Supporting metadata for extracted memories

[RO-HIT17]

Hi mem0 team

I’ve been using custom_fact_extraction_prompt and really like how much control it gives over what gets stored as memory. I wanted to ask about a possible extension to allow custom metadata to be attached to extracted facts.

In applications, like personalized tutoring, memories represent a student’s learning state, not just a static fact. Along with the extracted text, it’s useful to capture:

• the category of memory (progress, difficulty, misconception, preference, etc.)
• the progress level associated with it

For example:

• strong → good progress / concept well understood
• medium → actively working on it
• weak → struggling or failing

Examples:

• “Finished 50% of calculus chapter” → progress, strong
• “Still stuck on derivatives” → learning difficulty, weak

Would it be feasible for custom_fact_extraction_prompt to optionally return metadata along with each fact?

Something like:

{
  "facts": [
    {
      "text": "Finished 50% of calculus chapter",
      "metadata": {
        "category": "progress",
        "level": "strong"
      }
    }
  ]
}

Alternatively, is there an existing or recommended pattern for attaching progress levels or custom categories to extracted memories?

Thanks!

[KeepALifeUS]

Metadata for extracted memories is essential for production use. Here's a comprehensive approach:

Metadata Schema Design

from dataclasses import dataclass, field
from datetime import datetime
from typing import List, Dict, Optional
from enum import Enum

class MemorySource(Enum):
    USER_EXPLICIT = "user_explicit"      # User directly stated
    USER_IMPLICIT = "user_implicit"      # Inferred from behavior
    AGENT_DERIVED = "agent_derived"      # Agent concluded
    EXTERNAL_API = "external_api"        # From external source

class ConfidenceLevel(Enum):
    HIGH = "high"        # Directly stated
    MEDIUM = "medium"    # Reasonably inferred
    LOW = "low"          # Speculative

@dataclass
class MemoryMetadata:
    # Provenance
    source: MemorySource
    source_message_id: Optional[str] = None
    extraction_model: str = "gpt-4"
    
    # Confidence
    confidence: ConfidenceLevel = ConfidenceLevel.MEDIUM
    confidence_score: float = 0.8
    
    # Temporal
    created_at: datetime = field(default_factory=datetime.utcnow)
    valid_from: Optional[datetime] = None
    valid_until: Optional[datetime] = None  # For time-bound facts
    last_accessed: Optional[datetime] = None
    access_count: int = 0
    
    # Categorization
    category: str = "general"  # preference, fact, event, relationship
    tags: List[str] = field(default_factory=list)
    entities: List[str] = field(default_factory=list)
    
    # Versioning
    version: int = 1
    supersedes: Optional[str] = None  # ID of memory this updates
    superseded_by: Optional[str] = None

Usage in Retrieval

def smart_retrieve(query: str, user_id: str) -> List[Memory]:
    memories = mem0.search(query, user_id=user_id)
    
    # Filter and rank by metadata
    scored = []
    for mem in memories:
        score = mem.score  # Base vector similarity
        
        # Boost recent memories
        age_days = (datetime.utcnow() - mem.metadata.created_at).days
        recency_boost = 1.0 / (1 + age_days * 0.1)
        
        # Boost high confidence
        confidence_boost = {"high": 1.2, "medium": 1.0, "low": 0.8}
        
        # Penalize expired memories
        if mem.metadata.valid_until and mem.metadata.valid_until < datetime.utcnow():
            score *= 0.3
        
        final_score = score * recency_boost * confidence_boost[mem.metadata.confidence.value]
        scored.append((mem, final_score))
    
    return sorted(scored, key=lambda x: x[1], reverse=True)

Key Metadata to Track

Field	Why It Matters
source	Know if user said it vs agent inferred
confidence	Weight in decision-making
valid_until	Handle temporal facts ("meeting tomorrow")
supersedes	Track memory updates without losing history

More on memory patterns: https://github.com/KeepALifeUS/autonomous-agents