How Mem0 Works - Mem0

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Mem0 sits between your application and your model. You send conversation turns to add, then call search before the next model request to fetch relevant context. Your app decides which returned memories to include in the prompt.Use Mem0 when you want agents to remember useful facts across turns, sessions, or users without replaying the full transcript every time.

Memory extraction: Mem0 turns messages into stored facts with metadata, embeddings, and optional entity relationships.

The mental model

Without a memory layer	With Mem0
Keep appending chat history to the prompt	Store facts once, then retrieve them by query
Make the model re-read old turns	Give the model only the relevant memories
Lose context when a session ends	Scope memory by `user_id`, `agent_id`, `run_id`, and metadata

Messages vs memories

You send Mem0 messages. By default, Mem0 stores extracted memories, not a verbatim transcript.

Input	Stored memory
`"I prefer aisle seats"`	`User prefers aisle seats`
`"Let's use Postgres for this project"`	`Project decision: use Postgres`
Message metadata	Filterable fields such as category, app, user, or run

Use infer=False when you need to store raw content exactly as provided. Otherwise, keep inference enabled so retrieval works on clean, deduplicated facts.

Two phases: extraction and retrieval

Most applications use Mem0 in two places:

After a useful interaction, call add to store what should be remembered.
Before a model call, call search and pass the best results into your prompt.

1. Extraction (writing memory)

When new messages arrive, Mem0 extracts durable facts and stores them with the identifiers and metadata you provide.

Context lookup. Mem0 checks related existing memories so it can avoid storing the same fact again.
Fact extraction. An LLM extracts preferences, decisions, plans, and other details your agent can reuse.
Deduplication and embedding. Redundant facts are removed, then each memory is embedded for semantic search.
Entity linking. When configured, Mem0 links people, places, organizations, and concepts across memories.

The automatic extraction path is additive. If a user says, “I moved from Austin to Seattle,” Mem0 can store the new fact without silently rewriting the old one. Use explicit update or delete operations when your application needs to correct or remove a memory.

2. Retrieval (reading memory)

When you call search, Mem0 ranks stored memories against your query and filters.

Signal	What it does	Best for
Semantic	Vector similarity over embeddings	Conceptual questions
Keyword	Term matching for exact words and phrases	Names, IDs, and factual lookups
Entity	Boosts memories linked to entities in the query	Questions about a person, project, or account
Temporal	Scores candidates on time metadata extracted at write time against the query’s temporal intent	Temporal questions (“when did…”, current state, recency)

Platform retrieval fuses these signals in the managed service. OSS retrieval depends on your configured vector store, optional reranker, and graph store.

Always scope searches with filters such as user_id, agent_id, or run_id. This keeps memories from different users, agents, or sessions from mixing.

Where memories live

Mem0 stores different parts of a memory in stores built for different lookup patterns:

Store	Holds	Purpose
SQL database	Facts and metadata	The source of truth for each memory
Vector database	Embeddings	Semantic similarity search
Entity or graph store	Entities and relationships	Relationship-aware retrieval when graph memory is enabled

On Mem0 Platform, these stores are managed for you. In OSS, you choose and operate the backing stores through your configuration.

Build against this flow

Call add only for information worth reusing later: preferences, decisions, account facts, goals, and durable feedback.
Call search before the model response, then include only the returned memories that help answer the current request.
Use metadata for filters your product already cares about, such as workspace, feature area, tenant, or data source.
Avoid storing secrets, raw credentials, or unredacted sensitive data. Mem0 is designed to retrieve stored context.

Next steps

[**Memory types**
Choose the right scope for user, agent, run, and session memory.](https://docs.mem0.ai/core-concepts/memory-types)

[**Memory operations**
Add, search, update, and delete memories from your app.](https://docs.mem0.ai/core-concepts/memory-operations/add)

[**See the benchmarks**
Review the evaluation setup and benchmark results.](https://docs.mem0.ai/core-concepts/memory-evaluation)