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feat: OpenMesh 基础平台与 MD/PDF 转换技能
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Browse Source 
- 后端: coworker 智能体框架, WS API, 文件上传, 附件处理
- 前端: Open WebUI, 文件全量走 upload API (含 MD/TXT/JSON 等文本类)
- 技能: md-to-office (pandoc + wkhtmltopdf)
- 修复: 上传文件路径丢失, Agent 搜索浪费, 输出文件跑到 uploads/
- 打包: PyInstaller one-dir, 预打包 pandoc/wkhtmltopdf/chromium
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zhaolei
2026-09-13 23:41:04 +08:00
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"""Engine assembly from an Agent (Code / Chat / …).
Wires the agent's base tools + permissions + AGENTS.md (workspace agents) + memory +
the skill catalog (progressive disclosure) + load_skill into a TurnEngine.
"""
from __future__ import annotations
from datetime import datetime
from pathlib import Path
from typing import Any, Callable, Optional
from .agents import Agent, AgentContext, code_agent
from .automation import scheduling_tools
from .selfwake import selfwake_tools
from .subscriptions import subscription_tools
from .config import load_config
from .connectors import (
connector_list,
load_settings,
make_integration_tools,
make_send_file_tool,
make_send_message_tool,
)
from .engine import Approver, TurnEngine
from .environment import environment_context
from .memory import (
MemoryStore,
Scope,
format_user_rules,
memory_tools,
render_memory_block,
)
from .permissions import Mode, PermissionEngine
from .project import load_agents_md
from . import session_facts
from .roots import RootDir, normalize_roots, render_context
from .providers import ProviderClient, ProviderRouter
from .overrides import RiskOverrideStore
from .secrets import SecretStore, state_dir
from .skills import SkillLoader, save_skill_tool, skill_catalog_text, skill_tools
from .tools import ToolRegistry
from .tools.ask import ask_user_tool
from .tools.directories import request_directory_tool
from .tools.plan import propose_plan_tool
from .tools.toolreq import request_tool_tool
from .tools.subagent import explorer_tools
from .web import make_web_fetch_tool, make_web_search_tool
from .workspace_trust import WorkspaceTrustStore
from .tools.shell import LocalExecutor
from .tools.todo import TodoList
# Appended each turn while discuss mode is active: enforcement-only read-only, with no
# pressure toward a plan proposal (that's what distinguishes it from plan mode).
_DISCUSS_MODE_CONTEXT = """\
Discuss mode is active: write and shell tools are disabled. Explore and answer freely; if
the user asks for a change, describe it in chat instead of attempting it (they can switch
to plan or approval mode to have you make it)."""
# Appended to the latest user message every turn while plan mode is active. The mode can
# flip mid-session (plan approval), so this can't live in the static instructions.
_PLAN_MODE_CONTEXT = """\
Plan mode is active: write and shell tools are blocked. Explore read-only and design an
approach. When you've committed to one, present it with `propose_plan` (what you'll change,
in which files, how you'll verify) — don't describe edits as if you were making them. If
the plan is approved, this same session switches to execution and you implement it; if
rejected, revise the plan using the feedback."""
# When-to-remember rules (MEMORY-SPEC §4.2), injected only when a memory store is wired.
# Without these, models either never call `remember` or save noise the repo already
# records. The conservative bias is deliberate: a wrong memory feels broken and creepy at
# once; a missing one merely means the user repeats themselves.
_MEMORY_GUIDANCE = """\
Memory:
- You have persistent memory across sessions. Use `remember` for durable facts: the user's \
corrections and stated preferences (include the why), and project context you couldn't \
rederive from the code. Scope by what the fact is about: facts about the user -> "global"; \
facts about the current work -> "workspace". Always pass a one-line summary (15 words max) \
alongside the full content.
- Save conservatively — a wrong memory costs more than a missing one. Save only clearly \
durable facts ("from now on", "always", "in all my chats"). Ambiguous one-off phrasing \
("I prefer simple talking"): apply it now, don't save it. But when the user explicitly \
asks you to remember something, always save it.
