mirror of
https://github.com/TauricResearch/TradingAgents.git
synced 2026-05-01 14:33:10 +03:00
feat: add multi-provider LLM support with thinking configurations
Models added: - OpenAI: GPT-5.2, GPT-5.1, GPT-5, GPT-5 Mini, GPT-5 Nano, GPT-4.1 - Anthropic: Claude Opus 4.5/4.1, Claude Sonnet 4.5/4, Claude Haiku 4.5 - Google: Gemini 3 Pro/Flash, Gemini 2.5 Flash/Flash Lite - xAI: Grok 4, Grok 4.1 Fast (Reasoning/Non-Reasoning) Configs updated: - Add unified thinking_level for Gemini (maps to thinking_level for Gemini 3, thinking_budget for Gemini 2.5; handles Pro's lack of "minimal" support) - Add OpenAI reasoning_effort configuration - Add NormalizedChatGoogleGenerativeAI for consistent response handling Fixes: - Fix Bull/Bear researcher display truncation - Replace ChromaDB with BM25 for memory retrieval
This commit is contained in:
Yijia Xiao
143
cli/main.py
143
cli/main.py
@@ -484,6 +484,28 @@ def get_user_selections():
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selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
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selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider)
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# Step 7: Provider-specific thinking configuration
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thinking_level = None
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reasoning_effort = None
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provider_lower = selected_llm_provider.lower()
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if provider_lower == "google":
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console.print(
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create_question_box(
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"Step 7: Thinking Mode",
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"Configure Gemini thinking mode"
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)
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)
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thinking_level = ask_gemini_thinking_config()
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elif provider_lower == "openai":
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console.print(
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create_question_box(
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"Step 7: Reasoning Effort",
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"Configure OpenAI reasoning effort level"
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)
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)
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reasoning_effort = ask_openai_reasoning_effort()
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return {
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"ticker": selected_ticker,
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"analysis_date": analysis_date,
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@@ -493,6 +515,8 @@ def get_user_selections():
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"backend_url": backend_url,
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"shallow_thinker": selected_shallow_thinker,
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"deep_thinker": selected_deep_thinker,
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"google_thinking_level": thinking_level,
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"openai_reasoning_effort": reasoning_effort,
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}
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@@ -717,23 +741,45 @@ def update_research_team_status(status):
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message_buffer.update_agent_status(agent, status)
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def extract_content_string(content):
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"""Extract string content from various message formats."""
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"""Extract string content from various message formats.
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Returns None if no meaningful text content is found.
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"""
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import ast
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def is_empty(val):
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"""Check if value is empty using Python's truthiness."""
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if val is None or val == '':
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return True
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if isinstance(val, str):
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s = val.strip()
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if not s:
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return True
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try:
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return not bool(ast.literal_eval(s))
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except (ValueError, SyntaxError):
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return False # Can't parse = real text
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return not bool(val)
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if is_empty(content):
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return None
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if isinstance(content, str):
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return content
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elif isinstance(content, list):
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# Handle Anthropic's list format
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text_parts = []
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for item in content:
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if isinstance(item, dict):
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if item.get('type') == 'text':
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text_parts.append(item.get('text', ''))
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elif item.get('type') == 'tool_use':
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text_parts.append(f"[Tool: {item.get('name', 'unknown')}]")
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else:
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text_parts.append(str(item))
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return ' '.join(text_parts)
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else:
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return str(content)
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return content.strip()
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if isinstance(content, dict):
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text = content.get('text', '')
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return text.strip() if not is_empty(text) else None
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if isinstance(content, list):
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text_parts = [
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item.get('text', '').strip() if isinstance(item, dict) and item.get('type') == 'text'
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else (item.strip() if isinstance(item, str) else '')
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for item in content
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]
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result = ' '.join(t for t in text_parts if t and not is_empty(t))
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return result if result else None
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return str(content).strip() if not is_empty(content) else None
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def run_analysis():
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# First get all user selections
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@@ -747,6 +793,9 @@ def run_analysis():
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config["deep_think_llm"] = selections["deep_thinker"]
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config["backend_url"] = selections["backend_url"]
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config["llm_provider"] = selections["llm_provider"].lower()
