Merge pull request #63 from ggozad/feat/improve-research

Improve and clean research multi-agent implementation.

docs/agents.md

@@ -70,14 +70,14 @@ from haiku.rag.client import HaikuRAG
 from haiku.rag.research import ResearchOrchestrator
 
 client = HaikuRAG(path_to_db)
-orchestrator = ResearchOrchestrator(provider="openai", model="gpt-4o-mini")
+orchestrator = ResearchOrchestrator(provider="ollama", model="gpt-oss")
 
 report = await orchestrator.conduct_research(
     question="What are the main drivers and recent trends of global temperature anomalies since 1990?",
     client=client,
     max_iterations=2,
     confidence_threshold=0.8,
-    verbose=False,
+    verbose=True,
 )
 
 print(report.title)

src/haiku/rag/app.py

@@ -101,7 +101,6 @@ class HaikuRAGApp:
                     client=client,
                     max_iterations=max_iterations,
                     verbose=verbose,
-                    console=self.console if verbose else None,
                 )
 
                 # Display the report

src/haiku/rag/research/base.py

@@ -45,7 +45,8 @@ class BaseResearchAgent[T](ABC):
             model=model_obj,
             deps_type=ResearchDependencies,
             output_type=agent_output_type,
-            system_prompt=self.get_system_prompt(),
+            instructions=self.get_system_prompt(),
+            retries=3,
         )
 
         # Register tools
@@ -75,7 +76,6 @@ class BaseResearchAgent[T](ABC):
         """Return the system prompt for this agent."""
         pass
 
-    @abstractmethod
     def register_tools(self) -> None:
         """Register agent-specific tools."""
         pass

src/haiku/rag/research/dependencies.py

@@ -1,4 +1,6 @@
 from pydantic import BaseModel, Field
+from pydantic_ai import format_as_xml
+from rich.console import Console
 
 from haiku.rag.client import HaikuRAG
 from haiku.rag.research.base import SearchAnswer
@@ -43,3 +45,25 @@ class ResearchDependencies(BaseModel):
 
     client: HaikuRAG = Field(description="RAG client for document operations")
     context: ResearchContext = Field(description="Shared research context")
+    console: Console | None = None
+
+
+def _format_context_for_prompt(context: ResearchContext) -> str:
+    """Format the research context as XML for inclusion in prompts."""
+
+    context_data = {
+        "original_question": context.original_question,
+        "unanswered_questions": context.sub_questions,
+        "qa_responses": [
+            {
+                "question": qa.query,
+                "answer": qa.answer,
+                "context_snippets": qa.context,
+                "sources": qa.sources,
+            }
+            for qa in context.qa_responses
+        ],
+        "insights": context.insights,
+        "gaps": context.gaps,
+    }
+    return format_as_xml(context_data, root_tag="research_context")

src/haiku/rag/research/evaluation_agent.py

@@ -1,6 +1,11 @@
 from pydantic import BaseModel, Field
+from pydantic_ai.run import AgentRunResult
 
 from haiku.rag.research.base import BaseResearchAgent
+from haiku.rag.research.dependencies import (
+    ResearchDependencies,
+    _format_context_for_prompt,
+)
 from haiku.rag.research.prompts import EVALUATION_AGENT_PROMPT
 
 
@@ -34,9 +39,47 @@ class AnalysisEvaluationAgent(BaseResearchAgent[EvaluationResult]):
     def __init__(self, provider: str, model: str) -> None:
         super().__init__(provider, model, output_type=EvaluationResult)
 
+    async def run(
+        self, prompt: str, deps: ResearchDependencies, **kwargs
+    ) -> AgentRunResult[EvaluationResult]:
+        console = deps.console
+        if console:
+            console.print(
+                "\n[bold cyan]📊 Analyzing and evaluating research progress...[/bold cyan]"
+            )
+
+        # Format context for the evaluation agent
+        context_xml = _format_context_for_prompt(deps.context)
+        evaluation_prompt = f"""Analyze all gathered information and evaluate the completeness of research.
+
+{context_xml}
+
+Evaluate the research progress for the original question and identify any remaining gaps."""
+
+        result = await super().run(evaluation_prompt, deps, **kwargs)
+        output = result.output
+
+        # Store insights
+        for insight in output.key_insights:
+            deps.context.add_insight(insight)
+
+        # Add new questions to the sub-questions list
+        for new_q in output.new_questions:
+            if new_q not in deps.context.sub_questions:
+                deps.context.sub_questions.append(new_q)
+
+        if console:
+            if output.key_insights:
+                console.print("   [bold]Key insights:[/bold]")
+                for insight in output.key_insights:
+                    console.print(f"   • {insight}")
+            console.print(
+                f"   Confidence: [yellow]{output.confidence_score:.1%}[/yellow]"
+            )
+            status = "[green]Yes[/green]" if output.is_sufficient else "[red]No[/red]"
+            console.print(f"   Sufficient: {status}")
+
+        return result
+
     def get_system_prompt(self) -> str:
         return EVALUATION_AGENT_PROMPT
-
-    def register_tools(self) -> None:
-        """No additional tools needed - uses LLM capabilities directly."""
-        pass