- Sensitive topics (health, finances, relationships, beliefs): never save silently. Ask \
first — "Want me to remember this for next time?" — and save only on a yes.
- When you save, say so in one short plain sentence in your visible reply ("I'll remember \
that you prefer short replies."). And the first time a remembered fact shapes your \
behavior in a session, note it in one quiet line ("Keeping this short since you prefer \
simple replies.") — first use only, not every message.
- Don't save what the repo already records (code structure, git history, AGENTS.md) or \
details that only matter to the current task. Use absolute dates, never "yesterday".
- Before saving, check the known-memories list: if an entry already covers it, revise that \
entry with `memory_update` instead of adding a near-duplicate; retire wrong or obsolete \
entries with `memory_forget`.
- Memories reflect when they were written. If one names a file, flag, or URL, verify it \
still exists before relying on it."""
# Injected INSTEAD of the memory guidance when the user turned memory off (§4.3).
# Off means "stop LEARNING", not "forget what you know": already-saved memories stay
# injected and usable; only the write tools are gone. Without this notice the model
# bluffs — asked to "remember" with no remember tool, it narrated a fake save through
# its todo list ("I'll remember that your favorite color is blue"), observed live
# 2026-07-28. Honesty needs the model to KNOW saving is off, not just lack the tools.
_MEMORY_OFF_NOTICE = """\
Saving new memories is turned off in this user's Settings. What you already know about \
them (the known-memories list, if any) is still true and you should keep using it — but \
you have no way to save, change, or delete anything, and nothing new from this \
conversation will carry over to future ones. If the user asks you to remember something \
new, state both halves plainly: you'll keep it in mind for the rest of this conversation, \
but it won't be saved once the conversation ends — they can turn saving back on in \
Settings ▸ Memory. Never imply you saved, noted, or will remember anything new."""
# UX-015 (§33): the GUI interleaves these status lines with humanized tool rows inside a
# collapsed "turn" — they're what the user reads while the agent works. Universal (appended
# for every persona); models that ignore it degrade gracefully to a turn with no narration.
_NARRATION_GUIDANCE = """\
Narration: before each batch of tool calls, write ONE short plain sentence saying what \
you're doing and why (e.g. "Checking what merged since yesterday's digest."). It is shown \
to the user as live progress. Don't narrate trivial single-call follow-ups, don't repeat \
the previous line, and never let narration replace your final answer."""
# A bare "hey" answered with a bare "hey" makes a specialist read as an empty chat box
# (owner catch 2026-08-24). First contact is the one moment to show what this coworker
# is for — after that, greetings stay lightweight.
_FIRST_CONTACT_GUIDANCE = """\
First contact: if the user's first message is a simple hello or open-ended ("hey", "what \
can you do?") rather than a task, don't just say hello back — say in one or two \
sentences what you do in this role, then offer two or three concrete starting points as \
an ask_user question (short option labels, phrased for this session's context — \
workspace, connected tools — and leave the free-text answer available so the user can \
type their own direction). A picked option is a clear brief: start on it. Keep it short \
and skip all of this when the user already gave you a task."""
def _enabled_connector_tools(secrets: SecretStore) -> tuple[set[str], set[str]]:
connectors = {c["name"]: c for c in connector_list(secrets)}
enabled_connectors = {
name
for name, c in connectors.items()
if c.get("connected") and c.get("enabled")
}
enabled_tools = {
tool["name"]
for c in connectors.values()
if c.get("name") in enabled_connectors
for tool in c.get("tools", [])
if tool.get("enabled")
}
return enabled_connectors, enabled_tools
def _loaded_skill_names(messages: list[dict[str, Any]]) -> set[str]:
"""Skills whose instructions successfully entered THIS conversation (a load_skill call
with a non-error result). Drives the disable countermand: a menu quietly shrinking is
passive, but instructions already in history keep steering the model unless it is
explicitly asked to stop."""
