docs

CHANGELOG.md

@@ -1,6 +1,14 @@
 # Changelog
 ## [Unreleased]
 
+### Changed
+
+- **Iterative Research Planning**: Research graph now uses an iterative feedback loop instead of batch question processing
+  - Planner proposes ONE question at a time, sees the answer, then decides whether to continue
+  - Removes `gather_context` tool — planner proposes questions directly
+  - Simpler flow: `plan_next` → `search_one` → loop back until complete → `synthesize`
+  - Consolidated `build_conversational_graph()` into `build_research_graph(output_mode="conversational")`
+
 ## [0.27.2] - 2026-01-29
 
 ### Added

docs/agents.md

@@ -163,44 +163,39 @@ Frontend clients should extract state from under this key. See the [Web Applicat
 
 ## Research Graph
 
-The research workflow is implemented as a typed pydantic-graph. It plans, searches (in parallel batches), evaluates, and synthesizes into a final report.
+The research workflow is implemented as a typed pydantic-graph. It uses an iterative feedback loop where the planner proposes one question at a time, sees the answer, then decides whether to continue or synthesize.
 
 ```mermaid
 ---
 title: Research graph
 ---
 stateDiagram-v2
-  [*] --> plan
+  [*] --> plan_next
-  plan --> get_batch
+  plan_next --> search_one: Has next question
-  get_batch --> search_one: Has questions (map)
+  plan_next --> synthesize: Complete or max iterations
-  get_batch --> synthesize: No questions
+  search_one --> plan_next
-  search_one --> collect_answers
-  collect_answers --> decide
-  decide --> get_batch: Continue research
-  decide --> synthesize: Done researching
   synthesize --> [*]
 ```
 
 **Key nodes:**
 
-- **plan**: Builds up to 3 standalone sub-questions (uses an internal presearch tool)
+- **plan_next**: Evaluates gathered evidence and either proposes the next question to investigate or marks research as complete
-- **get_batch**: Retrieves remaining sub-questions for the current iteration
+- **search_one**: Answers a single question using the knowledge base
-- **search_one**: Answers a single sub-question using the KB (mapped in parallel)
-- **collect_answers**: Aggregates search results from parallel executions
-- **decide**: Evaluates confidence and determines whether to continue or synthesize
 - **synthesize**: Generates a final structured research report
 
 **Primary models:**
 
-- `SearchAnswer` — one per sub-question (query, answer, confidence, citations)
+- `IterativePlanResult` — planning decision (is_complete, next_question, reasoning)
-- `EvaluationResult` — confidence score, new questions, sufficiency assessment
+- `SearchAnswer` — answer to a single question (query, answer, confidence, citations)
 - `ResearchReport` — final report (title, executive summary, findings, conclusions, …)
+- `ConversationalAnswer` — alternative output for chat integration (answer, citations, confidence)
 
-**Parallel execution:**
+**Iterative flow:**
 
-- The `search_one` node is mapped over all questions in a batch
+- Each iteration: planner evaluates context → proposes one question → search answers it → loop back
-- Parallelism is controlled via `max_concurrency`
+- Planner can decompose complex questions (e.g., "benefits and drawbacks" → start with "benefits")
-- Decision nodes process results after each batch completes
+- Session context is used to resolve ambiguous references and inform planning
+- Loop terminates when planner marks `is_complete=True` or `max_iterations` is reached
 
 ### CLI Usage
 

evaluations/evaluations/benchmark.py

@@ -308,12 +308,7 @@ async def run_qa_benchmark(
 
             async def answer_question(question: str) -> str:
                 context = ResearchContext(original_question=question)
-                state = ResearchState.from_config(
+                state = ResearchState.from_config(context=context, config=config)
-                    context=context,
-                    config=config,
-                    max_iterations=2,
-                    confidence_threshold=0.0,
-                )
                 deps = ResearchDeps(client=rag)
                 report = await graph.run(state=state, deps=deps)
                 return report.executive_summary if report else ""