Feature/delta team/epic r04 (#7)
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## Summary

epic r04 - begin refactor

## Change Type

- [x] Cowork feature
- [ ] Bug fix
- [ ] Core AI contribution
- [ ] Test / hardening
- [ ] Performance
- [ ] Documentation

## Related Work

Cowork Task:

Core Repo: http://34.143.229.138/gitea-admin/fsg-ai-core-assets

Core AI Issue:

Core Task:

Related PR:

## Scope

What is intentionally included?

What is intentionally NOT included?

## Validation

- [ ] Unit tests
- [ ] Integration tests
- [ ] Manual verification
- [ ] Regression check

Commands / evidence:

## Security Impact

Permission / credential / network / customer data impact:

## Compatibility

- [ ] No breaking change
- [ ] Breaking change documented

## Reviewer Notes

Anything Cowork reviewers should pay attention to.

---------

Co-authored-by: Anh Tran Nguyen Minh <anhtnm1@fpt.com>
Co-authored-by: Huong Le Thi Thien <huongltt35@fpt.com>
Co-authored-by: Nam Pham Dinh Thanh <nampdt@fpt.com>
Co-authored-by: Vu Dam Tuan <vudt15@fpt.com>
Co-authored-by: Hiep Ha Van <hiephv3@fpt.com>
Co-authored-by: Lam Hoang Van <lamhv7@fpt.com>
Reviewed-on: #7
Co-authored-by: Duy Le Huu <duylh19@fpt.com>
This commit was merged in pull request #7.
This commit is contained in:
2026-08-31 05:15:13 +00:00
committed by gitea-admin
co-authored by anhtnm1 huongltt35 Nam Pham Dinh Thanh vudt15 Hiep Ha Van lamhv7
parent 86c27e2e79
commit f9f6bc01fd
496 changed files with 68421 additions and 19688 deletions
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"""Domain agents package: turn requests, agent events, and role definitions."""
from .agent_event import (
NOTICE_INFO,
NOTICE_PROGRESS,
NOTICE_WARNING,
AgentEvent,
AssistantMessageCompletedEvent,
ErrorEvent,
HistoryReadyEvent,
NoticeEvent,
OutputsAddedEvent,
OutputsRemovedEvent,
PlanStep,
PlanUpdatedEvent,
ReasoningChunkEvent,
TextChunkEvent,
ToolCallFinishedEvent,
ToolCallStartedEvent,
ToolOutputChunkEvent,
ToolPreview,
TurnCompletedEvent,
)
from .conversation_execution_request import (
PREFIX_SEPARATOR,
ConversationExecutionRequest,
)
__all__ = [
"NOTICE_INFO",
"NOTICE_PROGRESS",
"NOTICE_WARNING",
"PREFIX_SEPARATOR",
"AgentEvent",
"AssistantMessageCompletedEvent",
"ConversationExecutionRequest",
"ErrorEvent",
"HistoryReadyEvent",
"NoticeEvent",
"OutputsAddedEvent",
"OutputsRemovedEvent",
"PlanStep",
"PlanUpdatedEvent",
"ReasoningChunkEvent",
"TextChunkEvent",
"ToolCallFinishedEvent",
"ToolCallStartedEvent",
"ToolOutputChunkEvent",
"ToolPreview",
"TurnCompletedEvent",
]
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"""Typed events a turn emits while it runs (R04-T02).
The runtime currently speaks in bare dicts: ``emit({"type": "tool_result", "id":
..., "ok": ...})``. Nothing declares which keys a given type carries, so the
only specification is the 130-line ``if/elif`` chain in
``ui/chat_panel.py::_on_event`` — and a typo in an emitter surfaces as a widget
that silently renders nothing.
This module makes the vocabulary explicit. Each event is a frozen dataclass with
real fields, and each one knows how to serialise itself back to the exact legacy
dict the widget already reads (:meth:`AgentEvent.to_legacy_dict`), with
:func:`from_legacy_dict` parsing the other way. That two-way bridge is what lets
R04 introduce typed events WITHOUT touching the presentation layer — decomposing
``_on_event`` into a renderer is R08-T01's job, and forcing both changes into one
PR is exactly the "rewrite everything at once" the refactor plan forbids.
Scope note: this covers the interactive/scheduled **Cowork turn** vocabulary
(the ``run_cowork`` path R04 unifies). Co4E's own node events (``node_status``,
``stage_text``, ``run_done``) belong to ``Co4EWorkflowService`` in R07-T06 and
are deliberately left as dicts here — :func:`from_legacy_dict` returns ``None``
for them so a bridge can pass them straight through.
Layer rules (``docs/architecture/ADR-001-layered-architecture.md``): domain
layer, standard library only. No PySide6, no ``core/*`` imports.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, ClassVar, Dict, Iterable, Optional, Tuple
# Notice levels. "progress" is special-cased by the UI (it retargets the live
# thinking indicator instead of adding a bubble), so the vocabulary is pinned
# here rather than left to each emitter's string literal.
NOTICE_INFO = "info"
NOTICE_WARNING = "warning"
NOTICE_PROGRESS = "progress"