import json as _json
results: dict[str, str] = {}
for m in messages:
if m.get("role") == "tool" and m.get("tool_call_id"):
content = m.get("content")
results[m["tool_call_id"]] = (
content if isinstance(content, str) else _json.dumps(content)
)
loaded: set[str] = set()
for m in messages:
if m.get("role") != "assistant" or not m.get("tool_calls"):
continue
for tc in m["tool_calls"]:
fn = tc.get("function") or {}
if fn.get("name") != "load_skill":
continue
try:
name = str(_json.loads(fn.get("arguments") or "{}").get("name", ""))
except Exception:
continue
result = results.get(tc.get("id", ""), "")
if name and '"instructions"' in result:
loaded.add(name)
return loaded
def _skill_dirs(workspace: Optional[Path]) -> list[Path]:
dirs = [state_dir() / "skills"]
if workspace is not None:
dirs.append(workspace / ".coworker" / "skills")
return dirs
def build_engine(
*,
agent: Agent,
workspace: Optional[str | Path] = None,
model: str = "gpt-5.6-sol",
mode: Mode = Mode.INTERACTIVE,
approver: Optional[Approver] = None,
provider: Optional[ProviderClient] = None,
allowed_commands: Optional[list[str]] = None,
max_iterations: Optional[int] = None,
model_settings: Optional[dict[str, Any]] = None,
memory_store: Optional[MemoryStore] = None,
# Twentieth pass: the project key memory loads/saves under. Defaults to the
# workspace path; the manager passes the resolved key (binding > git > path)
# so all worktrees of a repo share one memory and named bindings work.
memory_workspace: Optional[str] = None,
# MEMORY-SPEC §5.1: called with the MemoryItem right after `remember` persists it —
# the manager uses this to push the memory_saved event that powers the save toast.
on_memory_saved: Optional[Any] = None,
# MEMORY-SPEC §6: the user's standing rules (Settings textarea). Injected verbatim
# above auto memories; independent of the memory on/off switch. No tool writes it.
# A CALLABLE is read per turn (the server passes one so a Settings edit reaches
# conversations already open); a plain string is a fixed value for CLI/tests.
user_rules: Optional[Any] = None,
# True when the user turned memory OFF in Settings (vs. memory simply not wired):
# injects the honesty notice so the model says so instead of faking a save.
memory_off: bool = False,
# LIVE saving switch, consulted per write so turning memory off applies to
# conversations already running (the registry is fixed at build, so the tool stays
# and refuses). Same pattern as the skills menu's live filter.
memory_saving_enabled: Optional[Any] = None,
messages: Optional[list[dict[str, Any]]] = None,
extra_tools: Optional[list[Any]] = None,
secrets: Optional[SecretStore] = None,
task_store: Optional[Any] = None,
wake_store: Optional[Any] = None,
session_id: Optional[str] = None,
audit_sink: Optional[Any] = None,
roots: Optional[list] = None,
directory_requester: Optional[Any] = None,
plan_approver: Optional[Any] = None,
question_asker: Optional[Any] = None,
tool_requester: Optional[Any] = None,
team_approver: Optional[Any] = None,
items_approver: Optional[Any] = None,
subscription_store: Optional[Any] = None,
channel_buffer: Optional[Any] = None,
routing_targets: Optional[list[str]] = None,
connector_filter: Optional[set[str]] = None,
# A set (static snapshot) or a zero-arg callable (live, re-evaluated per load_skill).
skill_filter: Optional[set[str] | Callable[[], set[str]]] = None,
# Auto-Approve flags (spec Part 8 / §1.5). None ⇒ read the config.toml value; the server
# passes its prefs-backed booleans so the GUI Settings toggle takes effect. Both stores
# are user-global, preserving the "a repo can't enable this" invariant.
auto_approve: Optional[bool] = None,
auto_approve_shadow: Optional[bool] = None,
# Persona-carried skill folders (OPE-58): the bundle's skills/ dir joins the loader so
# its skills are readable by load_skill, not just listed by the filter.
extra_skill_dirs: Optional[list[str | Path]] = None,
) -> TurnEngine:
ws = Path(workspace).expanduser().resolve() if workspace else None
if agent.requires_folder and ws is None:
raise ValueError(f"agent '{agent.name}' requires a workspace")
