ReMe/reme/components/agent_wrapper/codex_agent_wrapper.py

"""Codex Python SDK backend for the unified agent wrapper."""

from __future__ import annotations

import asyncio
from collections.abc import AsyncGenerator
from contextlib import suppress
from dataclasses import dataclass
from functools import partial
import hashlib
import json
import os
from pathlib import Path
import sys
import tempfile
from typing import Any, TYPE_CHECKING

from .base_agent_wrapper import BaseAgentWrapper
from ..component_registry import R
from ...enumeration import ChunkEnum
from ...schema import StreamChunk

if TYPE_CHECKING:
    from openai_codex import AsyncCodex, AsyncThread, CodexConfig, RunInput
    from openai_codex.types import Notification
else:
    # Keep the optional Codex SDK out of ReMe's package import path. Tests may
    # also replace this value before the SDK is loaded.
    AsyncCodex: Any = None


def _get_async_codex_class():
    """Return the optional Codex client class, importing its SDK on first use."""
    global AsyncCodex  # pylint: disable=global-statement
    if AsyncCodex is None:
        from openai_codex import AsyncCodex as AsyncCodexClass

        AsyncCodex = AsyncCodexClass
    return AsyncCodex


@dataclass(frozen=True)
class _CodexAuthConfig:
    """Resolved authentication settings for one Codex app-server."""

    mode: str
    api_key: str = ""
    base_url: str = ""


@dataclass(frozen=True)
class _CodexLaunchConfig:
    """Options fixed for the lifetime of one Codex app-server."""

    auth_mode: str
    api_key: str
    base_url: str
    codex_bin: str | None
    config_overrides: tuple[str, ...]
    experimental_api: bool


@R.register("codex")
class CodexAgentWrapper(BaseAgentWrapper):
    """Agent wrapper backed by the Codex Python SDK."""

    SDK_PACKAGE = "openai-codex"
    _CLIENT_OPTION_NAMES = frozenset(
        {
            "api_key",
            "auth_mode",
            "base_url",
            "codex_bin",
            "codex_home",
            "config_overrides",
            "cwd",
            "experimental_api",
            "launch_args_override",
        },
    )

    # pylint: disable=too-many-arguments
    def __init__(
        self,
        mcp_config: str | None = None,
        codex_home: str | Path | None = None,
        *,
        auth_mode: str = "auto",
        api_key: str = "",
        base_url: str = "",
        codex_bin: str | None = None,
        config_overrides: list[str] | tuple[str, ...] | None = None,
        experimental_api: bool = True,
        **kwargs,
    ) -> None:
        if "launch_args_override" in kwargs:
            raise TypeError("launch_args_override is not supported; configure codex_bin instead")
        super().__init__(**kwargs)
        self.mcp_config = mcp_config
        self._codex_home = codex_home
        self._launch_config = _CodexLaunchConfig(
            auth_mode=auth_mode,
            api_key=api_key,
            base_url=base_url,
            codex_bin=codex_bin,
            config_overrides=tuple(config_overrides or ()),
            experimental_api=experimental_api,
        )
        self._codex: AsyncCodex | None = None
        self._turn_lock = asyncio.Lock()
        self._mcp_snapshot_path: Path | None = None
        self._thread_tool_contexts: dict[str, str] = {}

    @property
    def session_path(self) -> Path:
        """Directory used for Codex state and persisted threads."""
        if self._codex_home:
            path = Path(self._codex_home).expanduser()
            return path if path.is_absolute() else self.workspace_path / path
        if self.app_context is None:
            return self.workspace_path / "mem_session" / "codex"
        return self.workspace_path / self.app_context.app_config.mem_session_dir / "codex"