src/haiku/rag/research/orchestrator.py

@@ -1,13 +1,15 @@
 from typing import Any
 
 from pydantic import BaseModel, Field
-from pydantic_ai.format_prompt import format_as_xml
 from pydantic_ai.run import AgentRunResult
 from rich.console import Console
 
 from haiku.rag.config import Config
 from haiku.rag.research.base import BaseResearchAgent
-from haiku.rag.research.dependencies import ResearchContext, ResearchDependencies
+from haiku.rag.research.dependencies import (
+    ResearchContext,
+    ResearchDependencies,
+)
 from haiku.rag.research.evaluation_agent import (
     AnalysisEvaluationAgent,
     EvaluationResult,
@@ -31,7 +33,9 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
     """Orchestrator agent that coordinates the research workflow."""
 
     def __init__(
-        self, provider: str | None = Config.RESEARCH_PROVIDER, model: str | None = None
+        self,
+        provider: str | None = Config.RESEARCH_PROVIDER,
+        model: str | None = None,
     ):
         # Use provided values or fall back to config defaults
         provider = provider or Config.RESEARCH_PROVIDER or Config.QA_PROVIDER
@@ -53,30 +57,15 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
     def get_system_prompt(self) -> str:
         return ORCHESTRATOR_PROMPT
 
-    def register_tools(self) -> None:
+    def _should_stop_research(
-        """Register orchestration tools."""
+        self,
-        # Tools are no longer needed - orchestrator directly calls agents
+        evaluation_result: AgentRunResult[EvaluationResult],
-        pass
+        confidence_threshold: float,
+    ) -> bool:
+        """Determine if research should stop based on evaluation."""
 
-    def _format_context_for_prompt(self, context: ResearchContext) -> str:
+        result = evaluation_result.output
-        """Format the research context as XML for inclusion in prompts."""
+        return result.is_sufficient and result.confidence_score >= confidence_threshold
-
-        context_data = {
-            "original_question": context.original_question,
-            "unanswered_questions": context.sub_questions,
-            "qa_responses": [
-                {
-                    "question": qa.query,
-                    "answer": qa.answer,
-                    "context_snippets": qa.context,
-                    "sources": qa.sources,
-                }
-                for qa in context.qa_responses
-            ],
-            "insights": context.insights,
-            "gaps": context.gaps,
-        }
-        return format_as_xml(context_data, root_tag="research_context")
 
     async def conduct_research(
         self,
@@ -85,7 +74,6 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
         max_iterations: int = 3,
         confidence_threshold: float = 0.8,
         verbose: bool = False,
-        console: Console | None = None,
     ) -> ResearchReport:
         """Conduct comprehensive research on a question.
 
@@ -95,7 +83,6 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
             max_iterations: Maximum number of search-analyze-clarify cycles
             confidence_threshold: Minimum confidence level to stop research (0-1)
             verbose: If True, print progress and intermediate results
-            console: Optional Rich console for output
 
         Returns:
             ResearchReport with comprehensive findings
@@ -104,44 +91,27 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
         # Initialize context
         context = ResearchContext(original_question=question)
         deps = ResearchDependencies(client=client, context=context)
+        if verbose:
+            deps.console = Console()
 
-        # Use provided console or create a new one
+        console = deps.console
-        console = console or Console() if verbose else None
-
-        # Run a simple presearch survey to summarize KB context
-        if console:
-            console.print(
-                "\n[bold cyan]🔎 Presearch: summarizing KB context...[/bold cyan]"
-            )
-
-        presearch_result = await self.presearch_agent.run(question, deps=deps)
-
         # Create initial research plan
         if console:
             console.print("\n[bold cyan]📋 Creating research plan...[/bold cyan]")
 