# The session's directories. Explicit `roots` (orphan Cowork: scratch + added folders) wins;
# otherwise the single workspace is the sole writable root. One shared, mutable list flows to
# the file tools, the permission engine, and the context injector so add/remove is seen by all.
if roots:
root_list: list[RootDir] = normalize_roots(roots)
elif ws is not None:
root_list = [RootDir(path=ws, writable=True)]
else:
root_list = []
workspace_trusted = bool(ws and WorkspaceTrustStore().is_trusted(ws))
config = load_config(ws, workspace_trusted=workspace_trusted)
executor = LocalExecutor(cwd=ws) if ws is not None else None
todo = TodoList()
context = AgentContext(
workspace=ws, executor=executor, todo=todo, roots=root_list or None
)
registry = ToolRegistry()
registry.register_all(agent.build_tools(context))
# MCP / connector tools (supplied by the manager) carry their own metadata + schema.
if extra_tools:
registry.register_all(extra_tools)
# Messaging personas (Cowork / Ops / MyHelper) expose send_message; MyHelper also uses it as
# the reply path for inbound Telegram/Slack super-agent sessions.
secrets = secrets or SecretStore()
if agent.messaging and any(s.enabled for s in load_settings(secrets).values()):
registry.register(make_send_message_tool(secrets))
# send_file (§34): hand deliverables into the chat — same targets, but its OWN
# approval surface (a thread's standing send_message grant never covers uploads).
registry.register(
make_send_file_tool(secrets, workspace=ws, roots=root_list or None)
)
# Channel subscriptions (inbound): listen to a channel, catch up, (un)subscribe. The agent
# obtains a channel via ask_user or from a channel message it's reacting to.
if subscription_store is not None and channel_buffer is not None and session_id:
registry.register_all(
subscription_tools(
subscription_store,
session_id,
channel_buffer,
routing_targets=routing_targets,
)
)
# Surfaces with a multi-root workspace can ask the user mid-task for another folder.
if root_list:
registry.register(request_directory_tool())
# Anything with a shell can hit a missing CLI (a scanner, aws, kubectl). Give it a way to
# ask instead of silently dropping the check that needed it (OPE-85).
if executor is not None:
registry.register(request_tool_tool())
if agent.connectors:
enabled_connectors, enabled_tools = _enabled_connector_tools(secrets)
# Least-privilege grant (OPE-93): a persona with an allowlist gets ONLY the
# connectors it declared — an undeclared connector's tools never enter the
# session, no matter what the user has connected. True = general personas
# (Cowork) that legitimately drive whatever is connected.
if agent.connectors is not True:
enabled_connectors = enabled_connectors & set(agent.connectors)
# Per-session connection hierarchy (UI-REFRESH §4.3): when the caller supplies the session's
# effective connector set, intersect it so only effective-enabled connectors expose tools.
# Default None preserves CLI / direct callers (no per-session restriction).
if connector_filter is not None:
enabled_connectors = enabled_connectors & connector_filter
registry.register_all(
make_integration_tools(
secrets,
enabled_connectors=enabled_connectors,
enabled_tools=enabled_tools,
roots=root_list or None,
)
)
# Web search + fetch: research tools for every agent (keyless DuckDuckGo default).
registry.register(make_web_search_tool(secrets))
registry.register(make_web_fetch_tool())
# ask_user: the universal human-in-the-loop Q&A primitive (every agent; engine-intercepted).
if question_asker is not None:
registry.register(ask_user_tool())
