merge upstream main into analyst-phase1-observability

cli/main.py

@@ -467,7 +467,7 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
 def get_user_selections():
     """Get all user selections before starting the analysis display."""
     # Display ASCII art welcome message
-    with open("./cli/static/welcome.txt", "r", encoding="utf-8") as f:
+    with open(Path(__file__).parent / "static" / "welcome.txt", "r") as f:
         welcome_ascii = f.read()
 
     # Create welcome box content
@@ -506,7 +506,9 @@ def get_user_selections():
     # Step 1: Ticker symbol
     console.print(
         create_question_box(
-            "Step 1: Ticker Symbol", "Enter the ticker symbol to analyze", "SPY"
+            "Step 1: Ticker Symbol",
+            "Enter the exact ticker symbol to analyze, including exchange suffix when needed (examples: SPY, CNC.TO, 7203.T, 0700.HK)",
+            "SPY",
         )
     )
     selected_ticker = get_ticker()
@@ -522,10 +524,19 @@ def get_user_selections():
     )
     analysis_date = get_analysis_date()
 
-    # Step 3: Select analysts
+    # Step 3: Output language
     console.print(
         create_question_box(
-            "Step 3: Analysts Team", "Select your LLM analyst agents for the analysis"
+            "Step 3: Output Language",
+            "Select the language for analyst reports and final decision"
+        )
+    )
+    output_language = ask_output_language()
+
+    # Step 4: Select analysts
+    console.print(
+        create_question_box(
+            "Step 4: Analysts Team", "Select your LLM analyst agents for the analysis"
         )
     )
     selected_analysts = select_analysts()
@@ -533,40 +544,41 @@ def get_user_selections():
         f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
     )
 
-    # Step 4: Research depth
+    # Step 5: Research depth
     console.print(
         create_question_box(
-            "Step 4: Research Depth", "Select your research depth level"
+            "Step 5: Research Depth", "Select your research depth level"
         )
     )
     selected_research_depth = select_research_depth()
 
-    # Step 5: OpenAI backend
+    # Step 6: LLM Provider
     console.print(
         create_question_box(
-            "Step 5: OpenAI backend", "Select which service to talk to"
+            "Step 6: LLM Provider", "Select your LLM provider"
         )
     )
     selected_llm_provider, backend_url = select_llm_provider()
-    
-    # Step 6: Thinking agents
+
+    # Step 7: Thinking agents
     console.print(
         create_question_box(
-            "Step 6: Thinking Agents", "Select your thinking agents for analysis"
+            "Step 7: 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)
 
-    # Step 7: Provider-specific thinking configuration
+    # Step 8: Provider-specific thinking configuration
     thinking_level = None
     reasoning_effort = None
+    anthropic_effort = None
 
     provider_lower = selected_llm_provider.lower()
     if provider_lower == "google":
         console.print(
             create_question_box(
-                "Step 7: Thinking Mode",
+                "Step 8: Thinking Mode",
                 "Configure Gemini thinking mode"
             )
         )
@@ -574,11 +586,19 @@ def get_user_selections():
     elif provider_lower == "openai":
         console.print(
             create_question_box(
-                "Step 7: Reasoning Effort",
+                "Step 8: Reasoning Effort",
                 "Configure OpenAI reasoning effort level"
             )
         )
         reasoning_effort = ask_openai_reasoning_effort()
+    elif provider_lower == "anthropic":
+        console.print(
+            create_question_box(
+                "Step 8: Effort Level",
+                "Configure Claude effort level"
+            )
+        )
+        anthropic_effort = ask_anthropic_effort()
 
     return {
         "ticker": selected_ticker,
@@ -591,6 +611,8 @@ def get_user_selections():
         "deep_thinker": selected_deep_thinker,
         "google_thinking_level": thinking_level,
         "openai_reasoning_effort": reasoning_effort,
+        "anthropic_effort": anthropic_effort,
+        "output_language": output_language,
     }
 
 
@@ -793,9 +815,11 @@ ANALYST_REPORT_MAP = {
 
 
 def update_analyst_statuses(message_buffer, chunk, wall_time_tracker=None):
-    """Update all analyst statuses based on current report state.
+    """Update analyst statuses based on accumulated report state.
 
     Logic:
+    - Store new report content from the current chunk if present
+    - Check accumulated report_sections (not just current chunk) for status
     - Analysts with reports = completed
     - First analyst without report = in_progress
     - Remaining analysts without reports = pending
@@ -813,11 +837,16 @@ def update_analyst_statuses(message_buffer, chunk, wall_time_tracker=None):
 
         agent_name = ANALYST_AGENT_NAMES[analyst_key]
         report_key = ANALYST_REPORT_MAP[analyst_key]
-        has_report = bool(chunk.get(report_key))
+
+        # Capture new report content from current chunk
+        if chunk.get(report_key):
+            message_buffer.update_report_section(report_key, chunk[report_key])
+
+        # Determine status from accumulated sections, not just current chunk
+        has_report = bool(message_buffer.report_sections.get(report_key))
 
         if has_report:
             message_buffer.update_agent_status(agent_name, "completed")
-            message_buffer.update_report_section(report_key, chunk[report_key])
         elif not found_active:
             message_buffer.update_agent_status(agent_name, "in_progress")
             found_active = True
@@ -919,6 +948,8 @@ def run_analysis():
     # Provider-specific thinking configuration
     config["google_thinking_level"] = selections.get("google_thinking_level")
     config["openai_reasoning_effort"] = selections.get("openai_reasoning_effort")
+    config["anthropic_effort"] = selections.get("anthropic_effort")
+    config["output_language"] = selections.get("output_language", "English")
 
     # Create stats callback handler for tracking LLM/tool calls
     stats_handler = StatsCallbackHandler()
@@ -961,7 +992,7 @@ def run_analysis():
             func(*args, **kwargs)
             timestamp, message_type, content = obj.messages[-1]
             content = content.replace("\n", " ")  # Replace newlines with spaces
-            with open(log_file, "a", encoding="utf-8") as f:
+            with open(log_file, "a") as f:
                 f.write(f"{timestamp} [{message_type}] {content}\n")
         return wrapper
     
@@ -972,7 +1003,7 @@ def run_analysis():
             func(*args, **kwargs)
             timestamp, tool_name, args = obj.tool_calls[-1]
             args_str = ", ".join(f"{k}={v}" for k, v in args.items())
-            with open(log_file, "a", encoding="utf-8") as f:
+            with open(log_file, "a") as f:
                 f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n")
         return wrapper
 
@@ -985,8 +1016,9 @@ def run_analysis():
                 content = obj.report_sections[section_name]
                 if content:
                     file_name = f"{section_name}.md"
-                    with open(report_dir / file_name, "w", encoding="utf-8") as f:
-                        f.write(content)
+                    text = "\n".join(str(item) for item in content) if isinstance(content, list) else content
+                    with open(report_dir / file_name, "w") as f:
+                        f.write(text)
         return wrapper
 
     message_buffer.add_message = save_message_decorator(message_buffer, "add_message")