docs+skills: add main UI/UX visual-truth PRD and skill links
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331
.agents/skills/rlm-mem/auto_memory.py
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331
.agents/skills/rlm-mem/auto_memory.py
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#!/usr/bin/env python3
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"""
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Automatic Memory Update System for RLM-MEM.
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This module provides hooks to automatically remember things as we work,
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without requiring explicit "remember this" commands.
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Usage:
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from auto_memory import AutoMemory
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auto_mem = AutoMemory('brain/memory')
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# Call at start of session
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auto_mem.start_session()
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# Call when completing a task
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auto_mem.record_task_completion(task_id, what_was_done, outcome)
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# Call when making a decision
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auto_mem.record_decision(decision, rationale, alternatives_considered)
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# Call when discovering user preference
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auto_mem.record_preference(what_was_learned, context)
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# Call at end of session
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auto_mem.end_session()
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"""
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import os
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import sys
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from datetime import datetime
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from typing import List, Optional, Dict, Any
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from pathlib import Path
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# Add project root to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent))
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from brain.scripts import (
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LayeredMemoryStore,
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LayeredChunkStoreAdapter,
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MemoryPolicy,
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RememberOperation
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)
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class AutoMemory:
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"""
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Automatically remembers things as we work, without explicit commands.
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This integrates with the agent workflow to capture:
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- Task completions and outcomes
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- Decisions and rationale
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- User preferences discovered
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- File changes and patterns
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- Session context
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"""
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def __init__(self, memory_path: str = '.agents/memory', conversation_id: Optional[str] = None):
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# We ignore memory_path for the layered store as it uses MemoryPolicy
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# But we keep the argument for backward compatibility in signature
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self.policy = MemoryPolicy(project_root=Path.cwd())
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# We need a stable agent ID for auto-memory.
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# In a real environment, this might come from env vars.
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self.raw_store = LayeredMemoryStore(policy=self.policy, agent_id="auto-memory-agent")
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self.store = LayeredChunkStoreAdapter(self.raw_store)
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self.remember = RememberOperation(self.store)
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self.conversation_id = conversation_id or f"session-{datetime.now().strftime('%Y-%m-%d-%H%M')}"
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self.session_start = datetime.now()
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self.things_learned: List[Dict[str, Any]] = []
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def start_session(self, context: str = ""):
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"""Record session start with context."""
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self.remember.remember(
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content=f"Session started at {self.session_start.isoformat()}. Context: {context or 'General work session'}",
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conversation_id=self.conversation_id,
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tags=['session', 'start'],
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confidence=1.0,
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chunk_type='note'
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)
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def record_task_completion(self, task_id: str, what_was_done: str,
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outcome: str, files_modified: List[str] = None):
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"""
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Record that a task was completed.
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Args:
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task_id: Identifier for the task (e.g., bead ID)
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what_was_done: Description of what was accomplished
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outcome: Success, failure, partial, etc.
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files_modified: List of files that were changed
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"""
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files_str = f"\nFiles modified: {', '.join(files_modified)}" if files_modified else ""
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content = f"""Task {task_id} completed.
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What was done: {what_was_done}
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Outcome: {outcome}{files_str}"""
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self.remember.remember(
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content=content,
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conversation_id=self.conversation_id,
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tags=['task', 'completion', task_id],
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confidence=0.95,
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chunk_type='note'
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)
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self.things_learned.append({
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'type': 'task',
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'id': task_id,
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'outcome': outcome
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})
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def record_decision(self, decision: str, rationale: str,
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alternatives: List[str] = None, confidence: float = 0.9):
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"""
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Record a decision that was made.
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Args:
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decision: What was decided
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rationale: Why this decision was made
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alternatives: Other options considered
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confidence: How confident we are in this decision (0-1)
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"""
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alt_str = ""
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if alternatives:
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alt_str = f"\nAlternatives considered: {', '.join(alternatives)}"
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content = f"""Decision: {decision}
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Rationale: {rationale}{alt_str}"""
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self.remember.remember(
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content=content,
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conversation_id=self.conversation_id,
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tags=['decision', 'architecture'],
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confidence=confidence,
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chunk_type='decision'
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)
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self.things_learned.append({
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'type': 'decision',
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'decision': decision
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})
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def record_preference(self, what_was_learned: str, context: str = "",
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confidence: float = 0.85):
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"""
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Record a user preference discovered during work.
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Args:
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what_was_learned: The preference discovered
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context: When/how we learned this
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confidence: How sure we are (0-1)
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"""
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ctx_str = f"\nContext: {context}" if context else ""
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content = f"""User preference: {what_was_learned}{ctx_str}"""
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self.remember.remember(
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content=content,
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conversation_id=self.conversation_id,
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tags=['preference', 'user'],
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confidence=confidence,
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chunk_type='preference'
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)
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self.things_learned.append({
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'type': 'preference',
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'content': what_was_learned
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})
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def record_file_pattern(self, pattern_type: str, description: str, examples: List[str]):
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"""
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Record a pattern observed in the codebase.