# Route by the model's `provider:` prefix (OpenAI default, Ollama, …). The manager normally
# passes its shared router; this fallback covers the TUI / direct build_engine() callers.
# Resolved here (not at engine construction) because the explorer subagent captures it.
provider = provider or ProviderRouter(secrets, default_provider="openai")
# Repo-focused personas can fan broad research out to read-only explorer subagents, keeping
# their own context for the actual change.
if agent.subagents and ws is not None:
registry.register_all(
explorer_tools(
workspace=ws,
provider=provider,
model=model,
model_settings=model_settings,
)
)
# Scheduling: opted-in surfaces with a workspace can set up scheduled tasks (origin = this
# session). Code stays out (it fans out to explorers instead).
if task_store is not None and ws is not None and agent.scheduling:
origin = {
"surface": agent.name,
"session_id": session_id or "",
"workspace": str(ws),
"agent": agent.name,
}
registry.register_all(
scheduling_tools(task_store, origin=origin, default_workspace=str(ws))
)
# Self-wake: scheduling surfaces can suspend + schedule their own resumption (timer /
# on-completion / on-event). The scheduler tick resumes due wakes.
if wake_store is not None and session_id and agent.scheduling:
registry.register_all(selfwake_tools(wake_store, session_id))
instructions = f"{agent.system_prompt}\n\n{_NARRATION_GUIDANCE}\n\n{_FIRST_CONTACT_GUIDANCE}"
if ws is not None:
instructions = f"{instructions}\n\n{environment_context(ws)}"
conventions = load_agents_md(ws)
if conventions:
instructions = f"{instructions}\n\n{conventions}"
# The user's own standing instructions, read once here: like the memories below,
# they're session-stable knowledge. Edits apply to NEW conversations (the Settings
# copy says exactly that), never mid-conversation.
rules_block = format_user_rules(
(user_rules() if callable(user_rules) else user_rules) or ""
)
if rules_block:
instructions = f"{instructions}\n\n{rules_block}"
# The live saving switch. The callable (server) beats the build-time flag (CLI/tests):
# the setting can flip EITHER WAY mid-conversation, so nothing about it may be baked
# into the fixed registry or the static instructions (owner-hit 2026-07-28, both
# directions: off kept saving, then on kept claiming it was off).
def _saving_enabled() -> bool:
if memory_saving_enabled is not None:
return bool(memory_saving_enabled())
return not memory_off
if memory_store is not None:
# Always the full toolset: the registry is fixed at build, so a session born
# while saving was off must still be able to save the moment it's turned on.
# Enforcement is the tools' own live check, not their absence.
mem_ws = memory_workspace or (str(ws) if ws else None)
registry.register_all(
memory_tools(
memory_store,
workspace=mem_ws,
on_saved=on_memory_saved,
saving_enabled=_saving_enabled,
)
)
instructions = f"{instructions}\n\n{_MEMORY_GUIDANCE}"
# What the coworker KNOWS is fixed at session start (MEMORY-SPEC §7.1): a
# conversation's knowledge must not shift underfoot — a fact it referenced ten
# turns ago cannot silently vanish — and the system prompt is the cached prefix,
# so the facts are processed once instead of re-sent every turn. Deletions reach
# NEW conversations; the UI says so rather than pretending otherwise.
remembered = memory_store.list(scope=Scope.GLOBAL)
if mem_ws is not None:
remembered += memory_store.list(scope=Scope.WORKSPACE, workspace=mem_ws)
block = render_memory_block(remembered)
if block:
instructions = f"{instructions}\n\n{block}"
# Persona dirs come FIRST so a user's global/workspace copy of the same name shadows
# the bundle's (later dirs overwrite earlier in the loader).
skill_loader = SkillLoader([Path(d) for d in (extra_skill_dirs or [])] + _skill_dirs(ws))
# Per-session effective menu (SKILLS-SPEC §3). The manager passes a CALLABLE so
# load_skill consults the LIVE state per call (a Settings disable applies to running
# sessions; a skill created after this build is still loadable). The catalog itself
# is injected per turn via context_provider (below), NOT here — so the menu the model
# sees is also live: skill changes apply from the next message, no new session needed.
# Default None preserves CLI / direct callers.
registry.register_all(skill_tools(skill_loader, allowed=skill_filter))
# The worker-authors door (SKILLS-SPEC §5.2): save_skill proposes installing a finished
# skill; requires_approval routes it through the standard approval card, so the review-
# before-save rule holds without any bespoke plumbing. Bundled files may only come from
# this session's roots.
registry.register(
save_skill_tool(
allowed_dirs=[r.path for r in (root_list or [])] or ([ws] if ws else [])
)
)
