forked from cardosofelipe/fast-next-template
Felipe Cardoso
cf6291ac8e
Auto-fixed linting errors and formatting issues: - Removed unused imports (F401): pytest, Any, AnalysisType, MemoryType, OutcomeType - Removed unused variable (F841): hooks variable in test - Applied consistent formatting across memory service and test files 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
636 lines
21 KiB
Python
636 lines
21 KiB
Python
# app/services/memory/integration/lifecycle.py
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"""
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Agent Lifecycle Hooks for Memory System.
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Provides memory management hooks for agent lifecycle events:
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- spawn: Initialize working memory for new agent instance
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- pause: Checkpoint working memory state
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- resume: Restore working memory from checkpoint
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- terminate: Consolidate session to episodic memory
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"""
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import logging
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from collections.abc import Callable, Coroutine
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from dataclasses import dataclass, field
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from datetime import UTC, datetime
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from typing import Any
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from uuid import UUID
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.services.memory.episodic import EpisodicMemory
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from app.services.memory.types import EpisodeCreate, Outcome
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from app.services.memory.working import WorkingMemory
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logger = logging.getLogger(__name__)
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@dataclass
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class LifecycleEvent:
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"""Event data for lifecycle hooks."""
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event_type: str # spawn, pause, resume, terminate
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project_id: UUID
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agent_instance_id: UUID
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agent_type_id: UUID | None = None
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session_id: str | None = None
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timestamp: datetime = field(default_factory=lambda: datetime.now(UTC))
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metadata: dict[str, Any] = field(default_factory=dict)
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@dataclass
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class LifecycleResult:
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"""Result of a lifecycle operation."""
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success: bool
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event_type: str
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message: str | None = None
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data: dict[str, Any] = field(default_factory=dict)
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duration_ms: float = 0.0
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# Type alias for lifecycle hooks
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LifecycleHook = Callable[[LifecycleEvent], Coroutine[Any, Any, None]]
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class LifecycleHooks:
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"""
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Collection of lifecycle hooks.
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Allows registration of custom hooks for lifecycle events.
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Hooks are called after the core memory operations.
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"""
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def __init__(self) -> None:
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"""Initialize lifecycle hooks."""
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self._spawn_hooks: list[LifecycleHook] = []
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self._pause_hooks: list[LifecycleHook] = []
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self._resume_hooks: list[LifecycleHook] = []
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self._terminate_hooks: list[LifecycleHook] = []
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def on_spawn(self, hook: LifecycleHook) -> LifecycleHook:
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"""Register a spawn hook."""
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self._spawn_hooks.append(hook)
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return hook
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def on_pause(self, hook: LifecycleHook) -> LifecycleHook:
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"""Register a pause hook."""
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self._pause_hooks.append(hook)
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return hook
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def on_resume(self, hook: LifecycleHook) -> LifecycleHook:
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"""Register a resume hook."""
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self._resume_hooks.append(hook)
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return hook
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def on_terminate(self, hook: LifecycleHook) -> LifecycleHook:
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"""Register a terminate hook."""
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self._terminate_hooks.append(hook)
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return hook
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async def run_spawn_hooks(self, event: LifecycleEvent) -> None:
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"""Run all spawn hooks."""
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for hook in self._spawn_hooks:
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try:
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await hook(event)
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except Exception as e:
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logger.warning(f"Spawn hook failed: {e}")
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async def run_pause_hooks(self, event: LifecycleEvent) -> None:
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"""Run all pause hooks."""
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for hook in self._pause_hooks:
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try:
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await hook(event)
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except Exception as e:
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logger.warning(f"Pause hook failed: {e}")
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async def run_resume_hooks(self, event: LifecycleEvent) -> None:
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"""Run all resume hooks."""
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for hook in self._resume_hooks:
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try:
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await hook(event)
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except Exception as e:
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logger.warning(f"Resume hook failed: {e}")
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async def run_terminate_hooks(self, event: LifecycleEvent) -> None:
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"""Run all terminate hooks."""
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for hook in self._terminate_hooks:
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try:
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await hook(event)
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except Exception as e:
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logger.warning(f"Terminate hook failed: {e}")
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class AgentLifecycleManager:
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"""
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Manager for agent lifecycle and memory integration.
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Handles memory operations during agent lifecycle events:
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- spawn: Creates new working memory for the session
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- pause: Saves working memory state to checkpoint
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- resume: Restores working memory from checkpoint
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- terminate: Consolidates working memory to episodic memory
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"""
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# Key prefix for checkpoint storage
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CHECKPOINT_PREFIX = "__checkpoint__"
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def __init__(
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self,
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session: AsyncSession,
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embedding_generator: Any | None = None,
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hooks: LifecycleHooks | None = None,
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) -> None:
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"""
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Initialize the lifecycle manager.