# --------------------------------------------------------------------------- #
# Value objects shared by several events.
# --------------------------------------------------------------------------- #
@dataclass(frozen=True)
class ToolPreview:
"""The human-readable preview of a proposed tool call.
Mirrors ``core/tools.py::describe_action``'s return shape exactly (three
string keys, nothing else), so wrapping it in a type is lossless. ``kind``
drives which bubble the UI renders: "diff" -> coloured before/after,
"command" -> terminal block, "info" -> plain text.
"""
kind: str = "info"
title: str = ""
text: str = ""
def to_dict(self) -> Dict[str, str]:
"""Ba khoá đúng như ``core/tools.py::describe_action`` trả về — không thêm không bớt."""
return {"kind": self.kind, "title": self.title, "text": self.text}
@classmethod
def from_dict(cls, raw: Any) -> Optional["ToolPreview"]:
"""Parse a legacy preview dict; ``None`` when there was none.
A non-dict value degrades to ``None`` rather than raising: a malformed
preview must cost the user a nicer bubble, never the whole turn.
"""
if not isinstance(raw, dict) or not raw:
return None
return cls(kind=str(raw.get("kind", "info")), title=str(raw.get("title", "")),
text=str(raw.get("text", "")))
@dataclass(frozen=True)
class PlanStep:
"""One entry of the agent's ``update_plan`` checklist.
``status`` is kept a plain string on purpose: ``core/plan.py`` already owns
validation (clamping anything unknown to "pending" against
pending/running/done/error), and duplicating that vocabulary here would give
the app two sources of truth to drift apart.
"""
title: str
status: str = "pending"
def to_dict(self) -> Dict[str, str]:
"""Một bước kế hoạch dưới dạng dict, để đưa vào payload sự kiện."""
return {"title": self.title, "status": self.status}
def _as_str_tuple(values: Iterable[Any]) -> Tuple[str, ...]:
"""Freeze an iterable of paths into a tuple of strings.
Emitters hand us live lists (``record["outputs"]``, ``_cleanup``'s result);
copying decouples the event from later mutation of that list.
"""
return tuple(str(v) for v in (values or ()))
# --------------------------------------------------------------------------- #
# Base class.
# --------------------------------------------------------------------------- #
class AgentEvent:
"""Base for every turn event.
Not a dataclass itself (it holds no data) — subclasses are the frozen
dataclasses. ``EVENT_TYPE`` is the legacy wire name, which stays the single
identifier shared between the typed world and the dict world.
"""
EVENT_TYPE: ClassVar[str] = ""
def _payload(self) -> Dict[str, Any]:
"""Type-specific keys of the legacy dict (without ``type``)."""
return {}
def to_legacy_dict(self) -> Dict[str, Any]:
"""The exact dict shape ``ui/chat_panel.py::_on_event`` dispatches on."""
return {"type": self.EVENT_TYPE, **self._payload()}
# --------------------------------------------------------------------------- #
# Streaming events.
# --------------------------------------------------------------------------- #
@dataclass(frozen=True)
class TextChunkEvent(AgentEvent):
"""A fragment of the assistant's visible answer."""
EVENT_TYPE: ClassVar[str] = "text"
delta: str = ""
def _payload(self) -> Dict[str, Any]:
"""``{delta}`` — một mẩu câu trả lời đang phát dần."""
return {"delta": self.delta}
@dataclass(frozen=True)
class ReasoningChunkEvent(AgentEvent):
"""A fragment of a reasoning model's thinking, shown in a collapsed box."""
EVENT_TYPE: ClassVar[str] = "reasoning"
delta: str = ""
def _payload(self) -> Dict[str, Any]:
"""``{delta}`` — một mẩu suy luận nội bộ của model."""
return {"delta": self.delta}
@dataclass(frozen=True)
class AssistantMessageCompletedEvent(AgentEvent):
"""One assistant message finished streaming.
Emitted once per provider call, so a tool-using turn produces SEVERAL of
these — it marks an autosave point, not the end of the turn. The end of the
turn is :class:`TurnCompletedEvent`.
"""
EVENT_TYPE: ClassVar[str] = "assistant_done"
content: str = ""
def _payload(self) -> Dict[str, Any]:
"""``{content}`` — nội dung trọn vẹn của một lượt gọi provider."""
return {"content": self.content}
# --------------------------------------------------------------------------- #
# Tool-call lifecycle.
# --------------------------------------------------------------------------- #
@dataclass(frozen=True)
class ToolCallStartedEvent(AgentEvent):
"""A tool call is about to run (after any security/permission gate).
