fix(streaming): keep large file generation responsive (#4354)

* fix(streaming): keep large file generation responsive

* fix(streaming): address follow-up review feedback

* fix(streaming): address final review feedback

backend/AGENTS.md

@@ -14,6 +14,7 @@ DeerFlow is a LangGraph-based AI super agent system with a full-stack architectu
 
 **Runtime**:
 - `make dev`, Docker dev, and production all run the agent runtime in Gateway via `RunManager` + `run_agent()` + `StreamBridge` (`packages/harness/deerflow/runtime/`). Nginx exposes that runtime at `/api/langgraph/*` and rewrites it to Gateway's native `/api/*` routers.
+- Gateway streams `write_file` and `str_replace` argument deltas in bounded batches when clients also subscribe to `values`; messages-only consumers retain the original per-chunk contract, while `values` preserves the complete tool call.
 - Scheduled-task executions must reuse that same Gateway run lifecycle. The scheduler may decide *when* work runs, but it must dispatch through the existing run path rather than introducing a parallel execution stack.
 
 **Project Structure**:

backend/packages/harness/deerflow/runtime/runs/worker.py

@@ -20,6 +20,7 @@ import copy
 import inspect
 import logging
 import os
+import sys
 import threading
 import weakref
 from collections.abc import AsyncIterator
@@ -103,6 +104,126 @@ async def _checkpoint_thread_lock(thread_id: str) -> AsyncIterator[None]:
 
 # Valid stream_mode values for LangGraph's graph.astream()
 _VALID_LG_MODES = {"values", "updates", "checkpoints", "tasks", "debug", "messages", "custom"}
+# Keep this streaming policy separate from middleware write-authorization sets.
+_LARGE_FILE_TOOL_NAMES = frozenset({"str_replace", "write_file"})
+_LARGE_FILE_TOOL_BATCH_SIZE = 32
+
+
+@dataclass
+class _LargeFileToolChunkBatcher:
+    """Batch file-body argument deltas to avoid quadratic browser parsing.
+
+    Normal assistant text and non-file tool calls remain token-streamed. Large
+    file arguments still update progressively, but in bounded batches instead
+    of forcing the browser to reparse the growing JSON on every model token.
+    """
+
+    batch_size: int = _LARGE_FILE_TOOL_BATCH_SIZE
+    tool_names: dict[tuple[str, str, str], str] = field(default_factory=dict)
+    pending_identity: tuple[str, str, str] | None = None
+    pending_message: Any | None = None
+    pending_metadata: dict[str, Any] = field(default_factory=dict)
+    pending_count: int = 0
+
+    def push(self, chunk: Any) -> list[Any]:
+        if not isinstance(chunk, tuple) or len(chunk) != 2:
+            return [*self.flush(), chunk]
+
+        message, metadata = chunk
+        message_id = getattr(message, "id", None)
+        tool_call_chunks = getattr(message, "tool_call_chunks", None)
+        if not isinstance(message_id, str) or not message_id or not isinstance(tool_call_chunks, list) or len(tool_call_chunks) != 1:
+            return [*self.flush(), chunk]
+
+        tool_chunk = tool_call_chunks[0]
+        if not isinstance(tool_chunk, dict):
+            return [*self.flush(), chunk]
+        index = tool_chunk.get("index")
+        tool_call_id = tool_chunk.get("id")
+        if isinstance(index, int):
+            discriminator = f"index:{index}"
+        elif isinstance(tool_call_id, str) and tool_call_id:
+            discriminator = f"id:{tool_call_id}"
+        else:
+            discriminator = "single"
+        raw_namespace = None
+        if isinstance(metadata, dict):
+            raw_namespace = metadata.get("langgraph_checkpoint_ns") or metadata.get("checkpoint_ns")
+        namespace = raw_namespace if isinstance(raw_namespace, str) else ""
+        identity = (namespace, message_id, discriminator)
+        name_fragment = tool_chunk.get("name")
+        tool_name = self.tool_names.get(identity, "")
+        if tool_name not in _LARGE_FILE_TOOL_NAMES and isinstance(name_fragment, str) and name_fragment:
+            tool_name += name_fragment
+            if any(candidate.startswith(tool_name) for candidate in _LARGE_FILE_TOOL_NAMES):
+                self.tool_names[identity] = tool_name
+            else:
+                self.tool_names.pop(identity, None)
+        # Batching starts only after the accumulated name matches; split or
+        # incomplete name fragments stream per-chunk until then.
+        if tool_name not in _LARGE_FILE_TOOL_NAMES:
+            return [*self.flush(), chunk]
+
+        model_copy = getattr(message, "model_copy", None)
+        if not callable(model_copy):
+            return [*self.flush(), chunk]
+        additional_kwargs = getattr(message, "additional_kwargs", None)
+        sanitized_additional_kwargs = additional_kwargs
+        if isinstance(additional_kwargs, dict) and ("function_call" in additional_kwargs or "tool_calls" in additional_kwargs):
+            sanitized_additional_kwargs = {key: value for key, value in additional_kwargs.items() if key not in {"function_call", "tool_calls"}}
+        has_non_tool_payload = bool(getattr(message, "content", None) or sanitized_additional_kwargs or getattr(message, "usage_metadata", None) or getattr(message, "response_metadata", None))
+        outputs: list[Any] = []
+        if self.pending_identity is not None and self.pending_identity != identity:
+            outputs.extend(self.flush())
+        if has_non_tool_payload:
+            visible_message = model_copy(
+                update={
+                    "additional_kwargs": sanitized_additional_kwargs,
+                    "invalid_tool_calls": [],
+                    "tool_call_chunks": [],
+                    "tool_calls": [],
+                }
+            )
+            outputs.append((visible_message, metadata))
+
+        tool_only_message = model_copy(
+            update={
+                "additional_kwargs": {},
+                "content": "",
+                "invalid_tool_calls": [],
+                "response_metadata": {},
+                "tool_calls": [],
+                "usage_metadata": None,
+            }
+        )
+        self.pending_identity = identity
+        self.pending_message = tool_only_message if self.pending_message is None else self.pending_message + tool_only_message
+        if isinstance(metadata, dict):
+            self.pending_metadata.update(metadata)
+        self.pending_count += 1
+        if self.pending_count >= self.batch_size:
+            outputs.extend(self.flush())
+        return outputs
+
+    def flush(self) -> list[Any]:
+        if self.pending_message is None:
+            return []
+        chunk = (self.pending_message, self.pending_metadata)
+        self.pending_identity = None
+        self.pending_message = None
+        self.pending_metadata = {}
+        self.pending_count = 0
+        return [chunk]
+
+    def finish(self) -> list[Any]:
+        """Flush and release identities at a values or end-of-stream boundary.
+
+        A regular batch-size or interleaved-mode flush must retain identities
+        because continuation chunks commonly omit the tool name.
+        """
+        chunks = self.flush()
+        self.tool_names.clear()
+        return chunks
 
