diff --git a/.github/workflows/test-suite.yml b/.github/workflows/test-suite.yml
index d8c88a6dc..b2a8bb60a 100644
--- a/.github/workflows/test-suite.yml
+++ b/.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
diff --git a/README.md b/README.md
index 386c41b10..2299daf4a 100644
--- a/README.md
+++ b/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.
 
 
diff --git a/examples/smart_scraper_graph/litellm/smart_scraper_litellm.py b/examples/smart_scraper_graph/litellm/smart_scraper_litellm.py
new file mode 100644
index 000000000..f4ac16f92
--- /dev/null
+++ b/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))
diff --git a/pyproject.toml b/pyproject.toml
index afad4ece5..4805a4ef3 100644
--- a/pyproject.toml
+++ b/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",
diff --git a/scrapegraphai/graphs/abstract_graph.py b/scrapegraphai/graphs/abstract_graph.py
index 21f90460c..ab98617cb 100644
--- a/scrapegraphai/graphs/abstract_graph.py
+++ b/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
 
diff --git a/scrapegraphai/models/__init__.py b/scrapegraphai/models/__init__.py
index db01cd2e4..05519fcaf 100644
--- a/scrapegraphai/models/__init__.py
+++ b/scrapegraphai/models/__init__.py
@@ -6,6 +6,7 @@
 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
@@ -13,4 +14,4 @@
 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"]
diff --git a/scrapegraphai/models/litellm.py b/scrapegraphai/models/litellm.py
new file mode 100644
index 000000000..023c257b4
--- /dev/null
+++ b/scrapegraphai/models/litellm.py
@@ -0,0 +1,268 @@
+"""
+LiteLLM Module
+"""
+
+import math
+from typing import Any, Dict, List, Optional, Tuple
+
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import AIMessage, BaseMessage, convert_to_openai_messages
+from langchain_core.messages.ai import UsageMetadata
+from langchain_core.outputs import ChatGeneration, ChatResult
+from pydantic import ConfigDict, Field, model_validator
+
+LITELLM_INSTALL_HINT = (
+    "The litellm package is not installed. "
+    "Please install it using `pip install scrapegraphai[litellm]`."
+)
+
+
+def _import_litellm():
+    try:
+        import litellm
+    except ImportError as exc:
+        raise ImportError(LITELLM_INSTALL_HINT) from exc
+    return litellm
+
+
+def get_litellm_model_tokens(model: str) -> Optional[int]:
+    """
+    Look up the max input tokens of a model in LiteLLM's model map.
+
+    Args:
+        model (str): A LiteLLM model name, e.g. ``anthropic/claude-sonnet-4-6``.
+
+    Returns:
+        Optional[int]: The context window, or None if LiteLLM does not know it.
+    """
+    litellm = _import_litellm()
+    try:
+        info = litellm.get_model_info(model)
+    except Exception as exc:
+        if not _is_model_not_mapped(exc):
+            raise
+        return None
+    # ``max_tokens`` in LiteLLM's model map is usually the output limit, so it
+    # is not used as a context window; unknown windows fall back to the
+    # graph's default instead.
+    return info.get("max_input_tokens") or None
+
+
+def get_litellm_token_costs(
+    model: str, prompt_tokens: int, completion_tokens: int, usage: Any = None
+) -> Optional[Tuple[float, float]]:
+    """
+    Split the cost of a call into prompt and completion cost with LiteLLM's
+    price map.
+
+    Args:
+        model (str): A LiteLLM model name, e.g. ``anthropic/claude-sonnet-4-6``.
+        prompt_tokens (int): Number of prompt tokens.
+        completion_tokens (int): Number of completion tokens.
+        usage: The response usage object, so cached tokens are priced too.
+
+    Returns:
+        Optional[Tuple[float, float]]: ``(prompt_cost, completion_cost)`` in
+        USD, or None if LiteLLM cannot price the model.
+    """
+    litellm = _import_litellm()
+    try:
+        prompt_cost, completion_cost = litellm.cost_per_token(
+            model=model,
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            usage_object=usage,
+        )
+    except Exception:
+        # The split only refines a cost LiteLLM already reported, so a failure
+        # here must not fail a completion that succeeded.
