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Add support for other backends, such as OpenRouter and olama

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This aims to offer alternative OpenAI capable api's.
This offers people to experiment with running the application locally
This commit is contained in:
maxer137
2025-06-11 14:19:25 +02:00
parent a879868396
commit 99789f9cd1
6 changed files with 110 additions and 41 deletions
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18
cli/main.py
View File

@@ -444,20 +444,29 @@ def get_user_selections():
)
selected_research_depth = select_research_depth()
# Step 5: Thinking agents
# Step 5: OpenAI backend
console.print(
create_question_box(
"Step 5: Thinking Agents", "Select your thinking agents for analysis"
"Step 5: OpenAI backend", "Select which service to talk to"
)
)
selected_shallow_thinker = select_shallow_thinking_agent()
selected_deep_thinker = select_deep_thinking_agent()
selected_openai_backend = select_openai_backend()
# 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_openai_backend)
selected_deep_thinker = select_deep_thinking_agent(selected_openai_backend)
return {
"ticker": selected_ticker,
"analysis_date": analysis_date,
"analysts": selected_analysts,
"research_depth": selected_research_depth,
"openai_backend": selected_openai_backend,
"shallow_thinker": selected_shallow_thinker,
"deep_thinker": selected_deep_thinker,
}
@@ -694,6 +703,7 @@ def run_analysis():
config["max_risk_discuss_rounds"] = selections["research_depth"]
config["quick_think_llm"] = selections["shallow_thinker"]
config["deep_think_llm"] = selections["deep_thinker"]
config["openai_backend"] = selections["openai_backend"]
# Initialize the graph
graph = TradingAgentsGraph(

89
cli/utils.py
View File

@@ -122,22 +122,32 @@ def select_research_depth() -> int:
return choice
def select_shallow_thinking_agent() -> str:
def select_shallow_thinking_agent(backend) -> str:
"""Select shallow thinking llm engine using an interactive selection."""
# Define shallow thinking llm engine options with their corresponding model names
SHALLOW_AGENT_OPTIONS = [
("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"),
]
SHALLOW_AGENT_OPTIONS = {
"https://api.openai.com/v1": [
("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"),
],
"https://openrouter.ai/api/v1": [
("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"),
],
"http://localhost:11434/v1": [
("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
for display, value in SHALLOW_AGENT_OPTIONS[backend]
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
@@ -158,25 +168,34 @@ def select_shallow_thinking_agent() -> str:
return choice
def select_deep_thinking_agent() -> str:
def select_deep_thinking_agent(backend) -> str:
"""Select deep thinking llm engine using an interactive selection."""
# Define deep thinking llm engine options with their corresponding model names
DEEP_AGENT_OPTIONS = [
("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"),
]
DEEP_AGENT_OPTIONS = {
"https://api.openai.com/v1": [
("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"),
],
"https://openrouter.ai/api/v1": [
("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"),
],
"http://localhost:11434/v1": [
("qwen3", "qwen3"),
]
}
choice = questionary.select(
"Select Your [Deep-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in DEEP_AGENT_OPTIONS
for display, value in DEEP_AGENT_OPTIONS[backend]
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
@@ -193,3 +212,35 @@ def select_deep_thinking_agent() -> str:
exit(1)
return choice
def select_openai_backend() -> str:
"""Select the OpenAI api url using interactive selection."""
# Define OpenAI api options with their corresponding endpoints
OPENAI_BASE_URLS = [
("OpenAI - Requires an OpenAPI Key", "https://api.openai.com/v1"),
("Openrouter - Requires an OpenRouter API Key", "https://openrouter.ai/api/v1"),
("Ollama - Local", "http://localhost:11434/v1")
]
choice = questionary.select(
"Select your [OpenAI endpoint]:",
choices=[
questionary.Choice(display, value=value)
for display, value in OPENAI_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)
return choice

12
tradingagents/agents/utils/memory.py
View File

@@ -1,19 +1,23 @@
import chromadb
from chromadb.config import Settings
from openai import OpenAI
import numpy as np
class FinancialSituationMemory:
def __init__(self, name):
self.client = OpenAI()
def __init__(self, name, config):
if config["openai_backend"] == "http://localhost:11434/v1":
self.embedding = "nomic-embed-text"
else:
self.embedding = "text-embedding-ada-002"
self.client = OpenAI(base_url=config["openai_backend"])
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="text-embedding-ada-002", input=text
model=self.embedding, input=text
)
return response.data[0].embedding

15
tradingagents/dataflows/interface.py
View File

@@ -703,10 +703,11 @@ def get_YFin_data(
def get_stock_news_openai(ticker, curr_date):
client = OpenAI()
config = get_config()
client = OpenAI(base_url=config["openai_backend"])
response = client.responses.create(
model="gpt-4.1-mini",
model=config["quick_think_llm"],
input=[
{
"role": "system",
@@ -737,10 +738,11 @@ def get_stock_news_openai(ticker, curr_date):
def get_global_news_openai(curr_date):
client = OpenAI()
config = get_config()
client = OpenAI(base_url=config["openai_backend"])
response = client.responses.create(
model="gpt-4.1-mini",
model=config["quick_think_llm"],
input=[
{
"role": "system",
@@ -771,10 +773,11 @@ def get_global_news_openai(curr_date):
def get_fundamentals_openai(ticker, curr_date):
client = OpenAI()
config = get_config()
client = OpenAI(base_url=config["openai_backend"])
response = client.responses.create(
model="gpt-4.1-mini",
model=config["quick_think_llm"],
input=[
{
"role": "system",

1
tradingagents/default_config.py
View File

@@ -10,6 +10,7 @@ DEFAULT_CONFIG = {
# LLM settings
"deep_think_llm": "o4-mini",
"quick_think_llm": "gpt-4o-mini",
"openai_backend": "https://api.openai.com/v1",
# Debate and discussion settings
"max_debate_rounds": 1,
"max_risk_discuss_rounds": 1,

14
tradingagents/graph/trading_graph.py
View File

@@ -55,18 +55,18 @@ class TradingAgentsGraph:
)
# Initialize LLMs
self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"])
self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["openai_backend"],)
self.quick_thinking_llm = ChatOpenAI(
model=self.config["quick_think_llm"], temperature=0.1
model=self.config["quick_think_llm"], temperature=0.1, base_url=self.config["openai_backend"],
)
self.toolkit = Toolkit(config=self.config)
# Initialize memories
self.bull_memory = FinancialSituationMemory("bull_memory")
self.bear_memory = FinancialSituationMemory("bear_memory")
self.trader_memory = FinancialSituationMemory("trader_memory")
self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory")
self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory")
self.bull_memory = FinancialSituationMemory("bull_memory", self.config)
self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
# Create tool nodes
self.tool_nodes = self._create_tool_nodes()