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docs: remove criteria retrieval docs for non-existent feature (#6282)

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Kartik
2026-07-14 20:06:05 +05:30
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docs/api-reference/organizations-projects.mdx
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@@ -79,7 +79,7 @@ new_project = client.project.create(
### Update Project Settings
Modify project configuration including custom instructions, categories, language preferences, retrieval criteria, and memory decay:
Modify project configuration including custom instructions, categories, language preferences, and memory decay:
```python
# Update project with custom categories
@@ -98,14 +98,6 @@ client.project.update(
# Use the input language for memory storage and retrieval
client.project.update(multilingual=True)
# Set retrieval criteria to control which memories are surfaced in search
client.project.update(
retrieval_criteria=[
{"name": "relevance", "description": "How directly relevant this memory is to the current topic or user query", "weight": 3},
{"name": "access_frequency", "description": "How often this memory has been accessed or surfaced recently", "weight": 1}
]
)
# Enable Memory Decay (boosts recently-accessed memories at search time)
client.project.update(decay=True)
@@ -120,34 +112,6 @@ client.project.update(
)
```
#### Set Retrieval Criteria
`retrieval_criteria` is a per-project list of dictionaries (`List[Dict]`) that shapes how memories are ranked and filtered during search. Each dictionary has three fields: `name` (identifier), `description` (interpreted by the LLM to score each memory), and `weight` (relative influence on the final score). Use this to focus retrieval on intent-aligned or signal-specific memories:
```python
client.project.update(
retrieval_criteria=[
{
"name": "joy",
"description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the memory. A higher score reflects greater joy.",
"weight": 3
},
{
"name": "curiosity",
"description": "Assess the extent to which the memory reflects inquisitiveness or interest in exploring new information. A higher score reflects stronger curiosity.",
"weight": 2
},
{
"name": "access_frequency",
"description": "How often this memory has been accessed or surfaced recently.",
"weight": 1
}
]
)
```
Pass an empty list to clear all criteria and restore default retrieval behaviour.
#### Toggle Memory Decay
`decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay): a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:
docs/docs.json
+4 -1
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@@ -84,7 +84,6 @@
"pages": [
"platform/features/advanced-retrieval",
"platform/advanced-memory-operations",
"platform/features/criteria-retrieval",
"platform/features/custom-instructions",
"platform/features/memory-decay"
]
@@ -1231,6 +1230,10 @@
{
"source": "/open-source/multimodal-support",
"destination": "/open-source/features/multimodal-support"
},
{
"source": "/platform/features/criteria-retrieval",
"destination": "/platform/features/advanced-retrieval"
}
]
}
docs/llms.txt
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@@ -204,7 +204,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
### Features - Advanced Retrieval
- [Advanced Retrieval](https://docs.mem0.ai/platform/features/advanced-retrieval) [Platform]: Use when the user needs keyword search, reranking, or hybrid retrieval.
- [Criteria-Based Retrieval](https://docs.mem0.ai/platform/features/criteria-retrieval) [Platform]: Use when targeting memories by custom criteria, not just semantic similarity.
- [Temporal Reasoning](https://docs.mem0.ai/platform/features/temporal-reasoning) [Platform]: Use when time-aware searches like last week, upcoming, or right now need better result ordering.
- [Custom Instructions](https://docs.mem0.ai/platform/features/custom-instructions) [Platform]: Use when tailoring what Mem0 extracts and stores on Platform.
- [Memory Decay](https://docs.mem0.ai/platform/features/memory-decay) [Platform]: Use when search results should boost recently-reinforced memories and dampen stale ones. Opt in per project; applies at search time and never filters candidates out.
docs/platform/features/criteria-retrieval.mdx
-201
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@@ -1,201 +0,0 @@
---
title: Criteria Retrieval
description: "Rank and retrieve memories based on custom-defined criteria like emotional tone, intent, and behavioral signals."
