Replace console/stream with AG-UI event protocol

haiku_rag_slim/haiku/rag/agui/__init__.py

@@ -1,5 +1,6 @@
 """Generic AG-UI protocol support for haiku.rag graphs."""
 
+from haiku.rag.agui.cli_renderer import AGUIConsoleRenderer
 from haiku.rag.agui.emitter import AGUIEmitter
 from haiku.rag.agui.events import (
     AGUIEvent,
@@ -18,8 +19,10 @@ from haiku.rag.agui.events import (
     emit_text_message_start,
 )
 from haiku.rag.agui.state import compute_state_delta
+from haiku.rag.agui.stream import stream_graph
 
 __all__ = [
+    "AGUIConsoleRenderer",
     "AGUIEmitter",
     "AGUIEvent",
     "compute_state_delta",
@@ -36,4 +39,5 @@ __all__ = [
     "emit_text_message_content",
     "emit_text_message_end",
     "emit_text_message_start",
+    "stream_graph",
 ]

haiku_rag_slim/haiku/rag/agui/cli_renderer.py

@@ -0,0 +1,152 @@
+"""Generic CLI renderer for AG-UI events with Rich console output."""
+
+from collections.abc import AsyncIterator
+from typing import Any
+
+from pydantic import BaseModel
+from rich.console import Console
+
+from haiku.rag.agui.events import AGUIEvent
+
+
+class AGUIConsoleRenderer:
+    """Renders AG-UI events to Rich console with formatted output.
+
+    Generic renderer that processes AG-UI protocol events and renders them
+    with Rich formatting. Works with any graph that emits AG-UI events.
+    """
+
+    def __init__(self, console: Console | None = None):
+        """Initialize the renderer.
+
+        Args:
+            console: Optional Rich console instance (creates new one if not provided)
+        """
+        self.console = console or Console()
+        self._state: BaseModel | None = None
+
+    async def render(self, events: AsyncIterator[AGUIEvent]) -> Any | None:
+        """Process events and render to console, return final result.
+
+        Args:
+            events: Async iterator of AG-UI events
+
+        Returns:
+            The final result from RunFinished event, or None
+        """
+        result = None
+
+        async for event in events:
+            event_type = event.get("type")
+
+            if event_type == "RUN_STARTED":
+                self._render_run_started(event)
+            elif event_type == "RUN_FINISHED":
+                result = event.get("result")
+                self._render_run_finished()
+            elif event_type == "RUN_ERROR":
+                self._render_error(event)
+            elif event_type == "STEP_STARTED":
+                self._render_step_started(event)
+            elif event_type == "STEP_FINISHED":
+                self._render_step_finished(event)
+            elif event_type == "TEXT_MESSAGE_CHUNK":
+                self._render_text_message(event)
+            elif event_type == "TEXT_MESSAGE_START":
+                pass  # Start of streaming message, no output needed
+            elif event_type == "TEXT_MESSAGE_CONTENT":
+                self._render_text_content(event)
+            elif event_type == "TEXT_MESSAGE_END":
+                pass  # End of streaming message, no output needed
+            elif event_type == "STATE_SNAPSHOT":
+                self._state = event.get("snapshot")
+            elif event_type == "STATE_DELTA":
+                self._apply_state_delta(event)
+            elif event_type == "ACTIVITY_SNAPSHOT":
+                self._render_activity(event)
+            elif event_type == "ACTIVITY_DELTA":
+                pass  # Activity deltas don't need separate rendering
+
+        return result
+
+    def _render_run_started(self, event: AGUIEvent) -> None:
+        """Render run start event.
+
+        Args:
+            event: RunStarted event
+        """
+        # Currently silent - could render run metadata later
+
+    def _render_run_finished(self) -> None:
+        """Render run completion."""
+        # Currently silent - the result is rendered separately
+
+    def _render_error(self, event: AGUIEvent) -> None:
+        """Render error event.
+
+        Args:
+            event: RunError event
+        """
+        message = event.get("message", "Unknown error")
+        self.console.print(f"[bold red]❌ Error:[/bold red] {message}")
+
+    def _render_step_started(self, event: AGUIEvent) -> None:
+        """Render step start event.
+
+        Args:
+            event: StepStarted event
+        """
+        step_name = event.get("stepName", "")
+        if step_name:
+            # Format step name for display
+            display_name = step_name.replace("_", " ").title()
+            self.console.print(f"\n[bold cyan]{display_name}[/bold cyan]")
+
+    def _render_step_finished(self, event: AGUIEvent) -> None:
+        """Render step finish event.
+
+        Args:
+            event: StepFinished event
+        """
+        # Step completion is implicit from the next step or activity
+
+    def _render_text_message(self, event: AGUIEvent) -> None:
+        """Render complete text message.
+
+        Args:
+            event: TextMessageChunk event
+        """
+        delta = event.get("delta", "")
+        # The delta contains the text content to display
+        self.console.print(delta)
+
+    def _render_text_content(self, event: AGUIEvent) -> None:
+        """Render streaming text content delta.
+
+        Args:
+            event: TextMessageContent event
+        """
+        delta = event.get("delta", "")
+        # Print delta without newline for streaming effect
+        self.console.print(delta, end="")
+
+    def _render_activity(self, event: AGUIEvent) -> None:
+        """Render activity update.
+
+        Args:
+            event: ActivitySnapshot event
+        """
+        content = event.get("content", "")
+
+        # Render activity content with emphasis
+        if content:
+            self.console.print(f"[dim]{content}[/dim]")
+
+    def _apply_state_delta(self, event: AGUIEvent) -> None:
+        """Apply state delta to current state.
+
+        Args:
+            event: StateDelta event
+        """
+        # Currently not applying deltas - could implement state patching later
+        # For now, the text messages contain all the information we need to display