+        return None
+    return float(prompt_cost), float(completion_cost)
+
+
+def _is_model_not_mapped(exc: Exception) -> bool:
+    """
+    Whether ``exc`` is how LiteLLM reports a model missing from its model map.
+
+    litellm 1.94 raises a bare ``Exception``; newer releases raise
+    ``ModelNotMappedError``, and ``BadRequestError`` when no provider can be
+    inferred from the model name (e.g. a proxy alias).
+    """
+    if type(exc) is Exception:
+        return True
+    try:
+        from litellm.exceptions import BadRequestError
+    except ImportError:
+        BadRequestError = None
+    try:
+        from litellm.exceptions import ModelNotMappedError
+    except ImportError:
+        ModelNotMappedError = None
+    known = tuple(e for e in (BadRequestError, ModelNotMappedError) if e is not None)
+    return bool(known) and isinstance(exc, known)
+
+
+class LiteLLM(BaseChatModel):
+    """
+    A chat model that calls the LiteLLM SDK, so any provider LiteLLM supports
+    (OpenAI, Anthropic, Gemini, Bedrock, Vertex AI, Azure, Groq, Ollama, ...)
+    can be used with a ``litellm/<provider>/<model>`` model string.
+
+    Credentials are read by LiteLLM from the provider's usual environment
+    variables (``ANTHROPIC_API_KEY``, ``AWS_*``, ...) unless ``api_key`` is set.
+    ``drop_params`` defaults to True so OpenAI-only parameters are dropped for
+    providers that do not accept them.
+
+    Args:
+        llm_config (dict): Configuration parameters for the language model.
+    """
+
+    model_config = ConfigDict(populate_by_name=True, protected_namespaces=())
+
+    model: str
+    api_key: Optional[str] = None
+    api_base: Optional[str] = Field(default=None, alias="base_url")
+    temperature: Optional[float] = None
+    max_tokens: Optional[int] = None
+    timeout: Optional[float] = None
+    max_retries: Optional[int] = None
+    drop_params: bool = True
+    # Set by AbstractGraph for every provider; responses are returned whole.
+    streaming: bool = False
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+
+    @model_validator(mode="before")
+    @classmethod
+    def _collect_model_kwargs(cls, values: Any) -> Any:
+        """Forward unknown config keys (top_p, seed, ...) to LiteLLM."""
+        if not isinstance(values, dict):
+            return values
+        known = set(cls.model_fields) | {"base_url"}
+        extra = {k: v for k, v in values.items() if k not in known}
+        if not extra:
+            return values
+        values = {k: v for k, v in values.items() if k in known}
+        values["model_kwargs"] = {**values.get("model_kwargs", {}), **extra}
+        return values
+
+    @property
+    def _llm_type(self) -> str:
+        return "litellm"
+
+    @property
+    def model_name(self) -> str:
+        """Model name used by the graph for logging and cost tracking."""
+        return self.model
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        return {"model": self.model, "api_base": self.api_base}
+
+    def _completion_params(
+        self, messages: List[BaseMessage], stop: Optional[List[str]], **kwargs: Any
+    ) -> Dict[str, Any]:
+        params: Dict[str, Any] = {
+            "model": self.model,
+            "messages": convert_to_openai_messages(messages),
+            "drop_params": self.drop_params,
+        }
+        optional = {
+            "api_key": self.api_key,
+            "api_base": self.api_base,
+            "temperature": self.temperature,
+            "max_tokens": self.max_tokens,
+            "timeout": self.timeout,
+            "num_retries": self.max_retries,
+            "stop": stop,
+        }
+        # Unset values are left out so LiteLLM falls back to provider env vars
+        # and provider defaults.
