feat: add LiteLLM as LLM provider

.env.example

@@ -29,6 +29,17 @@ OLLAMA_BASE_URL="http://localhost:11434"
 # Pull a model first: ollama pull llama3.1:8b
 OLLAMA_MODEL="llama3.1:8b"
 
+# LLM Provider
+# Set to "litellm" to use LiteLLM SDK instead of Ollama.
+# Supports 100+ providers: OpenAI, Anthropic, Bedrock, Vertex, Groq, etc.
+# Install with: pip install moneyprinter[litellm]
+LLM_PROVIDER="ollama"
+
+# LiteLLM Settings (only used when LLM_PROVIDER=litellm)
+# Model uses LiteLLM naming: "gpt-4o-mini", "anthropic/claude-sonnet-4-20250514", etc.
+# Provider API keys are read from standard env vars (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)
+LITELLM_MODEL="gpt-4o-mini"
+
 # AssemblyAI API Key
 # Sign up at https://www.assemblyai.com/ to receive an API key.
 ASSEMBLY_AI_API_KEY=""

Backend/gpt.py

@@ -16,6 +16,15 @@
 OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "llama3.1:8b")
 OLLAMA_TIMEOUT = float(os.getenv("OLLAMA_TIMEOUT", "180"))
 
+# LLM provider: "ollama" (default) or "litellm"
+LLM_PROVIDER = os.getenv("LLM_PROVIDER", "ollama").lower()
+LITELLM_MODEL = os.getenv("LITELLM_MODEL", "gpt-4o-mini")
+LITELLM_TIMEOUT = float(os.getenv("LITELLM_TIMEOUT", str(OLLAMA_TIMEOUT)))
+
+if LLM_PROVIDER not in ("ollama", "litellm"):
+    log(f"[!] Unknown LLM_PROVIDER '{LLM_PROVIDER}', falling back to 'ollama'", "warning")
+    LLM_PROVIDER = "ollama"
+
 
 def _ollama_client() -> Client:
     return Client(host=OLLAMA_BASE_URL, timeout=OLLAMA_TIMEOUT)
@@ -31,7 +40,40 @@ def _extract_model_name(model_obj) -> str:
     return ""
 
 
-def list_ollama_models() -> Tuple[List[str], str]:
+def list_models() -> Tuple[List[str], str]:
+    """
+    Returns available model names and configured default model.
+    Delegates to Ollama or LiteLLM depending on LLM_PROVIDER.
+
+    Returns:
+        Tuple[List[str], str]: (available model names, default model)
+    """
+    if LLM_PROVIDER == "litellm":
+        return _list_litellm_models()
+    return _list_ollama_models()
+
+
+def _list_litellm_models() -> Tuple[List[str], str]:
+    """Returns a curated list of popular LiteLLM model identifiers."""
+    models = [
+        LITELLM_MODEL,
+        "gpt-4.1",
+        "gpt-4.1-mini",
+        "anthropic/claude-sonnet-4-20250514",
+        "anthropic/claude-haiku-4-5-20251001",
+        "groq/llama-3.3-70b-versatile",
+        "bedrock/anthropic.claude-sonnet-4-20250514-v1:0",
+        "vertex_ai/gemini-2.5-flash",
+    ]
+    unique = list(dict.fromkeys(models))
+    return unique, LITELLM_MODEL
+
+
+# Keep old name as alias for backward compatibility
+list_ollama_models = list_models
+
+
+def _list_ollama_models() -> Tuple[List[str], str]:
     """
     Returns available Ollama model names and configured default model.
 
@@ -64,22 +106,47 @@ def list_ollama_models() -> Tuple[List[str], str]:
     return unique_names, default_model
 
 
+def _litellm_generate_response(prompt: str, model_name: str) -> str:
+    """Generate a response using the LiteLLM SDK."""
+    import litellm
+
+    try:
+        response = litellm.completion(
+            model=model_name,
+            messages=[{"role": "user", "content": prompt}],
+            stream=False,
+            drop_params=True,
+            timeout=LITELLM_TIMEOUT,
+        )
+    except Exception as err:
+        raise RuntimeError(f"LiteLLM request failed: {err}") from err
+
+    if not response.choices:
+        raise RuntimeError("LiteLLM returned no choices.")
+
+    content = (response.choices[0].message.content or "").strip()
+    if not content:
+        raise RuntimeError("LiteLLM returned an empty response.")
+    return content
+
+
 def generate_response(prompt: str, ai_model: str) -> str:
     """
-    Generate a script for a video, depending on the subject of the video.
+    Generate a response from the configured LLM provider.
 
     Args:
-        video_subject (str): The subject of the video.
-        ai_model (str): The AI model to use for generation.
-
+        prompt (str): The prompt to send to the model.
+        ai_model (str): The model identifier to use.
 
     Returns:
-
         str: The response from the AI model.
-
     """
 
-    model_name = (ai_model or "").strip() or OLLAMA_MODEL
+    default_model = LITELLM_MODEL if LLM_PROVIDER == "litellm" else OLLAMA_MODEL
+    model_name = (ai_model or "").strip() or default_model
+
+    if LLM_PROVIDER == "litellm":
+        return _litellm_generate_response(prompt, model_name)
 
     try:
         client = _ollama_client()

Backend/main.py

@@ -6,7 +6,7 @@
 from sqlalchemy import and_, case, select
 
 from db import SessionLocal, init_db
-from gpt import list_ollama_models
+from gpt import list_models, LLM_PROVIDER, LITELLM_MODEL
 from logstream import log
 from repository import create_job, get_job, list_job_events, request_cancel
 from utils import ENV_FILE, SONGS_DIR, check_env_vars, clean_dir
@@ -26,7 +26,7 @@
 @app.route("/api/models", methods=["GET"])
 def models():
     try:
-        available_models, default_model = list_ollama_models()
+        available_models, default_model = list_models()
         return jsonify(
             {
                 "status": "success",
@@ -35,13 +35,14 @@ def models():
             }
         )
     except Exception as err:
-        log(f"[-] Error fetching Ollama models: {str(err)}", "error")
+        log(f"[-] Error fetching models: {str(err)}", "error")
+        fallback = LITELLM_MODEL if LLM_PROVIDER == "litellm" else os.getenv("OLLAMA_MODEL", "llama3.1:8b")
         return jsonify(
             {
                 "status": "error",
-                "message": "Could not fetch Ollama models. Is Ollama running?",
-                "models": [os.getenv("OLLAMA_MODEL", "llama3.1:8b")],
-                "default": os.getenv("OLLAMA_MODEL", "llama3.1:8b"),
+                "message": f"Could not fetch models from {LLM_PROVIDER}.",
+                "models": [fallback],
+                "default": fallback,
             }
         )
 

pyproject.toml

@@ -26,6 +26,9 @@ dependencies = [
     "psycopg[binary]==3.2.3",
 ]
 
+[project.optional-dependencies]
+litellm = ["litellm>=1.80.0,<1.87.0"]
+
 [dependency-groups]
 dev = [
     "pytest==8.4.1",