haiku_rag_slim/haiku/rag/agui/emitter.py

@@ -13,7 +13,6 @@ from haiku.rag.agui.events import (
     emit_run_error,
     emit_run_finished,
     emit_run_started,
-    emit_state_delta,
     emit_state_snapshot,
     emit_step_finished,
     emit_step_started,
@@ -60,18 +59,19 @@ class AGUIEmitter[StateT: BaseModel, ResultT]:
         """Get the run ID for this emitter."""
         return self._run_id
 
-    def start_run(self, input_data: str, initial_state: StateT) -> None:
+    def start_run(self, initial_state: StateT) -> None:
         """Emit RunStarted and initial StateSnapshot.
 
         Args:
-            input_data: The input that started the run
             initial_state: The initial state of the graph
         """
-        # If thread_id wasn't provided, generate from input hash
+        # If thread_id wasn't provided, generate from state hash
         if not self._thread_id or self._thread_id == str(uuid4()):
-            self._thread_id = self._generate_thread_id(input_data)
+            state_json = initial_state.model_dump_json()
+            self._thread_id = self._generate_thread_id(state_json)
 
-        self._emit(emit_run_started(self._thread_id, self._run_id, input_data))
+        # RunStarted (state snapshot follows immediately with full state)
+        self._emit(emit_run_started(self._thread_id, self._run_id))
         self._emit(emit_state_snapshot(initial_state))
         self._last_state = initial_state
 
@@ -100,21 +100,13 @@ class AGUIEmitter[StateT: BaseModel, ResultT]:
         self._emit(emit_text_message(message, role))
 
     def update_state(self, new_state: StateT) -> None:
-        """Emit StateDelta for state change, or StateSnapshot if no previous state.
+        """Emit StateSnapshot for state change.
 
         Args:
             new_state: The updated state
         """
-        if self._last_state:
-            # Emit delta if we have a previous state
-            delta_event = emit_state_delta(self._last_state, new_state)
-            # Only emit if there are actual changes
-            if delta_event.get("operations"):
-                self._emit(delta_event)
-        else:
-            # Emit snapshot if this is the first state update
-            self._emit(emit_state_snapshot(new_state))
-
+        # Always emit full snapshot (not delta) for complete state visibility
+        self._emit(emit_state_snapshot(new_state))
         self._last_state = new_state
 
     def update_activity(
@@ -125,9 +117,11 @@ class AGUIEmitter[StateT: BaseModel, ResultT]:
         Args:
             activity_type: Type of activity (e.g., "planning", "searching")
             content: Description of the activity
-            message_id: Optional message ID to associate activity with
+            message_id: Optional message ID to associate activity with (auto-generated if None)
         """
-        self._emit(emit_activity(activity_type, content, message_id))
+        if message_id is None:
+            message_id = str(uuid4())
+        self._emit(emit_activity(message_id, activity_type, content))
 
     def finish_run(self, result: ResultT) -> None:
         """Emit RunFinished event.

haiku_rag_slim/haiku/rag/agui/events.py

@@ -11,23 +11,27 @@ from haiku.rag.agui.state import compute_state_delta
 AGUIEvent = dict[str, Any]
 
 
-def emit_run_started(thread_id: str, run_id: str, input_data: str) -> dict[str, Any]:
+def emit_run_started(
+    thread_id: str, run_id: str, input_data: str | None = None
+) -> dict[str, Any]:
     """Create a RunStarted event.
 
     Args:
         thread_id: Unique identifier for the conversation thread
         run_id: Unique identifier for this run
-        input_data: The input that started the run
+        input_data: Optional input that started the run
 
     Returns:
         RunStarted event dict
     """
-    return {
+    event: dict[str, Any] = {
         "type": "RUN_STARTED",
         "threadId": thread_id,
         "runId": run_id,
-        "input": input_data,
     }
+    if input_data:
+        event["input"] = input_data
+    return event
 
 
 def emit_run_finished(thread_id: str, run_id: str, result: Any) -> dict[str, Any]:
@@ -119,7 +123,7 @@ def emit_text_message(content: str, role: str = "assistant") -> dict[str, Any]:
         "type": "TEXT_MESSAGE_CHUNK",
         "messageId": message_id,
         "role": role,
-        "content": content,
+        "delta": content,
     }
 
 
@@ -197,46 +201,47 @@ def emit_state_delta(old_state: BaseModel, new_state: BaseModel) -> dict[str, An
     Returns:
         StateDelta event dict
     """
-    operations = compute_state_delta(old_state, new_state)
+    delta = compute_state_delta(old_state, new_state)
     return {
         "type": "STATE_DELTA",
-        "operations": operations,
+        "delta": delta,
     }
 
 
 def emit_activity(
+    message_id: str,
     activity_type: str,
     content: str,
-    message_id: str | None = None,
 ) -> dict[str, Any]:
     """Create an ActivitySnapshot event.
 