Field names are the typed ones (``call_id``, ``arguments``); the legacy keys
``id``/``args`` are produced only at the serialisation boundary, so new code
never has to shadow the ``id`` builtin.
"""
EVENT_TYPE: ClassVar[str] = "tool_proposed"
call_id: str = ""
name: str = ""
arguments: Dict[str, Any] = field(default_factory=dict)
preview: Optional[ToolPreview] = None
def _payload(self) -> Dict[str, Any]:
"""``{id, name, args}``, kèm ``preview`` chỉ khi thật sự có.
Không có xem trước thì BỎ HẲN khoá thay vì gửi ``None``: widget đang viết
``ev.get("preview") or {}``, tức nó đã quen với việc khoá vắng mặt.
"""
payload: Dict[str, Any] = {"id": self.call_id, "name": self.name,
"args": dict(self.arguments)}
# Omitted rather than sent as None: the widget does
# ``preview = ev.get("preview") or {}`` and an absent key is the shape it
# already handles for tools without a preview.
if self.preview is not None:
payload["preview"] = self.preview.to_dict()
return payload
@dataclass(frozen=True)
class ToolOutputChunkEvent(AgentEvent):
"""Live stdout/stderr from a running command, appended to its step bubble."""
EVENT_TYPE: ClassVar[str] = "tool_output"
call_id: str = ""
name: str = ""
delta: str = ""
def _payload(self) -> Dict[str, Any]:
"""``{id, name, delta}`` — một mẩu đầu ra tool đang chạy."""
return {"id": self.call_id, "name": self.name, "delta": self.delta}
@dataclass(frozen=True)
class ToolCallFinishedEvent(AgentEvent):
"""A tool call returned. ``path``/``produced`` name files it created."""
EVENT_TYPE: ClassVar[str] = "tool_result"
call_id: str = ""
name: str = ""
ok: bool = False
output: str = ""
path: str = "" # the single file this call wrote, if any
produced: Tuple[str, ...] = () # extra deliverables a command produced
def __post_init__(self) -> None:
# Callers pass a live list; freeze it so the event cannot change later.
"""Đóng băng danh sách tệp đầu ra thành tuple.
Bên gọi truyền vào một list đang sống; không sao chép thì sự kiện đã phát đi
vẫn đổi nội dung được về sau — sự kiện phải là ảnh chụp bất biến.
"""
object.__setattr__(self, "produced", _as_str_tuple(self.produced))
def _payload(self) -> Dict[str, Any]:
"""``{id, name, ok, output}``, kèm ``error``/``produced`` chỉ khi có.
Hai khoá sau vắng mặt khi rỗng, đúng như ``chat_agent`` vẫn phát: phía
nhận kiểm bằng ``ev.get(...)`` nên thêm giá trị rỗng là đổi hành vi.
"""
payload: Dict[str, Any] = {"id": self.call_id, "name": self.name,
"ok": self.ok, "output": self.output}
# Both keys stay ABSENT when empty, matching what chat_agent emits today:
# downstream code tests them with ``ev.get(...)`` truthiness and iterates
# ``ev.get("produced", [])``, so adding empty values would be a change.
if self.path:
payload["path"] = self.path
if self.produced:
payload["produced"] = list(self.produced)
return payload
# --------------------------------------------------------------------------- #
# Side-channel events (plan, notices, output folder).
# --------------------------------------------------------------------------- #
@dataclass(frozen=True)
class PlanUpdatedEvent(AgentEvent):
"""The agent published a new version of its step checklist (full list)."""
EVENT_TYPE: ClassVar[str] = "plan_set"
steps: Tuple[PlanStep, ...] = ()
def __post_init__(self) -> None:
"""Đóng băng danh sách bước kế hoạch thành tuple."""
object.__setattr__(self, "steps", tuple(self.steps or ()))
def _payload(self) -> Dict[str, Any]:
"""``{steps}`` — cả danh sách bước, vì agent gửi lại trọn kế hoạch mỗi lần cập nhật."""
return {"steps": [s.to_dict() for s in self.steps]}
@dataclass(frozen=True)
class NoticeEvent(AgentEvent):
"""An aside outside the model's own answer.
Three sources today: context auto-compaction (info), a blocked
security check (warning), and attachment reading progress (progress).
"""
EVENT_TYPE: ClassVar[str] = "notice"
text: str = ""
level: str = NOTICE_INFO
def _payload(self) -> Dict[str, Any]:
"""``{level, text}`` — một dòng thông báo (info / cảnh báo / tiến độ)."""
return {"level": self.level, "text": self.text}
@dataclass(frozen=True)
class OutputsAddedEvent(AgentEvent):
"""Deliverables appeared in the turn's output folder."""
EVENT_TYPE: ClassVar[str] = "outputs_added"
paths: Tuple[str, ...] = ()
def __post_init__(self) -> None:
"""Đóng băng danh sách đường dẫn thành tuple."""
object.__setattr__(self, "paths", _as_str_tuple(self.paths))
def _payload(self) -> Dict[str, Any]:
"""``{paths}`` — các tệp vừa sinh ra, đổi về list cho JSON hoá được."""
