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main works, cli bugs

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This commit is contained in:
Edward Sun
2025-06-15 22:20:59 -07:00
parent 90b23e72f5
commit da84ef43aa
11 changed files with 5577 additions and 50 deletions
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55
cli/main.py
View File

@@ -295,10 +295,27 @@ def update_display(layout, spinner_text=None):
# Add regular messages
for timestamp, msg_type, content in message_buffer.messages:
# Convert content to string if it's not already
content_str = content
if isinstance(content, list):
# Handle list of content blocks (Anthropic format)
text_parts = []
for item in content:
if isinstance(item, dict):
if item.get('type') == 'text':
text_parts.append(item.get('text', ''))
elif item.get('type') == 'tool_use':
text_parts.append(f"[Tool: {item.get('name', 'unknown')}]")
else:
text_parts.append(str(item))
content_str = ' '.join(text_parts)
elif not isinstance(content_str, str):
content_str = str(content)
# Truncate message content if too long
if isinstance(content, str) and len(content) > 200:
content = content[:197] + "..."
all_messages.append((timestamp, msg_type, content))
if len(content_str) > 200:
content_str = content_str[:197] + "..."
all_messages.append((timestamp, msg_type, content_str))
# Sort by timestamp
all_messages.sort(key=lambda x: x[0])
@@ -450,7 +467,7 @@ def get_user_selections():
"Step 5: OpenAI backend", "Select which service to talk to"
)
)
selected_openai_backend = select_openai_backend()
selected_llm_provider, backend_url = select_llm_provider()
# Step 6: Thinking agents
console.print(
@@ -458,15 +475,16 @@ def get_user_selections():
"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)
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,
"openai_backend": selected_openai_backend,
"llm_provider": selected_llm_provider.lower(),
"backend_url": backend_url,
"shallow_thinker": selected_shallow_thinker,
"deep_thinker": selected_deep_thinker,
}
@@ -692,6 +710,24 @@ def update_research_team_status(status):
for agent in research_team:
message_buffer.update_agent_status(agent, status)
def extract_content_string(content):
"""Extract string content from various message formats."""
if isinstance(content, str):
return content
elif isinstance(content, list):
# Handle Anthropic's list format
text_parts = []
for item in content:
if isinstance(item, dict):
if item.get('type') == 'text':
text_parts.append(item.get('text', ''))
elif item.get('type') == 'tool_use':
text_parts.append(f"[Tool: {item.get('name', 'unknown')}]")
else:
text_parts.append(str(item))
return ' '.join(text_parts)
else:
return str(content)
def run_analysis():
# First get all user selections
@@ -703,7 +739,8 @@ 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"]
config["backend_url"] = selections["backend_url"]
config["llm_provider"] = selections["llm_provider"].lower()
# Initialize the graph
graph = TradingAgentsGraph(
@@ -764,7 +801,7 @@ def run_analysis():
# Extract message content and type
if hasattr(last_message, "content"):
content = last_message.content
content = extract_content_string(last_message.content) # Use the helper function
msg_type = "Reasoning"
else:
content = str(last_message)

72
cli/utils.py
View File

@@ -122,23 +122,34 @@ def select_research_depth() -> int:
return choice
def select_shallow_thinking_agent(backend) -> str:
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 = {
"https://api.openai.com/v1": [
"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"),
],
"https://openrouter.ai/api/v1": [
"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"),
],
"http://localhost:11434/v1": [
"ollama": [
("llama3.2 local", "llama3.2"),
]
}
@@ -147,7 +158,7 @@ def select_shallow_thinking_agent(backend) -> str:
"Select Your [Quick-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in SHALLOW_AGENT_OPTIONS[backend]
for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
@@ -168,12 +179,12 @@ def select_shallow_thinking_agent(backend) -> str:
return choice
def select_deep_thinking_agent(backend) -> str:
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 = {
"https://api.openai.com/v1": [
"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"),
@@ -182,11 +193,24 @@ def select_deep_thinking_agent(backend) -> str:
("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"),
"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"),
],
"http://localhost:11434/v1": [
"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": [
("qwen3", "qwen3"),
]
}
@@ -195,7 +219,7 @@ def select_deep_thinking_agent(backend) -> str:
"Select Your [Deep-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in DEEP_AGENT_OPTIONS[backend]
for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
@@ -213,21 +237,22 @@ def select_deep_thinking_agent(backend) -> str:
return choice
def select_openai_backend() -> str:
def select_llm_provider() -> tuple[str, 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")
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 [OpenAI endpoint]:",
"Select your LLM Provider:",
choices=[
questionary.Choice(display, value=value)
for display, value in OPENAI_BASE_URLS
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(
@@ -243,4 +268,7 @@ def select_openai_backend() -> str:
console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
exit(1)
return choice
display_name, url = choice
print(f"You selected: {display_name}\tURL: {url}")
return display_name, url