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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.

The search flow follows these 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, version="v2", filters=filters)

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Example: Mem0 Open Source

from mem0 import Memory

m = Memory()
related_memories = m.search("Should I drink coffee or tea?", user_id="alice")
  • Use descriptive natural queries (Mem0 can interpret intent)
  • Apply filters for scoped, faster lookup
  • Use version: "v2" for enhanced results
  • Consider wildcard filters (e.g., run_id: "*") for broader matches
  • Tune with top_k, threshold, or rerank if needed

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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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