    @property
    def wrapper_session_path(self) -> Path:
        """ReMe-owned session data, kept separate from a shared OAuth CODEX_HOME."""
        if self.app_context is None:
            return self.workspace_path / "mem_session" / "codex"
        return self.workspace_path / self.app_context.app_config.mem_session_dir / "codex"

    def _ensure_skills(self, skills: list[str] | str | None) -> None:
        """Expose selected project skills through Codex's repo-level directory."""
        sources = self._resolve_project_skills(skills)
        if not sources:
            return

        target_root = self.project_path / ".agents" / "skills"
        target_root.mkdir(parents=True, exist_ok=True)
        for name, source in sources.items():
            target = target_root / name
            if target.is_symlink():
                if target.resolve() == source.resolve():
                    continue
                raise FileExistsError(f"Codex skill conflict: {target} points to {target.resolve(strict=False)}")
            if target.exists():
                raise FileExistsError(f"Codex skill conflict: {target} already exists and was preserved")
            relative_source = os.path.relpath(source, target.parent)
            target.symlink_to(relative_source, target_is_directory=True)

    def _explicit_mcp_config(self, kwargs: dict[str, Any]) -> str | None:
        value = kwargs.get("mcp_config") if "mcp_config" in kwargs else self.mcp_config
        if value is None:
            return None
        source = Path(str(value)).expanduser()
        if source.suffix in {".yaml", ".yml", ".json"}:
            if not source.is_absolute():
                source = self.workspace_path / source
            return str(source.absolute())
        return str(value)

    def _effective_config_snapshot(self) -> Path:
        """Create one private snapshot that remains valid for the client lifetime."""
        if self._mcp_snapshot_path is not None:
            return self._mcp_snapshot_path
        if self.app_context is None:
            raise RuntimeError("Cannot snapshot MCP config without an app_context")
        snapshot_dir = self.wrapper_session_path / "reme-mcp"
        snapshot_dir.mkdir(parents=True, exist_ok=True)
        fd, raw_path = tempfile.mkstemp(prefix="config-", suffix=".json", dir=snapshot_dir)
        try:
            os.fchmod(fd, 0o600)
            with os.fdopen(fd, "w", encoding="utf-8") as stream:
                json.dump(self.app_context.app_config.model_dump(mode="json"), stream)
        except BaseException:
            with suppress(OSError):
                os.close(fd)
            Path(raw_path).unlink(missing_ok=True)
            raise
        self._mcp_snapshot_path = Path(raw_path)
        return self._mcp_snapshot_path

    def _mcp_config_source(self, kwargs: dict[str, Any]) -> str:
        return self._explicit_mcp_config(kwargs) or str(self._effective_config_snapshot())

    @staticmethod
    def _resolve_auth_config(auth_mode: str, api_key: str = "", base_url: str = "") -> _CodexAuthConfig:
        """Resolve login from wrapper options.

        The Codex child process still inherits the parent process environment.
        """
        requested_mode = str(auth_mode or "auto").lower()
        if requested_mode not in {"auto", "api_key", "oauth"}:
            raise ValueError("auth_mode must be one of: auto, api_key, oauth")
        if requested_mode == "oauth":
            return _CodexAuthConfig(mode="oauth")

        api_key = api_key if isinstance(api_key, str) else ""
        if requested_mode == "api_key" and not api_key:
            raise ValueError("auth_mode='api_key' requires a non-empty API key")
        if not api_key:
            return _CodexAuthConfig(mode="oauth")

        base_url = base_url if isinstance(base_url, str) else ""
        return _CodexAuthConfig(mode="api_key", api_key=api_key, base_url=base_url)

    def _build_client_config(self, auth: _CodexAuthConfig) -> CodexConfig:
        from openai_codex import CodexConfig

        env = dict(self.subprocess_environment)
        self.session_path.mkdir(parents=True, exist_ok=True)
        env["CODEX_HOME"] = str(self.session_path)

        overrides = list(self._launch_config.config_overrides)
        if auth.base_url:
            overrides.append(f"openai_base_url={json.dumps(auth.base_url)}")
        login_method = "api" if auth.mode == "api_key" else "chatgpt"
        overrides.append(f"forced_login_method={json.dumps(login_method)}")
        return CodexConfig(
            codex_bin=self._launch_config.codex_bin,
            config_overrides=tuple(overrides),
            cwd=str(self.cwd),
            env=env,
            client_name="reme",
            client_title="ReMe",
            experimental_api=self._launch_config.experimental_api,
        )