return {"paths": list(self.paths)}
@dataclass(frozen=True)
class OutputsRemovedEvent(AgentEvent):
"""Intermediate/generator files were cleaned up — drop them from Output."""
EVENT_TYPE: ClassVar[str] = "outputs_removed"
paths: Tuple[str, ...] = ()
def __post_init__(self) -> None:
"""Đóng băng danh sách đường dẫn thành tuple."""
object.__setattr__(self, "paths", _as_str_tuple(self.paths))
def _payload(self) -> Dict[str, Any]:
"""``{paths}`` — các tệp trung gian vừa được dọn."""
return {"paths": list(self.paths)}
@dataclass(frozen=True)
class HistoryReadyEvent(AgentEvent):
"""The turn's conversation now exists on disk and can be opened.
Emitted by the unattended (Schedule Task) path so the scheduler refreshes
History only once the session is really there.
"""
EVENT_TYPE: ClassVar[str] = "history_ready"
session_id: str = ""
def _payload(self) -> Dict[str, Any]:
"""``{session_id}`` — lịch sử đã ghi xong, kèm id phiên để mở lại."""
return {"session_id": self.session_id}
# --------------------------------------------------------------------------- #
# Turn-level events introduced by R04 (no legacy consumer yet).
# --------------------------------------------------------------------------- #
@dataclass(frozen=True)
class TurnCompletedEvent(AgentEvent):
"""The whole turn ended — exactly once per turn.
Nothing consumes ``"turn_completed"`` yet: the widget's ``if/elif`` chain
simply has no branch for it, so emitting it is inert until R08 wires a
renderer. It exists now because the state it carries (was the turn
cancelled? did it hit the step ceiling?) is currently reconstructed by the
UI from side effects rather than being told to it.
"""
EVENT_TYPE: ClassVar[str] = "turn_completed"
final_text: str = ""
steps_used: int = 0
cancelled: bool = False
budget_exhausted: bool = False # stopped at effective_max_steps
def _payload(self) -> Dict[str, Any]:
"""``{final_text, steps_used, cancelled, budget_exhausted}`` — tổng kết cả lượt."""
return {"final_text": self.final_text, "steps_used": self.steps_used,
"cancelled": self.cancelled, "budget_exhausted": self.budget_exhausted}
@dataclass(frozen=True)
class ErrorEvent(AgentEvent):
"""The turn hit an error.
``recoverable`` separates "this turn is over" from "something failed but the
loop carried on" — a distinction the current code loses, because both end up
as a bare ``except Exception`` plus a text bubble.
"""
EVENT_TYPE: ClassVar[str] = "error"
message: str = ""
recoverable: bool = False
def _payload(self) -> Dict[str, Any]:
"""``{message, recoverable}`` — lỗi kèm cờ có thể thử lại hay không."""
return {"message": self.message, "recoverable": self.recoverable}
__all__ = [
"NOTICE_INFO", "NOTICE_WARNING", "NOTICE_PROGRESS",
"AgentEvent", "ToolPreview", "PlanStep",
"TextChunkEvent", "ReasoningChunkEvent", "AssistantMessageCompletedEvent",
"ToolCallStartedEvent", "ToolOutputChunkEvent", "ToolCallFinishedEvent",
"PlanUpdatedEvent", "NoticeEvent", "OutputsAddedEvent", "OutputsRemovedEvent",
"HistoryReadyEvent", "TurnCompletedEvent", "ErrorEvent",
]
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"""Legacy dict -> typed :mod:`agent_event` translation (R04-T02).
Kept in its own module for two reasons. It is a **temporary compatibility
shim**: once R08-T01 turns ``ui/chat_panel.py::_on_event`` into an event
renderer that consumes typed events directly, nothing needs to parse dicts any
more and this whole file gets deleted — a deletion that stays trivial only while
it is isolated. And it keeps ``agent_event.py`` inside the 400-LOC limit the
architecture rules impose, without diluting either file's single job: one
declares the vocabulary, the other bridges it to the old wire format.
Serialisation the other way lives on the events themselves
(``AgentEvent.to_legacy_dict``), because an event has to be emittable without
anyone importing a codec.
SEAM · dựng 2026-08-23 · chưa nối dây (F-05)
------------------------------------------------------------
Được nối khi: ``presentation/chat`` dùng thẳng sự kiện có kiểu, không còn đọc dict cũ nữa.
Để dormant thì sao: Shim này để xoá, không để giữ. Còn nó thì khuôn dict cũ
vẫn là một hợp đồng phải duy trì.
Cổng ``scripts/check_orphan_modules.py`` đếm tuổi seam từ ngày trên
và nhắc khi quá ``SEAM_MAX_AGE_DAYS``. Đổi nội dung dòng đó thì cổng
đọc theo — đừng sửa ngày để làm im lời nhắc.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Tuple
from .agent_event import (
AgentEvent,