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Args:
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session: Database session
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embedding_generator: Optional embedding generator
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hooks: Optional lifecycle hooks
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"""
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self._session = session
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self._embedding_generator = embedding_generator
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self._hooks = hooks or LifecycleHooks()
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# Lazy-initialized services
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self._episodic: EpisodicMemory | None = None
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async def _get_episodic(self) -> EpisodicMemory:
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"""Get or create episodic memory service."""
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if self._episodic is None:
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self._episodic = await EpisodicMemory.create(
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self._session,
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self._embedding_generator,
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)
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return self._episodic
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@property
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def hooks(self) -> LifecycleHooks:
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"""Get the lifecycle hooks."""
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return self._hooks
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async def spawn(
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self,
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project_id: UUID,
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agent_instance_id: UUID,
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session_id: str,
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agent_type_id: UUID | None = None,
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initial_state: dict[str, Any] | None = None,
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metadata: dict[str, Any] | None = None,
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) -> LifecycleResult:
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"""
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Handle agent spawn - initialize working memory.
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Creates a new working memory instance for the agent session
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and optionally populates it with initial state.
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Args:
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project_id: Project scope
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agent_instance_id: Agent instance ID
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session_id: Session ID for working memory
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agent_type_id: Optional agent type ID
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initial_state: Optional initial state to populate
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metadata: Optional metadata for the event
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Returns:
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LifecycleResult with spawn outcome
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"""
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start_time = datetime.now(UTC)
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try:
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# Create working memory for the session
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working = await WorkingMemory.for_session(
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session_id=session_id,
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project_id=str(project_id),
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agent_instance_id=str(agent_instance_id),
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)
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# Populate initial state if provided
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items_set = 0
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if initial_state:
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for key, value in initial_state.items():
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await working.set(key, value)
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items_set += 1
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# Create and run event hooks
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event = LifecycleEvent(
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event_type="spawn",
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project_id=project_id,
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agent_instance_id=agent_instance_id,
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agent_type_id=agent_type_id,
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session_id=session_id,
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metadata=metadata or {},
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)
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await self._hooks.run_spawn_hooks(event)
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duration_ms = (datetime.now(UTC) - start_time).total_seconds() * 1000
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logger.info(
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f"Agent {agent_instance_id} spawned with session {session_id}, "
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f"initial state: {items_set} items"
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)
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return LifecycleResult(
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success=True,
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event_type="spawn",
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message="Agent spawned successfully",
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data={
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"session_id": session_id,
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"initial_items": items_set,
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},
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duration_ms=duration_ms,
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)
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except Exception as e:
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logger.error(f"Spawn failed for agent {agent_instance_id}: {e}")
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return LifecycleResult(
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success=False,
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event_type="spawn",
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message=f"Spawn failed: {e}",
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)
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async def pause(
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self,
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project_id: UUID,
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agent_instance_id: UUID,
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session_id: str,
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checkpoint_id: str | None = None,
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metadata: dict[str, Any] | None = None,
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) -> LifecycleResult:
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"""
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Handle agent pause - checkpoint working memory.
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Saves the current working memory state to a checkpoint
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that can be restored later with resume().
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Args:
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project_id: Project scope
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agent_instance_id: Agent instance ID
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session_id: Session ID
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checkpoint_id: Optional checkpoint identifier
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metadata: Optional metadata for the event
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Returns:
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LifecycleResult with checkpoint data
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"""
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start_time = datetime.now(UTC)
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checkpoint_id = checkpoint_id or f"checkpoint_{int(start_time.timestamp())}"
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try:
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working = await WorkingMemory.for_session(
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session_id=session_id,
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project_id=str(project_id),
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agent_instance_id=str(agent_instance_id),
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)
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# Get all current state
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all_keys = await working.list_keys()
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# Filter out checkpoint keys
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state_keys = [
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k for k in all_keys if not k.startswith(self.CHECKPOINT_PREFIX)
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]
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state: dict[str, Any] = {}
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for key in state_keys:
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value = await working.get(key)
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if value is not None:
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state[key] = value
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# Store checkpoint
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checkpoint_key = f"{self.CHECKPOINT_PREFIX}{checkpoint_id}"
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await working.set(
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checkpoint_key,
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{
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"state": state,
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"timestamp": start_time.isoformat(),
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"keys_count": len(state),
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},
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ttl_seconds=86400 * 7, # Keep checkpoint for 7 days
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)
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# Run hooks
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event = LifecycleEvent(
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event_type="pause",
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project_id=project_id,
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agent_instance_id=agent_instance_id,
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session_id=session_id,
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metadata={**(metadata or {}), "checkpoint_id": checkpoint_id},
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)
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await self._hooks.run_pause_hooks(event)
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duration_ms = (datetime.now(UTC) - start_time).total_seconds() * 1000
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logger.info(
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f"Agent {agent_instance_id} paused, checkpoint {checkpoint_id} "
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f"saved with {len(state)} items"
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)
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return LifecycleResult(
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success=True,
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event_type="pause",
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message="Agent paused successfully",
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data={
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"checkpoint_id": checkpoint_id,
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"items_saved": len(state),
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"timestamp": start_time.isoformat(),
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},
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duration_ms=duration_ms,
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)
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except Exception as e:
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logger.error(f"Pause failed for agent {agent_instance_id}: {e}")
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return LifecycleResult(
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success=False,
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event_type="pause",
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message=f"Pause failed: {e}",
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)
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async def resume(
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self,
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project_id: UUID,
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agent_instance_id: UUID,
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session_id: str,
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checkpoint_id: str,
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clear_current: bool = True,
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metadata: dict[str, Any] | None = None,
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) -> LifecycleResult:
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"""
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Handle agent resume - restore from checkpoint.