    @classmethod
    def _reject_client_options(cls, kwargs: dict[str, Any]) -> None:
        invalid = sorted(cls._CLIENT_OPTION_NAMES.intersection(kwargs))
        if invalid:
            names = ", ".join(invalid)
            raise TypeError(f"Codex client options must be configured on the wrapper: {names}")

    def _mcp_server_config(self, kwargs: dict[str, Any]) -> dict[str, Any] | None:
        from ..job import BackgroundJob, StreamJob

        job_names = list(dict.fromkeys(kwargs.get("job_tools") or []))
        if not job_names:
            return None
        jobs = self._resolve_job_tools(job_names)
        unsupported = [job.name for job in jobs if isinstance(job, (BackgroundJob, StreamJob))]
        if unsupported:
            raise TypeError(f"Codex job_tools must be non-stream request jobs: {', '.join(unsupported)}")

        config_source = self._mcp_config_source(kwargs)
        args = [
            "-m",
            "reme.components.agent_wrapper.codex_mcp_server",
            "--config",
            config_source,
            "--workspace",
            str(self.workspace_path),
        ]
        for name in job_names:
            args.extend(["--job", name])
        args.extend(["--tool-context-id", str(kwargs.get("tool_context_id") or "")])
        return {
            "command": sys.executable,
            "args": args,
            "cwd": str(self.project_path),
            "required": True,
            "enabled_tools": job_names,
            "startup_timeout_sec": kwargs.get("mcp_startup_timeout", 30),
            "tool_timeout_sec": kwargs.get("mcp_tool_timeout", 300),
        }

    def _thread_config(self, kwargs: dict[str, Any]) -> dict[str, Any] | None:
        config = dict(kwargs.get("config") or {})
        if proxy_environment := self.command_proxy_environment:
            shell_environment_policy = dict(config.get("shell_environment_policy") or {})
            environment = dict(shell_environment_policy.get("set") or {})
            environment.update(proxy_environment)
            shell_environment_policy["set"] = environment
            config["shell_environment_policy"] = shell_environment_policy
        if server := self._mcp_server_config(kwargs):
            servers = dict(config.get("mcp_servers") or {})
            server_key = hashlib.sha256(json.dumps(server, sort_keys=True).encode()).hexdigest()[:12]
            servers[f"reme_jobs_{server_key}"] = server
            config["mcp_servers"] = servers
        return config or None

    @staticmethod
    def _enum(enum_cls: Any, value: Any, default: Any = None) -> Any:
        if value is None:
            return default
        return value if isinstance(value, enum_cls) else enum_cls(value)

    async def _open_thread(self, codex: AsyncCodex, kwargs: dict[str, Any]) -> AsyncThread:
        from openai_codex import ApprovalMode, Sandbox
        from openai_codex.types import Personality, ThreadSource, ThreadStartSource

        resume = kwargs.get("resume") or ""
        session_id = kwargs.get("session_id") or ""
        if resume and session_id and resume != session_id:
            raise ValueError("resume and session_id must identify the same Codex thread")
        thread_id = resume or session_id
        fork_session = bool(kwargs.get("fork_session", False))
        if fork_session and not thread_id:
            raise ValueError("fork_session=True requires resume or session_id")
        requested_tool_context = str(kwargs.get("tool_context_id") or "")
        if not fork_session and thread_id in self._thread_tool_contexts:
            if requested_tool_context != self._thread_tool_contexts[thread_id]:
                raise ValueError("tool_context_id cannot change when resuming a Codex thread")

        common = {
            "approval_mode": self._enum(ApprovalMode, kwargs.get("approval_mode"), ApprovalMode.auto_review),
            "base_instructions": kwargs.get("base_instructions"),
            "config": self._thread_config(kwargs),
            "cwd": str(self.cwd),
            "developer_instructions": kwargs.get("system_prompt"),
            "model": kwargs.get("model"),
            "model_provider": kwargs.get("model_provider"),
            "sandbox": self._enum(Sandbox, kwargs.get("sandbox"), Sandbox.full_access),
            "service_tier": kwargs.get("service_tier"),
        }
        personality = self._enum(Personality, kwargs.get("personality"))
        thread_source = self._enum(ThreadSource, kwargs.get("thread_source"))
        if fork_session:
            thread = await codex.thread_fork(
                thread_id,
                ephemeral=kwargs.get("ephemeral"),
                thread_source=thread_source,
                **common,
            )
        elif thread_id:
            thread = await codex.thread_resume(thread_id, personality=personality, **common)
        else:
            thread = await codex.thread_start(
                ephemeral=kwargs.get("ephemeral"),
                personality=personality,
                service_name=kwargs.get("service_name"),
                session_start_source=self._enum(ThreadStartSource, kwargs.get("session_start_source")),
                thread_source=thread_source,
                **common,
            )
        self._thread_tool_contexts[thread.id] = requested_tool_context
        return thread

