feat: add multi-provider LLM support with LiteLLM
- Replace openai with litellm for unified LLM interface - Support 100+ providers: OpenAI, Anthropic, Gemini, Azure, Ollama, etc. - Add custom API base URL support (LLM_API_BASE) - Add .env file support with python-dotenv - Add .env.example configuration template 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@@ -1,6 +1,22 @@
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"""
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LLM Service - Agent Brain Module.
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Provides AI-powered responses for agents using OpenAI's API.
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Provides AI-powered responses for agents using LiteLLM (supports multiple providers).
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Supported providers (via environment variables):
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- OpenAI: OPENAI_API_KEY → model="gpt-3.5-turbo" or "gpt-4"
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- Anthropic: ANTHROPIC_API_KEY → model="claude-3-haiku-20240307" or "claude-3-sonnet-20240229"
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- Google: GEMINI_API_KEY → model="gemini/gemini-pro"
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- Azure OpenAI: AZURE_API_KEY + AZURE_API_BASE → model="azure/<deployment-name>"
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- OpenRouter: OPENROUTER_API_KEY → model="openrouter/<model>"
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- Ollama (local): OLLAMA_API_BASE → model="ollama/llama2"
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- Custom/Self-hosted: LLM_API_KEY + LLM_API_BASE → any OpenAI-compatible endpoint
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- And 100+ more providers...
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Configuration:
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- LLM_MODEL: Model to use (default: gpt-3.5-turbo)
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- LLM_API_BASE: Custom API base URL (for self-hosted or proxy services)
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- LLM_API_KEY: Generic API key (used with LLM_API_BASE)
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- LLM_MOCK_MODE: Set to "true" to force mock mode
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"""
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import logging
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@@ -41,42 +57,90 @@ MOCK_REACTIONS = {
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],
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}
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# Default model configuration
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DEFAULT_MODEL = "gpt-3.5-turbo"
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class LLMService:
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"""
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Service for generating AI-powered agent reactions.
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Falls back to mock responses if API key is not configured.
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Service for generating AI-powered agent reactions using LiteLLM.
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Supports multiple LLM providers through a unified interface.
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Falls back to mock responses if no API key is configured.
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"""
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def __init__(self) -> None:
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"""Initialize the LLM service with OpenAI client or mock mode."""
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self._api_key = os.environ.get("OPENAI_API_KEY")
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self._client = None
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self._mock_mode = False
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"""Initialize the LLM service with LiteLLM or mock mode."""
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self._model = os.environ.get("LLM_MODEL", DEFAULT_MODEL)
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self._api_base = os.environ.get("LLM_API_BASE") # Custom base URL
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self._api_key = os.environ.get("LLM_API_KEY") # Generic API key
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self._mock_mode = os.environ.get("LLM_MOCK_MODE", "").lower() == "true"
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self._acompletion = None
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if not self._api_key:
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if self._mock_mode:
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logger.info("LLMService running in MOCK mode (forced by LLM_MOCK_MODE)")
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return
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# Check for any supported API key (order matters for provider detection)
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api_keys = {
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"OPENAI_API_KEY": "OpenAI",
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"ANTHROPIC_API_KEY": "Anthropic",
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"GEMINI_API_KEY": "Google Gemini",
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"AZURE_API_KEY": "Azure OpenAI",
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"AZURE_API_BASE": "Azure OpenAI",
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"OPENROUTER_API_KEY": "OpenRouter",
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"COHERE_API_KEY": "Cohere",
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"HUGGINGFACE_API_KEY": "HuggingFace",
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"OLLAMA_API_BASE": "Ollama (local)",
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"LLM_API_KEY": "Custom (with LLM_API_BASE)",
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"LLM_API_BASE": "Custom endpoint",
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}
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found_provider = None
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for key, provider in api_keys.items():
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if os.environ.get(key):
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found_provider = provider
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break
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if not found_provider:
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logger.warning(
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"OPENAI_API_KEY not found in environment. "
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"LLMService running in MOCK mode - using predefined responses."
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"No LLM API key found in environment. "
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"LLMService running in MOCK mode - using predefined responses. "
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f"Supported keys: {', '.join(api_keys.keys())}"
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)
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self._mock_mode = True
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else:
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try:
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from openai import AsyncOpenAI
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self._client = AsyncOpenAI(api_key=self._api_key)
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logger.info("LLMService initialized with OpenAI API")
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except ImportError:
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logger.error("openai package not installed. Running in MOCK mode.")
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self._mock_mode = True
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except Exception as e:
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logger.error(f"Failed to initialize OpenAI client: {e}. Running in MOCK mode.")
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self._mock_mode = True
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return
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try:
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from litellm import acompletion
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self._acompletion = acompletion
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# Log configuration details
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config_info = f"provider: {found_provider}, model: {self._model}"
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if self._api_base:
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config_info += f", api_base: {self._api_base}"
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logger.info(f"LLMService initialized with LiteLLM ({config_info})")
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except ImportError:
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logger.error("litellm package not installed. Running in MOCK mode.")
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self._mock_mode = True
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except Exception as e:
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logger.error(f"Failed to initialize LiteLLM: {e}. Running in MOCK mode.")
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self._mock_mode = True
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@property
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def is_mock_mode(self) -> bool:
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"""Check if service is running in mock mode."""
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return self._mock_mode
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@property
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def model(self) -> str:
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"""Get the current model name."""
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return self._model
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@property
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def api_base(self) -> str | None:
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"""Get the custom API base URL if configured."""
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return self._api_base
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def _get_mock_response(self, event_type: str = "feed") -> str:
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"""Get a random mock response for testing without API."""
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responses = MOCK_REACTIONS.get(event_type, MOCK_REACTIONS["feed"])
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@@ -112,15 +176,22 @@ class LLMService:
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f"Respond in first person, as if speaking out loud."
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)
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response = await self._client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=[
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# Build kwargs for acompletion
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kwargs = {
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"model": self._model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": event_description}
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],
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max_tokens=50,
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temperature=0.8,
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)
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"max_tokens": 50,
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"temperature": 0.8,
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}
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if self._api_base:
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kwargs["api_base"] = self._api_base
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if self._api_key:
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kwargs["api_key"] = self._api_key
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response = await self._acompletion(**kwargs)
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return response.choices[0].message.content.strip()
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@@ -165,15 +236,22 @@ class LLMService:
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f"Speak naturally, as if talking to yourself or nearby survivors."
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)
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response = await self._client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=[
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# Build kwargs for acompletion
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kwargs = {
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"model": self._model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": "What are you thinking right now?"}
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],
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max_tokens=40,
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temperature=0.9,
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)
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"max_tokens": 40,
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"temperature": 0.9,
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}
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if self._api_base:
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kwargs["api_base"] = self._api_base
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if self._api_key:
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kwargs["api_key"] = self._api_key
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response = await self._acompletion(**kwargs)
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return response.choices[0].message.content.strip()
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