+        params.update({k: v for k, v in optional.items() if v is not None})
+        params.update(self.model_kwargs)
+        params.update(kwargs)
+        return params
+
+    def _create_chat_result(self, response: Any) -> ChatResult:
+        choice = response.choices[0]
+        usage = getattr(response, "usage", None)
+        hidden_params = getattr(response, "_hidden_params", None) or {}
+
+        usage_metadata = None
+        token_usage: Dict[str, int] = {}
+        input_tokens = output_tokens = 0
+        if usage is not None:
+            input_tokens = getattr(usage, "prompt_tokens", 0) or 0
+            output_tokens = getattr(usage, "completion_tokens", 0) or 0
+            total_tokens = getattr(usage, "total_tokens", 0) or (
+                input_tokens + output_tokens
+            )
+            usage_metadata = UsageMetadata(
+                input_tokens=input_tokens,
+                output_tokens=output_tokens,
+                total_tokens=total_tokens,
+            )
+            token_usage = {
+                "prompt_tokens": input_tokens,
+                "completion_tokens": output_tokens,
+                "total_tokens": total_tokens,
+            }
+
+        response_metadata: Dict[str, Any] = {
+            "model_name": getattr(response, "model", None) or self.model,
+            "finish_reason": getattr(choice, "finish_reason", None),
+        }
+        if hidden_params.get("response_cost") is not None:
+            response_metadata["response_cost"] = hidden_params["response_cost"]
+            costs = get_litellm_token_costs(
+                self.model, input_tokens, output_tokens, usage=usage
+            )
+            # Only report the split when it adds up to what LiteLLM charged,
+            # e.g. not for a proxy alias priced under the upstream model name.
+            if costs is not None and math.isclose(
+                sum(costs), float(hidden_params["response_cost"]), rel_tol=1e-6
+            ):
+                response_metadata["prompt_cost"] = costs[0]
+                response_metadata["completion_cost"] = costs[1]
+
+        message = AIMessage(
+            content=choice.message.content or "",
+            usage_metadata=usage_metadata,
+            response_metadata=response_metadata,
+        )
+        return ChatResult(
+            generations=[ChatGeneration(message=message)],
+            llm_output={
+                "token_usage": token_usage,
+                "model_name": response_metadata["model_name"],
+            },
+        )
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        litellm = _import_litellm()
+        response = litellm.completion(
+            **self._completion_params(messages, stop, **kwargs)
+        )
+        return self._create_chat_result(response)
+
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        litellm = _import_litellm()
+        response = await litellm.acompletion(
+            **self._completion_params(messages, stop, **kwargs)
+        )
+        return self._create_chat_result(response)
diff --git a/scrapegraphai/utils/custom_callback.py b/scrapegraphai/utils/custom_callback.py
index c2a881a60..f33dde1cb 100644
--- a/scrapegraphai/utils/custom_callback.py
+++ b/scrapegraphai/utils/custom_callback.py
@@ -109,11 +109,25 @@ def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
             generation = response.generations[0][0]
         except IndexError:
             generation = None
+        reported_cost = None
+        reported_split = None
         if isinstance(generation, ChatGeneration):
             try:
                 message = generation.message
                 if isinstance(message, AIMessage):
                     usage_metadata = message.usage_metadata
+                    # Models that price their own calls (e.g. LiteLLM) report
+                    # the cost of the request in the response metadata.
+                    metadata = message.response_metadata
+                    reported_cost = metadata.get("response_cost")
+                    if (
+                        metadata.get("prompt_cost") is not None
+                        and metadata.get("completion_cost") is not None
+                    ):
+                        reported_split = (
+                            float(metadata["prompt_cost"]),
+                            float(metadata["completion_cost"]),
+                        )
                 else:
                     usage_metadata = None
             except AttributeError:
@@ -155,13 +169,20 @@ def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
                 input_tokens=prompt_tokens,
                 service_tier=self.service_tier,
             )
+            request_cost = prompt_cost + completion_cost
+        elif reported_split is not None:
+            prompt_cost, completion_cost = reported_split
+            request_cost = prompt_cost + completion_cost
+        elif reported_cost is not None:
+            # The model priced the call but not its parts, so only the total
+            # is known.
+            request_cost = float(reported_cost)
         else:
-            completion_cost = 0
-            prompt_cost = 0
+            request_cost = 0
 
         # update shared state behind lock
         with self._lock:
-            self.total_cost += prompt_cost + completion_cost
+            self.total_cost += request_cost
             self.total_tokens += token_usage.get("total_tokens", 0)
             self.prompt_tokens += prompt_tokens
             self.completion_tokens += completion_tokens
diff --git a/tests/test_litellm_model.py b/tests/test_litellm_model.py
new file mode 100644
index 000000000..23785cb5a
--- /dev/null
+++ b/tests/test_litellm_model.py
@@ -0,0 +1,297 @@
+"""Tests for the LiteLLM model integration."""