 
 def _build_runtime_context(
@@ -539,40 +660,58 @@ async def run_agent(
 
         async def _stream_once(input_payload: Any, stream_config: RunnableConfig) -> None:
             nonlocal llm_error_fallback_message
-            async with _checkpoint_thread_lock(thread_id):
-                if len(lg_modes) == 1 and not stream_subgraphs:
-                    # Single mode, no subgraphs: astream yields raw chunks
-                    single_mode = lg_modes[0]
-                    async for chunk in agent.astream(input_payload, config=stream_config, stream_mode=single_mode):
+            file_tool_chunk_batcher = _LargeFileToolChunkBatcher() if "values" in requested_modes else None
+            try:
+                async with _checkpoint_thread_lock(thread_id):
+                    if len(lg_modes) == 1 and not stream_subgraphs:
+                        # Single mode, no subgraphs: astream yields raw chunks
+                        single_mode = lg_modes[0]
+                        async for chunk in agent.astream(input_payload, config=stream_config, stream_mode=single_mode):
+                            if record.abort_event.is_set():
+                                logger.info("Run %s abort requested — stopping", run_id)
+                                break
+                            llm_error_fallback_message = llm_error_fallback_message or _extract_llm_error_fallback_message(chunk, pre_existing_message_ids)
+                            sse_event = _lg_mode_to_sse_event(single_mode)
+                            await bridge.publish(run_id, sse_event, serialize(chunk, mode=single_mode))
+                            if single_mode == "custom":
+                                await subagent_events.add(chunk)
+                        return
+                    # Multiple modes or subgraphs: astream yields tuples
+                    async for item in agent.astream(
+                        input_payload,
+                        config=stream_config,
+                        stream_mode=lg_modes,
+                        subgraphs=stream_subgraphs,
+                    ):
                         if record.abort_event.is_set():
                             logger.info("Run %s abort requested — stopping", run_id)
                             break
+
+                        mode, chunk = _unpack_stream_item(item, lg_modes, stream_subgraphs)
+                        if mode is None:
+                            continue
+
                         llm_error_fallback_message = llm_error_fallback_message or _extract_llm_error_fallback_message(chunk, pre_existing_message_ids)
-                        sse_event = _lg_mode_to_sse_event(single_mode)
-                        await bridge.publish(run_id, sse_event, serialize(chunk, mode=single_mode))
-                        if single_mode == "custom":
+                        sse_event = _lg_mode_to_sse_event(mode)
+                        if file_tool_chunk_batcher is not None and mode != "messages":
+                            pending_chunks = file_tool_chunk_batcher.finish() if mode == "values" else file_tool_chunk_batcher.flush()
+                            for publish_chunk in pending_chunks:
+                                await bridge.publish(run_id, "messages", serialize(publish_chunk, mode="messages"))
+                        chunks_to_publish = file_tool_chunk_batcher.push(chunk) if mode == "messages" and file_tool_chunk_batcher is not None else [chunk]
+                        for publish_chunk in chunks_to_publish:
+                            await bridge.publish(run_id, sse_event, serialize(publish_chunk, mode=mode))
+                        if mode == "custom":
                             await subagent_events.add(chunk)
-                    return
-                # Multiple modes or subgraphs: astream yields tuples
-                async for item in agent.astream(
-                    input_payload,
-                    config=stream_config,
-                    stream_mode=lg_modes,
-                    subgraphs=stream_subgraphs,
-                ):
-                    if record.abort_event.is_set():
-                        logger.info("Run %s abort requested — stopping", run_id)
-                        break
-
-                    mode, chunk = _unpack_stream_item(item, lg_modes, stream_subgraphs)
-                    if mode is None:
-                        continue
-
-                    llm_error_fallback_message = llm_error_fallback_message or _extract_llm_error_fallback_message(chunk, pre_existing_message_ids)
-                    sse_event = _lg_mode_to_sse_event(mode)
-                    await bridge.publish(run_id, sse_event, serialize(chunk, mode=mode))
-                    if mode == "custom":
-                        await subagent_events.add(chunk)
+            finally:
+                stream_error = sys.exception()
+                if file_tool_chunk_batcher is not None:
+                    try:
+                        for publish_chunk in file_tool_chunk_batcher.finish():
+                            await bridge.publish(run_id, "messages", serialize(publish_chunk, mode="messages"))
+                    except Exception:
+                        if stream_error is None:
+                            raise
+                        logger.debug("Could not flush pending file-tool chunks for run %s", run_id, exc_info=True)
 