     Args:
+        message_id: Message ID to associate activity with (required)
         activity_type: Type of activity (e.g., "planning", "searching")
         content: Description of the activity
-        message_id: Optional message ID to associate activity with
 
     Returns:
         ActivitySnapshot event dict
     """
-    event: dict[str, Any] = {
+    return {
         "type": "ACTIVITY_SNAPSHOT",
+        "messageId": message_id,
         "activityType": activity_type,
         "content": content,
     }
-    if message_id:
-        event["messageId"] = message_id
-    return event
 
 
 def emit_activity_delta(
-    message_id: str, operations: list[dict[str, Any]]
+    message_id: str,
+    activity_type: str,
+    patch: list[dict[str, Any]],
 ) -> dict[str, Any]:
     """Create an ActivityDelta event with JSON Patch operations.
 
     Args:
         message_id: Message ID of the activity being updated
-        operations: JSON Patch operations to apply
+        activity_type: Type of activity being updated
+        patch: JSON Patch operations to apply
 
     Returns:
         ActivityDelta event dict
@@ -244,5 +249,6 @@ def emit_activity_delta(
     return {
         "type": "ACTIVITY_DELTA",
         "messageId": message_id,
-        "operations": operations,
+        "activityType": activity_type,
+        "patch": patch,
     }

haiku_rag_slim/haiku/rag/agui/stream.py

@@ -0,0 +1,80 @@
+"""Generic graph streaming with AG-UI events."""
+
+import asyncio
+from collections.abc import AsyncIterator
+from contextlib import suppress
+from typing import Any, Protocol
+
+from pydantic import BaseModel
+
+from haiku.rag.agui.emitter import AGUIEmitter
+from haiku.rag.agui.events import AGUIEvent
+
+
+class GraphDeps(Protocol):
+    """Protocol for graph dependencies that support AG-UI emission."""
+
+    agui_emitter: AGUIEmitter[Any, Any] | None
+
+
+async def stream_graph(
+    graph: Any,
+    state: BaseModel,
+    deps: GraphDeps,
+) -> AsyncIterator[AGUIEvent]:
+    """Run a graph and yield AG-UI events as they occur.
+
+    This is a generic streaming function that works with any pydantic-graph
+    that follows the AG-UI pattern:
+    - State must be a Pydantic BaseModel
+    - Deps must have an optional agui_emitter attribute
+    - Graph must be a pydantic-graph Graph instance
+
+    Args:
+        graph: The pydantic-graph Graph to execute
+        state: Initial state (Pydantic BaseModel)
+        deps: Graph dependencies with agui_emitter support
+
+    Yields:
+        AG-UI event dictionaries
+
+    Raises:
+        TypeError: If deps doesn't support agui_emitter
+        RuntimeError: If graph doesn't produce a result
+    """
+    if not hasattr(deps, "agui_emitter"):
+        raise TypeError("deps must have an 'agui_emitter' attribute")
+
+    # Create AG-UI emitter
+    emitter: AGUIEmitter[Any, Any] = AGUIEmitter()
+    deps.agui_emitter = emitter
+
+    async def _execute() -> None:
+        try:
+            # Start the run with initial state
+            emitter.start_run(initial_state=state)
+
+            # Execute the graph
+            result = await graph.run(state=state, deps=deps)
+
+            if result is None:
+                raise RuntimeError("Graph did not produce a result")
+
+            # Finish the run with the result
+            emitter.finish_run(result)
+        except Exception as exc:
+            # Emit error event
+            emitter.error(exc)
+        finally:
+            await emitter.close()
+
+    runner = asyncio.create_task(_execute())
+
+    try:
+        async for event in emitter:
+            yield event
+    finally:
+        if not runner.done():
+            runner.cancel()
+        with suppress(asyncio.CancelledError):
+            await runner

haiku_rag_slim/haiku/rag/app.py

@@ -8,6 +8,7 @@ from rich.console import Console
 from rich.markdown import Markdown
 from rich.progress import Progress
 
+from haiku.rag.agui import AGUIConsoleRenderer, stream_graph
 from haiku.rag.client import HaikuRAG
 from haiku.rag.config import AppConfig, Config
 from haiku.rag.mcp import create_mcp_server
@@ -15,7 +16,6 @@ from haiku.rag.monitor import FileWatcher
 from haiku.rag.research.dependencies import ResearchContext
 from haiku.rag.research.graph import build_research_graph
 from haiku.rag.research.state import ResearchDeps, ResearchState
-from haiku.rag.research.stream import stream_research_graph
 from haiku.rag.store.models.chunk import Chunk
 from haiku.rag.store.models.document import Document
 
@@ -243,46 +243,36 @@ class HaikuRAGApp:
             except Exception as e:
                 self.console.print(f"[red]Error: {e}[/red]")
 
-    async def research(
-        self,
-        question: str,
-        verbose: bool = False,
-    ):
+    async def research(self, question: str):
         """Run research via the pydantic-graph pipeline.
 
