Search Memory

Retrieve relevant memories from Mem0 using powerful semantic and filtered search capabilities.

How Mem0 Searches Memory

Mem0’s search operation lets agents ask natural-language questions and get back the memories that matter most. It’s the bridge between everything you’ve stored and the next response your agent writes.

Why it matters

• Retrieves the right facts without rebuilding prompts from scratch.
• Supports both managed Platform and OSS so you can test locally and deploy at scale.
• Keeps results relevant with filters, rerankers, and thresholds.

Key terms

• Query – Natural-language question or statement you pass to search.
• Filters – JSON logic (AND/OR, comparison operators) that narrows results by user, categories, dates, etc.
• top_k / threshold – Controls how many memories return and the minimum similarity score.
• Rerank – Optional second pass that boosts precision when a reranker is configured.

Architecture

Architecture diagram illustrating the memory search process.

1. Query processing

   Mem0 cleans and enriches your natural-language query so the downstream embedding search is accurate.

2. Vector search

   Embeddings locate the closest memories using cosine similarity across your scoped dataset.

3. Filtering & reranking

   Logical filters narrow candidates; rerankers or thresholds fine-tune ordering.

4. Results delivery

   Formatted memories (with metadata and timestamps) return to your agent or calling service.

This pipeline runs the same way for the hosted Platform API and the OSS SDK.

---

Search with Mem0 Platform

Python

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)

---

Search with Mem0 Open Source

Python

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"}}
)

---

Expect an array of memory documents. Platform responses include vectors, metadata, and timestamps; OSS returns your stored schema.

Filter patterns

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"}}
    ]
})

---

Tips for better search

• 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

More Details

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

Managed vs OSS differences

Capability	Mem0 Platform	Mem0 OSS
Filters	Logical operators (AND, OR, comparisons) with field-level access	Basic field filters, extend via Python hooks
Reranking	Toggle rerank=True with managed reranker catalog	Requires configuring local or third-party rerankers
Thresholds	Request-level configuration (threshold, top_k)	Controlled via SDK parameters
Response metadata	Includes confidence scores, timestamps, dashboard visibility	Determined by your storage backend

Put it into practice

• Revisit the Add Memory guide to ensure you capture the context you expect to retrieve.
• Configure rerankers and filters in Advanced Retrieval for higher precision.

See it live

• Support Inbox with Mem0 demonstrates scoped search with rerankers.
• Tavily Search with Mem0 shows hybrid search in action.

Tune Update Concepts

Build Hybrid Search