AssistantMessageCompletedEvent,
ErrorEvent,
HistoryReadyEvent,
NOTICE_INFO,
NoticeEvent,
OutputsAddedEvent,
OutputsRemovedEvent,
PlanStep,
PlanUpdatedEvent,
ReasoningChunkEvent,
TextChunkEvent,
ToolCallFinishedEvent,
ToolCallStartedEvent,
ToolOutputChunkEvent,
ToolPreview,
TurnCompletedEvent,
)
def _plan_steps_from_legacy(raw: Any) -> Tuple[PlanStep, ...]:
"""Parse the legacy ``steps`` list, dropping anything unusable.
A step with no title cannot be rendered or ticked off, so it is discarded
instead of becoming a blank row in the Plan panel.
"""
if not isinstance(raw, list):
return ()
steps: List[PlanStep] = []
for item in raw:
if not isinstance(item, dict):
continue
title = str(item.get("title", "")).strip()
if not title:
continue
steps.append(PlanStep(title=title, status=str(item.get("status", "pending"))))
return tuple(steps)
def _parse_tool_started(raw: Dict[str, Any]) -> ToolCallStartedEvent:
"""Rebuild a ``tool_proposed`` event, mapping ``id``/``args`` to typed names."""
args = raw.get("args")
return ToolCallStartedEvent(
call_id=str(raw.get("id", "")), name=str(raw.get("name", "")),
arguments=dict(args) if isinstance(args, dict) else {},
preview=ToolPreview.from_dict(raw.get("preview")),
)
def _parse_tool_finished(raw: Dict[str, Any]) -> ToolCallFinishedEvent:
"""Rebuild a ``tool_result`` event; the optional file keys may be absent."""
return ToolCallFinishedEvent(
call_id=str(raw.get("id", "")), name=str(raw.get("name", "")),
ok=bool(raw.get("ok", False)), output=str(raw.get("output", "")),
path=str(raw.get("path", "") or ""), produced=raw.get("produced") or (),
)
# One parser per wire name. A table (rather than an if/elif chain) keeps adding
# an event a single-line change and makes the supported set introspectable.
_PARSERS = {
TextChunkEvent.EVENT_TYPE: lambda raw: TextChunkEvent(delta=str(raw.get("delta", ""))),
ReasoningChunkEvent.EVENT_TYPE: lambda raw: ReasoningChunkEvent(
delta=str(raw.get("delta", ""))),
AssistantMessageCompletedEvent.EVENT_TYPE: lambda raw: AssistantMessageCompletedEvent(
content=str(raw.get("content", ""))),
ToolCallStartedEvent.EVENT_TYPE: _parse_tool_started,
ToolOutputChunkEvent.EVENT_TYPE: lambda raw: ToolOutputChunkEvent(
call_id=str(raw.get("id", "")), name=str(raw.get("name", "")),
delta=str(raw.get("delta", ""))),
ToolCallFinishedEvent.EVENT_TYPE: _parse_tool_finished,
PlanUpdatedEvent.EVENT_TYPE: lambda raw: PlanUpdatedEvent(
steps=_plan_steps_from_legacy(raw.get("steps"))),
NoticeEvent.EVENT_TYPE: lambda raw: NoticeEvent(
text=str(raw.get("text", "")), level=str(raw.get("level", NOTICE_INFO))),
OutputsAddedEvent.EVENT_TYPE: lambda raw: OutputsAddedEvent(paths=raw.get("paths") or ()),
OutputsRemovedEvent.EVENT_TYPE: lambda raw: OutputsRemovedEvent(paths=raw.get("paths") or ()),
HistoryReadyEvent.EVENT_TYPE: lambda raw: HistoryReadyEvent(
session_id=str(raw.get("session_id", ""))),
TurnCompletedEvent.EVENT_TYPE: lambda raw: TurnCompletedEvent(
final_text=str(raw.get("final_text", "")), steps_used=int(raw.get("steps_used", 0) or 0),
cancelled=bool(raw.get("cancelled", False)),
budget_exhausted=bool(raw.get("budget_exhausted", False))),
ErrorEvent.EVENT_TYPE: lambda raw: ErrorEvent(
message=str(raw.get("message", "")), recoverable=bool(raw.get("recoverable", False))),
}
def from_legacy_dict(payload: Any) -> Optional[AgentEvent]:
"""Parse an emitted dict into a typed event, or ``None`` if it isn't ours.
``None`` (rather than an exception) is the contract that makes incremental
adoption possible: a bridge sitting between the runtime and the widget can
type the events it recognises and forward everything else — Co4E's node
events, or anything a future emitter adds — completely untouched.
"""
if not isinstance(payload, dict):
return None
parser = _PARSERS.get(str(payload.get("type", "")))
return parser(payload) if parser is not None else None
__all__ = ["from_legacy_dict"]
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"""What one finished turn produced (R04-T03).
The outcome of a turn is currently spread over three shapes: ``run_cowork``
returns the mutated message list, ``task_executors._run_agent`` returns a
``(answer_text, timed_out, incomplete_reason)`` tuple, and the UI reconstructs
the rest (did it get cancelled? did it hit the ceiling?) from side effects. Each
caller therefore knows a slightly different amount about the same turn.
:class:`AgentResult` is the single answer. Frozen, like the request that started