-        # Include the presearch summary to ground the planning step.
+        # Run a simple presearch survey to summarize KB context
-
+        presearch_result = await self.presearch_agent.run(question, deps=deps)
-        planning_context_xml = format_as_xml(
-            {
-                "original_question": question,
-                "presearch_summary": presearch_result.output or "",
-            },
-            root_tag="planning_context",
-        )
-
         plan_prompt = (
             "Create a research plan for the main question below.\n\n"
             f"Main question: {question}\n\n"
             "Use this brief presearch summary to inform the plan. Focus the 3 sub-questions "
             "on the most important aspects not already obvious from the current KB context.\n\n"
-            f"{planning_context_xml}"
+            f"{presearch_result.output}"
         )
 
         plan_result: AgentRunResult[ResearchPlan] = await self.run(
             plan_prompt, deps=deps
         )
-
         context.sub_questions = plan_result.output.sub_questions
 
         if console:
@@ -152,7 +122,6 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
             console.print("   [bold]Sub-questions:[/bold]")
             for i, sq in enumerate(plan_result.output.sub_questions, 1):
                 console.print(f"      {i}. {sq}")
-            console.print()
 
         # Execute research iterations
         for iteration in range(max_iterations):
@@ -163,7 +132,6 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
 
             # Check if we have questions to search
             if not context.sub_questions:
-                # No more questions to explore
                 if console:
                     console.print(
                         "[yellow]No more questions to explore. Concluding research.[/yellow]"
@@ -171,90 +139,20 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
                 break
 
             # Use current sub-questions for this iteration
-            questions_to_search = context.sub_questions
+            questions_to_search = context.sub_questions[:]
 
             # Search phase - answer all questions in this iteration
             if console:
                 console.print(
                     f"\n[bold cyan]🔍 Searching & Answering {len(questions_to_search)} questions:[/bold cyan]"
                 )
-                for i, q in enumerate(questions_to_search, 1):
-                    console.print(f"   {i}. {q}")
 
-            # Run searches for all questions and remove answered ones
-            answered_questions = []
             for search_question in questions_to_search:
-                try:
+                await self.search_agent.run(search_question, deps=deps)
-                    await self.search_agent.run(search_question, deps=deps)
-                except Exception as e:  # pragma: no cover - defensive
-                    if console:
-                        console.print(
-                            f"\n   [red]×[/red] Omitting failed question: {search_question} ({e})"
-                        )
-                finally:
-                    answered_questions.append(search_question)
-
-                if console and context.qa_responses:
-                    # Show the last QA response (which should be for this question)
-                    latest_qa = context.qa_responses[-1]
-                    answer_preview = (
-                        latest_qa.answer[:150] + "..."
-                        if len(latest_qa.answer) > 150
-                        else latest_qa.answer
-                    )
-                    console.print(
-                        f"\n   [green]✓[/green] {search_question[:50]}..."
-                        if len(search_question) > 50
-                        else f"\n   [green]✓[/green] {search_question}"
-                    )
-                    console.print(f"      {answer_preview}")
-
-            # Remove answered questions from the list
-            for question in answered_questions:
-                if question in context.sub_questions:
-                    context.sub_questions.remove(question)
 
             # Analysis and Evaluation phase
-            if console:
-                console.print(
-                    "\n[bold cyan]📊 Analyzing and evaluating research progress...[/bold cyan]"
-                )
 
-            # Format context for the evaluation agent
+            evaluation_result = await self.evaluation_agent.run("", deps=deps)
-            context_xml = self._format_context_for_prompt(context)
-            evaluation_prompt = f"""Analyze all gathered information and evaluate the completeness of research.
-
-{context_xml}
-
-Evaluate the research progress for the original question and identify any remaining gaps."""
-
-            evaluation_result = await self.evaluation_agent.run(
-                evaluation_prompt,
-                deps=deps,
-            )
-
-            if console and evaluation_result.output:
-                output = evaluation_result.output
-                if output.key_insights:
-                    console.print("   [bold]Key insights:[/bold]")
-                    for insight in output.key_insights:
-                        console.print(f"   • {insight}")
-                console.print(
-                    f"   Confidence: [yellow]{output.confidence_score:.1%}[/yellow]"
-                )
-                status = (
-                    "[green]Yes[/green]" if output.is_sufficient else "[red]No[/red]"
-                )
-                console.print(f"   Sufficient: {status}")
-
-            # Store insights
-            for insight in evaluation_result.output.key_insights:
-                context.add_insight(insight)
-
-            # Add new questions to the sub-questions list
-            for new_q in evaluation_result.output.new_questions:
-                if new_q not in context.sub_questions:
-                    context.sub_questions.append(new_q)
 