         # 7. Stream the requested turn, then optionally continue hidden goal turns.
         # Clear any stale stop_reason before the first (user-visible) turn only.

backend/tests/test_run_worker_rollback.py

@@ -6,7 +6,7 @@ from typing import Annotated, Any, NotRequired, TypedDict
 from unittest.mock import AsyncMock, call, patch
 
 import pytest
-from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
+from langchain_core.messages import AIMessage, AIMessageChunk, AnyMessage, HumanMessage
 from langgraph.channels.delta import DeltaChannel
 from langgraph.checkpoint.base import empty_checkpoint
 from langgraph.checkpoint.memory import InMemorySaver
@@ -31,6 +31,7 @@ from deerflow.runtime.runs.worker import (
     _ensure_interrupted_title,
     _extract_llm_error_fallback_message,
     _install_runtime_context,
+    _LargeFileToolChunkBatcher,
     _rollback_to_pre_run_checkpoint,
     _try_extract_from_message,
     run_agent,
@@ -93,6 +94,197 @@ def _build_message_append_graph(state_schema: type, checkpointer: Any):
     return builder.compile(checkpointer=checkpointer)
 
 
+@pytest.mark.parametrize("tool_name", ["write_file", "str_replace"])
+def test_large_file_tool_chunk_batcher_streams_bounded_batches(tool_name: str):
+    batcher = _LargeFileToolChunkBatcher(batch_size=2)
+    first = AIMessageChunk(
+        content="",
+        id="ai-1",
+        tool_call_chunks=[
+            {
+                "id": "call-1",
+                "index": 0,
+                "name": tool_name,
+                "args": '{"path":"/mnt/user-data/outputs/report.md","content":"Hel',
+            }
+        ],
+    )
+    continuation = AIMessageChunk(
+        content="",
+        id="ai-1",
+        tool_call_chunks=[{"index": 0, "name": None, "args": 'lo"}'}],
+    )
+
+    assert batcher.push((first, {})) == []
+    published = batcher.push((continuation, {}))
+    assert len(published) == 1
+    message, metadata = published[0]
+    assert metadata == {}
+    assert message.tool_calls[0]["args"]["content"] == "Hello"
+    assert batcher.flush() == []
+
+
+def test_large_file_tool_chunk_batcher_preserves_visible_and_non_file_chunks():
+    batcher = _LargeFileToolChunkBatcher()
+    visible_text = AIMessageChunk(content="Writing the report now.", id="ai-1")
+    search_tool = AIMessageChunk(
+        content="",
+        id="ai-2",
+        tool_call_chunks=[
+            {
+                "id": "call-2",
+                "index": 0,
+                "name": "web_search",
+                "args": '{"query":"vector databases"}',
+            }
+        ],
+    )
+    write_with_reasoning = AIMessageChunk(
+        content="",
+        id="ai-3",
+        additional_kwargs={"reasoning_content": "Choosing a filename."},
+        tool_call_chunks=[
+            {
+                "id": "call-3",
+                "index": 0,
+                "name": "write_file",
+                "args": '{"path":"/mnt/user-data/outputs/report.md"}',
+            }
+        ],
+    )
+
+    assert batcher.push((visible_text, {})) == [(visible_text, {})]
+    assert batcher.push((search_tool, {})) == [(search_tool, {})]
+    visible_reasoning = batcher.push((write_with_reasoning, {}))
+    assert len(visible_reasoning) == 1
+    filtered_message, filtered_metadata = visible_reasoning[0]
+    assert filtered_metadata == {}
+    assert filtered_message.additional_kwargs == {"reasoning_content": "Choosing a filename."}
+    assert filtered_message.tool_call_chunks == []
+    pending_file_chunks = batcher.flush()
+    assert len(pending_file_chunks) == 1
+    assert pending_file_chunks[0][0].tool_call_chunks[0]["name"] == "write_file"
+
+
+def test_large_file_tool_chunk_batcher_separates_subgraph_namespaces():
+    batcher = _LargeFileToolChunkBatcher()
+    first = AIMessageChunk(
+        content="",
+        id="shared-ai-id",