+
+import asyncio
+import sys
+import types
+from types import SimpleNamespace
+from unittest import mock
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from langchain_core.outputs import ChatGeneration, LLMResult
+
+from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.models import LiteLLM
+from scrapegraphai.models.litellm import (
+    get_litellm_model_tokens,
+    get_litellm_token_costs,
+)
+from scrapegraphai.utils.custom_callback import CustomCallbackHandler
+
+
+def _fake_response(content="hello", cost=0.00042):
+    response = SimpleNamespace(
+        model="claude-sonnet-4-6",
+        choices=[
+            SimpleNamespace(
+                message=SimpleNamespace(content=content), finish_reason="stop"
+            )
+        ],
+        usage=SimpleNamespace(prompt_tokens=12, completion_tokens=3, total_tokens=15),
+    )
+    response._hidden_params = {"response_cost": cost}
+    return response
+
+
+@pytest.fixture
+def fake_litellm(monkeypatch):
+    """Install a stub litellm module so the tests run without the extra."""
+    module = types.ModuleType("litellm")
+    module.completion = mock.MagicMock(return_value=_fake_response())
+    module.acompletion = mock.AsyncMock(return_value=_fake_response("async"))
+    module.get_model_info = mock.MagicMock(
+        return_value={"max_input_tokens": 200000, "max_tokens": 64000}
+    )
+    module.cost_per_token = mock.MagicMock(return_value=(0.00036, 0.00006))
+    monkeypatch.setitem(sys.modules, "litellm", module)
+    return module
+
+
+def test_invoke_calls_litellm_completion(fake_litellm):
+    model = LiteLLM(model="anthropic/claude-sonnet-4-6", temperature=0)
+
+    result = model.invoke([SystemMessage("be brief"), HumanMessage("hi")])
+
+    kwargs = fake_litellm.completion.call_args.kwargs
+    assert kwargs["model"] == "anthropic/claude-sonnet-4-6"
+    assert kwargs["messages"] == [
+        {"role": "system", "content": "be brief"},
+        {"role": "user", "content": "hi"},
+    ]
+    assert kwargs["temperature"] == 0
+    assert kwargs["drop_params"] is True
+    assert result.content == "hello"
+
+
+def test_unset_credentials_are_not_forwarded(fake_litellm):
+    LiteLLM(model="anthropic/claude-sonnet-4-6").invoke("hi")
+
+    kwargs = fake_litellm.completion.call_args.kwargs
+    for key in ("api_key", "api_base", "temperature", "max_tokens", "num_retries"):
+        assert key not in kwargs
+    assert "streaming" not in kwargs
+
+
+def test_config_is_mapped_to_litellm_arguments(fake_litellm):
+    model = LiteLLM(
+        model="openai/gpt-4o-mini",
+        api_key="sk-test",
+        base_url="http://localhost:4000",
+        max_tokens=256,
+        max_retries=3,
+        top_p=0.5,
+        streaming=False,
+    )
+
+    model.invoke("hi")
+
+    kwargs = fake_litellm.completion.call_args.kwargs
+    assert kwargs["api_key"] == "sk-test"
+    assert kwargs["api_base"] == "http://localhost:4000"
+    assert kwargs["max_tokens"] == 256
+    assert kwargs["num_retries"] == 3
+    assert kwargs["top_p"] == 0.5
+    assert "streaming" not in kwargs
+
+
+def test_drop_params_can_be_disabled(fake_litellm):
+    LiteLLM(model="openai/gpt-4o-mini", drop_params=False).invoke("hi")
+
+    assert fake_litellm.completion.call_args.kwargs["drop_params"] is False
