openai,elevenlabs,groq,google,azure,sarvam,soundfile,silero,webrtc,local-smart-turn-v3,speechmatics]' && \
+RUN pip install --user --no-cache-dir '/tmp/pipecat[cartesia,deepgram,openai,elevenlabs,groq,google,azure,sarvam,soundfile,silero,webrtc,local-smart-turn-v3,speechmatics,openrouter]' && \
     # Clean up pip cache and temporary pipecat directory
     rm -rf /root/.cache/pip /tmp/pipecat
 
diff --git a/api/routes/knowledge_base.py b/api/routes/knowledge_base.py
index fbe4381..b9fbaae 100644
--- a/api/routes/knowledge_base.py
+++ b/api/routes/knowledge_base.py
@@ -360,6 +360,9 @@ async def search_chunks(
             db_client=db_client,
             api_key=embeddings_api_key,
             model_id=embeddings_model or "text-embedding-3-small",
+            base_url=getattr(user_config.embeddings, "base_url", None)
+            if user_config.embeddings
+            else None,
         )
 
         # Perform search
diff --git a/api/services/configuration/check_validity.py b/api/services/configuration/check_validity.py
index 351aec3..3f25ef2 100644
--- a/api/services/configuration/check_validity.py
+++ b/api/services/configuration/check_validity.py
@@ -33,6 +33,7 @@ class UserConfigurationValidator:
             ServiceProviders.OPENAI.value: self._check_openai_api_key,
             ServiceProviders.DEEPGRAM.value: self._check_deepgram_api_key,
             ServiceProviders.GROQ.value: self._check_groq_api_key,
+            ServiceProviders.OPENROUTER.value: self._check_openrouter_api_key,
             ServiceProviders.ELEVENLABS.value: self._validate_elevenlabs_api_key,
             ServiceProviders.GOOGLE.value: self._check_google_api_key,
             ServiceProviders.AZURE.value: self._check_azure_api_key,
@@ -150,5 +151,8 @@ class UserConfigurationValidator:
     def _check_sarvam_api_key(self, model: str, api_key: str) -> bool:
         return True
 
+    def _check_openrouter_api_key(self, model: str, api_key: str) -> bool:
+        return True
+
     def _check_speechmatics_api_key(self, model: str, api_key: str) -> bool:
         return True
diff --git a/api/services/configuration/registry.py b/api/services/configuration/registry.py
index f1d0b69..4443b5a 100644
--- a/api/services/configuration/registry.py
+++ b/api/services/configuration/registry.py
@@ -15,6 +15,7 @@ class ServiceProviders(str, Enum):
     OPENAI = "openai"
     DEEPGRAM = "deepgram"
     GROQ = "groq"
+    OPENROUTER = "openrouter"
     CARTESIA = "cartesia"
     # NEUPHONIC = "neuphonic"
     ELEVENLABS = "elevenlabs"
@@ -30,6 +31,7 @@ class BaseServiceConfiguration(BaseModel):
         ServiceProviders.OPENAI,
         ServiceProviders.DEEPGRAM,
         ServiceProviders.GROQ,
+        ServiceProviders.OPENROUTER,
         ServiceProviders.ELEVENLABS,
         ServiceProviders.GOOGLE,
         ServiceProviders.AZURE,
@@ -131,6 +133,15 @@ GROQ_MODELS = [
     "llama-3.1-8b-instant",
     "openai/gpt-oss-120b",
 ]
+OPENROUTER_MODELS = [
+    "openai/gpt-4.1",
+    "openai/gpt-4.1-mini",
+    "anthropic/claude-sonnet-4",
+    "google/gemini-2.5-flash",
+    "google/gemini-2.0-flash",
+    "meta-llama/llama-3.3-70b-instruct",
+    "deepseek/deepseek-chat-v3-0324",
+]
 AZURE_MODELS = ["gpt-4.1-mini"]
 DOGRAH_LLM_MODELS = ["default", "accurate", "fast", "lite", "zen"]
 
@@ -160,6 +171,16 @@ class GroqLLMService(BaseLLMConfiguration):
     api_key: str
 
