feat: add LiteLLM as provider for 100+ LLM backends

agentic_security/llm_providers/__init__.py

@@ -7,6 +7,7 @@ from agentic_security.llm_providers.base import (
 )
 from agentic_security.llm_providers.openai_provider import OpenAIProvider
 from agentic_security.llm_providers.anthropic_provider import AnthropicProvider
+from agentic_security.llm_providers.litellm_provider import LiteLLMProvider
 from agentic_security.llm_providers.factory import create_provider, get_provider_class
 
 __all__ = [
@@ -17,6 +18,7 @@ __all__ = [
     "LLMRateLimitError",
     "OpenAIProvider",
     "AnthropicProvider",
+    "LiteLLMProvider",
     "create_provider",
     "get_provider_class",
 ]

agentic_security/llm_providers/factory.py

@@ -14,9 +14,11 @@ def _ensure_registered() -> None:
         return
     from agentic_security.llm_providers.openai_provider import OpenAIProvider
     from agentic_security.llm_providers.anthropic_provider import AnthropicProvider
+    from agentic_security.llm_providers.litellm_provider import LiteLLMProvider
 
     _PROVIDERS["openai"] = OpenAIProvider
     _PROVIDERS["anthropic"] = AnthropicProvider
+    _PROVIDERS["litellm"] = LiteLLMProvider
 
 
 def register_provider(name: str, provider_class: type[BaseLLMProvider]) -> None:

agentic_security/llm_providers/litellm_provider.py

@@ -0,0 +1,114 @@
+"""LiteLLM provider — unified access to 100+ LLM backends."""
+
+from typing import Any
+
+from agentic_security.llm_providers.base import (
+    BaseLLMProvider,
+    LLMMessage,
+    LLMProviderError,
+    LLMRateLimitError,
+    LLMResponse,
+)
+
+
+class LiteLLMProvider(BaseLLMProvider):
+    """LLM provider using LiteLLM SDK for 100+ backends.
+
+    Accepts any LiteLLM model string (e.g. ``openai/gpt-4o``,
+    ``anthropic/claude-sonnet-4-6``, ``groq/llama-3.3-70b-versatile``).
+    """
+
+    DEFAULT_MODEL = "openai/gpt-4o-mini"
+
+    def __init__(
+        self,
+        model: str = DEFAULT_MODEL,
+        api_key: str | None = None,
+        api_base: str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(model, **kwargs)
+        self._api_key = api_key
+        self._api_base = api_base
+
+    def _call_kwargs(self) -> dict[str, Any]:
+        kwargs: dict[str, Any] = {"model": self.model, "drop_params": True}
+        if self._api_key:
+            kwargs["api_key"] = self._api_key
+        if self._api_base:
+            kwargs["api_base"] = self._api_base
+        return kwargs
+
+    @classmethod
+    def get_supported_models(cls) -> list[str]:
+        return [
+            "openai/gpt-4o",
+            "openai/gpt-4o-mini",
+            "anthropic/claude-sonnet-4-6",
+            "anthropic/claude-haiku-4-5",
+            "groq/llama-3.3-70b-versatile",
+            "together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo",
+        ]
+
+    def _messages_to_dicts(self, messages: list[LLMMessage]) -> list[dict[str, str]]:
+        return [{"role": m.role, "content": m.content} for m in messages]
+
+    def _parse_response(self, response: Any) -> LLMResponse:
+        choice = response.choices[0]
+        usage = None
+        if response.usage:
+            usage = {
+                "prompt_tokens": response.usage.prompt_tokens,
+                "completion_tokens": response.usage.completion_tokens,
+                "total_tokens": response.usage.total_tokens,
+            }
+        return LLMResponse(
+            content=choice.message.content or "",
+            model=getattr(response, "model", self.model),
+            finish_reason=choice.finish_reason,
+            usage=usage,
+        )
+
+    def _handle_error(self, e: Exception) -> None:
+        qualname = f"{type(e).__module__}.{type(e).__name__}"
+        if qualname == "litellm.exceptions.RateLimitError":
+            raise LLMRateLimitError(str(e)) from e
+        raise LLMProviderError(str(e)) from e
+
+    async def generate(self, prompt: str, **kwargs: Any) -> LLMResponse:
+        messages = [LLMMessage(role="user", content=prompt)]
+        if system_prompt := kwargs.pop("system_prompt", None):
+            messages.insert(0, LLMMessage(role="system", content=system_prompt))
+        return await self.chat(messages, **kwargs)
+
+    async def chat(self, messages: list[LLMMessage], **kwargs: Any) -> LLMResponse:
+        import litellm
+
+        try:
+            response = await litellm.acompletion(
+                messages=self._messages_to_dicts(messages),
+                **{**self._call_kwargs(), **kwargs},
+            )
+            return self._parse_response(response)
+        except Exception as e:
+            self._handle_error(e)
+            raise
+
+    def sync_generate(self, prompt: str, **kwargs: Any) -> LLMResponse:
+        messages = [LLMMessage(role="user", content=prompt)]
+        if system_prompt := kwargs.pop("system_prompt", None):
+            messages.insert(0, LLMMessage(role="system", content=system_prompt))
+        return self.sync_chat(messages, **kwargs)
+
+    def sync_chat(self, messages: list[LLMMessage], **kwargs: Any) -> LLMResponse:
+        import litellm
+
+        try:
+            response = litellm.completion(
+                messages=self._messages_to_dicts(messages),
+                **{**self._call_kwargs(), **kwargs},
+            )
+            return self._parse_response(response)
+        except Exception as e:
+            self._handle_error(e)
+            raise