+        tool_call_chunks=[{"id": "call-a", "index": 0, "name": "write_file", "args": '{"path":"a.md","content":"A'}],
+    )
+    second = AIMessageChunk(
+        content="",
+        id="shared-ai-id",
+        tool_call_chunks=[{"id": "call-b", "index": 0, "name": "write_file", "args": '{"path":"b.md","content":"B'}],
+    )
+
+    assert batcher.push((first, {"langgraph_checkpoint_ns": "task-a"})) == []
+    published = batcher.push((second, {"langgraph_checkpoint_ns": "task-b"}))
+
+    assert len(published) == 1
+    assert published[0][1]["langgraph_checkpoint_ns"] == "task-a"
+    assert batcher.flush()[0][1]["langgraph_checkpoint_ns"] == "task-b"
+
+
+@pytest.mark.parametrize("metadata", [None, "not-a-dict"])
+def test_large_file_tool_chunk_batcher_accepts_non_dict_metadata(metadata: Any):
+    batcher = _LargeFileToolChunkBatcher(batch_size=1)
+    message = AIMessageChunk(
+        content="",
+        id="ai-file",
+        tool_call_chunks=[
+            {
+                "id": "call-file",
+                "index": 0,
+                "name": "write_file",
+                "args": '{"path":"report.md","content":"draft"}',
+            }
+        ],
+    )
+
+    published = batcher.push((message, metadata))
+
+    assert len(published) == 1
+    assert published[0][0].tool_call_chunks[0]["args"] == '{"path":"report.md","content":"draft"}'
+    assert published[0][1] == {}
+
+
+def test_large_file_tool_chunk_batcher_does_not_retain_non_file_names():
+    batcher = _LargeFileToolChunkBatcher()
+
+    for index in range(100):
+        message = AIMessageChunk(
+            content="",
+            id=f"ai-{index}",
+            tool_call_chunks=[
+                {
+                    "id": f"call-{index}",
+                    "index": 0,
+                    "name": "web_search",
+                    "args": '{"query":"deerflow"}',
+                }
+            ],
+        )
+
+        assert batcher.push((message, {})) == [(message, {})]
+
+    assert batcher.tool_names == {}
+
+
+def test_large_file_tool_chunk_batcher_starts_batching_after_split_name_matches():
+    batcher = _LargeFileToolChunkBatcher(batch_size=1)
+    name_prefix = AIMessageChunk(
+        content="",
+        id="ai-file",
+        tool_call_chunks=[{"id": "call-file", "index": 0, "name": "write_", "args": ""}],
+    )
+    name_suffix = AIMessageChunk(
+        content="",
+        id="ai-file",
+        tool_call_chunks=[
+            {
+                "index": 0,
+                "name": "file",
+                "args": '{"path":"report.md","content":"draft"}',
+            }
+        ],
+    )
+
+    assert batcher.push((name_prefix, {})) == [(name_prefix, {})]
+    assert set(batcher.tool_names.values()) == {"write_"}
+    assert len(batcher.push((name_suffix, {}))) == 1
+    assert set(batcher.tool_names.values()) == {"write_file"}
+
+
+def test_large_file_tool_chunk_batcher_keeps_identity_across_batches_then_releases_it():
+    batcher = _LargeFileToolChunkBatcher(batch_size=1)
+    first = AIMessageChunk(
+        content="",
+        id="ai-file",
+        tool_call_chunks=[
+            {
+                "id": "call-file",
+                "index": 0,
+                "name": "write_file",
+                "args": '{"path":"report.md","content":"Hel',
+            }
+        ],
+    )
+    continuation = AIMessageChunk(
+        content="",
+        id="ai-file",
+        tool_call_chunks=[{"index": 0, "name": None, "args": 'lo"}'}],
+    )
+
+    assert len(batcher.push((first, {}))) == 1
+    assert len(batcher.push((continuation, {}))) == 1
+    assert set(batcher.tool_names.values()) == {"write_file"}
+
+    assert batcher.finish() == []
+    assert batcher.tool_names == {}
+
+
 def test_build_runtime_context_includes_app_config_when_present():
     app_config = object()
 