         Args:
             question: The research question
-            verbose: Show verbose output
         """
         async with HaikuRAG(db_path=self.db_path, config=self.config) as client:
             try:
-                if verbose:
-                    self.console.print("[bold cyan]Starting research[/bold cyan]")
-                    self.console.print(f"[bold blue]Question:[/bold blue] {question}")
-                    self.console.print()
+                self.console.print("[bold cyan]Starting research[/bold cyan]")
+                self.console.print(f"[bold blue]Question:[/bold blue] {question}")
+                self.console.print()
 
                 graph = build_research_graph(config=self.config)
                 context = ResearchContext(original_question=question)
                 state = ResearchState.from_config(context=context, config=self.config)
-                deps = ResearchDeps(
-                    client=client, console=self.console if verbose else None
-                )
+                deps = ResearchDeps(client=client)
 
-                report = None
-                async for event in stream_research_graph(graph, state, deps):
-                    if event.type == "report":
-                        report = event.report
-                        break
-                    if event.type == "error":
-                        self.console.print(
-                            f"[red]Error during research: {event.message}[/red]"
-                        )
-                        return
+                # Use AG-UI renderer to process events
+                renderer = AGUIConsoleRenderer(self.console)
+                report_dict = await renderer.render(stream_graph(graph, state, deps))
 
-                if report is None:
+                if report_dict is None:
                     self.console.print("[red]Research did not produce a report.[/red]")
                     return
 
+                # Convert dict to ResearchReport model
+                from haiku.rag.research.models import ResearchReport
+
+                report = ResearchReport.model_validate(report_dict)
+
                 # Display the report
                 self.console.print("[bold green]Research Report[/bold green]")
                 self.console.rule()

haiku_rag_slim/haiku/rag/cli.py

@@ -299,14 +299,9 @@ def research(
         "--db",
         help="Path to the LanceDB database file",
     ),
-    verbose: bool = typer.Option(
-        False,
-        "--verbose",
-        help="Show verbose progress output",
-    ),
 ):
     app = create_app(db)
-    asyncio.run(app.research(question=question, verbose=verbose))
+    asyncio.run(app.research(question=question))
 
 
 @cli.command("settings", help="Display current configuration settings")

haiku_rag_slim/haiku/rag/research/dependencies.py

@@ -1,7 +1,6 @@
 from collections.abc import Iterable
 
 from pydantic import BaseModel, Field
-from rich.console import Console
 
 from haiku.rag.client import HaikuRAG
 from haiku.rag.graph_common.models import SearchAnswer
@@ -10,7 +9,6 @@ from haiku.rag.research.models import (
     InsightAnalysis,
     InsightRecord,
 )
-from haiku.rag.research.stream import ResearchStream
 
 
 class ResearchContext(BaseModel):
@@ -184,10 +182,6 @@ class ResearchDependencies(BaseModel):
 
     client: HaikuRAG = Field(description="RAG client for document operations")
     context: ResearchContext = Field(description="Shared research context")
-    console: Console | None = None
-    stream: ResearchStream | None = Field(
-        default=None, description="Optional research event stream"
-    )
 
 
 def _merge_unique(existing: list[str], incoming: Iterable[str]) -> list[str]:

haiku_rag_slim/haiku/rag/research/graph.py

@@ -53,50 +53,51 @@ def build_research_graph(
         state = ctx.state
         deps = ctx.deps
 
-        log(deps, state, "\n[bold cyan]📋 Creating research plan...[/bold cyan]")
+        if deps.agui_emitter:
+            deps.agui_emitter.start_step("plan")
+            deps.agui_emitter.update_activity("planning", "Creating research plan")
 
-        plan_agent = Agent(
-            model=get_model(provider, model),
-            output_type=ResearchPlan,
-            instructions=(
-                PLAN_PROMPT
-                + "\n\nUse the gather_context tool once on the main question before planning."
-            ),
-            retries=3,
-            deps_type=ResearchDependencies,
-        )
+        try:
+            plan_agent = Agent(
+                model=get_model(provider, model),
+                output_type=ResearchPlan,
+                instructions=(
+                    PLAN_PROMPT
+                    + "\n\nUse the gather_context tool once on the main question before planning."
+                ),
+                retries=3,
+                output_retries=3,
+                deps_type=ResearchDependencies,
+            )
 
-        @plan_agent.tool
-        async def gather_context(
-            ctx2: RunContext[ResearchDependencies], query: str, limit: int = 6
-        ) -> str:
-            results = await ctx2.deps.client.search(query, limit=limit)
-            expanded = await ctx2.deps.client.expand_context(results)
-            return "\n\n".join(chunk.content for chunk, _ in expanded)
+            @plan_agent.tool
+            async def gather_context(
+                ctx2: RunContext[ResearchDependencies], query: str, limit: int = 6
+            ) -> str:
+                results = await ctx2.deps.client.search(query, limit=limit)
+                expanded = await ctx2.deps.client.expand_context(results)
+                return "\n\n".join(chunk.content for chunk, _ in expanded)
 
-        prompt = (
-            "Plan a focused approach for the main question.\n\n"
-            f"Main question: {state.context.original_question}"
-        )
+            prompt = (
+                "Plan a focused approach for the main question.\n\n"
+                f"Main question: {state.context.original_question}"
+            )
 
-        agent_deps = ResearchDependencies(
-            client=deps.client,
-            context=state.context,
-            console=deps.console,
-            stream=deps.stream,
-        )
-        plan_result = await plan_agent.run(prompt, deps=agent_deps)
-        state.context.sub_questions = list(plan_result.output.sub_questions)
+            agent_deps = ResearchDependencies(
+                client=deps.client,
+                context=state.context,
+            )
+            plan_result = await plan_agent.run(prompt, deps=agent_deps)
+            state.context.sub_questions = list(plan_result.output.sub_questions)
 