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Restores working memory state from a previously saved checkpoint.
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Args:
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project_id: Project scope
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agent_instance_id: Agent instance ID
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session_id: Session ID
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checkpoint_id: Checkpoint to restore from
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clear_current: Whether to clear current state before restoring
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metadata: Optional metadata for the event
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Returns:
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LifecycleResult with restore outcome
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"""
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start_time = datetime.now(UTC)
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try:
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working = await WorkingMemory.for_session(
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session_id=session_id,
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project_id=str(project_id),
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agent_instance_id=str(agent_instance_id),
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)
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# Get checkpoint
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checkpoint_key = f"{self.CHECKPOINT_PREFIX}{checkpoint_id}"
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checkpoint = await working.get(checkpoint_key)
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if checkpoint is None:
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return LifecycleResult(
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success=False,
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event_type="resume",
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message=f"Checkpoint '{checkpoint_id}' not found",
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)
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# Clear current state if requested
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if clear_current:
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all_keys = await working.list_keys()
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for key in all_keys:
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if not key.startswith(self.CHECKPOINT_PREFIX):
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await working.delete(key)
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# Restore state from checkpoint
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state = checkpoint.get("state", {})
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items_restored = 0
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for key, value in state.items():
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await working.set(key, value)
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items_restored += 1
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# Run hooks
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event = LifecycleEvent(
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event_type="resume",
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project_id=project_id,
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agent_instance_id=agent_instance_id,
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session_id=session_id,
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metadata={**(metadata or {}), "checkpoint_id": checkpoint_id},
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)
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await self._hooks.run_resume_hooks(event)
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duration_ms = (datetime.now(UTC) - start_time).total_seconds() * 1000
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logger.info(
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f"Agent {agent_instance_id} resumed from checkpoint {checkpoint_id}, "
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f"restored {items_restored} items"
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)
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return LifecycleResult(
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success=True,
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event_type="resume",
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message="Agent resumed successfully",
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data={
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"checkpoint_id": checkpoint_id,
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"items_restored": items_restored,
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"checkpoint_timestamp": checkpoint.get("timestamp"),
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},
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duration_ms=duration_ms,
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)
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except Exception as e:
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logger.error(f"Resume failed for agent {agent_instance_id}: {e}")
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return LifecycleResult(
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success=False,
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event_type="resume",
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message=f"Resume failed: {e}",
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)
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async def terminate(
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self,
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project_id: UUID,
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agent_instance_id: UUID,
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session_id: str,
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task_description: str | None = None,
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outcome: Outcome = Outcome.SUCCESS,
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lessons_learned: list[str] | None = None,
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consolidate_to_episodic: bool = True,
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cleanup_working: bool = True,
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metadata: dict[str, Any] | None = None,
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) -> LifecycleResult:
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"""
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Handle agent termination - consolidate to episodic memory.
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Consolidates the session's working memory into an episodic memory
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entry, then optionally cleans up the working memory.