 
+@register_llm
+class OpenRouterLLMConfiguration(BaseLLMConfiguration):
+    provider: Literal[ServiceProviders.OPENROUTER] = ServiceProviders.OPENROUTER
+    model: str = Field(
+        default="openai/gpt-4.1", json_schema_extra={"examples": OPENROUTER_MODELS}
+    )
+    api_key: str
+    base_url: str = Field(default="https://openrouter.ai/api/v1")
+
+
 @register_llm
 class AzureLLMService(BaseLLMConfiguration):
     provider: Literal[ServiceProviders.AZURE] = ServiceProviders.AZURE
@@ -183,6 +204,7 @@ LLMConfig = Annotated[
     Union[
         OpenAILLMService,
         GroqLLMService,
+        OpenRouterLLMConfiguration,
         GoogleLLMService,
         AzureLLMService,
         DograhLLMService,
@@ -497,8 +519,22 @@ class OpenAIEmbeddingsConfiguration(BaseEmbeddingsConfiguration):
     api_key: str
 
 
+OPENROUTER_EMBEDDING_MODELS = ["openai/text-embedding-3-small"]
+
+
+@register_embeddings
+class OpenRouterEmbeddingsConfiguration(BaseEmbeddingsConfiguration):
+    provider: Literal[ServiceProviders.OPENROUTER] = ServiceProviders.OPENROUTER
+    model: str = Field(
+        default="openai/text-embedding-3-small",
+        json_schema_extra={"examples": OPENROUTER_EMBEDDING_MODELS},
+    )
+    api_key: str
+    base_url: str = Field(default="https://openrouter.ai/api/v1")
+
+
 EmbeddingsConfig = Annotated[
-    Union[OpenAIEmbeddingsConfiguration],
+    Union[OpenAIEmbeddingsConfiguration, OpenRouterEmbeddingsConfiguration],
     Field(discriminator="provider"),
 ]
 
diff --git a/api/services/gen_ai/embedding/openai_service.py b/api/services/gen_ai/embedding/openai_service.py
index ec4598b..89a58a3 100644
--- a/api/services/gen_ai/embedding/openai_service.py
+++ b/api/services/gen_ai/embedding/openai_service.py
@@ -50,6 +50,7 @@ class OpenAIEmbeddingService(BaseEmbeddingService):
         api_key: Optional[str] = None,
         model_id: str = DEFAULT_MODEL_ID,
         max_tokens: int = 512,
+        base_url: Optional[str] = None,
     ):
         """Initialize the OpenAI embedding service.
 
@@ -59,6 +60,7 @@ class OpenAIEmbeddingService(BaseEmbeddingService):
                     initialized and operations will fail with a clear error.
             model_id: OpenAI embedding model ID (default: text-embedding-3-small)
             max_tokens: Maximum number of tokens per chunk (default: 512)
+            base_url: Optional base URL for the API (e.g. for OpenRouter)
         """
         self.db = db_client
         self.model_id = model_id
@@ -67,7 +69,10 @@ class OpenAIEmbeddingService(BaseEmbeddingService):
         # Only initialize OpenAI client if API key is provided
         self._api_key_configured = bool(api_key)
         if self._api_key_configured:
-            self.client = AsyncOpenAI(api_key=api_key)
+            client_kwargs = {"api_key": api_key}
+            if base_url:
+                client_kwargs["base_url"] = base_url
+            self.client = AsyncOpenAI(**client_kwargs)
             logger.info(f"OpenAI embedding service initialized with model: {model_id}")
         else:
             self.client = None
diff --git a/api/services/pipecat/run_pipeline.py b/api/services/pipecat/run_pipeline.py
index 4d4ea9d..1f6356a 100644
--- a/api/services/pipecat/run_pipeline.py
+++ b/api/services/pipecat/run_pipeline.py
@@ -538,9 +538,11 @@ async def _run_pipeline(
     # Extract embeddings configuration from user config
     embeddings_api_key = None
     embeddings_model = None
+    embeddings_base_url = None
     if user_config and user_config.embeddings:
         embeddings_api_key = user_config.embeddings.api_key
         embeddings_model = user_config.embeddings.model
+        embeddings_base_url = getattr(user_config.embeddings, "base_url", None)
 
     engine = PipecatEngine(
         llm=llm,
@@ -550,6 +552,7 @@ async def _run_pipeline(
         node_transition_callback=node_transition_callback,
         embeddings_api_key=embeddings_api_key,
         embeddings_model=embeddings_model,
+        embeddings_base_url=embeddings_base_url,
     )
 