-        log(deps, state, "\n[bold green]✅ Plan Created:[/bold green]")
-        log(
-            deps,
-            state,
-            f"   [bold]Main Question:[/bold] {state.context.original_question}",
-        )
-        log(deps, state, "   [bold]Sub-questions:[/bold]")
-        for i, sq in enumerate(state.context.sub_questions, 1):
-            log(deps, state, f"      {i}. {sq}")
+            # Log the plan results
+            log(deps, state, f"Main Question: {state.context.original_question}")
+            log(deps, state, "Sub-questions:")
+            for i, sq in enumerate(state.context.sub_questions, 1):
+                log(deps, state, f"  {i}. {sq}")
+        finally:
+            if deps.agui_emitter:
+                deps.agui_emitter.finish_step()
 
     @g.step
     async def search_one(
@@ -106,26 +107,30 @@ def build_research_graph(
         deps = ctx.deps
         sub_q = ctx.inputs
 
-        # Create semaphore if not already provided
-        if deps.semaphore is None:
-            import asyncio
+        if deps.agui_emitter:
+            deps.agui_emitter.start_step("search_one")
 
-            deps.semaphore = asyncio.Semaphore(state.max_concurrency)
+        try:
+            # Create semaphore if not already provided
+            if deps.semaphore is None:
+                import asyncio
 
-        # Use semaphore to control concurrency
-        async with deps.semaphore:
-            return await _do_search(state, deps, sub_q)
+                deps.semaphore = asyncio.Semaphore(state.max_concurrency)
+
+            # Use semaphore to control concurrency
+            async with deps.semaphore:
+                return await _do_search(state, deps, sub_q)
+        finally:
+            if deps.agui_emitter:
+                deps.agui_emitter.finish_step()
 
     async def _do_search(
         state: ResearchState,
         deps: ResearchDeps,
         sub_q: str,
     ) -> SearchAnswer:
-        log(
-            deps,
-            state,
-            f"\n[bold cyan]🔍 Searching & Answering:[/bold cyan] {sub_q}",
-        )
+        if deps.agui_emitter:
+            deps.agui_emitter.update_activity("searching", f"Searching: {sub_q}")
 
         agent = Agent(
             model=get_model(provider, model),
@@ -160,21 +165,16 @@ def build_research_graph(
         agent_deps = ResearchDependencies(
             client=deps.client,
             context=state.context,
-            console=deps.console,
-            stream=deps.stream,
         )
         try:
             result = await agent.run(sub_q, deps=agent_deps)
             answer = result.output
             if answer:
                 state.context.add_qa_response(answer)
-                preview = answer.answer[:150] + (
-                    "…" if len(answer.answer) > 150 else ""
-                )
-                log(deps, state, f"   [green]✓[/green] {preview}")
+                log(deps, state, f"Answer: {answer.answer}")
             return answer
         except Exception as e:
-            log(deps, state, f"[red]Search failed:[/red] {e}")
+            log(deps, state, f"Search failed: {e}")
             failure_answer = SearchAnswer(
                 query=sub_q,
                 answer=f"Search failed after retries: {str(e)}",
@@ -204,148 +204,139 @@ def build_research_graph(
         state = ctx.state
         deps = ctx.deps
 
-        log(
-            deps,
-            state,
-            "\n[bold cyan]🧭 Synthesizing new insights and gap status...[/bold cyan]",
-        )
+        if deps.agui_emitter:
+            deps.agui_emitter.start_step("analyze_insights")
+            deps.agui_emitter.update_activity(
+                "analyzing", "Synthesizing insights and gaps"
+            )
 
-        agent = Agent(
-            model=get_model(provider, model),
-            output_type=InsightAnalysis,
-            instructions=INSIGHT_AGENT_PROMPT,
-            retries=3,
-            deps_type=ResearchDependencies,
-        )
+        try:
+            agent = Agent(
+                model=get_model(provider, model),
+                output_type=InsightAnalysis,
+                instructions=INSIGHT_AGENT_PROMPT,
+                retries=3,
+                output_retries=3,
+                deps_type=ResearchDependencies,
+            )
 
-        context_xml = format_context_for_prompt(state.context)
-        prompt = (
-            "Review the latest research context and update the shared ledger of insights, gaps,"
-            " and follow-up questions.\n\n"
-            f"{context_xml}"
-        )
-        agent_deps = ResearchDependencies(
-            client=deps.client,
-            context=state.context,
-            console=deps.console,
-            stream=deps.stream,
-        )
-        result = await agent.run(prompt, deps=agent_deps)
-        analysis: InsightAnalysis = result.output
+            context_xml = format_context_for_prompt(state.context)
+            prompt = (
+                "Review the latest research context and update the shared ledger of insights, gaps,"
+                " and follow-up questions.\n\n"
+                f"{context_xml}"
+            )
+            agent_deps = ResearchDependencies(
+                client=deps.client,
+                context=state.context,
+            )
+            result = await agent.run(prompt, deps=agent_deps)
+            analysis: InsightAnalysis = result.output
 
-        state.context.integrate_analysis(analysis)
-        state.last_analysis = analysis
+            state.context.integrate_analysis(analysis)
+            state.last_analysis = analysis
 
