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TradingAgents
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feat: Migrate to Gemini and remove other AI providers

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google-labs-jules[bot] 2025-08-16 00:36:13 +00:00
parent a438acdbbd
commit 9254cde228
8 changed files with 66 additions and 216 deletions
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45
API_KEY_MANAGEMENT.md Normal file
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@ -0,0 +1,45 @@
# API Key Management
This document provides instructions for managing the API keys required to run the TradingAgents framework.
## Google API Key
The TradingAgents framework uses Google's Generative AI models. You will need a Google API key to use the service.
### Obtaining a Google API Key
1. Go to the [Google AI Studio](https://aistudio.google.com/).
2. Log in with your Google account.
3. Click on "Get API key" to create a new API key.
### Setting the Google API Key
To use the Google API key, you need to set it as an environment variable named `GOOGLE_API_KEY`.
#### For Linux and macOS:
You can set the environment variable in your shell's configuration file (e.g., `.bashrc`, `.zshrc`).
```bash
export GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY"
```
Replace `"YOUR_GOOGLE_API_KEY"` with the API key you obtained from the Google AI Studio.
After adding the line to your configuration file, restart your shell or run the following command to apply the changes:
```bash
source ~/.bashrc # or source ~/.zshrc
```
#### For Windows:
You can set the environment variable through the system settings.
1. Search for "Environment Variables" in the Start menu and select "Edit the system environment variables".
2. In the System Properties window, click on the "Environment Variables..." button.
3. In the Environment Variables window, click on "New..." under the "System variables" section.
4. Set the "Variable name" to `GOOGLE_API_KEY` and the "Variable value" to your Google API key.
5. Click "OK" to close all windows.
You may need to restart your command prompt or IDE for the changes to take effect.

10
README.md
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@ -119,9 +119,9 @@ You will also need the FinnHub API for financial data. All of our code is implem
export FINNHUB_API_KEY=$YOUR_FINNHUB_API_KEY
```
You will need the OpenAI API for all the agents.
You will need the Google API for all the agents. See `API_KEY_MANAGEMENT.md` for more details.
```bash
export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY
export GOOGLE_API_KEY=$YOUR_GOOGLE_API_KEY
```
### CLI Usage
@ -150,7 +150,7 @@ An interface will appear showing results as they load, letting you track the age
### Implementation Details
We built TradingAgents with LangGraph to ensure flexibility and modularity. We utilize `o1-preview` and `gpt-4o` as our deep thinking and fast thinking LLMs for our experiments. However, for testing purposes, we recommend you use `o4-mini` and `gpt-4.1-mini` to save on costs as our framework makes **lots of** API calls.
We built TradingAgents with LangGraph to ensure flexibility and modularity. We utilize `gemini-2.5-pro` and `gemini-2.5-flash` as our deep thinking and fast thinking LLMs.
### Python Usage
@ -175,8 +175,8 @@ from tradingagents.default_config import DEFAULT_CONFIG
# Create a custom config
config = DEFAULT_CONFIG.copy()
config["deep_think_llm"] = "gpt-4.1-nano" # Use a different model
config["quick_think_llm"] = "gpt-4.1-nano" # Use a different model
config["deep_think_llm"] = "gemini-2.5-pro"
config["quick_think_llm"] = "gemini-2.5-flash"
config["max_debate_rounds"] = 1 # Increase debate rounds
config["online_tools"] = True # Use online tools or cached data

30
cli/main.py
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@ -23,7 +23,7 @@ from rich.rule import Rule
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 get_ticker, get_analysis_date, select_analysts, select_research_depth
console = Console()
@ -463,32 +463,11 @@ def get_user_selections():
)
selected_research_depth = select_research_depth()
# Step 5: OpenAI backend
console.print(
create_question_box(
"Step 5: OpenAI backend", "Select which service to talk to"
)
)
selected_llm_provider, backend_url = select_llm_provider()
# Step 6: Thinking agents
console.print(
create_question_box(
"Step 6: Thinking Agents", "Select your thinking agents for analysis"
)
)
selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider)
return {
"ticker": selected_ticker,
"analysis_date": analysis_date,
"analysts": selected_analysts,
"research_depth": selected_research_depth,
"llm_provider": selected_llm_provider.lower(),
"backend_url": backend_url,
"shallow_thinker": selected_shallow_thinker,
"deep_thinker": selected_deep_thinker,
}
@ -739,10 +718,9 @@ def run_analysis():
config = DEFAULT_CONFIG.copy()
config["max_debate_rounds"] = selections["research_depth"]
config["max_risk_discuss_rounds"] = selections["research_depth"]
config["quick_think_llm"] = selections["shallow_thinker"]
config["deep_think_llm"] = selections["deep_thinker"]
config["backend_url"] = selections["backend_url"]
config["llm_provider"] = selections["llm_provider"].lower()
config["quick_think_llm"] = "gemini-2.5-flash"
config["deep_think_llm"] = "gemini-2.5-pro"
config["llm_provider"] = "google"
# Initialize the graph
graph = TradingAgentsGraph(

152
cli/utils.py
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@ -122,155 +122,3 @@ def select_research_depth() -> int:
return choice
def select_shallow_thinking_agent(provider) -> str:
"""Select shallow thinking llm engine using an interactive selection."""
