fix: Refine clarification workflow state handling (#641)

* fix: support local models by making thought field optional in Plan model

- Make thought field optional in Plan model to fix Pydantic validation errors with local models
- Add Ollama configuration example to conf.yaml.example
- Update documentation to include local model support
- Improve planner prompt with better JSON format requirements

Fixes local model integration issues where models like qwen3:14b would fail
due to missing thought field in JSON output.

* feat: Add intelligent clarification feature for research queries

- Add multi-turn clarification process to refine vague research questions
- Implement three-dimension clarification standard (Tech/App, Focus, Scope)
- Add clarification state management in coordinator node
- Update coordinator prompt with detailed clarification guidelines
- Add UI settings to enable/disable clarification feature (disabled by default)
- Update workflow to handle clarification rounds recursively
- Add comprehensive test coverage for clarification functionality
- Update documentation with clarification feature usage guide

Key components:
- src/graph/nodes.py: Core clarification logic and state management
- src/prompts/coordinator.md: Detailed clarification guidelines
- src/workflow.py: Recursive clarification handling
- web/: UI settings integration
- tests/: Comprehensive test coverage
- docs/: Updated configuration guide

* fix: Improve clarification conversation continuity

- Add comprehensive conversation history to clarification context
- Include previous exchanges summary in system messages
- Add explicit guidelines for continuing rounds in coordinator prompt
- Prevent LLM from starting new topics during clarification
- Ensure topic continuity across clarification rounds

Fixes issue where LLM would restart clarification instead of building upon previous exchanges.

* fix: Add conversation history to clarification context

* fix: resolve clarification feature message to planer, prompt, test issues

- Optimize coordinator.md prompt template for better clarification flow
- Simplify final message sent to planner after clarification
- Fix API key assertion issues in test_search.py

* fix: Add configurable max_clarification_rounds and comprehensive tests

- Add max_clarification_rounds parameter for external configuration
- Add comprehensive test cases for clarification feature in test_app.py
- Fixes issues found during interactive mode testing where:
  - Recursive call failed due to missing initial_state parameter
  - Clarification exited prematurely at max rounds
  - Incorrect logging of max rounds reached

* Move clarification tests to test_nodes.py and add max_clarification_rounds to zh.json

* fix: add max_clarification_rounds parameter passing from frontend to backend

- Add max_clarification_rounds parameter in store.ts sendMessage function
- Add max_clarification_rounds type definition in chat.ts
- Ensure frontend settings page clarification rounds are correctly passed to backend

* fix: refine clarification workflow state handling and coverage

- Add clarification history reconstruction
- Fix clarified topic accumulation
- Add clarified_research_topic state field
- Preserve clarification state in recursive calls
- Add comprehensive test coverage

* refactor: optimize coordinator logic and type annotations

- Simplify handoff topic logic in coordinator_node
- Update type annotations from Tuple to tuple
- Improve code readability and maintainability

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>

src/workflow.py

@@ -5,6 +5,7 @@ import logging
 
 from src.config.configuration import get_recursion_limit
 from src.graph import build_graph
+from src.graph.utils import build_clarified_topic_from_history
 
 # Configure logging
 logging.basicConfig(
@@ -65,6 +66,8 @@ async def run_agent_workflow_async(
             "auto_accepted_plan": True,
             "enable_background_investigation": enable_background_investigation,
         }
+        initial_state["research_topic"] = user_input
+        initial_state["clarified_research_topic"] = user_input
 
         # Only set clarification parameter if explicitly provided
         # If None, State class default will be used (enable_clarification=False)
@@ -137,7 +140,18 @@ async def run_agent_workflow_async(
             current_state["messages"] = final_state["messages"] + [
                 {"role": "user", "content": user_response}
             ]
-            # Recursive call for clarification continuation
+            for key in (
+                "clarification_history",
+                "clarification_rounds",
+                "clarified_research_topic",
+                "research_topic",
+                "locale",
+                "enable_clarification",
+                "max_clarification_rounds",
+            ):
+                if key in final_state:
+                    current_state[key] = final_state[key]
+
             return await run_agent_workflow_async(
                 user_input=user_response,
                 max_plan_iterations=max_plan_iterations,