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Overview

The search operation allows you to retrieve relevant memories based on a natural language query and optional filters like user ID, agent ID, categories, and more. This is the foundation of giving your agents memory-aware behavior. Mem0 supports:
  • Semantic similarity search
  • Metadata filtering (with advanced logic)
  • Reranking and thresholds
  • Cross-agent, multi-session context resolution
This applies to both:
  • Mem0 Platform (hosted API with full-scale features)
  • Mem0 Open Source (local-first with LLM inference and local vector DB)

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Architecture

Architecture diagram illustrating the memory search process.

When you call search, Mem0 performs the following steps:
  1. Query Processing An LLM refines and optimizes your natural language query.
  2. Vector Search Semantic embeddings are used to find the most relevant memories using cosine similarity.
  3. Filtering & Ranking Logical and comparison-based filters are applied. Memories are scored, filtered, and optionally reranked.
  4. Results Delivery Relevant memories are returned with associated metadata and timestamps.

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Example: Mem0 Platform

from mem0 import MemoryClient

client = MemoryClient(api_key="your-api-key")

query = "What do you know about me?"
filters = {
   "OR": [
      {"user_id": "alice"},
      {"agent_id": {"in": ["travel-assistant", "customer-support"]}}
   ]
}

results = client.search(query, filters=filters)

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Example: Mem0 Open Source

from mem0 import Memory

m = Memory()

# Simple search
related_memories = m.search("Should I drink coffee or tea?", user_id="alice")

# Search with filters
memories = m.search(
    "food preferences",
    user_id="alice",
    filters={"categories": {"contains": "diet"}}
)

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Using Filters

Filters help narrow down search results. Common use cases: Filter by Session Context: 
# Get memories from a specific agent session
m.search("query", user_id="alice", agent_id="chatbot", run_id="session-123")
Filter by Date Range: 
# Platform only - date filtering
client.search("recent memories", filters={
    "AND": [
        {"user_id": "alice"},
        {"created_at": {"gte": "2024-07-01"}}
    ]
})
Filter by Categories: 
# Platform only - category filtering
client.search("preferences", filters={
    "AND": [
        {"user_id": "alice"},
        {"categories": {"contains": "food"}}
    ]
})
  • Use natural language: Mem0 understands intent, so describe what you’re looking for naturally
  • Scope with session IDs: Always provide at least user_id to scope search to relevant memories
  • Combine filters: Use AND/OR logic to create precise queries (Platform)
  • Consider wildcard filters: Use wildcard filters (e.g., run_id: "*") for broader matches
  • Tune parameters: Adjust top_k for result count, threshold for relevance cutoff
  • Enable reranking: Use rerank=True (default) when you have a reranker configured

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More Details

For the full list of filter logic, comparison operators, and optional search parameters, see the Search Memory API Reference.

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