     # Create pipeline components with audio configuration
diff --git a/api/services/pipecat/service_factory.py b/api/services/pipecat/service_factory.py
index 572289f..129aa00 100644
--- a/api/services/pipecat/service_factory.py
+++ b/api/services/pipecat/service_factory.py
@@ -19,6 +19,7 @@ from pipecat.services.groq.llm import GroqLLMService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.openai.stt import OpenAISTTService
 from pipecat.services.openai.tts import OpenAITTSService
+from pipecat.services.openrouter.llm import OpenRouterLLMService
 from pipecat.services.sarvam.stt import SarvamSTTService
 from pipecat.services.sarvam.tts import SarvamTTSService
 from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
@@ -253,6 +254,13 @@ def create_llm_service(user_config):
             model=model,
             params=OpenAILLMService.InputParams(temperature=0.1),
         )
+    elif user_config.llm.provider == ServiceProviders.OPENROUTER.value:
+        return OpenRouterLLMService(
+            api_key=user_config.llm.api_key,
+            model=model,
+            base_url=user_config.llm.base_url,
+            params=OpenAILLMService.InputParams(temperature=0.1),
+        )
     elif user_config.llm.provider == ServiceProviders.GOOGLE.value:
         # Use the correct InputParams class for Google to avoid propagating OpenAI-specific
         # NOT_GIVEN sentinels that break Pydantic validation in GoogleLLMService.
diff --git a/api/services/workflow/pipecat_engine.py b/api/services/workflow/pipecat_engine.py
index b960bdf..8e6c74c 100644
--- a/api/services/workflow/pipecat_engine.py
+++ b/api/services/workflow/pipecat_engine.py
@@ -70,6 +70,7 @@ class PipecatEngine:
         ] = None,
         embeddings_api_key: Optional[str] = None,
         embeddings_model: Optional[str] = None,
+        embeddings_base_url: Optional[str] = None,
     ):
         self.task = task
         self.llm = llm
@@ -110,6 +111,7 @@ class PipecatEngine:
         # Embeddings configuration (passed from run_pipeline.py)
         self._embeddings_api_key: Optional[str] = embeddings_api_key
         self._embeddings_model: Optional[str] = embeddings_model
+        self._embeddings_base_url: Optional[str] = embeddings_base_url
 
     async def _get_organization_id(self) -> Optional[int]:
         """Get and cache the organization ID from workflow run."""
@@ -352,6 +354,7 @@ class PipecatEngine:
                     limit=3,  # Return top 3 most relevant chunks
                     embeddings_api_key=self._embeddings_api_key,
                     embeddings_model=self._embeddings_model,
+                    embeddings_base_url=self._embeddings_base_url,
                 )
 
                 await function_call_params.result_callback(result)
diff --git a/api/services/workflow/tools/knowledge_base.py b/api/services/workflow/tools/knowledge_base.py
index 9732619..ca79efc 100644
--- a/api/services/workflow/tools/knowledge_base.py
+++ b/api/services/workflow/tools/knowledge_base.py
@@ -28,6 +28,7 @@ async def retrieve_from_knowledge_base(
     limit: int = 3,
     embeddings_api_key: Optional[str] = None,
     embeddings_model: Optional[str] = None,
+    embeddings_base_url: Optional[str] = None,
 ) -> Dict[str, Any]:
     """Retrieve relevant information from the knowledge base using vector similarity search.
 