    def _turn_kwargs(self, kwargs: dict[str, Any]) -> dict[str, Any]:
        from openai_codex import ApprovalMode, Sandbox
        from openai_codex.types import Personality, ReasoningEffort, ReasoningSummary

        return {
            "approval_mode": self._enum(ApprovalMode, kwargs.get("approval_mode")),
            "cwd": str(self.cwd),
            "effort": self._enum(ReasoningEffort, kwargs.get("effort")),
            "model": kwargs.get("model"),
            "output_schema": kwargs.get("output_schema"),
            "personality": self._enum(Personality, kwargs.get("personality")),
            "sandbox": self._enum(Sandbox, kwargs.get("sandbox")),
            "service_tier": kwargs.get("service_tier"),
            "summary": self._enum(ReasoningSummary, kwargs.get("summary")),
        }

    async def _get_codex(self) -> AsyncCodex:
        """Lazily start the component-owned client from its fixed launch configuration."""
        if self._codex is not None:
            return self._codex
        auth = self._resolve_auth_config(
            self._launch_config.auth_mode,
            self._launch_config.api_key,
            self._launch_config.base_url,
        )
        codex = _get_async_codex_class()(self._build_client_config(auth))
        try:
            if auth.mode == "api_key":
                await codex.login_api_key(auth.api_key)
            else:
                account = await codex.account()
                if account.account is None:
                    raise RuntimeError(f"No ChatGPT OAuth login found in CODEX_HOME: {self.session_path}")
        except BaseException:
            await codex.close()
            raise
        self._codex = codex
        return codex

    async def _close(self) -> None:
        """Close the persistent app-server and remove its private config snapshot."""
        async with self._turn_lock:
            codex, self._codex = self._codex, None
            self._thread_tool_contexts.clear()
            try:
                if codex is not None:
                    await codex.close()
            finally:
                if self._mcp_snapshot_path is not None:
                    self._mcp_snapshot_path.unlink(missing_ok=True)
                    self._mcp_snapshot_path = None

    @staticmethod
    def _serialize(value: Any) -> Any:
        from pydantic_core import to_jsonable_python

        return to_jsonable_python(value, by_alias=True)

    async def reply(self, inputs: RunInput, **kwargs) -> dict:
        """Run one Codex turn and return its final response."""
        self._reject_client_options(kwargs)
        kwargs = self._merged_kwargs(kwargs)
        self._ensure_skills(kwargs.get("skills"))
        await self.start()
        async with self._turn_lock:
            codex = await self._get_codex()
            thread = await self._open_thread(codex, kwargs)
            result = await thread.run(inputs, **self._turn_kwargs(kwargs))

        final_response = result.final_response or ""
        response = {
            "session_id": thread.id,
            "last_message": final_response,
            "result": final_response,
            "turn": self._serialize(result),
        }
        if kwargs.get("output_schema") is not None:
            try:
                response["structured_output"] = json.loads(final_response)
            except json.JSONDecodeError as exc:
                raise ValueError("Codex returned invalid JSON for the requested output_schema") from exc
        return response

    async def compact_session(self, session_id: str) -> None:
        """Start native compaction of a Codex thread."""
        await self.start()
        async with self._turn_lock:
            codex = await self._get_codex()
            thread = await codex.thread_resume(session_id)
            await thread.compact()

