# memharness - Framework-agnostic memory infrastructure for AI agents
# Copyright (c) 2026 Ayush Sonuu
# Licensed under MIT License

"""
Garbage collection agent for memory cleanup.

Archives or deletes old memories based on age and access patterns.
"""

from __future__ import annotations

from datetime import datetime, timedelta
from typing import TYPE_CHECKING, Any

if TYPE_CHECKING:
    from langchain_core.language_models import BaseChatModel

    from memharness.core.harness import MemoryHarness


class GCAgent:
    """
    Agent that performs garbage collection on old memories.

    This agent is purely deterministic and doesn't use an LLM.
    It archives or deletes memories based on age thresholds.

    Example:
        ```python
        agent = GCAgent(harness, archive_after_days=90, delete_after_days=365)
        result = await agent.run()
        # Returns: {"archived": 10, "deleted": 3}
        ```
    """

    def __init__(
        self,
        harness: MemoryHarness,
        llm: BaseChatModel | None = None,
        archive_after_days: int = 90,
        delete_after_days: int = 365,
    ) -> None:
        """
        Initialize the GC agent.

        Args:
            harness: The MemoryHarness instance to operate on.
            llm: Optional LLM (not used by GC agent).
            archive_after_days: Archive memories older than this many days.
            delete_after_days: Delete memories older than this many days.
        """
        self.harness = harness
        self.llm = llm  # Not used, but kept for interface consistency
        self.archive_after_days = archive_after_days
        self.delete_after_days = delete_after_days

    async def archive_old_memories(self) -> int:
        """
        Archive memories older than the archive threshold.

        Returns:
            Number of memories archived.
        """
        # Calculate cutoff date
        datetime.now() - timedelta(days=self.archive_after_days)

        # This is a simplified implementation
        # In production, you'd query the backend for old memories and mark them as archived
        archived_count = 0

        # TODO: Implement actual archival logic
        # Example:
        # old_memories = await self.harness.backend.search_by_date(before=cutoff)
        # for memory in old_memories:
        #     memory.metadata["archived"] = True
        #     await self.harness.backend.update(memory)
        #     archived_count += 1

        return archived_count

    async def delete_old_memories(self) -> int:
        """
        Delete memories older than the deletion threshold.

        Returns:
            Number of memories deleted.
        """
        # Calculate cutoff date
        datetime.now() - timedelta(days=self.delete_after_days)

        # This is a simplified implementation
        # In production, you'd query the backend for very old memories and delete them
        deleted_count = 0

        # TODO: Implement actual deletion logic
        # Example:
        # very_old_memories = await self.harness.backend.search_by_date(before=cutoff)
        # for memory in very_old_memories:
        #     await self.harness.backend.delete(memory.memory_id)
        #     deleted_count += 1

        return deleted_count

    async def run(self, **kwargs: Any) -> dict[str, Any]:
        """
        Run the garbage collection agent.

        Args:
            **kwargs: Additional arguments (ignored).

        Returns:
            Dictionary with 'archived' and 'deleted' counts.
        """
        archived = await self.archive_old_memories()
        deleted = await self.delete_old_memories()

        return {
            "archived": archived,
            "deleted": deleted,
            "archive_after_days": self.archive_after_days,
            "delete_after_days": self.delete_after_days,
        }