-        if analysis.commentary:
-            log(deps, state, f"   Summary: {analysis.commentary}")
-        if analysis.highlights:
-            log(deps, state, "   [bold]Updated insights:[/bold]")
-            for insight in analysis.highlights:
-                label = insight.status.value
-                log(
-                    deps,
-                    state,
-                    f"   • ({label}) {insight.summary}",
-                )
-        if analysis.gap_assessments:
-            log(deps, state, "   [bold yellow]Gap updates:[/bold yellow]")
-            for gap in analysis.gap_assessments:
-                status = "resolved" if gap.resolved else "open"
-                severity = gap.severity.value
-                log(
-                    deps,
-                    state,
-                    f"   • ({severity}/{status}) {gap.description}",
-                )
-        if analysis.resolved_gaps:
-            log(deps, state, "   [green]Resolved gaps:[/green]")
-            for resolved in analysis.resolved_gaps:
-                log(deps, state, f"   • {resolved}")
-        if analysis.new_questions:
-            log(deps, state, "   [cyan]Proposed follow-ups:[/cyan]")
-            for question in analysis.new_questions:
-                log(deps, state, f"   • {question}")
+            if analysis.commentary:
+                log(deps, state, f"Summary: {analysis.commentary}")
+            if analysis.highlights:
+                log(deps, state, "Updated insights:")
+                for insight in analysis.highlights:
+                    label = insight.status.value
+                    log(deps, state, f"  • ({label}) {insight.summary}")
+            if analysis.gap_assessments:
+                log(deps, state, "Gap updates:")
+                for gap in analysis.gap_assessments:
+                    status = "resolved" if gap.resolved else "open"
+                    severity = gap.severity.value
+                    log(deps, state, f"  • ({severity}/{status}) {gap.description}")
+            if analysis.resolved_gaps:
+                log(deps, state, "Resolved gaps:")
+                for resolved in analysis.resolved_gaps:
+                    log(deps, state, f"  • {resolved}")
+            if analysis.new_questions:
+                log(deps, state, "Proposed follow-ups:")
+                for question in analysis.new_questions:
+                    log(deps, state, f"  • {question}")
+        finally:
+            if deps.agui_emitter:
+                deps.agui_emitter.finish_step()
 
     @g.step
     async def decide(ctx: StepContext[ResearchState, ResearchDeps, None]) -> bool:
         state = ctx.state
         deps = ctx.deps
 
-        log(
-            deps,
-            state,
-            "\n[bold cyan]📊 Evaluating research sufficiency...[/bold cyan]",
-        )
-
-        agent = Agent(
-            model=get_model(provider, model),
-            output_type=EvaluationResult,
-            instructions=DECISION_AGENT_PROMPT,
-            retries=3,
-            deps_type=ResearchDependencies,
-        )
-
-        context_xml = format_context_for_prompt(state.context)
-        analysis_xml = format_analysis_for_prompt(state.last_analysis)
-        prompt_parts = [
-            "Assess whether the research now answers the original question with adequate confidence.",
-            context_xml,
-            analysis_xml,
-        ]
-        if state.last_eval is not None:
-            prev = state.last_eval
-            prompt_parts.append(
-                "<previous_evaluation>"
-                f"<confidence>{prev.confidence_score:.2f}</confidence>"
-                f"<is_sufficient>{str(prev.is_sufficient).lower()}</is_sufficient>"
-                f"<reasoning>{prev.reasoning}</reasoning>"
-                "</previous_evaluation>"
+        if deps.agui_emitter:
+            deps.agui_emitter.start_step("decide")
+            deps.agui_emitter.update_activity(
+                "evaluating", "Evaluating research sufficiency"
             )
-        prompt = "\n\n".join(part for part in prompt_parts if part)
 
-        agent_deps = ResearchDependencies(
-            client=deps.client,
-            context=state.context,
-            console=deps.console,
-            stream=deps.stream,
-        )
-        decision_result = await agent.run(prompt, deps=agent_deps)
-        output = decision_result.output
+        try:
+            agent = Agent(
+                model=get_model(provider, model),
+                output_type=EvaluationResult,
+                instructions=DECISION_AGENT_PROMPT,
+                retries=3,
+                output_retries=3,
+                deps_type=ResearchDependencies,
+            )
 
-        state.last_eval = output
-        state.iterations += 1
+            context_xml = format_context_for_prompt(state.context)
+            analysis_xml = format_analysis_for_prompt(state.last_analysis)
+            prompt_parts = [
+                "Assess whether the research now answers the original question with adequate confidence.",
+                context_xml,
+                analysis_xml,
+            ]
+            if state.last_eval is not None:
+                prev = state.last_eval
+                prompt_parts.append(
+                    "<previous_evaluation>"
+                    f"<confidence>{prev.confidence_score:.2f}</confidence>"
+                    f"<is_sufficient>{str(prev.is_sufficient).lower()}</is_sufficient>"
+                    f"<reasoning>{prev.reasoning}</reasoning>"
+                    "</previous_evaluation>"
+                )
+            prompt = "\n\n".join(part for part in prompt_parts if part)
 
-        for new_q in output.new_questions:
-            if new_q not in state.context.sub_questions:
-                state.context.sub_questions.append(new_q)
+            agent_deps = ResearchDependencies(
+                client=deps.client,
+                context=state.context,
+            )
+            decision_result = await agent.run(prompt, deps=agent_deps)
+            output = decision_result.output
 