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Args:
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pattern_type: e.g., 'naming', 'structure', 'testing'
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description: What the pattern is
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examples: Examples of the pattern
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"""
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examples_str = '\n'.join(f" - {ex}" for ex in examples[:3])
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content = f"""Code pattern ({pattern_type}): {description}
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Examples:
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{examples_str}"""
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self.remember.remember(
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content=content,
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conversation_id=self.conversation_id,
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tags=['pattern', pattern_type, 'codebase'],
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confidence=0.9,
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chunk_type='pattern'
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)
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def record_issue_resolution(self, issue: str, solution: str,
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root_cause: str = ""):
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"""
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Record how an issue was resolved.
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Args:
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issue: What went wrong
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solution: How it was fixed
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root_cause: Why it happened (optional)
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"""
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root_str = f"\nRoot cause: {root_cause}" if root_cause else ""
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content = f"""Issue: {issue}
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Solution: {solution}{root_str}"""
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self.remember.remember(
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content=content,
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conversation_id=self.conversation_id,
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tags=['issue', 'resolution', 'fix'],
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confidence=0.95,
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chunk_type='note'
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)
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def end_session(self, summary: str = ""):
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"""Record session end with summary."""
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duration = datetime.now() - self.session_start
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things_str = "\n".join(
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f" - {item['type']}: {item.get('id', item.get('decision', item.get('content', 'unknown')))[:50]}"
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for item in self.things_learned[-10:] # Last 10 things
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)
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content = f"""Session ended at {datetime.now().isoformat()}.
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Duration: {duration}
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Things learned/recorded: {len(self.things_learned)}
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Recent activity:
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{things_str}
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Summary: {summary or 'Work session completed'}"""
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self.remember.remember(
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content=content,
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conversation_id=self.conversation_id,
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tags=['session', 'end', 'summary'],
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confidence=1.0,
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chunk_type='note'
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)
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def get_stats(self) -> Dict[str, Any]:
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"""Get stats about what we've remembered."""
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return {
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'things_learned_this_session': len(self.things_learned),
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'conversation_id': self.conversation_id,
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'session_duration': datetime.now() - self.session_start,
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'store_stats': self.store.get_stats()
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}
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# Convenience function for quick recording
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def quick_remember(content: str, tags: List[str] = None,
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memory_path: str = '.agents/memory',
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conversation_id: str = None):
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"""
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Quickly remember something without creating an AutoMemory instance.
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Usage:
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from auto_memory import quick_remember
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quick_remember(
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content="User prefers explicit types",
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tags=['preference', 'python']
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)
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"""
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policy = MemoryPolicy(project_root=Path.cwd())
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raw_store = LayeredMemoryStore(policy=policy, agent_id="quick-remember-agent")
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store = LayeredChunkStoreAdapter(raw_store)
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remember = RememberOperation(store)
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result = remember.remember(
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content=content,
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conversation_id=conversation_id or f"quick-{datetime.now().isoformat()}",
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tags=tags or ['note'],
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confidence=0.9,
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chunk_type='note'
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)
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return result
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if __name__ == "__main__":
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# Demo the auto memory system
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print("=" * 60)
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print("AUTO MEMORY SYSTEM DEMO")
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print("=" * 60)
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auto_mem = AutoMemory('brain/memory', conversation_id='demo-auto-memory')
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# Simulate a work session
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auto_mem.start_session("Working on RLM-MEM Enhanced documentation")
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auto_mem.record_task_completion(
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task_id="D5.2",
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what_was_done="Created comprehensive skill documentation with examples",
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outcome="success",
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files_modified=["SKILL.md", "ARCHITECTURE.md", "API.md"]
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)
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auto_mem.record_preference(
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what_was_learned="User wants automatic memory updates without explicit commands",
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context="User said 'I should not need to tell you to remember things'",
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confidence=0.95
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)
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auto_mem.record_decision(
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decision="Store memories in brain/memory/ instead of temp directories",
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rationale="Need persistence across sessions",
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alternatives=["Use temp directories", "Store in SQLite", "Use external DB"],
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confidence=0.9
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)
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auto_mem.record_file_pattern(
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pattern_type="testing",
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description="Tests use descriptive names with numbered phases",
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examples=["test_complete_system.py", "test_rlm_mem_original_format.py"]
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)
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auto_mem.end_session("Demo completed successfully")
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stats = auto_mem.get_stats()
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print(f"\nSession recorded:")
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print(f" Conversation ID: {stats['conversation_id']}")
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print(f" Things learned: {stats['things_learned_this_session']}")
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print(f" Total chunks in store: {stats['store_stats']['total_chunks']}")
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print("\n[OK] Auto memory system is ready to use!")
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