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Args:
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project_id: Project scope
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agent_instance_id: Agent instance ID
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session_id: Session ID
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task_description: Description of what was accomplished
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outcome: Task outcome (SUCCESS, FAILURE, PARTIAL)
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lessons_learned: Optional list of lessons learned
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consolidate_to_episodic: Whether to create episodic entry
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cleanup_working: Whether to clear working memory
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metadata: Optional metadata for the event
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Returns:
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LifecycleResult with termination outcome
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"""
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start_time = datetime.now(UTC)
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try:
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working = await WorkingMemory.for_session(
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session_id=session_id,
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project_id=str(project_id),
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agent_instance_id=str(agent_instance_id),
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)
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# Gather session state for consolidation
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all_keys = await working.list_keys()
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state_keys = [
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k for k in all_keys if not k.startswith(self.CHECKPOINT_PREFIX)
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]
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session_state: dict[str, Any] = {}
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for key in state_keys:
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value = await working.get(key)
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if value is not None:
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session_state[key] = value
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episode_id: str | None = None
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# Consolidate to episodic memory
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if consolidate_to_episodic:
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episodic = await self._get_episodic()
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description = task_description or f"Session {session_id} completed"
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episode_data = EpisodeCreate(
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project_id=project_id,
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agent_instance_id=agent_instance_id,
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session_id=session_id,
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task_type="session_completion",
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task_description=description[:500],
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outcome=outcome,
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outcome_details=f"Session terminated with {len(session_state)} state items",
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actions=[
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{
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"type": "session_terminate",
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"state_keys": list(session_state.keys()),
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"outcome": outcome.value,
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}
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],
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context_summary=str(session_state)[:1000] if session_state else "",
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lessons_learned=lessons_learned or [],
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duration_seconds=0.0, # Unknown at this point
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tokens_used=0,
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importance_score=0.6, # Moderate importance for session ends
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)
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episode = await episodic.record_episode(episode_data)
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episode_id = str(episode.id)
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# Clean up working memory
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items_cleared = 0
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if cleanup_working:
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for key in all_keys:
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await working.delete(key)
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items_cleared += 1
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# Run hooks
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event = LifecycleEvent(
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event_type="terminate",
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project_id=project_id,
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agent_instance_id=agent_instance_id,
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session_id=session_id,
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metadata={**(metadata or {}), "episode_id": episode_id},
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)
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await self._hooks.run_terminate_hooks(event)
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duration_ms = (datetime.now(UTC) - start_time).total_seconds() * 1000
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logger.info(
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f"Agent {agent_instance_id} terminated, session {session_id} "
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f"consolidated to episode {episode_id}"
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)
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|
|
|
return LifecycleResult(
|
|
success=True,
|
|
event_type="terminate",
|
|
message="Agent terminated successfully",
|
|
data={
|
|
"episode_id": episode_id,
|
|
"state_items_consolidated": len(session_state),
|
|
"items_cleared": items_cleared,
|
|
"outcome": outcome.value,
|
|
},
|
|
duration_ms=duration_ms,
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Terminate failed for agent {agent_instance_id}: {e}")
|
|
return LifecycleResult(
|
|
success=False,
|
|
event_type="terminate",
|
|
message=f"Terminate failed: {e}",
|
|
)
|
|
|
|
async def list_checkpoints(
|
|
self,
|
|
project_id: UUID,
|
|
agent_instance_id: UUID,
|
|
session_id: str,
|
|
) -> list[dict[str, Any]]:
|
|
"""
|
|
List available checkpoints for a session.
|
|
|
|
Args:
|
|
project_id: Project scope
|
|
agent_instance_id: Agent instance ID
|
|
session_id: Session ID
|
|
|
|
Returns:
|
|
List of checkpoint metadata dicts
|
|
"""
|
|
working = await WorkingMemory.for_session(
|
|
session_id=session_id,
|
|
project_id=str(project_id),
|
|
agent_instance_id=str(agent_instance_id),
|
|
)
|
|
|
|
all_keys = await working.list_keys()
|
|
checkpoints: list[dict[str, Any]] = []
|
|
|
|
for key in all_keys:
|
|
if key.startswith(self.CHECKPOINT_PREFIX):
|
|
checkpoint_id = key[len(self.CHECKPOINT_PREFIX) :]
|
|
checkpoint = await working.get(key)
|
|
if checkpoint:
|
|
checkpoints.append(
|
|
{
|
|
"checkpoint_id": checkpoint_id,
|
|
"timestamp": checkpoint.get("timestamp"),
|
|
"keys_count": checkpoint.get("keys_count", 0),
|
|
}
|
|
)
|
|
|
|
# Sort by timestamp (newest first)
|
|
checkpoints.sort(
|
|
key=lambda c: c.get("timestamp", ""),
|
|
reverse=True,
|
|
)
|
|
|
|
return checkpoints
|
|
|
|
|
|
# Factory function
|
|
async def get_lifecycle_manager(
|
|
session: AsyncSession,
|
|
embedding_generator: Any | None = None,
|
|
hooks: LifecycleHooks | None = None,
|
|
) -> AgentLifecycleManager:
|
|
"""Create a lifecycle manager instance."""
|
|
return AgentLifecycleManager(
|
|
session=session,
|
|
embedding_generator=embedding_generator,
|
|
hooks=hooks,
|
|
)
|