the turn, so a result cannot be edited into disagreeing with what actually
happened.
Layer rules (``docs/architecture/ADR-001-layered-architecture.md``): domain
layer — standard library plus sibling domain types only.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Dict, Tuple
from .agent_event import PlanStep, TurnCompletedEvent
@dataclass(frozen=True)
class AgentResult:
"""The outcome of one conversation turn."""
# The conversation AFTER the turn (system prompt, history, the new user
# message, every assistant reply and tool result).
messages: Tuple[Dict[str, Any], ...] = ()
steps_used: int = 0 # provider calls this turn consumed
cancelled: bool = False # the user pressed Stop
budget_exhausted: bool = False # stopped at effective_max_steps
# The agent's final checklist, so a caller can ask "did it really finish?"
# (``core/plan.py::plan_incomplete_reason``) without replaying the events.
plan_steps: Tuple[PlanStep, ...] = ()
# Non-empty when the turn ended on a failure. A string rather than the
# exception: the domain layer must not depend on where the error came from,
# and the message is what every consumer (bubble, error.txt, audit) shows.
error: str = ""
def __post_init__(self) -> None:
"""Freeze the collections the runtime hands over.
Both arrive as live lists that the caller keeps appending to after the
turn (the UI merges messages back into its own history), so copying here
is what keeps a result a record rather than a moving target.
"""
object.__setattr__(self, "messages", tuple(self.messages or ()))
object.__setattr__(self, "plan_steps", tuple(self.plan_steps or ()))
@property
def final_text(self) -> str:
"""The answer to show the user.
Scans backwards for the last assistant message with real content, which
is not the same as ``messages[-1]``: a turn that was cancelled or that
ran out of steps mid-loop ends on a tool message, and a reasoning-only
reply leaves a blank assistant message behind. Same rule as
``core/task_executors.py::_last_assistant_text``, which this replaces.
"""
for message in reversed(self.messages):
if message.get("role") == "assistant" and (message.get("content") or "").strip():
return str(message["content"])
return ""
@property
def ok(self) -> bool:
"""Whether the turn ran to a normal end.
Hitting the step ceiling still counts as ok: the agent did work and
produced an answer, it just was not allowed to keep going — which the
transcript says in its own note rather than by failing the turn.
"""
return not self.error and not self.cancelled
def to_turn_completed_event(self) -> TurnCompletedEvent:
"""The end-of-turn event carrying this outcome to subscribers."""
return TurnCompletedEvent(
final_text=self.final_text, steps_used=self.steps_used,
cancelled=self.cancelled, budget_exhausted=self.budget_exhausted,
)
__all__ = ["AgentResult"]
@@ -0,0 +1,222 @@
"""The immutable snapshot of ONE chat turn (R04-T01).
Today a turn's inputs live in a closure plus a 15-key ``ctx`` dict built inside
``ui/chat_panel.py::_start_turn``, and the worker thread reads the widget back
(``self._model``, ``self.title``, ``self.project_id``) while it runs. That is
the mechanism behind the whole class of "I changed the model mid-answer and the
running turn behaved oddly" reports: the turn has no snapshot of its own, so
every later click on the UI is visible to work already in flight.
:class:`ConversationExecutionRequest` is that missing snapshot. Everything the
runtime needs for one turn is captured once, on the UI thread, at submit time,
and then handed to code that runs on a worker thread. Frozen, so no caller —
widget or service — can retroactively change a decision the turn already acted
on.
Layer rules (see ``docs/architecture/ADR-001-layered-architecture.md``): this is
the domain layer, so standard library only. No PySide6, no ``requests``, no
filesystem access, and deliberately no import of ``core/*`` — a request only
*describes* a turn; running it is the application layer's job
(``application/conversations/``).
"""
from __future__ import annotations
from dataclasses import dataclass, field, replace
from pathlib import Path
from typing import Any, Dict, Optional, Tuple
# Separator between an instruction prefix (a ``/skill`` block, an ``/agent``
# persona) and the user's own request. Kept as a constant because the prefix is
# assembled in the presentation layer while the body is only known later on the
# worker thread — both halves must agree on the exact separator or the model
# sees a different prompt shape than it did before this refactor.
PREFIX_SEPARATOR = "\n\n---\n\n"