@@ -136,6 +328,200 @@ def test_install_runtime_context_overrides_internal_pre_existing_message_ids():
     assert config["context"][CURRENT_RUN_PRE_EXISTING_MESSAGE_IDS_KEY] == frozenset({"old-ai"})
 
 
+@pytest.mark.anyio
+async def test_run_agent_batches_incremental_file_args_and_keeps_complete_values():
+    run_manager = RunManager()
+    record = await run_manager.create("thread-file-stream")
+    bridge = SimpleNamespace(
+        publish=AsyncMock(),
+        publish_end=AsyncMock(),
+        cleanup=AsyncMock(),
+    )
+    complete_message = AIMessage(
+        content="",
+        id="ai-file",
+        tool_calls=[
+            {
+                "id": "call-file",
+                "name": "write_file",
+                "args": {
+                    "path": "/mnt/user-data/outputs/report.md",
+                    "content": "Hello world",
+                },
+                "type": "tool_call",
+            }
+        ],
+    )
+
+    class DummyAgent:
+        async def astream(self, graph_input, config=None, stream_mode=None, subgraphs=False):
+            del graph_input, config, stream_mode, subgraphs
+            yield (
+                "messages",
+                (
+                    AIMessageChunk(
+                        content="",
+                        id="ai-file",
+                        tool_call_chunks=[
+                            {
+                                "id": "call-file",
+                                "index": 0,
+                                "name": "write_file",
+                                "args": '{"path":"/mnt/user-data/outputs/report.md","content":"Hello',
+                            }
+                        ],
+                    ),
+                    {},
+                ),
+            )
+            yield (
+                "messages",
+                (
+                    AIMessageChunk(
+                        content="",
+                        id="ai-file",
+                        tool_call_chunks=[{"index": 0, "name": None, "args": ' world"}'}],
+                    ),
+                    {},
+                ),
+            )
+            yield ("values", {"messages": [complete_message]})
+
+    await run_agent(
+        bridge,
+        run_manager,
+        record,
+        ctx=RunContext(checkpointer=None),
+        agent_factory=lambda **_kwargs: DummyAgent(),
+        graph_input={},
+        config={},
+        stream_modes=["messages-tuple", "values"],
+    )
+
+    message_events = [call.args for call in bridge.publish.await_args_list if call.args[1] == "messages"]
+    assert len(message_events) == 1
+    assert message_events[0][2][0]["tool_calls"][0]["args"]["content"] == "Hello world"
+    values_events = [call.args[2] for call in bridge.publish.await_args_list if call.args[1] == "values"]
+    assert any(event["messages"][0]["tool_calls"][0]["args"]["content"] == "Hello world" for event in values_events)
+
+
+@pytest.mark.parametrize(
+    ("stream_error", "flush_publish_error", "expected_error"),
+    [
+        (True, False, "stream failed"),
+        (True, True, "stream failed"),
+        (False, True, "flush publish failed"),
+    ],
+)
+@pytest.mark.anyio
+async def test_run_agent_handles_pending_file_args_when_stream_or_flush_raises(stream_error: bool, flush_publish_error: bool, expected_error: str):
+    run_manager = RunManager()
+    record = await run_manager.create("thread-file-stream-error")
+
+    async def publish(_run_id: str, event: str, _data: Any):
+        if flush_publish_error and event == "messages":
+            raise RuntimeError("flush publish failed")
+
+    bridge = SimpleNamespace(
+        publish=AsyncMock(side_effect=publish),
+        publish_end=AsyncMock(),
+        cleanup=AsyncMock(),
+    )
+
+    class DummyAgent:
+        async def astream(self, graph_input, config=None, stream_mode=None, subgraphs=False):
+            del graph_input, config, stream_mode, subgraphs
+            yield (
+                "messages",
+                (
+                    AIMessageChunk(
+                        content="",
+                        id="ai-file",
+                        tool_call_chunks=[
+                            {
+                                "id": "call-file",
+                                "index": 0,
+                                "name": "write_file",
+                                "args": '{"path":"report.md","content":"partial',
+                            }
+                        ],
+                    ),
+                    {},
+                ),
+            )
+            if stream_error:
+                raise RuntimeError("stream failed")
+
+    await run_agent(
+        bridge,
+        run_manager,
+        record,
+        ctx=RunContext(checkpointer=None),
+        agent_factory=lambda **_kwargs: DummyAgent(),
+        graph_input={},
+        config={},
+        stream_modes=["messages-tuple", "values"],
+    )
+
+    message_events = [call.args for call in bridge.publish.await_args_list if call.args[1] == "messages"]
+    assert len(message_events) == 1
+    assert message_events[0][2][0]["tool_call_chunks"][0]["args"].endswith('"content":"partial')
+    error_events = [call.args for call in bridge.publish.await_args_list if call.args[1] == "error"]
+    assert error_events[0][2]["message"] == expected_error
+
+
+@pytest.mark.anyio
+async def test_run_agent_keeps_file_chunks_unbatched_without_values_mode():
+    run_manager = RunManager()
+    record = await run_manager.create("thread-file-messages-only")
+    bridge = SimpleNamespace(
+        publish=AsyncMock(),
+        publish_end=AsyncMock(),
+        cleanup=AsyncMock(),
+    )
+    chunks = [
+        AIMessageChunk(
+            content="",
+            id="ai-file",
+            tool_call_chunks=[
+                {
+                    "id": "call-file",
+                    "index": 0,
+                    "name": "write_file",
+                    "args": '{"path":"report.md","content":"Hel',
+                }
+            ],
+        ),
+        AIMessageChunk(
+            content="",
+            id="ai-file",
+            tool_call_chunks=[{"index": 0, "name": None, "args": 'lo"}'}],
+        ),
+    ]
+
+    class DummyAgent:
+        async def astream(self, graph_input, config=None, stream_mode=None, subgraphs=False):
+            del graph_input, config, stream_mode, subgraphs
+            for chunk in chunks:
+                yield (chunk, {})
+
+    await run_agent(
+        bridge,
+        run_manager,
+        record,
+        ctx=RunContext(checkpointer=None),
+        agent_factory=lambda **_kwargs: DummyAgent(),
+        graph_input={},
+        config={},
+        stream_modes=["messages-tuple"],
+    )
+
+    message_events = [call.args[2] for call in bridge.publish.await_args_list if call.args[1] == "messages"]
+    assert len(message_events) == 2
+    assert message_events[0][0]["tool_call_chunks"][0]["args"].endswith('"content":"Hel')
+    assert message_events[1][0]["tool_call_chunks"][0]["args"] == 'lo"}'
+
+
 @pytest.mark.anyio
 async def test_run_agent_threads_explicit_app_config_into_config_only_factory():
     run_manager = RunManager()

frontend/AGENTS.md

@@ -65,6 +65,7 @@ The frontend is a stateful chat application. Users create **threads** (conversat
 