-        if output.key_insights:
-            log(deps, state, "   [bold]Key insights:[/bold]")
-            for insight in output.key_insights:
-                log(deps, state, f"   • {insight}")
+            state.last_eval = output
+            state.iterations += 1
 
-        if output.gaps:
-            log(deps, state, "   [bold yellow]Remaining gaps:[/bold yellow]")
-            for gap in output.gaps:
-                log(deps, state, f"   • {gap}")
+            for new_q in output.new_questions:
+                if new_q not in state.context.sub_questions:
+                    state.context.sub_questions.append(new_q)
 
-        log(
-            deps,
-            state,
-            f"   Confidence: [yellow]{output.confidence_score:.1%}[/yellow]",
-        )
-        status = "[green]Yes[/green]" if output.is_sufficient else "[red]No[/red]"
-        log(deps, state, f"   Sufficient: {status}")
+            if output.key_insights:
+                log(deps, state, "Key insights:")
+                for insight in output.key_insights:
+                    log(deps, state, f"  • {insight}")
 
-        should_continue = (
-            not output.is_sufficient
-            or output.confidence_score < state.confidence_threshold
-        ) and state.iterations < state.max_iterations
+            if output.gaps:
+                log(deps, state, "Remaining gaps:")
+                for gap in output.gaps:
+                    log(deps, state, f"  • {gap}")
 
-        if not should_continue:
-            log(deps, state, "\n[bold green]✅ Stopping research.[/bold green]")
+            log(deps, state, f"Confidence: {output.confidence_score:.1%}")
+            status = "Yes" if output.is_sufficient else "No"
+            log(deps, state, f"Sufficient: {status}")
 
-        return should_continue
+            should_continue = (
+                not output.is_sufficient
+                or output.confidence_score < state.confidence_threshold
+            ) and state.iterations < state.max_iterations
+
+            return should_continue
+        finally:
+            if deps.agui_emitter:
+                deps.agui_emitter.finish_step()
 
     @g.step
     async def synthesize(
@@ -354,36 +345,37 @@ def build_research_graph(
         state = ctx.state
         deps = ctx.deps
 
-        log(
-            deps,
-            state,
-            "\n[bold cyan]📝 Generating final research report...[/bold cyan]",
-        )
+        if deps.agui_emitter:
+            deps.agui_emitter.start_step("synthesize")
+            deps.agui_emitter.update_activity(
+                "synthesizing", "Generating final research report"
+            )
 
-        agent = Agent(
-            model=get_model(provider, model),
-            output_type=ResearchReport,
-            instructions=SYNTHESIS_AGENT_PROMPT,
-            retries=3,
-            deps_type=ResearchDependencies,
-        )
+        try:
+            agent = Agent(
+                model=get_model(provider, model),
+                output_type=ResearchReport,
+                instructions=SYNTHESIS_AGENT_PROMPT,
+                retries=3,
+                output_retries=3,
+                deps_type=ResearchDependencies,
+            )
 
-        context_xml = format_context_for_prompt(state.context)
-        prompt = (
-            "Generate a comprehensive research report based on all gathered information.\n\n"
-            f"{context_xml}\n\n"
-            "Create a detailed report that synthesizes all findings into a coherent response."
-        )
-        agent_deps = ResearchDependencies(
-            client=deps.client,
-            context=state.context,
-            console=deps.console,
-            stream=deps.stream,
-        )
-        result = await agent.run(prompt, deps=agent_deps)
-
-        log(deps, state, "[bold green]✅ Research complete![/bold green]")
-        return result.output
+            context_xml = format_context_for_prompt(state.context)
+            prompt = (
+                "Generate a comprehensive research report based on all gathered information.\n\n"
+                f"{context_xml}\n\n"
+                "Create a detailed report that synthesizes all findings into a coherent response."
+            )
+            agent_deps = ResearchDependencies(
+                client=deps.client,
+                context=state.context,
+            )
+            result = await agent.run(prompt, deps=agent_deps)
+            return result.output
+        finally:
+            if deps.agui_emitter:
+                deps.agui_emitter.finish_step()
 
     # Build the graph structure
     collect_answers = g.join(

haiku_rag_slim/haiku/rag/research/state.py

@@ -3,29 +3,35 @@ from dataclasses import dataclass
 from typing import TYPE_CHECKING
 
 from pydantic import BaseModel, Field
-from rich.console import Console
 
 from haiku.rag.client import HaikuRAG
 from haiku.rag.research.dependencies import ResearchContext
-from haiku.rag.research.models import EvaluationResult, InsightAnalysis
-from haiku.rag.research.stream import ResearchStream
+from haiku.rag.research.models import EvaluationResult, InsightAnalysis, ResearchReport
 
 if TYPE_CHECKING:
+    from haiku.rag.agui.emitter import AGUIEmitter
     from haiku.rag.config.models import AppConfig
 