---
Mem0's Criteria Retrieval feature allows you to retrieve memories based on your defined criteria. It goes beyond generic semantic relevance and ranks 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 truly 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 semantically relevant memories, favoring those that better match your intent.
This gives you nuanced, intent-aware memory search that adapts to your use case.
## 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
## Setting Up Criteria Retrieval
Let’s walk through how to configure and use Criteria Retrieval step by step.
### Initialize the Client
Before defining any criteria, make sure to initialize the `MemoryClient` with your credentials and project ID:
```python
from mem0 import MemoryClient
client = MemoryClient(api_key="your_mem0_api_key")
```
### 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)
```python
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
}
]
```
### Apply Criteria to Your Project
Once defined, register the criteria to your project:
```python
client.project.update(retrieval_criteria=retrieval_criteria)
```
Criteria apply project-wide. Once set, they affect all searches automatically.
## Example Walkthrough
After setting up your criteria, you can use them to filter and retrieve memories. Here's an example:
### Add Memories
```python
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")
```
### Run Standard vs. Criteria-Based Search
```python
# Search with criteria enabled
filters = {"user_id": "alice"}
results_with_criteria = client.search(
query="Why I am feeling happy today?",
filters=filters
)
# To disable criteria for a specific search
results_without_criteria = client.search(
query="Why I am feeling happy today?",
filters=filters,
use_criteria=False # Disable criteria-based scoring
)
```
### Compare Results
### Search Results (with Criteria)
```text
[
{"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, ...}
]
```
### Search Results (without Criteria)
```text
[
{"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, ...},
]
```
## Search Results Comparison
1. **Memory Ordering**: With criteria, memories with high joy scores (like feeling refreshed and drawing) are ranked higher. 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. 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, while "Storm curiosity" is recognized and scored accordingly.
## Key Differences vs. Standard Search
| Aspect | Standard Search | Criteria Retrieval |
|-------------------------|--------------------------------------|-------------------------------------------------|
| Ranking Logic | Semantic similarity only | Semantic + LLM-based criteria scoring |
| Control Over Relevance | None | Fully customizable with weighted criteria |
| Memory Reordering | Static based on similarity | Dynamically re-ranked by intent alignment |
| Emotional Sensitivity | No tone or trait awareness | Incorporates emotion, tone, or custom behaviors |
| Activation | Default (no criteria defined) | Enabled when criteria are defined in project |
<Note>
If no criteria are defined for a project, search behaves normally based on semantic similarity only.
</Note>
## Best Practices
- Choose 3-5 criteria that reflect your application's intent
- Make descriptions clear and distinct; these are interpreted by an LLM
- Use stronger weights to amplify the impact of important traits
- Avoid redundant or ambiguous criteria (e.g., "positivity" and "joy")
- Always handle empty result sets in your application logic
## 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 automatically influence all searches.
3. **Memory Retrieval**: When you perform a search, Mem0 first retrieves relevant memories based on the query.
4. **Weighted Scoring**: Each retrieved memory is evaluated and scored against your 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.
<Note>
Criteria retrieval is automatically enabled when criteria are defined in your project. Use `use_criteria=False` in search to temporarily disable it for a specific query. `use_criteria` is a server-side parameter passed through to the Platform API: it is not a typed option in the SDK's `SearchMemoryOptions` interface, but the server accepts and processes it when included in the request body.
</Note>
## Summary
- Define what "relevant" means using criteria
- Apply them per project via `project.update()`
- Criteria-aware search activates automatically when criteria are configured
- Build agents that reason not just with relevance, but **contextual importance**
---
Need help designing or tuning your criteria?
