diff --git a/.gitignore b/.gitignore index 4ebf99e3..6b6c685b 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,5 @@ eval_results/ eval_data/ *.egg-info/ .env +*.log +results/* \ No newline at end of file diff --git a/cli/main.py b/cli/main.py index 64616ee1..9e78cd9a 100644 --- a/cli/main.py +++ b/cli/main.py @@ -1,5 +1,4 @@ -from typing import Optional -import datetime +from datetime import datetime import typer from pathlib import Path from functools import wraps @@ -14,16 +13,23 @@ from rich.text import Text from rich.live import Live from rich.table import Table from collections import deque -import time -from rich.tree import Tree from rich import box from rich.align import Align -from rich.rule import Rule +from dotenv import load_dotenv + +# Load environment variables from .env file +load_dotenv() from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.default_config import DEFAULT_CONFIG from cli.models import AnalystType -from cli.utils import * +from cli.utils import ( + select_analysts, + select_research_depth, + select_shallow_thinking_agent, + select_deep_thinking_agent, + select_llm_provider +) console = Console() @@ -72,11 +78,11 @@ class MessageBuffer: } def add_message(self, message_type, content): - timestamp = datetime.datetime.now().strftime("%H:%M:%S") + timestamp = datetime.now().strftime("%H:%M:%S") self.messages.append((timestamp, message_type, content)) def add_tool_call(self, tool_name, args): - timestamp = datetime.datetime.now().strftime("%H:%M:%S") + timestamp = datetime.now().strftime("%H:%M:%S") self.tool_calls.append((timestamp, tool_name, args)) def update_agent_status(self, agent, status): @@ -434,7 +440,7 @@ def get_user_selections(): selected_ticker = get_ticker() # Step 2: Analysis date - default_date = datetime.datetime.now().strftime("%Y-%m-%d") + default_date = datetime.now().strftime("%Y-%m-%d") console.print( create_question_box( "Step 2: Analysis Date", @@ -501,12 +507,12 @@ def get_analysis_date(): """Get the analysis date from user input.""" while True: date_str = typer.prompt( - "", default=datetime.datetime.now().strftime("%Y-%m-%d") + "", default=datetime.now().strftime("%Y-%m-%d") ) try: # Validate date format and ensure it's not in the future - analysis_date = datetime.datetime.strptime(date_str, "%Y-%m-%d") - if analysis_date.date() > datetime.datetime.now().date(): + analysis_date = datetime.strptime(date_str, "%Y-%m-%d") + if analysis_date.date() > datetime.now().date(): console.print("[red]Error: Analysis date cannot be in the future[/red]") continue return date_str diff --git a/cli/utils.py b/cli/utils.py index 7b9682a6..49d4d228 100644 --- a/cli/utils.py +++ b/cli/utils.py @@ -1,8 +1,17 @@ +import os import questionary -from typing import List, Optional, Tuple, Dict +import re +from datetime import datetime +from rich.console import Console +from typing import List +from urllib.parse import urlparse from cli.models import AnalystType +console = Console() + +CUSTOM_MODEL_IDENTIFIER = "__CUSTOM_MODEL__" + ANALYST_ORDER = [ ("Market Analyst", AnalystType.MARKET), ("Social Media Analyst", AnalystType.SOCIAL), @@ -33,8 +42,6 @@ def get_ticker() -> str: def get_analysis_date() -> str: """Prompt the user to enter a date in YYYY-MM-DD format.""" - import re - from datetime import datetime def validate_date(date_str: str) -> bool: if not re.match(r"^\d{4}-\d{2}-\d{2}$", date_str): @@ -122,44 +129,195 @@ def select_research_depth() -> int: return choice -def select_shallow_thinking_agent(provider) -> str: - """Select shallow thinking llm engine using an interactive selection.""" +# Centralized model definitions - single source of truth +SHALLOW_AGENT_OPTIONS = { + "openai": [ + ("GPT-4o-mini - Fast and efficient for quick tasks", "gpt-4o-mini"), + ("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"), + ("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"), + ("GPT-4o - Standard model with solid capabilities", "gpt-4o"), + ], + "anthropic": [ + ("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"), + ("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"), + ("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"), + ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"), + ], + "google": [ + ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"), + ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"), + ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"), + ], + "openrouter": [ + ("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"), + ("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"), + ("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"), + ], + "ollama": [ + ("llama3.1 local", "llama3.1"), + ("llama3.2 local", "llama3.2"), + ] +} - # Define shallow thinking llm engine options with their corresponding model names - SHALLOW_AGENT_OPTIONS = { - "openai": [ - ("GPT-4o-mini - Fast and efficient