Criteria Retrieval

Mem0’s Criteria Retrieval feature allows you to retrieve memories based on your defined criteria. It goes beyond generic semantic relevance and rank memories based on what matters to your application - emotional tone, intent, behavioral signals, or other custom traits.

Instead of just searching for “how similar a memory is to this query?”, you can define what relevance really means for your project. For example:

• Prioritize joyful memories when building a wellness assistant
• Downrank negative memories in a productivity-focused agent
• Highlight curiosity in a tutoring agent

You define criteria - custom attributes like “joy”, “negativity”, “confidence”, or “urgency”, and assign weights to control how they influence scoring. When you search, Mem0 uses these to re-rank memories that are semantically relevant, favoring those that better match your intent.

This gives you nuanced, intent-aware memory search that adapts to your use case.

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When to Use Criteria Retrieval

Use Criteria Retrieval if:

• You’re building an agent that should react to emotions or behavioral signals
• You want to guide memory selection based on context, not just content
• You have domain-specific signals like “risk”, “positivity”, “confidence”, etc. that shape recall

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Setting Up Criteria Retrieval

Let’s walk through how to configure and use Criteria Retrieval step by step.

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Initialize the Client

Before defining any criteria, make sure to initialize the MemoryClient with your credentials and project ID:

from mem0 import MemoryClient

client = MemoryClient(
    api_key="your_mem0_api_key",
    org_id="your_organization_id",
    project_id="your_project_id"
)

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Define Your Criteria

Each criterion includes:

• A name (used in scoring)
• A description (interpreted by the LLM)
• A weight (how much it influences the final score)

retrieval_criteria = [
    {
        "name": "joy",
        "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the sentence. A higher score reflects greater joy.",
        "weight": 3
    },
    {
        "name": "curiosity",
        "description": "Assess the extent to which the sentence reflects inquisitiveness, interest in exploring new information, or asking questions. A higher score reflects stronger curiosity.",
        "weight": 2
    },
    {
        "name": "emotion",
        "description": "Evaluate the presence and depth of sadness or negative emotional tone, including expressions of disappointment, frustration, or sorrow. A higher score reflects greater sadness.",
        "weight": 1
    }
]

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Apply Criteria to Your Project

Once defined, register the criteria to your project:

client.project.update(retrieval_criteria=retrieval_criteria)

Criteria apply project-wide. Once set, they affect all searches using version="v2".

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Example Walkthrough

After setting up your criteria, you can use them to filter and retrieve memories. Here’s an example:

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Add Memories

messages = [
    {"role": "user", "content": "What a beautiful sunny day! I feel so refreshed and ready to take on anything!"},
    {"role": "user", "content": "I've always wondered how storms form—what triggers them in the atmosphere?"},
    {"role": "user", "content": "It's been raining for days, and it just makes everything feel heavier."},
    {"role": "user", "content": "Finally I get time to draw something today, after a long time!! I am super happy today."}
]

client.add(messages, user_id="alice")

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Run Standard vs. Criteria-Based Search

# With criteria
filters = {
    "AND": [
        {"user_id": "alice"}
    ]
}
results_with_criteria = client.search(
    query="Why I am feeling happy today?",
    filters=filters,
    version="v2"
)

# Without criteria
results_without_criteria = client.search(
    query="Why I am feeling happy today?",
    user_id="alice"
)

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Compare Results

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Search Results (with Criteria)

[
    {"memory": "User feels refreshed and ready to take on anything on a beautiful sunny day", "score": 0.666, ...},
    {"memory": "User finally has time to draw something after a long time", "score": 0.616, ...},
    {"memory": "User is happy today", "score": 0.500, ...},
    {"memory": "User is curious about how storms form and what triggers them in the atmosphere.", "score": 0.400, ...},
    {"memory": "It has been raining for days, making everything feel heavier.", "score": 0.116, ...}
]

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Search Results (without Criteria)

[
    {"memory": "User is happy today", "score": 0.607, ...},
    {"memory": "User feels refreshed and ready to take on anything on a beautiful sunny day", "score": 0.512, ...},
    {"memory": "It has been raining for days, making everything feel heavier.", "score": 0.4617, ...},
    {"memory": "User is curious about how storms form and what triggers them in the atmosphere.", "score": 0.340, ...},
    {"memory": "User finally has time to draw something after a long time", "score": 0.336, ...},
]

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Search Results Comparison

1. Memory Ordering: With criteria, memories with high joy scores (like feeling refreshed and drawing) are ranked higher, while without criteria, the most relevant memory (“User is happy today”) comes first.
2. Score Distribution: With criteria, scores are more spread out (0.116 to 0.666) and reflect the criteria weights, while without criteria, scores are more clustered (0.336 to 0.607) and based purely on relevance.
3. Trait Sensitivity: “Rainy day” content is penalized due to negative tone. “Storm curiosity” is recognized and scored accordingly.

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Key Differences vs. Standard Search

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Best Practices

• Choose 3–5 criteria that reflect your application’s intent
• Make descriptions clear and distinct, those are interpreted by an LLM
• Use stronger weights to amplify impact of important traits
• Avoid redundant or ambiguous criteria (e.g. “positivity” + “joy”)
• Always handle empty result sets in your application logic

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How It Works

1. Criteria Definition: Define custom criteria with a name, description, and weight. These describe what matters in a memory (e.g., joy, urgency, empathy).
2. Project Configuration: Register these criteria using project.update(). They apply at the project level and influence all searches using version="v2".
3. Memory Retrieval: When you perform a search with version="v2", Mem0 first retrieves relevant memories based on the query and your defined criteria.
4. Weighted Scoring: Each retrieved memory is evaluated and scored against the defined criteria and weights.

This lets you prioritize memories that align with your agent’s goals and not just those that look similar to the query.

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Summary

• Define what “relevant” means using criteria
• Apply them per project via project.update()
• Use version="v2" to activate criteria-aware search
• Build agents that reason not just with relevance, but contextual importance

Need help designing or tuning your criteria?