+
+
+def test_usage_and_cost_are_reported(fake_litellm):
+    message = LiteLLM(model="anthropic/claude-sonnet-4-6").invoke("hi")
+
+    assert message.usage_metadata == {
+        "input_tokens": 12,
+        "output_tokens": 3,
+        "total_tokens": 15,
+    }
+    assert message.response_metadata["response_cost"] == 0.00042
+    assert message.response_metadata["prompt_cost"] == 0.00036
+    assert message.response_metadata["completion_cost"] == 0.00006
+    assert message.response_metadata["finish_reason"] == "stop"
+    kwargs = fake_litellm.cost_per_token.call_args.kwargs
+    assert kwargs["model"] == "anthropic/claude-sonnet-4-6"
+    assert (kwargs["prompt_tokens"], kwargs["completion_tokens"]) == (12, 3)
+
+
+def test_cost_split_omitted_when_litellm_cannot_price_model(fake_litellm):
+    fake_litellm.cost_per_token.side_effect = Exception("not mapped")
+
+    message = LiteLLM(model="openai/my-proxy-alias").invoke("hi")
+
+    assert message.response_metadata["response_cost"] == 0.00042
+    assert "prompt_cost" not in message.response_metadata
+    assert "completion_cost" not in message.response_metadata
+
+
+def test_cost_split_omitted_when_it_does_not_match_total(fake_litellm):
+    fake_litellm.cost_per_token.return_value = (0.1, 0.2)
+
+    message = LiteLLM(model="anthropic/claude-sonnet-4-6").invoke("hi")
+
+    assert message.response_metadata["response_cost"] == 0.00042
+    assert "prompt_cost" not in message.response_metadata
+
+
+def test_token_costs_split_prompt_and_completion(fake_litellm):
+    assert get_litellm_token_costs("anthropic/claude-sonnet-4-6", 12, 3) == (
+        0.00036,
+        0.00006,
+    )
+
+
+def test_cost_split_failure_keeps_the_completion(fake_litellm):
+    fake_litellm.cost_per_token.side_effect = KeyError("boom")
+
+    assert get_litellm_token_costs("anthropic/claude-sonnet-4-6", 12, 3) is None
+    message = LiteLLM(model="anthropic/claude-sonnet-4-6").invoke("hi")
+
+    assert message.response_metadata["response_cost"] == 0.00042
+    assert "prompt_cost" not in message.response_metadata
+
+
+def test_ainvoke_calls_litellm_acompletion(fake_litellm):
+    model = LiteLLM(model="gemini/gemini-2.5-flash")
+
+    result = asyncio.run(model.ainvoke("hi"))
+
+    assert result.content == "async"
+    assert fake_litellm.acompletion.call_args.kwargs["model"] == (
+        "gemini/gemini-2.5-flash"
+    )
+    fake_litellm.completion.assert_not_called()
+
+
+def test_missing_litellm_raises_install_hint(monkeypatch):
+    monkeypatch.setitem(sys.modules, "litellm", None)
+
+    with pytest.raises(ImportError, match=r"scrapegraphai\[litellm\]"):
+        LiteLLM(model="openai/gpt-4o-mini").invoke("hi")
+
+
+def test_model_tokens_come_from_litellm_model_map(fake_litellm):
+    assert get_litellm_model_tokens("anthropic/claude-sonnet-4-6") == 200000
+    fake_litellm.get_model_info.assert_called_once_with("anthropic/claude-sonnet-4-6")
+
+
+def test_model_tokens_none_for_unknown_model(fake_litellm):
+    fake_litellm.get_model_info.side_effect = Exception("not mapped")
+
+    assert get_litellm_model_tokens("my-proxy-alias") is None
+
+
+def test_model_tokens_ignore_max_tokens_output_limit(fake_litellm):
+    # LiteLLM sets max_tokens to the output limit when it is known.