for quick tasks", "gpt-4o-mini"), - ("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"), - ("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"), - ("GPT-4o - Standard model with solid capabilities", "gpt-4o"), - ], - "anthropic": [ - ("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"), - ("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"), - ("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"), - ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"), - ], - "google": [ - ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"), - ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"), - ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"), - ], - "openrouter": [ - ("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"), - ("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"), - ("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"), - ], - "ollama": [ - ("llama3.1 local", "llama3.1"), - ("llama3.2 local", "llama3.2"), - ] - } +DEEP_AGENT_OPTIONS = { + "openai": [ + ("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"), + ("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"), + ("GPT-4o - Standard model with solid capabilities", "gpt-4o"), + ("o4-mini - Specialized reasoning model (compact)", "o4-mini"), + ("o3-mini - Advanced reasoning model (lightweight)", "o3-mini"), + ("o3 - Full advanced reasoning model", "o3"), + ("o1 - Premier reasoning and problem-solving model", "o1"), + ], + "anthropic": [ + ("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"), + ("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"), + ("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"), + ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"), + ("Claude Opus 4 - Most powerful Anthropic model", "claude-opus-4-0"), + ], + "google": [ + ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"), + ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"), + ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"), + ("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"), + ], + "openrouter": [ + ("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"), + ("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"), + ], + "ollama": [ + ("llama3.1 local", "llama3.1"), + ("qwen3", "qwen3"), + ] +} + + +def _get_all_models_for_custom_provider(model_type: str) -> list: + """Get unified model list for custom provider with all available models from all providers. + + Args: + model_type: Either 'shallow' or 'deep' to get the appropriate model set + + Returns: + List of (description, model_value) tuples + """ + # Use the centralized model definitions + if model_type == "shallow": + provider_models = SHALLOW_AGENT_OPTIONS + else: # deep + provider_models = DEEP_AGENT_OPTIONS + + # Combine all models with provider labels + all_models = [] + for provider_name, models in provider_models.items(): + provider_display_name = provider_name.title() + for description, model_value in models: + labeled_description = f"{description} ({provider_display_name})" + all_models.append((labeled_description, model_value)) + + # Add custom model option at the end + all_models.append(("Custom Model - Enter your own model name", CUSTOM_MODEL_IDENTIFIER)) + + return all_models + + +def _select_custom_provider_model(model_type: str, title: str, default_model: str) -> str: + """Handle model selection for custom provider with unified model list. + + Args: + model_type: Either 'shallow' or 'deep' + title: Title for the selection prompt + default_model: Default model name for custom input + + Returns: + Selected model name + """ + all_models = _get_all_models_for_custom_provider(model_type) choice = questionary.select( - "Select Your [Quick-Thinking LLM Engine]:", + title, choices=[ questionary.Choice(display, value=value) - for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()] + for display, value in all_models + ], + instruction="\n- Use arrow keys to navigate\n- Press Enter to select\n- Your custom endpoint should support the selected model", + style=questionary.Style( + [ + ("selected", "fg:magenta noinherit"), + ("highlighted", "fg:magenta noinherit"), + ("pointer", "fg:magenta noinherit"), + ] + ), + ).ask() + + if choice is None: + raise ValueError(f"No {model_type} thinking model selected") + + # Handle custom model input + if choice == CUSTOM_MODEL_IDENTIFIER: + custom_model = questionary.text( + f"Enter your custom {model_type} thinking model name:", + default=default_model, + instruction="\n- Enter the exact model name as supported by your custom endpoint\n- Press Enter to confirm" + ).ask() + + if not custom_model: + raise ValueError(f"No custom {model_type} model name entered") + + return custom_model + + return choice + + +def _select_thinking_agent(provider: str, model_type: str) -> str: + """Unified function to select thinking agents for both shallow and deep models. + + Args: + provider: The LLM provider name + model_type: Either 'shallow' or 'deep' + + Returns: + str: The selected model name + """ + # Configuration for different model types + config = { + "shallow": { + "title": "Select Your [Quick-Thinking LLM Engine]:", + "custom_title": "Select Your [Quick-Thinking LLM Engine] (Custom Provider - All Models Available):", + "default_model": "gpt-4o-mini", + "options": SHALLOW_AGENT_OPTIONS, + "error_message": "No shallow thinking llm engine selected. Exiting..." + }, + "deep": { + "title": "Select Your [Deep-Thinking LLM Engine]:", + "custom_title": "Select Your [Deep-Thinking LLM Engine] (Custom Provider - All Models Available):", + "default_model": "o4-mini", + "options": DEEP_AGENT_OPTIONS, + "error_message": "No deep thinking llm engine selected. Exiting..." + } + } + + model_config = config[model_type] + + # Handle custom provider - use unified model selection + if provider.lower().startswith("custom"): + try: + return _select_custom_provider_model( + model_type=model_type, + title=model_config["custom_title"], + default_model=model_config["default_model"] + ) + except ValueError as e: + console.print(f"\n[red]Error: {e}[/red]") + exit(1) + + # Use centralized model definitions + choice = questionary.select( + model_config["title"], + choices=[ + questionary.Choice(display, value=value) + for display, value in model_config["options"][provider.lower()] ], instruction="\n- Use arrow keys to navigate\n- Press Enter to select", style=questionary.Style( @@ -172,83 +330,104 @@ def select_shallow_thinking_agent(provider) -> str: ).ask() if choice is None: - console.print( - "\n[red]No shallow thinking llm engine selected. Exiting...[/red]" - ) + console.print(f"\n[red]{model_config['error_message']}[/red]") exit(1) return choice +def select_shallow_thinking_agent(provider) -> str: + """Select shallow thinking llm engine using an interactive selection.""" + return _select_thinking_agent(provider, "shallow") + + def select_deep_thinking_agent(provider) -> str: """Select deep thinking llm engine using an interactive selection.""" + return _select_thinking_agent(provider, "deep") - # Define deep thinking llm engine options with their corresponding model names - DEEP_AGENT_OPTIONS = { - "openai": [ - ("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"), - ("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"), - ("GPT-4o - Standard model with solid capabilities", "gpt-4o"), - ("o4-mini - Specialized reasoning model (compact)", "o4-mini"), - ("o3-mini - Advanced reasoning model (lightweight)", "o3-mini"), - ("o3 - Full advanced reasoning model", "o3"), - ("o1 - Premier reasoning and problem-solving model", "o1"), - ], - "anthropic": [ - ("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"), - ("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"), - ("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"), - ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"), - ("Claude Opus 4 - Most powerful Anthropic model", " claude-opus-4-0"), - ], - "google": [ - ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"), - ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"), - ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"), - ("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"), - ], - "openrouter": [ - ("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"), - ("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"), - ], - "ollama": [ - ("llama3.1 local", "llama3.1"), - ("qwen3", "qwen3"), - ] - } - - choice = questionary.select( - "Select Your [Deep-Thinking LLM Engine]:", - choices=[ - questionary.Choice(display, value=value) - for display, value in DEEP_AGENT_OPTIONS[provider.lower()] - ], - instruction="\n- Use arrow keys to navigate\n- Press Enter to select", - style=questionary.Style( - [ - ("selected", "fg:magenta noinherit"), - ("highlighted", "fg:magenta noinherit"), - ("pointer", "fg:magenta noinherit"), - ] - ), - ).ask() +def validate_custom_url(url: str) -> str: + """Validate that a custom URL is properly formatted and has a valid hostname. - if choice is None: - console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]") - exit(1) + Args: + url: The URL to validate + + Returns: + str: The validated URL + + Raises: + ValueError: If the URL is invalid or malformed + """ + + if not url: + return "" + + # Basic URL format validation + url_pattern = re.compile( + r'^https?://' # http:// or https:// + r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+[A-Z]{2,6}\.?|' # domain... + r'localhost|' # localhost... + r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip + r'(?::\d+)?' # optional port + r'(?:/?|[/?]