 1. Optional composer helpers such as `core/input-polish` can rewrite the local draft before submission, and `core/voice-input` can transcribe browser microphone input into that same local draft; confirmed user input then flows to thread hooks (`core/threads/hooks.ts`) → LangGraph SDK streaming
 2. Stream events update thread state (messages, artifacts, todos, goal)
+   File-tool artifact auto-open work must run in an effect with timer cleanup; never schedule timers while rendering streamed `write_file` or `str_replace` updates.
 3. `useThreadHistory` loads persisted conversation pages from `GET /api/threads/{id}/messages/page`, preserving the backend's thread-global event `seq`; rendering overlays checkpoint/live copies at their matching canonical identities (a summarized checkpoint may contain a protected early input plus a recent tail), suppresses checkpoint/transient prefixes whose canonical position is still behind an unloaded cursor page instead of collapsing that unknown gap before a recent anchor, then adds optimistic messages without timestamp re-sorting. History invalidation preserves already-loaded pages so their established ordering positions are not discarded.
 4. Stop actions call the LangGraph SDK stream stop path; `core/threads/hooks.ts` invalidates current-thread, thread-history, token-usage, and sidebar/search caches immediately and schedules one follow-up refetch because SDK stop may finish via abort + fire-and-forget cancel before backend title finalization commits
 5. TanStack Query manages server state; localStorage stores user settings

frontend/src/components/workspace/messages/message-group.tsx

@@ -15,7 +15,7 @@ import {
   SquareTerminalIcon,
   WrenchIcon,
 } from "lucide-react";
-import { memo, useMemo, useState } from "react";
+import { memo, useEffect, useMemo, useState } from "react";
 
 import {
   ChainOfThought,
@@ -26,7 +26,10 @@ import {
 } from "@/components/ai-elements/chain-of-thought";
 import { CodeBlock } from "@/components/ai-elements/code-block";
 import { Button } from "@/components/ui/button";
-import { resolveArtifactURL } from "@/core/artifacts/utils";
+import {
+  buildWriteFileArtifactURL,
+  resolveArtifactURL,
+} from "@/core/artifacts/utils";
 import { useI18n } from "@/core/i18n/hooks";
 import { formatTokenCount } from "@/core/messages/usage";
 import type { TokenDebugStep } from "@/core/messages/usage-model";
@@ -554,6 +557,40 @@ function ToolCall({
     ) : (
       fallback
     );
+  const writeFilePath =
+    (name === "write_file" || name === "str_replace") &&
+    typeof args.path === "string"
+      ? args.path
+      : undefined;
+  const writeFileArtifactUrl = writeFilePath
+    ? buildWriteFileArtifactURL({
+        filepath: writeFilePath,
+        messageId,
+        toolCallId: id,
+      })
+    : null;
+  const autoOpenArtifactUrl =
+    isLoading &&
+    isLast &&
+    autoOpen &&
+    autoSelect &&
+    writeFileArtifactUrl &&
+    !result
+      ? writeFileArtifactUrl
+      : null;
+
+  useEffect(() => {
+    if (!autoOpenArtifactUrl || selectedArtifact === autoOpenArtifactUrl) {
+      return;
+    }
+
+    const timeout = window.setTimeout(() => {
+      select(autoOpenArtifactUrl, true);
+      setOpen(true);
+    }, 100);
+
+    return () => window.clearTimeout(timeout);
+  }, [autoOpenArtifactUrl, select, selectedArtifact, setOpen]);
 
   if (name.startsWith("browser_")) {
     const shot = browserView?.screenshot;
@@ -749,38 +786,24 @@ function ToolCall({
     if (!description) {
       description = t.toolCalls.writeFile;
     }
-    const path: string | undefined = (args as { path: string })?.path;
-    if (isLoading && isLast && autoOpen && autoSelect && path && !result) {
-      setTimeout(() => {
-        const url = new URL(
-          `write-file:${path}?message_id=${messageId}&tool_call_id=${id}`,
-        ).toString();
-        if (selectedArtifact === url) {
-          return;
-        }
-        select(url, true);
-        setOpen(true);
-      }, 100);
-    }
 
     return (
       <ChainOfThoughtStep
         key={id}
-        className="cursor-pointer"
+        className={writeFileArtifactUrl ? "cursor-pointer" : undefined}
         label={resolveLabel(description)}
         icon={NotebookPenIcon}
         onClick={() => {
-          select(
-            new URL(
-              `write-file:${path}?message_id=${messageId}&tool_call_id=${id}`,
-            ).toString(),
-          );
+          if (!writeFileArtifactUrl) {
+            return;
+          }
+          select(writeFileArtifactUrl);
           setOpen(true);
         }}
       >
-        {path && (
+        {writeFilePath && (
           <ChainOfThoughtSearchResult className="cursor-pointer">
-            {path}
+            {writeFilePath}
           </ChainOfThoughtSearchResult>
         )}
       </ChainOfThoughtStep>

frontend/src/core/artifacts/utils.ts

@@ -27,6 +27,26 @@ function encodeArtifactPath(filepath: string) {
     .join("/");
 }
 
+export function buildWriteFileArtifactURL({
+  filepath,
+  messageId,
+  toolCallId,
+}: {
+  filepath: string;
+  messageId?: string;
+  toolCallId?: string;
+}) {
+  const url = new URL("write-file:/");
+  url.pathname = filepath.replaceAll("%", "%25");
+  if (messageId) {
+    url.searchParams.set("message_id", messageId);
+  }
+  if (toolCallId) {
+    url.searchParams.set("tool_call_id", toolCallId);
+  }
+  return url.toString();
+}
+
 function decodeRelativeArtifactPath(filepath: string) {
   return filepath.split("/").map(decodePathSegment).join("/");
 }