    @classmethod
    # pylint: disable=too-many-return-statements
    def _event_to_chunks(cls, event: Notification, session_id: str) -> list[StreamChunk]:
        """Convert one Codex app-server notification to unified stream chunks."""
        method, payload = event.method, event.payload
        make_chunk = partial(cls._chunk, session_id=session_id)
        if method == "turn/started":
            return [make_chunk(ChunkEnum.REPLY_START, metadata={"turn_id": payload.turn.id})]
        if method == "item/agentMessage/delta":
            return [make_chunk(ChunkEnum.CONTENT, block_id=payload.item_id, chunk=payload.delta)]
        if method in {"item/reasoning/summaryTextDelta", "item/reasoning/textDelta", "item/plan/delta"}:
            return [make_chunk(ChunkEnum.THINK, block_id=payload.item_id, chunk=payload.delta)]
        if method in {"item/commandExecution/outputDelta", "item/fileChange/outputDelta"}:
            return [
                make_chunk(
                    ChunkEnum.TOOL_RESULT,
                    block_id=payload.item_id,
                    tool_call_id=payload.item_id,
                    chunk=payload.delta,
                ),
            ]
        if method == "item/mcpToolCall/progress":
            return [
                make_chunk(
                    ChunkEnum.TOOL_RESULT,
                    block_id=payload.item_id,
                    tool_call_id=payload.item_id,
                    chunk=payload.message,
                ),
            ]
        if method in {"item/autoApprovalReview/started", "item/autoApprovalReview/completed"}:
            action = cls._serialize(payload.action)
            review = cls._serialize(payload.review)
            review_id = payload.review_id
            target_item_id = getattr(payload, "target_item_id", None)
            status = "started" if method.endswith("/started") else "completed"
            decision_source = cls._serialize(getattr(payload, "decision_source", None))
            return [
                make_chunk(
                    ChunkEnum.APPROVAL,
                    block_id=target_item_id or review_id,
                    tool_call_id=target_item_id,
                    chunk=action,
                    metadata={
                        "review_id": review_id,
                        "status": status,
                        "review": review,
                        "decision_source": decision_source,
                        "turn_id": payload.turn_id,
                    },
                ),
            ]
        if method in {"item/started", "item/completed"}:
            item = payload.item.root
            item_type, item_id = item.type, item.id
            tool_types = {"commandExecution", "fileChange", "mcpToolCall", "dynamicToolCall", "collabAgentToolCall"}
            if item_type not in tool_types:
                return []
            name = getattr(item, "tool", None) or item_type
            chunk_type = ChunkEnum.TOOL_CALL if method == "item/started" else ChunkEnum.TOOL_RESULT
            return [
                make_chunk(
                    chunk_type,
                    block_id=item_id,
                    tool_call_id=item_id,
                    tool_call_name=name,
                    chunk=cls._serialize(item),
                ),
            ]
        if method == "thread/tokenUsage/updated":
            usage = payload.token_usage.last
            data = cls._serialize(usage)
            return [
                make_chunk(
                    ChunkEnum.USAGE,
                    chunk=data,
                    input_tokens=usage.input_tokens,
                    output_tokens=usage.output_tokens,
                ),
            ]
        if method == "error":
            return [
                make_chunk(
                    ChunkEnum.ERROR,
                    chunk=payload.error.message,
                    metadata={"will_retry": payload.will_retry},
                ),
            ]
        if method == "turn/completed":
            turn = payload.turn
            chunks = []
            if turn.error:
                chunks.append(make_chunk(ChunkEnum.ERROR, chunk=turn.error.message))
            chunks.append(
                make_chunk(
                    ChunkEnum.REPLY_END,
                    metadata={
                        "turn_id": turn.id,
                        "status": turn.status.value,
                        "duration_ms": turn.duration_ms,
                    },
                ),
            )
            return chunks
        if getattr(payload, "turn_id", None):
            return [
                make_chunk(
                    ChunkEnum.DATA,
                    block_id=getattr(payload, "item_id", None),
                    chunk=cls._serialize(payload),
                    metadata={"codex_method": method},
                ),
            ]
        return []

    async def reply_stream(self, inputs: RunInput, **kwargs) -> AsyncGenerator[StreamChunk, None]:
        """Stream Codex app-server notifications as unified chunks."""
        self._reject_client_options(kwargs)
        kwargs = self._merged_stream_kwargs(kwargs)
        self._ensure_skills(kwargs.get("skills"))
        await self.start()
        async with self._turn_lock:
            codex = await self._get_codex()
            thread = await self._open_thread(codex, kwargs)
            turn = await thread.turn(inputs, **self._turn_kwargs(kwargs))
            stream = turn.stream()
            completed = False
            try:
                async for event in stream:
                    if event.method == "turn/completed":
                        completed = True
                    for chunk in self._event_to_chunks(event, thread.id):
                        yield chunk
            finally:
                if not completed:
                    try:
                        await turn.interrupt()
                    except Exception as exc:  # pylint: disable=broad-exception-caught
                        self.logger.warning(f"Failed to interrupt Codex turn {turn.id}: {exc}")
                await stream.aclose()