@dataclass(frozen=True)
class ConversationExecutionRequest:
"""Everything needed to execute one conversation turn.
Frozen for the reason above; use :meth:`with_model` / :meth:`with_output_dir`
to derive an adjusted copy rather than mutating one another thread may be
reading.
Note on depth: ``messages`` is a *shallow* snapshot (a tuple holding the
same message dicts the caller passed). That matches the existing
``snapshot = list(self.messages)`` semantics in ``_start_turn`` exactly —
the turn is protected from the history list being appended to or replaced,
which is what actually happens between turns. Making it deep would silently
change how ``_finalize_turn`` merges the turn's messages back, so the
stronger guarantee is left to R04-T03 where that merge moves.
"""
# -- identity ------------------------------------------------------- #
turn_id: str # unique within a session ("t1", "t2", ...)
session_id: str # the conversation this turn belongs to
surface: str = "cowork" # routing/mode key: "cowork" | "co4e" | "ai_edit"
project_id: str = "" # workspace the turn is confined to
title: str = "" # conversation title; also names saved files
# -- what the user asked -------------------------------------------- #
# The typed request, already stripped of any ``/skill`` or ``/agent``
# directive (those become ``instruction_prefix``).
prompt: str = ""
instruction_prefix: str = "" # skill rules + agent persona for this turn
# Prepended when the model/agent was switched mid-conversation, asking the
# model to re-check the previous step before continuing. Invisible in the
# chat bubble — it only travels in the payload sent to the provider.
review_note: str = ""
# Attachment PATHS, not their text: extracting a .docx can pip-install a
# parser or shell out to LibreOffice, which must not run on the UI thread.
# The runtime reads them later and passes the result to :meth:`user_content`.
attachments: Tuple[str, ...] = ()
# Conversation history as of submit time; the new user message is NOT part
# of it (the runtime appends it once the body is composed).
messages: Tuple[Dict[str, Any], ...] = ()
# -- which model answers -------------------------------------------- #
# Already resolved upstream: an Admin-agent pin, the tab's own picker, or a
# routing override published by ``RoutingApplicationService`` (R03). The
# runtime does not re-decide, so a switch cannot land mid-turn.
provider_id: str = ""
model: str = "" # "" = the provider's configured default
# -- standing instructions ------------------------------------------ #
project_context: str = "" # Claude-Projects-style shared instructions
session_notes: str = "" # e.g. files this conversation already produced
# -- tool scope and turn limits -------------------------------------- #
# None = every enabled built-in tool. An explicit (possibly empty) tuple
# restricts the ADVERTISED tools, which is how a "read-only" step is made
# literally unable to write.
allowed_tools: Optional[Tuple[str, ...]] = None
max_steps: int = 30 # interactive cap
completion_max_steps: int = 200 # runaway ceiling for run-to-completion work
run_to_completion: bool = False # Co4E flow steps need the higher ceiling
enforce_rules: bool = True # False for sandboxed Co4E runs
gate_mode: str = "auto" # "confirm" -> ask before run_command/install
agent_role: str = "cowork" # audit-log attribution ("cowork" | "task" | ...)
# -- where its files go ---------------------------------------------- #
output_dir: Optional[Path] = None # this turn's isolated sandbox
home_output_root: Optional[Path] = None # conversation Output root to promote into
# -- unattended execution (Schedule Task) ----------------------------- #
unattended: bool = False # no human watching; plan tracking is enforced
timeout_sec: Optional[int] = None # None = no wall-clock limit
# Escape hatch for surface-specific data a future task needs to thread
# through without another schema change (same role as
# ``ProviderDescriptor.extras``).
extras: Dict[str, Any] = field(default_factory=dict)
# -- validation / normalisation --------------------------------------- #
def __post_init__(self) -> None:
"""Reject unusable requests and freeze the mutable inputs.
Validation lives here (not at the call site) so a request that exists is
always safe to key by: the audit log, the History autosave and the