frontend/tests/e2e/artifact-batched-stream.spec.ts

@@ -0,0 +1,234 @@
+import { createServer } from "node:http";
+import type { AddressInfo } from "node:net";
+
+import { expect, test } from "@playwright/test";
+
+import { mockLangGraphAPI } from "./utils/mock-api";
+
+const THREAD_ID = "00000000-0000-0000-0000-000000004354";
+const RUN_ID = "00000000-0000-0000-0000-000000004355";
+const MISSING_PATH_THREAD_ID = "00000000-0000-0000-0000-000000004356";
+const ARTIFACT_PATH = "/artifact-fixtures/batched-report.md";
+
+const INITIAL_MESSAGES = [
+  {
+    type: "human",
+    id: "msg-human-batched-artifact",
+    content: [{ type: "text", text: "Create a batched markdown report" }],
+  },
+];
+
+function batchedWriteFileStreamFrames() {
+  const chunks = [
+    {
+      content: "",
+      additional_kwargs: {},
+      response_metadata: {},
+      type: "AIMessageChunk",
+      name: null,
+      id: "msg-ai-batched-artifact",
+      tool_calls: [
+        {
+          name: "write_file",
+          args: { path: ARTIFACT_PATH, content: "Hello " },
+          id: "call-batched-artifact",
+          type: "tool_call",
+        },
+      ],
+      invalid_tool_calls: [],
+      usage_metadata: null,
+      tool_call_chunks: [
+        {
+          name: "write_file",
+          args: `{"path":"${ARTIFACT_PATH}","content":"Hello `,
+          id: "call-batched-artifact",
+          index: 0,
+          type: "tool_call_chunk",
+        },
+      ],
+      chunk_position: null,
+    },
+    {
+      content: "",
+      additional_kwargs: {},
+      response_metadata: {},
+      type: "AIMessageChunk",
+      name: null,
+      id: "msg-ai-batched-artifact",
+      tool_calls: [],
+      invalid_tool_calls: [
+        {
+          name: null,
+          args: 'world"}',
+          id: null,
+          error: null,
+          type: "invalid_tool_call",
+        },
+      ],
+      usage_metadata: null,
+      tool_call_chunks: [
+        {
+          name: null,
+          args: 'world"}',
+          id: null,
+          index: 0,
+          type: "tool_call_chunk",
+        },
+      ],
+      chunk_position: null,
+    },
+  ];
+  const events = [
+    {
+      event: "metadata",
+      data: { run_id: RUN_ID, thread_id: THREAD_ID },
+    },
+    {
+      event: "values",
+      data: {
+        messages: [
+          ...INITIAL_MESSAGES,
+          {
+            type: "human",
+            id: "msg-human-batched-artifact-follow-up",
+            content: [{ type: "text", text: "Continue the report" }],
+          },
+        ],
+      },
+    },
+    ...chunks.map((chunk) => ({ event: "messages", data: [chunk, {}] })),
+  ];
+
+  return events.map(
+    (event) => `event: ${event.event}\ndata: ${JSON.stringify(event.data)}\n\n`,
+  );
+}
+
+async function startBatchedWriteFileStreamServer() {
+  const frames = batchedWriteFileStreamFrames();
+  const server = createServer((_request, response) => {
+    response.writeHead(200, {
+      "Access-Control-Allow-Origin": "*",
+      "Cache-Control": "no-cache",
+      "Content-Type": "text/event-stream",
+    });
+    response.write(frames.slice(0, 3).join(""));
+
+    const nextBatch = setTimeout(() => {
+      response.write(frames[3]);
+    }, 300);
+    const finishStream = setTimeout(() => {
+      response.end();
+    }, 2_000);
+    response.once("close", () => {
+      clearTimeout(nextBatch);
+      clearTimeout(finishStream);
+    });
+  });
+
+  await new Promise<void>((resolve, reject) => {
+    const handleError = (error: Error) => reject(error);
+    server.once("error", handleError);
+    server.listen(0, "127.0.0.1", () => {
+      server.off("error", handleError);
+      resolve();
+    });
+  });
+
+  const { port } = server.address() as AddressInfo;
+  return {
+    url: `http://127.0.0.1:${port}/runs/stream`,
+    async close() {
+      server.closeAllConnections();
+      await new Promise<void>((resolve, reject) => {
+        server.close((error) => (error ? reject(error) : resolve()));
+      });
+    },
+  };
+}
+
+test("assembles streamed write-file argument deltas in the artifact preview", async ({
+  page,
+}) => {
+  let streamStarted = false;
+  let releasePostStreamHistory!: () => void;
+  const postStreamHistoryReleased = new Promise<void>((resolve) => {
+    releasePostStreamHistory = resolve;
+  });
+  const streamServer = await startBatchedWriteFileStreamServer();
+  mockLangGraphAPI(page, {
+    threads: [
+      {
+        thread_id: THREAD_ID,
+        title: "Batched artifact streaming",
+        messages: INITIAL_MESSAGES,
+      },
+    ],
+  });
+  await page.route("**/api/langgraph/threads/*/history", async (route) => {
+    if (streamStarted) {
+      await postStreamHistoryReleased;
+    }
+    return route.fallback();
+  });
+  await page.route("**/api/langgraph/threads/*/runs/stream", (route) => {
+    streamStarted = true;
+    return route.continue({ url: streamServer.url });
+  });
+
+  try {
+    await page.goto(`/workspace/chats/${THREAD_ID}`);
+
+    const textarea = page.getByPlaceholder(/how can i assist you/i);
+    await expect(textarea).toBeVisible({ timeout: 15_000 });
+    await textarea.fill("Continue the report");
+    await textarea.press("Enter");
+
+    await expect(page.getByText(ARTIFACT_PATH)).toBeVisible({
+      timeout: 10_000,
+    });
+
+    const artifactsPanel = page.locator("#artifacts");
+    await expect(artifactsPanel).toBeVisible();
+    await expect(artifactsPanel.getByText("batched-report.md")).toBeVisible();
+    await expect(artifactsPanel.getByText("Hello world")).toBeVisible();
+  } finally {
+    releasePostStreamHistory();
+    await streamServer.close();
+  }
+});
+
+test("does not open an artifact for a file tool call without a path", async ({
+  page,
+}) => {
+  mockLangGraphAPI(page, {
+    threads: [
+      {
+        thread_id: MISSING_PATH_THREAD_ID,
+        title: "File tool without a path",
+        messages: [
+          ...INITIAL_MESSAGES,
+          {
+            type: "ai",
+            id: "msg-ai-missing-path",
+            content: "",
+            tool_calls: [
+              {
+                id: "call-missing-path",
+                name: "write_file",
+                args: { description: "Write file" },
+              },
+            ],
+          },
+        ],
+      },
+    ],
+  });
+
+  await page.goto(`/workspace/chats/${MISSING_PATH_THREAD_ID}`);
+
+  const writeFileStep = page.getByText("Write file", { exact: true });
+  await expect(writeFileStep).toBeVisible({ timeout: 15_000 });
+  await writeFileStep.click();
+  await expect(page.locator("#artifacts")).toBeHidden();
+});