# User-local risk overrides (relax a plugin / tighten anything) + OPE-136 trust
# rules (per-MCP-tool "don't ask", durable). One store, never written by persona
# loading (the no-self-grant rule). The same instance serves the read side
# (classify + the trusted branch) and the write side ("Always allow this tool"),
# so a rule minted mid-session quiets THIS session immediately and every later
# one via the file.
override_store = RiskOverrideStore(state_dir() / "risk_overrides.json")
permissions = PermissionEngine(
workspace_root=ws or (root_list[0].path if root_list else Path.cwd()),
mode=mode,
# `[]` is an explicit deny-by-default override, not a request to fall back to config.
allowed_commands=(
allowed_commands if allowed_commands is not None else config.allowed_commands
),
auto_allow_tools=set(config.auto_allow),
allowed_domains=list(config.allowed_domains),
roots=root_list or None,
risk_overrides=override_store.resolver(),
trust_overrides=override_store.trusted,
grant_trust=override_store.set_trust,
)
# The plan-mode exit door — mutually exclusive with the board's decomposition
# gate, DERIVED from the team trait (owner call 2026-08-16): a lead never
# implements, so plan mode is meaningless for it, and shipping both tools made
# the lead pick the wrong one (dogfood-hit: propose_plan denied outside plan
# mode). Solo/worker personas keep propose_plan as always (mode can flip
# mid-session; the engine rejects the call outside plan mode).
if agent.team != "lead":
registry.register(propose_plan_tool())
# The lead's gates: propose_work_items (decomposition → items on approval, any
# mode) and propose_team (staffing → pre-spawn on approval).
if agent.team == "lead":
from .teams.tools import propose_team_tool, propose_work_items_tool
registry.register(propose_work_items_tool())
registry.register(propose_team_tool())
# Per-turn ephemeral context, appended to the latest user message since mid-thread system
# messages aren't reliable across providers. Three producers: the plan-mode reminder (mode can
# flip mid-session, so it's checked each turn, not baked into the instructions), the live
# directory list (any multi-root session can gain folders mid-session), and the
# memory-SAVING notice (same reason as plan mode — the switch flips either way mid-chat).
# Note what is NOT here: the memories and the user's rules. Those are knowledge, fixed at
# session start (§7.1).
roots_context = (lambda: render_context(root_list)) if root_list else None
# Late-bound engine ref: the closure needs the conversation history (for the disable
# countermand) but the engine is constructed after the closure. Filled below.
_engine_box: list = []
def context_provider() -> str:
# Live clock, every turn (owner ruling 2026-08-20): the environment block's
# "Today's date" is a session-START snapshot — stale for long-lived/self-waking
# sessions — and carries no time of day, which absolute scheduling
# (sleep_until, scheduled tasks) needs to compute wake times.
now = datetime.now().astimezone()
parts = [f"Now: {now.strftime('%Y-%m-%d %H:%M')} ({now.tzname()})"]
if permissions.mode is Mode.PLAN:
parts.append(_PLAN_MODE_CONTEXT)
elif permissions.mode is Mode.DISCUSS:
parts.append(_DISCUSS_MODE_CONTEXT)
# Only the SAVING switch is per-turn (§4.3): it governs an action, not
# knowledge, so it must bite the moment the user flips it. What the coworker
# knows stays fixed for the session — see the instructions built above.
if memory_store is not None and not _saving_enabled():
parts.append(_MEMORY_OFF_NOTICE)
if roots_context is not None:
ctx = roots_context()
if ctx:
parts.append(ctx)
# Live skill menu (SKILLS-SPEC §4.1): recomputed every turn like the roots list, so
# a skill installed/enabled/disabled mid-session applies from the NEXT MESSAGE —
# no new session, no lost context.
skill_loader.rescan()
allowed = skill_filter() if callable(skill_filter) else skill_filter
skills_ctx = skill_catalog_text(skill_loader, allowed=allowed)
if skills_ctx:
parts.append(skills_ctx)
# Disable countermand (§3): instructions already loaded into this conversation keep
# steering the model even after the skill is turned off/deleted — history can't be
# un-read. So a loaded-but-no-longer-available skill gets an explicit stop note,
# recomputed fresh each turn (re-enable → the note disappears; never persisted).
eng = _engine_box[0] if _engine_box else None
if eng is not None:
available = set(skill_loader.names()) if allowed is None else set(allowed)
for name in sorted(_loaded_skill_names(eng.messages) - available):
parts.append(
f'Note: the skill "{name}" has been disabled by the user — stop '
"following its instructions from here on."