             # Check if research is sufficient
             if self._should_stop_research(evaluation_result, confidence_threshold):
@@ -265,36 +163,8 @@ Evaluate the research progress for the original question and identify any remain
                 break
 
         # Generate final report
-        if console:
-            console.print(
-                "\n[bold cyan]📝 Generating final research report...[/bold cyan]"
-            )
-
-        # Format context for the synthesis agent
-        final_context_xml = self._format_context_for_prompt(context)
-        synthesis_prompt = f"""Generate a comprehensive research report based on all gathered information.
-
-{final_context_xml}
-
-Create a detailed report that synthesizes all findings into a coherent response."""
-
         report_result: AgentRunResult[ResearchReport] = await self.synthesis_agent.run(
-            synthesis_prompt, deps=deps
+            "", deps=deps
         )
 
-        if console:
-            console.print("[bold green]✅ Research complete![/bold green]")
-
         return report_result.output
-
-    def _should_stop_research(
-        self,
-        evaluation_result: AgentRunResult[EvaluationResult],
-        confidence_threshold: float,
-    ) -> bool:
-        """Determine if research should stop based on evaluation."""
-
-        result = evaluation_result.output
-
-        # Stop if the agent indicates sufficient information AND confidence exceeds threshold
-        return result.is_sufficient and result.confidence_score >= confidence_threshold

src/haiku/rag/research/presearch_agent.py

@@ -15,6 +15,12 @@ class PresearchSurveyAgent(BaseResearchAgent[str]):
     async def run(
         self, prompt: str, deps: ResearchDependencies, **kwargs
     ) -> AgentRunResult[str]:
+        console = deps.console
+        if console:
+            console.print(
+                "\n[bold cyan]🔎 Presearch: summarizing KB context...[/bold cyan]"
+            )
+
         return await super().run(prompt, deps, **kwargs)
 
     def get_system_prompt(self) -> str:
@@ -28,7 +34,6 @@ class PresearchSurveyAgent(BaseResearchAgent[str]):
             limit: int = 6,
         ) -> str:
             """Return verbatim concatenation of relevant chunk texts."""
-            query = query.replace('"', "")
             results = await ctx.deps.client.search(query, limit=limit)
             expanded = await ctx.deps.client.expand_context(results)
             return "\n\n".join(chunk.content for chunk, _ in expanded)

src/haiku/rag/research/search_agent.py

@@ -21,10 +21,17 @@ class SearchSpecialistAgent(BaseResearchAgent[SearchAnswer]):
         Pydantic AI enforces `SearchAnswer` as the output model; we just store
         the QA response with the last search results as sources.
         """
-        result = await super().run(prompt, deps, **kwargs)
+        console = deps.console
+        if console:
+            console.print(f"\t{prompt}")
 
-        if result.output:
+        result = await super().run(prompt, deps, **kwargs)
-            deps.context.add_qa_response(result.output)
+        deps.context.add_qa_response(result.output)
+        deps.context.sub_questions.remove(prompt)
+        if console:
+            answer = result.output.answer
+            answer_preview = answer[:150] + "…" if len(answer) > 150 else answer
+            console.log(f"\n   [green]✓[/green] {answer_preview}")
 
         return result
 
@@ -41,9 +48,6 @@ class SearchSpecialistAgent(BaseResearchAgent[SearchAnswer]):
             limit: int = 5,
         ) -> str:
             """Search the KB and return a concise context pack."""
-            # Remove quotes from queries as this requires positional indexing in lancedb
-            # XXX: Investigate how to do that with lancedb
-            query = query.replace('"', "")
             search_results = await ctx.deps.client.search(query, limit=limit)
             expanded = await ctx.deps.client.expand_context(search_results)
 

src/haiku/rag/research/synthesis_agent.py

@@ -1,6 +1,11 @@
 from pydantic import BaseModel, Field
+from pydantic_ai.run import AgentRunResult
 
 from haiku.rag.research.base import BaseResearchAgent
+from haiku.rag.research.dependencies import (
+    ResearchDependencies,
+    _format_context_for_prompt,
+)
 from haiku.rag.research.prompts import SYNTHESIS_AGENT_PROMPT
 