frontend/tests/unit/components/workspace/messages/message-group.test.ts

@@ -1,25 +1,36 @@
 import type { Message } from "@langchain/langgraph-sdk";
-import { describe, expect, it, rs } from "@rstest/core";
+import { afterEach, describe, expect, it, rs } from "@rstest/core";
 import { createElement, type ComponentProps } from "react";
 import { renderToStaticMarkup } from "react-dom/server";
 
 import { MessageGroup } from "@/components/workspace/messages/message-group";
 import { I18nContext } from "@/core/i18n/context";
 
+const artifactsMockState = rs.hoisted(() => ({
+  autoOpen: false,
+  autoSelect: false,
+}));
+
 rs.mock("@/components/workspace/artifacts", () => ({
   useArtifacts: () => ({
     artifacts: [],
     setArtifacts: () => undefined,
     selectedArtifact: null,
-    autoSelect: false,
+    autoSelect: artifactsMockState.autoSelect,
     select: () => undefined,
     deselect: () => undefined,
     open: false,
-    autoOpen: false,
+    autoOpen: artifactsMockState.autoOpen,
     setOpen: () => undefined,
   }),
 }));
 
+afterEach(() => {
+  artifactsMockState.autoOpen = false;
+  artifactsMockState.autoSelect = false;
+  rs.restoreAllMocks();
+});
+
 describe("MessageGroup", () => {
   it("renders assistant text attached to a tool-calling processing message", () => {
     const html = renderGroup([
@@ -92,6 +103,35 @@ describe("MessageGroup", () => {
     expect(html).toContain("1 more step");
   });
 
+  it("does not schedule artifact auto-open during render", () => {
+    artifactsMockState.autoOpen = true;
+    artifactsMockState.autoSelect = true;
+    const timeoutSpy = rs.spyOn(globalThis, "setTimeout");
+    const html = renderGroup(
+      [
+        {
+          id: "ai-write",
+          type: "ai",
+          content: "",
+          tool_calls: [
+            {
+              id: "call-write",
+              name: "write_file",
+              args: {
+                path: "/mnt/user-data/outputs/report.md",
+                content: "# Report",
+              },
+            },
+          ],
+        } as Message,
+      ],
+      { isLoading: true },
+    );
+
+    expect(html).toContain("/mnt/user-data/outputs/report.md");
+    expect(timeoutSpy).not.toHaveBeenCalled();
+  });
+
   it("keeps tool-calling assistant text visible when reasoning is also present", () => {
     const html = renderGroup([
       {

frontend/tests/unit/core/artifacts/utils.test.ts

@@ -223,4 +223,33 @@ describe("artifact URL helpers", () => {
       ]),
     ).toBe("https://example.com/image.png");
   });
+
+  test("builds encoded write-file URLs without undefined query parameters", async () => {
+    const { buildWriteFileArtifactURL } = await loadFreshArtifactUtils();
+    const filepath = "/mnt/user-data/outputs/a b#c?%20.md";
+    expect(
+      buildWriteFileArtifactURL({
+        filepath: "/mnt/user-data/outputs/report.md",
+        messageId: "ai-1",
+        toolCallId: "call-1",
+      }),
+    ).toBe(
+      "write-file:/mnt/user-data/outputs/report.md?message_id=ai-1&tool_call_id=call-1",
+    );
+
+    const withIds = new URL(
+      buildWriteFileArtifactURL({
+        filepath,
+        messageId: "message #1",
+        toolCallId: "call ?1",
+      }),
+    );
+    expect(decodeURIComponent(withIds.pathname)).toBe(filepath);
+    expect(withIds.searchParams.get("message_id")).toBe("message #1");
+    expect(withIds.searchParams.get("tool_call_id")).toBe("call ?1");
+
+    const withoutIds = buildWriteFileArtifactURL({ filepath });
+    expect(withoutIds).not.toContain("undefined");
+    expect(new URL(withoutIds).search).toBe("");
+  });
 });