feat(models): add LiteLLM SDK model provider

.github/workflows/test-suite.yml

@@ -44,6 +44,7 @@ jobs:
           tests/test_depth_search_graph.py
           tests/test_error_page_detection.py
           tests/test_json_scraper_graph.py
+          tests/test_litellm_model.py
           tests/test_minimax_models.py
           tests/test_scrape_do.py
           tests/test_search_graph.py

README.md

@@ -157,6 +157,8 @@ For each of these graphs there is the multi version. It allows to make calls of
 
 It is possible to use different LLM through APIs, such as **OpenAI**, **Groq**, **Azure**, **Gemini**, **[MiniMax](docs/minimax.md)** and more, or local models using **Ollama**.
 
+To reach any provider supported by [LiteLLM](https://docs.litellm.ai/docs/providers) (Anthropic, Bedrock, Vertex AI, Azure, ...), install `scrapegraphai[litellm]` and set the model to `litellm/<provider>/<model>`, for example `litellm/anthropic/claude-sonnet-4-6`. The context window and the cost reported in the execution info come from LiteLLM's model map.
+
 Remember to have [Ollama](https://ollama.com/) installed and download the models using the **ollama pull** command, if you want to use local models.
 
 

examples/smart_scraper_graph/litellm/smart_scraper_litellm.py

@@ -0,0 +1,51 @@
+"""
+Basic example of scraping pipeline using SmartScraper with LiteLLM
+
+Install the extra first: pip install scrapegraphai[litellm]
+"""
+
+import os
+
+from dotenv import load_dotenv
+
+from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.utils import prettify_exec_info
+
+load_dotenv()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+# "litellm/<provider>/<model>" routes the call through the LiteLLM SDK, so any
+# provider LiteLLM supports works here, e.g. "litellm/anthropic/claude-sonnet-4-6",
+# "litellm/vertex_ai/gemini-2.5-flash" or "litellm/bedrock/<model id>".
+# Credentials are read from the provider's usual environment variables.
+graph_config = {
+    "llm": {
+        "model": os.getenv("LITELLM_MODEL", "litellm/anthropic/claude-sonnet-4-6"),
+        "temperature": 0,
+    },
+    "verbose": True,
+    "headless": False,
+}
+
+# ************************************************
+# Create the SmartScraperGraph instance and run it
+# ************************************************
+
+smart_scraper_graph = SmartScraperGraph(
+    prompt="Find some information about the founders.",
+    source="https://scrapegraphai.com/",
+    config=graph_config,
+)
+
+result = smart_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = smart_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))

pyproject.toml

@@ -69,6 +69,7 @@ requires-python = ">=3.12,<4.0"
 
 [project.optional-dependencies]
 burr = ["burr[start]==0.22.1"]
+litellm = ["litellm>=1.94.0,<1.105.0"]
 nvidia = ["langchain-nvidia-ai-endpoints>=0.1.0"]
 ocr = [
     "surya-ocr>=0.5.0",

scrapegraphai/graphs/abstract_graph.py

@@ -13,7 +13,8 @@
 from pydantic import BaseModel
 
 from ..helpers import models_tokens
-from ..models import XAI, CLoD, DeepSeek, MiniMax, Nvidia, OneApi
+from ..models import XAI, CLoD, DeepSeek, LiteLLM, MiniMax, Nvidia, OneApi
+from ..models.litellm import get_litellm_model_tokens
 from ..utils.logging import get_logger, set_verbosity_info, set_verbosity_warning
 
 logger = get_logger(__name__)
@@ -175,6 +176,7 @@ def _create_llm(self, llm_config: dict) -> object:
             "togetherai",
             "xai",
             "minimax",
+            "litellm",
         }
 
         if "/" in llm_params["model"]:
@@ -209,9 +211,17 @@ def _create_llm(self, llm_config: dict) -> object:
 
         if llm_params.get("model_tokens", None) is None:
             try:
-                self.model_token = models_tokens[llm_params["model_provider"]][
-                    llm_params["model"]
-                ]
+                if llm_params["model_provider"] == "litellm":
+                    # LiteLLM routes to any provider, so read the context
+                    # window from its model map instead of models_tokens.
+                    model_token = get_litellm_model_tokens(llm_params["model"])
+                    if model_token is None:
+                        raise KeyError(llm_params["model"])
+                    self.model_token = model_token
+                else:
+                    self.model_token = models_tokens[llm_params["model_provider"]][
+                        llm_params["model"]
+                    ]
             except KeyError:
                 logger.warning(
                     "Max input tokens for model %s/%s not found, "
@@ -243,6 +253,7 @@ def _create_llm(self, llm_config: dict) -> object:
                 "clod",
                 "xai",
                 "minimax",
+                "litellm",
             }:
                 if llm_params["model_provider"] == "bedrock":
                     llm_params["model_kwargs"] = {
@@ -263,6 +274,9 @@ def _create_llm(self, llm_config: dict) -> object:
                 if model_provider == "minimax":
                     return MiniMax(**llm_params)
 
+                if model_provider == "litellm":
+                    return LiteLLM(**llm_params)
+
                 if model_provider == "ernie":
                     from langchain_community.chat_models import ErnieBotChat
 

scrapegraphai/models/__init__.py

@@ -6,11 +6,12 @@
 from .cheaperinference import CheaperInference
 from .clod import CLoD
 from .deepseek import DeepSeek
+from .litellm import LiteLLM
 from .minimax import MiniMax
 from .nvidia import Nvidia
 from .oneapi import OneApi
 from .openai_itt import OpenAIImageToText
 from .openai_tts import OpenAITextToSpeech
 from .xai import XAI
 
-__all__ = ["AtlasCloud", "CheaperInference", "DeepSeek", "MiniMax", "OneApi", "OpenAIImageToText", "OpenAITextToSpeech", "CLoD", "XAI", "Nvidia"]
+__all__ = ["AtlasCloud", "CheaperInference", "DeepSeek", "LiteLLM", "MiniMax", "OneApi", "OpenAIImageToText", "OpenAITextToSpeech", "CLoD", "XAI", "Nvidia"]