 
@@ -30,11 +35,26 @@ class SynthesisAgent(BaseResearchAgent[ResearchReport]):
     def __init__(self, provider: str, model: str) -> None:
         super().__init__(provider, model, output_type=ResearchReport)
 
+    async def run(
+        self, prompt: str, deps: ResearchDependencies, **kwargs
+    ) -> AgentRunResult[ResearchReport]:
+        console = deps.console
+        if console:
+            console.print(
+                "\n[bold cyan]📝 Generating final research report...[/bold cyan]"
+            )
+
+        context_xml = _format_context_for_prompt(deps.context)
+        synthesis_prompt = f"""Generate a comprehensive research report based on all gathered information.
+
+{context_xml}
+
+Create a detailed report that synthesizes all findings into a coherent response."""
+        result = await super().run(synthesis_prompt, deps, **kwargs)
+        if console:
+            console.print("[bold green]✅ Research complete![/bold green]")
+
+        return result
+
     def get_system_prompt(self) -> str:
         return SYNTHESIS_AGENT_PROMPT
-
-    def register_tools(self) -> None:
-        """Register synthesis-specific tools."""
-        # The agent will use its LLM capabilities directly for synthesis
-        # The structured output will guide the report generation
-        pass

tests/generate_benchmark_db.py

@@ -12,7 +12,7 @@ from haiku.rag.client import HaikuRAG
 from haiku.rag.logging import configure_cli_logging
 from haiku.rag.qa import get_qa_agent
 
-logfire.configure()
+logfire.configure(send_to_logfire="if-token-present")
 logfire.instrument_pydantic_ai()
 configure_cli_logging()
 console = Console()

tests/research/test_evaluation_agent.py

@@ -1,3 +1,4 @@
+from haiku.rag.config import Config
 from haiku.rag.research.evaluation_agent import (
     AnalysisEvaluationAgent,
     EvaluationResult,
@@ -8,7 +9,9 @@ class TestAnalysisEvaluationAgent:
     """Lean tests for AnalysisEvaluationAgent without LLM mocking."""
 
     def test_agent_initialization(self):
-        agent = AnalysisEvaluationAgent(provider="openai", model="gpt-4")
+        agent = AnalysisEvaluationAgent(
-        assert agent.provider == "openai"
+            provider=Config.RESEARCH_PROVIDER, model=Config.RESEARCH_MODEL
-        assert agent.model == "gpt-4"
+        )
+        assert agent.provider == Config.RESEARCH_PROVIDER
+        assert agent.model == Config.RESEARCH_MODEL
         assert agent.output_type == EvaluationResult

tests/research/test_orchestrator.py

@@ -4,6 +4,7 @@ import pytest
 from pydantic_ai.models.test import TestModel
 
 from haiku.rag.client import HaikuRAG
+from haiku.rag.config import Config
 from haiku.rag.research.dependencies import ResearchContext, ResearchDependencies
 from haiku.rag.research.evaluation_agent import EvaluationResult
 from haiku.rag.research.orchestrator import ResearchOrchestrator, ResearchPlan
@@ -70,7 +71,9 @@ class TestResearchOrchestrator:
 
     def test_orchestrator_initialization(self):
         """Test that orchestrator initializes all agents correctly."""
-        orchestrator = ResearchOrchestrator(provider="openai", model="gpt-4")
+        orchestrator = ResearchOrchestrator(
+            provider=Config.RESEARCH_PROVIDER, model=Config.RESEARCH_MODEL
+        )
 
         # Check all agents are initialized
         assert orchestrator.search_agent is not None
@@ -78,21 +81,25 @@ class TestResearchOrchestrator:
         assert orchestrator.synthesis_agent is not None
 