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# Provider-specific thinking configuration
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config["google_thinking_level"] = selections.get("google_thinking_level")
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config["openai_reasoning_effort"] = selections.get("openai_reasoning_effort")
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# Initialize the graph
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graph = TradingAgentsGraph(
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@@ -853,18 +902,23 @@ def run_analysis():
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last_message = chunk["messages"][-1]
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# Extract message content and type
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content = None
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msg_type = "Reasoning"
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if hasattr(last_message, "content"):
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content = extract_content_string(last_message.content) # Use the helper function
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msg_type = "Reasoning"
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else:
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content = str(last_message)
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msg_type = "System"
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content = extract_content_string(last_message.content)
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elif last_message is not None:
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raw = str(last_message).strip()
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if raw and raw != '{}':
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content = raw
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msg_type = "System"
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# Add message to buffer
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message_buffer.add_message(msg_type, content)
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# Only add message to buffer if there's actual content
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if content:
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message_buffer.add_message(msg_type, content)
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# If it's a tool call, add it to tool calls
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if hasattr(last_message, "tool_calls"):
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# Handle tool calls separately
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if hasattr(last_message, "tool_calls") and last_message.tool_calls:
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for tool_call in last_message.tool_calls:
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# Handle both dictionary and object tool calls
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if isinstance(tool_call, dict):
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@@ -928,51 +982,30 @@ def run_analysis():
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# Update Bull Researcher status and report
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if "bull_history" in debate_state and debate_state["bull_history"]:
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# Keep all research team members in progress
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update_research_team_status("in_progress")
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# Extract latest bull response
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bull_responses = debate_state["bull_history"].split("\n")
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latest_bull = bull_responses[-1] if bull_responses else ""
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if latest_bull:
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message_buffer.add_message("Reasoning", latest_bull)
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# Update research report with bull's latest analysis
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message_buffer.update_report_section(
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"investment_plan",
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f"### Bull Researcher Analysis\n{latest_bull}",
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)
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message_buffer.update_report_section(
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"investment_plan",
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f"### Bull Researcher Analysis\n{debate_state['bull_history']}",
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)
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# Update Bear Researcher status and report
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if "bear_history" in debate_state and debate_state["bear_history"]:
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# Keep all research team members in progress
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update_research_team_status("in_progress")
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# Extract latest bear response
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bear_responses = debate_state["bear_history"].split("\n")
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latest_bear = bear_responses[-1] if bear_responses else ""
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if latest_bear:
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message_buffer.add_message("Reasoning", latest_bear)
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# Update research report with bear's latest analysis
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message_buffer.update_report_section(
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"investment_plan",
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f"{message_buffer.report_sections['investment_plan']}\n\n### Bear Researcher Analysis\n{latest_bear}",
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)
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message_buffer.update_report_section(
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"investment_plan",
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f"### Bear Researcher Analysis\n{debate_state['bear_history']}",
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)
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# Update Research Manager status and final decision
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if (
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"judge_decision" in debate_state
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and debate_state["judge_decision"]
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):
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# Keep all research team members in progress until final decision
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update_research_team_status("in_progress")
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message_buffer.add_message(
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"Reasoning",
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f"Research Manager: {debate_state['judge_decision']}",
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)
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# Update research report with final decision
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message_buffer.update_report_section(
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"investment_plan",
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f"{message_buffer.report_sections['investment_plan']}\n\n### Research Manager Decision\n{debate_state['judge_decision']}",
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f"### Research Manager Decision\n{debate_state['judge_decision']}",
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)
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# Mark all research team members as completed
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update_research_team_status("completed")
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# Set first risk analyst to in_progress
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message_buffer.update_agent_status(
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