@@ -43,6 +44,7 @@ async def retrieve_from_knowledge_base(
         limit: Maximum number of chunks to return (default: 3)
         embeddings_api_key: Optional API key for embedding service
         embeddings_model: Optional model ID for embedding service
+        embeddings_base_url: Optional base URL for embedding service
 
     Returns:
         Dictionary containing:
@@ -70,6 +72,7 @@ async def retrieve_from_knowledge_base(
                 limit,
                 embeddings_api_key,
                 embeddings_model,
+                embeddings_base_url,
             )
 
         # Create span with parent context
@@ -106,6 +109,7 @@ async def retrieve_from_knowledge_base(
                         limit,
                         embeddings_api_key,
                         embeddings_model,
+                        embeddings_base_url,
                     )
 
                     # Add result metadata to span
@@ -179,6 +183,7 @@ async def retrieve_from_knowledge_base(
                 limit,
                 embeddings_api_key,
                 embeddings_model,
+                embeddings_base_url,
             )
     else:
         # Tracing is disabled - perform retrieval without tracing
@@ -199,6 +204,7 @@ async def _perform_retrieval(
     limit: int,
     embeddings_api_key: Optional[str] = None,
     embeddings_model: Optional[str] = None,
+    embeddings_base_url: Optional[str] = None,
 ) -> Dict[str, Any]:
     """Internal function to perform the actual retrieval operation.
 
@@ -213,6 +219,7 @@ async def _perform_retrieval(
             max_tokens=128,  # This is only used for chunking, not for retrieval
             api_key=embeddings_api_key,
             model_id=embeddings_model or "text-embedding-3-small",
+            base_url=embeddings_base_url,
         )
 
         # Perform vector similarity search
diff --git a/api/tasks/knowledge_base_processing.py b/api/tasks/knowledge_base_processing.py
index e1a4cea..fe0d996 100644
--- a/api/tasks/knowledge_base_processing.py
+++ b/api/tasks/knowledge_base_processing.py
@@ -124,11 +124,13 @@ async def process_knowledge_base_document(
         # Try to get user's embeddings configuration
         embeddings_api_key = None
         embeddings_model = None
+        embeddings_base_url = None
         if document.created_by:
             user_config = await db_client.get_user_configurations(document.created_by)
             if user_config.embeddings:
                 embeddings_api_key = user_config.embeddings.api_key
                 embeddings_model = user_config.embeddings.model
+                embeddings_base_url = getattr(user_config.embeddings, "base_url", None)
                 logger.info(f"Using user embeddings config: model={embeddings_model}")
 
         # Check if API key is configured
@@ -148,6 +150,7 @@ async def process_knowledge_base_document(
             max_tokens=max_tokens,
             api_key=embeddings_api_key,
             model_id=embeddings_model or "text-embedding-3-small",
+            base_url=embeddings_base_url,
         )
 
         # Step 1: Convert document with docling
diff --git a/docs/deployment/docker.mdx b/docs/deployment/docker.mdx
index abc6c6e..6408e18 100644
--- a/docs/deployment/docker.mdx
+++ b/docs/deployment/docker.mdx
@@ -70,6 +70,10 @@ It will automatically:
 
 ### Start the Application
 
+<Note>
+Please ensure that Docker Compose is installed on your machine before proceeding further. You can check whether its installed by running `docker compose version` command. If its not installed, please install it by following your server provider documentation.
+</Note>
+
 After the setup script completes, start Dograh:
 
 ```bash
diff --git a/scripts/setup_pipecat.sh b/scripts/setup_pipecat.sh
index 3db9836..70b3663 100755
--- a/scripts/setup_pipecat.sh
+++ b/scripts/setup_pipecat.sh
@@ -16,7 +16,7 @@ git submodule update --init --recursive
 
 # Install pipecat in editable mode with all extras
 echo "Installing pipecat dependencies..."
-pip install -e ./pipecat[cartesia,deepgram,openai,elevenlabs,groq,google,azure,sarvam,soundfile,silero,webrtc,local-smart-turn-v3,speechmatics]
+pip install -e ./pipecat[cartesia,deepgram,openai,elevenlabs,groq,google,azure,sarvam,soundfile,silero,webrtc,local-smart-turn-v3,speechmatics,openrouter]
 
 # Install other requirements
 echo "Installing dograh API requirements..."