+    fake_litellm.get_model_info.return_value = {
+        "max_tokens": 8192,
+        "max_output_tokens": 8192,
+    }
+
+    assert get_litellm_model_tokens("gemini/gemini-gemma-2-9b-it") is None
+
+
+def test_model_tokens_reraise_unexpected_errors(fake_litellm):
+    fake_litellm.get_model_info.side_effect = RuntimeError("boom")
+
+    with pytest.raises(RuntimeError):
+        get_litellm_model_tokens("anthropic/claude-sonnet-4-6")
+
+
+def test_model_tokens_none_for_real_litellm_unknown_model():
+    pytest.importorskip("litellm")
+
+    assert get_litellm_model_tokens("my-proxy-alias") is None
+    assert get_litellm_model_tokens("openai/not-a-real-model") is None
+
+
+def test_token_costs_with_real_litellm_price_map():
+    litellm = pytest.importorskip("litellm")
+    model = "anthropic/claude-sonnet-4-6"
+    info = litellm.get_model_info(model)
+
+    prompt_cost, completion_cost = get_litellm_token_costs(model, 1000, 100)
+
+    assert prompt_cost == pytest.approx(1000 * info["input_cost_per_token"])
+    assert completion_cost == pytest.approx(100 * info["output_cost_per_token"])
+    assert get_litellm_token_costs("my-proxy-alias", 1000, 100) is None
+
+
+def _graph(llm_config):
+    return SmartScraperGraph(
+        prompt="List the products",
+        source="<html><body>products</body></html>",
+        config={"llm": llm_config},
+    )
+
+
+def test_graph_routes_litellm_prefix(fake_litellm):
+    graph = _graph({"model": "litellm/anthropic/claude-sonnet-4-6", "api_key": "k"})
+
+    assert isinstance(graph.llm_model, LiteLLM)
+    assert graph.llm_model.model == "anthropic/claude-sonnet-4-6"
+    assert graph.model_token == 200000
+    assert graph.model_tokens_defaulted is False
+
+
+def test_graph_model_tokens_override_litellm_lookup(fake_litellm):
+    graph = _graph({"model": "litellm/openai/gpt-4o-mini", "model_tokens": 1234})
+
+    assert graph.model_token == 1234
+    fake_litellm.get_model_info.assert_not_called()
+
+
+def test_graph_falls_back_when_litellm_does_not_know_model(fake_litellm):
+    fake_litellm.get_model_info.side_effect = Exception("not mapped")
+
+    graph = _graph({"model": "litellm/my-proxy-alias"})
+
+    assert graph.model_token == 8192
+    assert graph.model_tokens_defaulted is True
+
+
+def test_custom_callback_uses_reported_cost():
+    handler = CustomCallbackHandler("anthropic/claude-sonnet-4-6")
+    message = AIMessage(
+        content="ok",
+        usage_metadata={"input_tokens": 12, "output_tokens": 3, "total_tokens": 15},
+        response_metadata={"response_cost": 0.00042},
+    )
+
+    handler.on_llm_end(LLMResult(generations=[[ChatGeneration(message=message)]]))
+
+    assert handler.total_cost == pytest.approx(0.00042)
+    assert handler.total_tokens == 15
+    assert handler.successful_requests == 1
+
+
+def test_custom_callback_uses_reported_cost_split():
+    handler = CustomCallbackHandler("anthropic/claude-sonnet-4-6")
+    message = AIMessage(
+        content="ok",
+        usage_metadata={"input_tokens": 12, "output_tokens": 3, "total_tokens": 15},
+        response_metadata={
+            "response_cost": 0.00042,
+            "prompt_cost": 0.00036,
+            "completion_cost": 0.00006,
+        },
+    )
+
+    handler.on_llm_end(LLMResult(generations=[[ChatGeneration(message=message)]]))
+
+    assert handler.total_cost == pytest.approx(0.00042)
+    assert handler.prompt_tokens == 12
+    assert handler.completion_tokens == 3
+
+
+def test_custom_callback_end_to_end_with_litellm_model(fake_litellm):
+    handler = CustomCallbackHandler("anthropic/claude-sonnet-4-6")
+
+    LiteLLM(model="anthropic/claude-sonnet-4-6").invoke(
+        "hi", config={"callbacks": [handler]}
+    )
+
+    assert handler.total_cost == pytest.approx(0.00042)
+    assert handler.total_tokens == 15
diff --git a/uv.lock b/uv.lock
index bbf2bb3e7..7e426997e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,9 +2,12 @@ version = 1
 revision = 1
 requires-python = ">=3.12, <4.0"
 resolution-markers = [
-    "python_full_version >= '3.13' and sys_platform == 'darwin'",
-    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.14' and sys_platform == 'darwin'",
+    "python_full_version == '3.13.*' and sys_platform == 'darwin'",
+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "(python_full_version == '3.13.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
     "python_full_version < '3.13' and sys_platform == 'darwin'",
     "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "(python_full_version < '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
@@ -30,7 +33,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.13.2"
+version = "3.14.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -39,78 +42,135 @@ dependencies = [
     { name = "frozenlist" },
     { name = "multidict" },
     { name = "propcache" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
     { name = "yarl" },
 ]
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