per-turn output folder are all named from ``session_id``/``turn_id``.
Normalisation matters just as much: the caller hands us the composer's
own attachment LIST and the live history LIST, and both get cleared or
appended to for the next turn. Copying them into tuples here is what
actually makes the snapshot a snapshot. ``object.__setattr__`` is the
standard way to do this in a frozen dataclass.
"""
if not (self.turn_id or "").strip():
raise ValueError("ConversationExecutionRequest.turn_id must not be empty")
if not (self.session_id or "").strip():
raise ValueError("ConversationExecutionRequest.session_id must not be empty")
object.__setattr__(self, "attachments", tuple(self.attachments or ()))
object.__setattr__(self, "messages", tuple(self.messages or ()))
# None must survive: it means "no restriction", while an empty tuple
# means "deny every built-in tool" — two very different turns.
if self.allowed_tools is not None:
object.__setattr__(self, "allowed_tools", tuple(self.allowed_tools))
# Accept str paths so a call site holding a config value does not have to
# wrap it; everything downstream can then assume Path.
for name in ("output_dir", "home_output_root"):
value = getattr(self, name)
if value is not None and not isinstance(value, Path):
object.__setattr__(self, name, Path(value))
# -- derived turn policy ---------------------------------------------- #
@property
def has_prompt(self) -> bool:
"""Whether the user actually typed something (an attachment-only turn
legitimately has none). Mirrors ``RoutingRequest.has_prompt`` so both
DTOs answer the "is there anything to work with?" question the same way.
"""
return bool((self.prompt or "").strip())
@property
def effective_max_steps(self) -> int:
"""The tool-use budget for this turn.
Run-to-completion work (a Co4E flow step whose single instruction may
need many tool calls) gets the higher ceiling; interactive chat keeps the
tight cap. Either way the turn still ends the moment the model stops
calling tools — this is only the runaway limit.
"""
return self.completion_max_steps if self.run_to_completion else self.max_steps
@property
def requires_permission_gate(self) -> bool:
"""Whether ``run_command``/``install_package`` must be approved first.
Resolved by the caller (per-workspace Auto-run override, else the global
"confirm before running commands" setting) and frozen here, so toggling
the setting mid-turn cannot change the rules the turn started under.
"""
return self.gate_mode == "confirm"
# -- prompt composition ------------------------------------------------ #
def user_content(self, body: str = "") -> str:
"""The exact ``content`` to send as this turn's user message.
``body`` is the request text AFTER attachment extraction, which happens
on the worker thread — hence a method taking it as an argument rather
than a stored field. The assembly order reproduces the closure in
``_start_turn`` byte for byte, because changing what a model receives is
a behaviour change, not a refactor:
1. session notes are appended after the body;
2. the instruction prefix goes in front, behind a fixed separator;
3. the model-switch review note goes ahead of everything.
"""
content = body or ""
notes = self.session_notes or ""
if notes:
# Guard the empty-body case (attachment-only turn) so the payload
# never opens with a stray blank line.
content = f"{content}\n\n{notes}" if content else notes
prefix = self.instruction_prefix or ""
if prefix:
content = f"{prefix}{PREFIX_SEPARATOR}{content}"
review = self.review_note or ""
if review:
content = f"{review}\n\n{content}"
return content
# -- derivation --------------------------------------------------------- #
def with_model(self, provider_id: str = "", model: str = "") -> "ConversationExecutionRequest":
"""A copy pinned to another provider/model.
Needed when a decision lands between building the request and running it
(a routing override, an Admin-agent pin). Deriving a new request keeps
the "one turn, one immutable snapshot" rule intact instead of patching a
request another thread may already hold.
"""
return replace(self, provider_id=provider_id or self.provider_id,
model=model or self.model)
def with_output_dir(self, output_dir) -> "ConversationExecutionRequest":
"""A copy writing into a different sandbox — used when the caller only
learns the per-turn folder after the request is assembled."""
return replace(self, output_dir=output_dir)
__all__ = ["PREFIX_SEPARATOR", "ConversationExecutionRequest"]