 
 @dataclass
 class ResearchDeps:
+    """Dependencies for research graph execution."""
+
     client: HaikuRAG
-    console: Console | None = None
-    stream: ResearchStream | None = None
+    agui_emitter: "AGUIEmitter[ResearchState, ResearchReport] | None" = None
     semaphore: asyncio.Semaphore | None = None
 
     def emit_log(self, message: str, state: "ResearchState | None" = None) -> None:
-        if self.console:
-            self.console.print(message)
-        if self.stream:
-            self.stream.log(message, state)
+        """Emit a log message through AG-UI events.
+
+        Args:
+            message: The message to log
+            state: Optional state to include in state update
+        """
+        if self.agui_emitter:
+            self.agui_emitter.log(message)
+            if state:
+                self.agui_emitter.update_state(state)
 
 
 class ResearchState(BaseModel):

haiku_rag_slim/haiku/rag/research/stream.py

@@ -1,162 +0,0 @@
-import asyncio
-from collections.abc import AsyncIterator
-from dataclasses import dataclass
-from typing import TYPE_CHECKING, Literal
-
-from haiku.rag.research.models import ResearchReport
-
-if TYPE_CHECKING:  # pragma: no cover
-    from haiku.rag.research.state import ResearchState
-
-
-@dataclass(slots=True)
-class ResearchStateSnapshot:
-    question: str
-    sub_questions: list[str]
-    iterations: int
-    max_iterations: int
-    confidence_threshold: float
-    pending_sub_questions: int
-    answered_questions: int
-    insights: list[str]
-    gaps: list[str]
-    last_confidence: float | None
-    last_sufficient: bool | None
-
-    @classmethod
-    def from_state(cls, state: "ResearchState") -> "ResearchStateSnapshot":
-        context = state.context
-        last_confidence: float | None = None
-        last_sufficient: bool | None = None
-        if state.last_eval:
-            last_confidence = state.last_eval.confidence_score
-            last_sufficient = state.last_eval.is_sufficient
-
-        return cls(
-            question=context.original_question,
-            sub_questions=list(context.sub_questions),
-            iterations=state.iterations,
-            max_iterations=state.max_iterations,
-            confidence_threshold=state.confidence_threshold,
-            pending_sub_questions=len(context.sub_questions),
-            answered_questions=len(context.qa_responses),
-            insights=[
-                f"{insight.status.value}:{insight.summary}"
-                for insight in context.insights
-            ],
-            gaps=[
-                f"{gap.severity.value}/{'resolved' if gap.resolved else 'open'}:{gap.description}"
-                for gap in context.gaps
-            ],
-            last_confidence=last_confidence,
-            last_sufficient=last_sufficient,
-        )
-
-
-@dataclass(slots=True)
-class ResearchStreamEvent:
-    type: Literal["log", "report", "error"]
-    message: str | None = None
-    state: ResearchStateSnapshot | None = None
-    report: ResearchReport | None = None
-    error: str | None = None
-
-
-class ResearchStream:
-    """Queue-backed stream for research graph events."""
-
-    def __init__(self) -> None:
-        self._queue: asyncio.Queue[ResearchStreamEvent | None] = asyncio.Queue()
-        self._closed = False
-
-    def _snapshot(self, state: "ResearchState | None") -> ResearchStateSnapshot | None:
-        if state is None:
-            return None
-        return ResearchStateSnapshot.from_state(state)
-
-    def log(self, message: str, state: "ResearchState | None" = None) -> None:
-        if self._closed:
-            return
-        event = ResearchStreamEvent(
-            type="log", message=message, state=self._snapshot(state)
-        )
-        self._queue.put_nowait(event)
-
-    def report(self, report: ResearchReport, state: "ResearchState") -> None:
-        if self._closed:
-            return
-        event = ResearchStreamEvent(
-            type="report",
-            report=report,
-            state=self._snapshot(state),
-        )
-        self._queue.put_nowait(event)
-
-    def error(self, error: Exception, state: "ResearchState | None" = None) -> None:
-        if self._closed:
-            return
-        event = ResearchStreamEvent(
-            type="error",
-            message=str(error),
-            error=str(error),
-            state=self._snapshot(state),
-        )
-        self._queue.put_nowait(event)
-
-    async def close(self) -> None:
-        if self._closed:
-            return
-        self._closed = True
-        await self._queue.put(None)
-
-    def __aiter__(self) -> AsyncIterator[ResearchStreamEvent]:
-        return self._iter_events()
-
-    async def _iter_events(self) -> AsyncIterator[ResearchStreamEvent]:
-        while True:
-            event = await self._queue.get()
-            if event is None:
-                break
-            yield event
-
-
-async def stream_research_graph(
-    graph,
-    state: "ResearchState",
-    deps,
-) -> AsyncIterator[ResearchStreamEvent]:
-    """Run the research graph and yield streaming events as they occur."""
-
-    from contextlib import suppress
-
-    from haiku.rag.research.state import ResearchDeps
-
-    if not isinstance(deps, ResearchDeps):
-        raise TypeError("deps must be an instance of ResearchDeps")
-
-    stream = ResearchStream()
-    deps.stream = stream
-
-    async def _execute() -> None:
-        try:
-            report = await graph.run(state=state, deps=deps)
-
-            if report is None:
-                raise RuntimeError("Graph did not produce a report")
-
-            stream.report(report, state)
-        except Exception as exc:
-            stream.error(exc, state)
-        finally:
-            await stream.close()
-
-    runner = asyncio.create_task(_execute())
-
-    try:
-        async for event in stream:
-            yield event
-    finally:
-        if not runner.done():
-            runner.cancel()
-        with suppress(asyncio.CancelledError):
-            await runner