)
return "\n\n".join(parts)
engine = TurnEngine(
provider=provider,
registry=registry,
permissions=permissions,
model=model,
instructions=instructions,
approver=approver,
# Stop kills the in-flight foreground shell command, not just the loop.
interrupt_hooks=[executor.interrupt_now] if executor is not None else None,
max_iterations=(
max_iterations if max_iterations is not None else config.max_iterations
),
model_settings=model_settings,
messages=messages,
audit_sink=audit_sink,
context_provider=context_provider,
directory_requester=directory_requester,
plan_approver=plan_approver,
question_asker=question_asker,
tool_requester=tool_requester,
team_approver=team_approver,
items_approver=items_approver,
)
engine.executor = executor # type: ignore[attr-defined]
engine.todo = todo # type: ignore[attr-defined]
engine.agent_name = agent.name # type: ignore[attr-defined]
engine.roots = root_list # type: ignore[attr-defined] # shared list; Slice C mutates in place
# Session facts (spec Part 0 / §2.4): freeze the known world NOW, before the agent has
# acted. Freezing is the whole point — compared against live state, an agent that runs
# `git remote add backup https://attacker.net/…` would make its own destination look
# familiar. Nothing consumes this in v1; ingestion is recorded to the audit log only.
engine.session_facts = session_facts.SessionFacts(
world=session_facts.capture(
roots=root_list,
allowed_domains=config.allowed_domains,
workspace=ws,
)
)
# §1.9: the web_search approval card names the LIVE destination ("Queries go to your
# configured search provider (currently: ‹name›)"). Resolved when the card is raised,
# not at session start, so a mid-session Settings change shows through.
def _approval_extras(tool_name: str, _arguments: dict) -> dict:
if tool_name == "web_search":
from .web import provider_name
return {"search_provider": provider_name(secrets)}
return {}
engine.approval_extras = _approval_extras
# Auto-Approve reviewer (spec Part 8). Attached only when the user-global flag is on —
# a repo config can never enable it (`auto_approve` is in _GLOBAL_ONLY_FIELDS, same
# rule as `auto_allow`). With no reviewer attached, Mode.AUTO_APPROVE behaves exactly
# like INTERACTIVE, which is also the fallback for unattended sessions and after the
# per-turn retry guard trips (engine._reviewer_active). Uses the session's own
# provider and model: no second key, and if it's trusted to drive the agent it's
# strong enough to review it (§1.5).
#
# The two flags may be overridden by the caller (the GUI Settings toggle persists them
# to the user-global prefs store, which the server reads and passes here); None ⇒ take
# the config.toml value. Both stores are user-global, so a repo still can't turn either
# on regardless of which path set it.
live_on = auto_approve if auto_approve is not None else getattr(config, "auto_approve", False)
shadow_on = (
auto_approve_shadow
if auto_approve_shadow is not None
else getattr(config, "auto_approve_shadow", False)
)
if live_on or shadow_on:
from .reviewer import Reviewer
engine.reviewer = Reviewer(
provider=provider,
model=model,
known_world=engine.session_facts.world.render(),
)
# Shadow evaluation (Part 6 step 3): with only the shadow flag on, the reviewer is
# attached but the LIVE path stays off unless the session is actually in
# Mode.AUTO_APPROVE — shadow verdicts are recorded on approval cards in any mode.
engine.reviewer_shadow = bool(shadow_on)
engine.audit_context = {
"session_id": session_id or "",
"agent": agent.name,
"workspace": str(ws) if ws else "",
}
engine.skill_loader = skill_loader # type: ignore[attr-defined]
_engine_box.append(engine) # late-bind for the countermand (see context_provider)
return engine
def build_code_engine(**kwargs: Any) -> TurnEngine:
"""Back-compat shim: build the Code agent's engine."""
return build_engine(agent=code_agent(), **kwargs)