<Snippet file="get-help.mdx" />
docs/platform/platform-vs-oss.mdx
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@@ -60,7 +60,6 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
| **Multimodal support** | ✅ | ✅ |
| **Custom categories** | ✅ | Limited |
| **Advanced retrieval** | ✅ | ✅ |
| **Criteria retrieval** | ✅ | ❌ |
| **Temporal reasoning** | ✅ (v3) | ❌ |
| **Memory decay** | ✅ (v3) | ❌ |
| **Graph memory** | ✅ Built-in | ✅ External graph store |
integrations/mem0-plugin/skills/mem0/references/features.md
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@@ -8,7 +8,6 @@ Additional platform capabilities beyond core CRUD operations.
- [Entity Linking](#entity-linking)
- [Custom Categories](#custom-categories)
- [Custom Instructions](#custom-instructions)
- [Criteria Retrieval](#criteria-retrieval)
- [Feedback Mechanism](#feedback-mechanism)
- [Memory Export](#memory-export)
- [Group Chat](#group-chat)
@@ -165,44 +164,6 @@ await client.updateProject({ customInstructions: "Your guidelines here..." });
---
## Criteria Retrieval
Custom attribute-based memory ranking using LLM-evaluated criteria with weights. Goes beyond semantic similarity to prioritize memories based on domain-specific signals.
### Configuration
```python
# Define criteria at project level
retrieval_criteria = [
{"name": "joy", "description": "Positive emotions like happiness and excitement", "weight": 3},
{"name": "curiosity", "description": "Inquisitiveness and desire to learn", "weight": 2},
{"name": "urgency", "description": "Time-sensitive or high-priority items", "weight": 4},
]
client.project.update(retrieval_criteria=retrieval_criteria)
```
```typescript
await client.updateProject({
retrievalCriteria: [
{ name: 'joy', description: 'Positive emotions', weight: 3 },
{ name: 'urgency', description: 'Time-sensitive items', weight: 4 },
],
});
```
### Usage
Once configured, `client.search()` automatically applies criteria ranking:
```python
# Criteria-weighted results returned automatically
results = client.search("Why am I feeling happy?", filters={"user_id": "alice"})
```
**Best for:** Wellness assistants, tutoring platforms, productivity tools — any app needing intent-aware retrieval.
---
## Feedback Mechanism
Provide feedback on extracted memories to improve system quality over time.
skills/mem0/references/features.md
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@@ -8,7 +8,6 @@ Additional platform capabilities beyond core CRUD operations.
- [Entity Linking](#entity-linking)
- [Custom Categories](#custom-categories)
- [Custom Instructions](#custom-instructions)
- [Criteria Retrieval](#criteria-retrieval)
- [Feedback Mechanism](#feedback-mechanism)
- [Memory Export](#memory-export)
- [Group Chat](#group-chat)
@@ -165,44 +164,6 @@ await client.updateProject({ customInstructions: "Your guidelines here..." });
---
## Criteria Retrieval
Custom attribute-based memory ranking using LLM-evaluated criteria with weights. Goes beyond semantic similarity to prioritize memories based on domain-specific signals.
### Configuration
```python
# Define criteria at project level
retrieval_criteria = [
{"name": "joy", "description": "Positive emotions like happiness and excitement", "weight": 3},
{"name": "curiosity", "description": "Inquisitiveness and desire to learn", "weight": 2},
{"name": "urgency", "description": "Time-sensitive or high-priority items", "weight": 4},
]
client.project.update(retrieval_criteria=retrieval_criteria)
```
```typescript
await client.updateProject({
retrievalCriteria: [
{ name: 'joy', description: 'Positive emotions', weight: 3 },
{ name: 'urgency', description: 'Time-sensitive items', weight: 4 },
],
});
```
### Usage
Once configured, `client.search()` automatically applies criteria ranking:
```python
# Criteria-weighted results returned automatically
results = client.search("Why am I feeling happy?", filters={"user_id": "alice"})
```
**Best for:** Wellness assistants, tutoring platforms, productivity tools — any app needing intent-aware retrieval.
---
## Feedback Mechanism
Provide feedback on extracted memories to improve system quality over time.