\S+)$', re.IGNORECASE) + + if not url_pattern.match(url): + raise ValueError(f"Invalid CUSTOM_BASE_URL format: {url}. Please provide a valid URL (e.g., https://api.example.com/v1)") + + # Additional validation using urlparse + try: + parsed = urlparse(url) + if not parsed.netloc: + raise ValueError(f"Invalid CUSTOM_BASE_URL: {url}. No hostname found") + return url + except ValueError: + # Re-raise ValueError as-is + raise + except Exception as e: + raise ValueError(f"Invalid CUSTOM_BASE_URL: {url}. URL parsing error: {e}") + + +def get_custom_provider_info() -> tuple[str, str] | None: + """Get custom provider info if both URL and API key are provided. + + Returns: + tuple[str, str] | None: (display_name, url) if valid custom provider configured, None otherwise + + Raises: + SystemExit: If custom URL is provided but invalid (exits with error message) + """ + + custom_url = os.getenv("CUSTOM_BASE_URL") + custom_api_key = os.getenv("CUSTOM_API_KEY") + + if custom_url and custom_api_key: + try: + validated_url = validate_custom_url(custom_url) + parsed = urlparse(validated_url) + hostname = parsed.netloc + return f"Custom ({hostname})", validated_url + except ValueError as e: + console.print(f"[red]Error: {e}[/red]") + exit(1) + + return None - return choice def select_llm_provider() -> tuple[str, str]: - """Select the OpenAI api url using interactive selection.""" - # Define OpenAI api options with their corresponding endpoints + """Select the LLM provider with support for a custom OpenAI-compatible endpoint.""" + + # Define default providers BASE_URLS = [ ("OpenAI", "https://api.openai.com/v1"), ("Anthropic", "https://api.anthropic.com/"), ("Google", "https://generativelanguage.googleapis.com/v1"), ("Openrouter", "https://openrouter.ai/api/v1"), - ("Ollama", "http://localhost:11434/v1"), + ("Ollama", "http://localhost:11434/v1"), ] + + # Add custom provider at the beginning if available + custom_info = get_custom_provider_info() + if custom_info: + BASE_URLS.insert(0, custom_info) choice = questionary.select( "Select your LLM Provider:", @@ -267,10 +446,10 @@ def select_llm_provider() -> tuple[str, str]: ).ask() if choice is None: - console.print("\n[red]no OpenAI backend selected. Exiting...[/red]") + console.print("\n[red]No LLM provider selected. Exiting...[/red]") exit(1) - + display_name, url = choice print(f"You selected: {display_name}\tURL: {url}") - + return display_name, url diff --git a/example.env b/example.env new file mode 100644 index 00000000..46c84d80 --- /dev/null +++ b/example.env @@ -0,0 +1,20 @@ +# Copy this to your .env file and modify the URLs and API keys as needed + +# Custom OpenAI-Compatible Provider (optional) +# If provided, a "Custom" option will appear first in the provider list +# The custom endpoint must be OpenAI-compatible (REST API, not gRPC) +# CUSTOM_BASE_URL=https://www.example.com/v1 +# CUSTOM_API_KEY=sk-your-custom-api-key-here + +# Standard Provider API Keys, please replace with your own keys to use the corresponding provider +OPENAI_API_KEY=sk-your-openai-api-key-here +ANTHROPIC_API_KEY=sk-ant-your-anthropic-api-key-here +GOOGLE_API_KEY=your-google-api-key-here +OPENROUTER_API_KEY=sk-or-your-openrouter-api-key-here +# OLLAMA_API_KEY is usually not needed for local Ollama instances + +# Other Configuration +FINNHUB_API_KEY=your-finnhub-api-key-here + +# Optional, uncomment to modify +# TRADINGAGENTS_RESULTS_DIR=./results diff --git a/requirements.txt b/requirements.txt index a6154cd2..02555276 100644 --- a/requirements.txt +++ b/requirements.txt @@ -24,3 +24,4 @@ rich questionary langchain_anthropic langchain-google-genai +dotenv \ No newline at end of file diff --git a/tradingagents/agents/utils/memory.py b/tradingagents/agents/utils/memory.py index 69b8ab8c..76ca0a8e 100644 --- a/tradingagents/agents/utils/memory.py +++ b/tradingagents/agents/utils/memory.py @@ -1,6 +1,6 @@ import chromadb from chromadb.config import Settings -from openai import OpenAI +from tradingagents.utils.provider_utils import get_openai_client class FinancialSituationMemory: @@ -9,7 +9,8 @@ class FinancialSituationMemory: self.embedding = "nomic-embed-text" else: self.embedding = "text-embedding-3-small" - self.client = OpenAI(base_url=config["backend_url"]) + + self.client = get_openai_client(config) self.chroma_client = chromadb.Client(Settings(allow_reset=True)) self.situation_collection = self.chroma_client.create_collection(name=name) diff --git a/tradingagents/dataflows/interface.py b/tradingagents/dataflows/interface.py index 7fffbb4f..64b69d7e 100644 --- a/tradingagents/dataflows/interface.py +++ b/tradingagents/dataflows/interface.py @@ -12,9 +12,9 @@ import os import pandas as pd from tqdm import tqdm import yfinance as yf -from openai import OpenAI from .config import get_config, set_config, DATA_DIR +from tradingagents.utils.provider_utils import get_openai_client def get_finnhub_news( ticker: Annotated[ @@ -704,7 +704,7 @@ def get_YFin_data( def get_stock_news_openai(ticker, curr_date): config = get_config() - client = OpenAI(base_url=config["backend_url"]) + client = get_openai_client(config) response = client.responses.create( model=config["quick_think_llm"], @@ -739,7 +739,7 @@ def get_stock_news_openai(ticker, curr_date): def get_global_news_openai(curr_date): config = get_config() - client = OpenAI(base_url=config["backend_url"]) + client = get_openai_client(config) response = client.responses.create( model=config["quick_think_llm"], @@ -774,7 +774,7 @@ def get_global_news_openai(curr_date): def get_fundamentals_openai(ticker, curr_date): config = get_config() - client = OpenAI(base_url=config["backend_url"]) + client = get_openai_client(config) response = client.responses.create( model=config["quick_think_llm"], diff --git a/tradingagents/default_config.py b/tradingagents/default_config.py index 089e9c24..be6ccf26 100644 --- a/tradingagents/default_config.py +++ b/tradingagents/default_config.py @@ -12,7 +12,7 @@ DEFAULT_CONFIG = { "llm_provider": "openai", "deep_think_llm": "o4-mini", "quick_think_llm": "gpt-4o-mini", - "backend_url": "https://api.openai.com/v1", + "backend_url": "https://api.openai.com/v1", # Will be updated based on selected provider # Debate and discussion settings "max_debate_rounds": 1, "max_risk_discuss_rounds": 1, diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py index 80a29e53..bd623425 100644 --- a/tradingagents/graph/trading_graph.py +++ b/tradingagents/graph/trading_graph.py @@ -13,6 +13,7 @@ from langchain_google_genai import ChatGoogleGenerativeAI from langgraph.prebuilt import ToolNode from tradingagents.agents import * +from tradingagents.utils.provider_utils import get_api_key_for_provider from tradingagents.default_config import DEFAULT_CONFIG from tradingagents.agents.utils.memory import FinancialSituationMemory from tradingagents.agents.utils.agent_states import ( @@ -58,13 +59,16 @@ class TradingAgentsGraph: ) # Initialize LLMs - if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "openrouter": - self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"]) - self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"]) - elif self.config["llm_provider"].lower() == "anthropic": + provider = self.config["llm_provider"].lower() + + if provider in ("openai", "ollama", "openrouter") or provider.startswith("custom"): + api_key = get_api_key_for_provider(self.config) + self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"], api_key=api_key) + self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"], api_key=api_key) + elif provider == "anthropic": self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"]) self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"]) - elif self.config["llm_provider"].lower() == "google": + elif provider == "google": self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"]) self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"]) else: diff --git a/tradingagents/utils/provider_utils.py b/tradingagents/utils/provider_utils.py new file mode 100644 index 00000000..98281215 --- /dev/null +++ b/tradingagents/utils/provider_utils.py @@ -0,0 +1,62 @@ +""" +Utility functions for LLM provider configuration and API key management. +""" + +import os +import sys +from openai import OpenAI + +def get_api_key_for_provider(config): + """Get the appropriate API key based on the provider. + + Args: + config (dict): Configuration dictionary containing llm_provider + + Returns: + str: The API key for the provider, or None if not found + """ + provider = config.get("llm_provider", "openai").lower() + + # Handle custom provider first + if provider.startswith("custom"): + api_key = os.getenv("CUSTOM_API_KEY") + if not api_key: + print("Warning: CUSTOM_API_KEY not found in environment variables", file=sys.stderr) + return api_key + + # Map providers to their environment variables + api_key_mapping = { + "openai": "OPENAI_API_KEY", + "anthropic": "ANTHROPIC_API_KEY", + "google": "GOOGLE_API_KEY", + "openrouter": "OPENROUTER_API_KEY", + "ollama": "OLLAMA_API_KEY", + } + + env_var = api_key_mapping.get(provider, "OPENAI_API_KEY") + api_key = os.getenv(env_var) + + if not api_key and provider != "ollama": # Ollama typically doesn't need API keys + print(f"Warning: {env_var} not found in environment variables", file=sys.stderr) + + return api_key + + +def get_openai_client(config): + """Get a properly configured OpenAI client based on the provider configuration. + + This function centralizes OpenAI client creation with correct API key resolution + for all providers that use OpenAI-compatible interfaces (OpenAI, OpenRouter, + Ollama, and custom providers). + + Args: + config (dict): Configuration dictionary containing llm_provider and backend_url + + Returns: + OpenAI: Configured OpenAI client instance + """ + + api_key = get_api_key_for_provider(config) + backend_url = config.get("backend_url", "https://api.openai.com/v1") + + return OpenAI(base_url=backend_url, api_key=api_key)