         # Check they all use the same provider and model
-        assert orchestrator.search_agent.provider == "openai"
+        assert orchestrator.search_agent.provider == Config.RESEARCH_PROVIDER
-        assert orchestrator.search_agent.model == "gpt-4"
+        assert orchestrator.search_agent.model == Config.RESEARCH_MODEL
-        assert orchestrator.evaluation_agent.provider == "openai"
+        assert orchestrator.evaluation_agent.provider == Config.RESEARCH_PROVIDER
-        assert orchestrator.evaluation_agent.model == "gpt-4"
+        assert orchestrator.evaluation_agent.model == Config.RESEARCH_MODEL
-        assert orchestrator.synthesis_agent.provider == "openai"
+        assert orchestrator.synthesis_agent.provider == Config.RESEARCH_PROVIDER
-        assert orchestrator.synthesis_agent.model == "gpt-4"
+        assert orchestrator.synthesis_agent.model == Config.RESEARCH_MODEL
 
     def test_orchestrator_has_correct_output_type(self):
         """Test that orchestrator's output type is ResearchPlan."""
-        orchestrator = ResearchOrchestrator(provider="openai", model="gpt-4")
+        orchestrator = ResearchOrchestrator(
+            provider=Config.RESEARCH_PROVIDER, model=Config.RESEARCH_MODEL
+        )
         assert orchestrator.output_type == ResearchPlan
 
     def test_orchestrator_has_no_tools(self):
         """Test that orchestrator no longer registers tools (direct agent calls now)."""
-        orchestrator = ResearchOrchestrator(provider="openai", model="gpt-4")
+        orchestrator = ResearchOrchestrator(
+            provider=Config.RESEARCH_PROVIDER, model=Config.RESEARCH_MODEL
+        )
 
         # Get the tools from the agent
         tools = orchestrator.agent._function_toolset.tools
@@ -103,7 +110,9 @@ class TestResearchOrchestrator:
 
     def test_should_stop_research_logic(self):
         """Test the stopping logic based on EvaluationResult."""
-        orchestrator = ResearchOrchestrator(provider="openai", model="gpt-4")
+        orchestrator = ResearchOrchestrator(
+            provider=Config.RESEARCH_PROVIDER, model=Config.RESEARCH_MODEL
+        )
 
         # Create mock evaluation results
         from unittest.mock import MagicMock
@@ -148,7 +157,9 @@ class TestResearchOrchestrator:
     @pytest.mark.asyncio
     async def test_conduct_research_workflow(self, test_model, mock_client):
         """Test the basic research workflow using TestModel."""
-        orchestrator = ResearchOrchestrator(provider="openai", model="gpt-4")
+        orchestrator = ResearchOrchestrator(
+            provider=Config.RESEARCH_PROVIDER, model=Config.RESEARCH_MODEL
+        )
 
         # Setup mock client returns
         mock_chunks = [

tests/research/test_search_agent.py

@@ -1,3 +1,4 @@
+from haiku.rag.config import Config
 from haiku.rag.research import SearchAnswer, SearchSpecialistAgent
 
 
@@ -5,7 +6,9 @@ class TestSearchSpecialistAgent:
     """Lean tests for SearchSpecialistAgent without LLM mocking."""
 
     def test_agent_initialization(self):
-        agent = SearchSpecialistAgent(provider="openai", model="gpt-4")
+        agent = SearchSpecialistAgent(
-        assert agent.provider == "openai"
+            provider=Config.RESEARCH_PROVIDER, model=Config.RESEARCH_MODEL
-        assert agent.model == "gpt-4"
+        )
+        assert agent.provider == Config.RESEARCH_PROVIDER
+        assert agent.model == Config.RESEARCH_MODEL
         assert agent.output_type is SearchAnswer

tests/research/test_synthesis_agent.py

@@ -1,3 +1,4 @@
+from haiku.rag.config import Config
 from haiku.rag.research.synthesis_agent import ResearchReport, SynthesisAgent
 
 
@@ -5,7 +6,9 @@ class TestSynthesisAgent:
     """Lean tests for SynthesisAgent without LLM mocking."""
 
     def test_agent_initialization(self):
-        agent = SynthesisAgent(provider="openai", model="gpt-4")
+        agent = SynthesisAgent(
-        assert agent.provider == "openai"
+            provider=Config.RESEARCH_PROVIDER, model=Config.RESEARCH_MODEL
-        assert agent.model == "gpt-4"
+        )
+        assert agent.provider == Config.RESEARCH_PROVIDER
+        assert agent.model == Config.RESEARCH_MODEL
         assert agent.output_type == ResearchReport