# 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"),
]
}
choice = questionary.select(
"Select Your [Quick-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in SHALLOW_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()
if choice is None:
console.print(
"\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
)
exit(1)
return choice
def select_deep_thinking_agent(provider) -> str:
"""Select deep thinking llm engine using an interactive selection."""
# 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()
if choice is None:
console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
exit(1)
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
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"),
]
choice = questionary.select(
"Select your LLM Provider:",
choices=[
questionary.Choice(display, value=(display, value))
for display, value in BASE_URLS
],
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()
if choice is None:
console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
exit(1)
display_name, url = choice
print(f"You selected: {display_name}\tURL: {url}")
return display_name, url

7
main.py
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@ -3,10 +3,9 @@ from tradingagents.default_config import DEFAULT_CONFIG
# Create a custom config
config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "google" # Use a different model
config["backend_url"] = "https://generativelanguage.googleapis.com/v1" # Use a different backend
config["deep_think_llm"] = "gemini-2.0-flash" # Use a different model
config["quick_think_llm"] = "gemini-2.0-flash" # Use a different model
config["llm_provider"] = "google"
config["deep_think_llm"] = "gemini-2.5-pro"
config["quick_think_llm"] = "gemini-2.5-flash"
config["max_debate_rounds"] = 1 # Increase debate rounds
config["online_tools"] = True # Increase debate rounds

16
tradingagents/agents/utils/memory.py
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@ -1,25 +1,17 @@
import chromadb
from chromadb.config import Settings
from openai import OpenAI
from langchain_google_genai import GoogleGenerativeAIEmbeddings
class FinancialSituationMemory:
def __init__(self, name, config):
if config["backend_url"] == "http://localhost:11434/v1":
self.embedding = "nomic-embed-text"
else:
self.embedding = "text-embedding-3-small"
self.client = OpenAI(base_url=config["backend_url"])
self.embedding = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
self.chroma_client = chromadb.Client(Settings(allow_reset=True))
self.situation_collection = self.chroma_client.create_collection(name=name)
def get_embedding(self, text):
"""Get OpenAI embedding for a text"""
response = self.client.embeddings.create(
model=self.embedding, input=text
)
return response.data[0].embedding
"""Get Google embedding for a text"""
return self.embedding.embed_query(text)
def add_situations(self, situations_and_advice):
"""Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""

7
tradingagents/default_config.py
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@ -9,10 +9,9 @@ DEFAULT_CONFIG = {
"dataflows/data_cache",
),
# LLM settings
"llm_provider": "openai",
"deep_think_llm": "o4-mini",
"quick_think_llm": "gpt-4o-mini",
"backend_url": "https://api.openai.com/v1",
"llm_provider": "google",
"deep_think_llm": "gemini-2.5-pro",
"quick_think_llm": "gemini-2.5-flash",
# Debate and discussion settings
"max_debate_rounds": 1,
"max_risk_discuss_rounds": 1,

15
tradingagents/graph/trading_graph.py
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@ -6,8 +6,6 @@ import json
from datetime import date
from typing import Dict, Any, Tuple, List, Optional
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.prebuilt import ToolNode
@ -58,17 +56,8 @@ 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":
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":
self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
else:
raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")
self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
self.toolkit = Toolkit(config=self.config)