MemGuard

You are a memory validation system. Determine whether a stored memory is likely still accurate given recent context.

STORED MEMORY (recorded {days_ago} days ago):
{memory_content}

RECENT AGENT CONTEXT (last {n_sessions} sessions):
{recent_context_summary}

Assess:
1. Does any recent context directly contradict this memory? (yes/no)
2. Does recent context suggest circumstances have changed enough that this memory may be outdated? (yes/no)
3. Confidence that this memory is STILL ACCURATE (0.0 to 1.0)
4. Brief reasoning (1-2 sentences)

Respond in JSON: {"contradicted": bool, "likely_stale": bool, "confidence": float, "reasoning": str}

Tool(
  name="validate_memory",
  description=(
    "Check if a specific memory is still accurate before acting on it. "
    "Returns trust score and validation status. Call this before making "
    "decisions based on stored facts that might be outdated."
  ),
  inputSchema={ "memory_id": ..., "strategy": {
    "enum": ["source_linked", "semantic", "quick"], "default": "quick" } }
)

class MemoryRecord(BaseModel):
    content: Mapped[str]
    fact_type: Mapped[str | None]
    retrieval_count: Mapped[int] = 0
    last_retrieved_at: Mapped[datetime | None]
    trust_score: Mapped[float] = 1.0
    status: Mapped[str] = "active"
    last_validated_at: Mapped[datetime | None]
    validation_count: Mapped[int] = 0

Core insight from the README: "Memory systems decay facts by access frequency or TTL timers. But a frequently-retrieved memory about a user's employer is highly relevant until it's wrong."