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Agent Memory & RAG

MEM0

MEM0 is an open-source Python memory layer for AI agents and applications. It stores conversational or application context so agents can retrieve relevant long-term information across interactions.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

MEM0 — OpenViST project profile in Agent Memory & RAG.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use MEM0 when an AI application needs continuity across sessions, recall of user history or preferences, or a separate persistence and retrieval layer for agent context.

Who it is for

  • Developers building AI agents or conversational applications.
  • Teams creating personalized assistants or customer-support systems.
  • Python developers adding persistent memory to an existing agent application.
  • Teams evaluating library, self-hosted, or managed deployment options.

Documented uses

  • Persist user, session, and agent context.
  • Retrieve relevant long-term information for later interactions.
  • Support personalized AI assistants and adaptive applications.
  • Add a dedicated memory layer to a Python-based agent application.
Before you choose

Requirements & limits

The repository is public, open source, and licensed under Apache-2.0. The documentation describes library, self-hosted server, and cloud platform options.

  • The library is documented for testing and prototyping, while the self-hosted server is positioned for teams running their own infrastructure and the cloud platform for zero-operations production use.
  • The core project is Python-based and also documents an npm SDK.
  • Optional NLP support adds BM25 keyword matching and entity extraction for enhanced hybrid search.
  • The documented memory model covers user, session, and agent state.
  • The project supports both self-hosted and managed deployment paths.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the MEM0 library for a Python project

Set up the documented Python library and optional NLP support for memory retrieval.

Before you start

  • A Python environment.
  • The MEM0 repository documentation.
  • For enhanced hybrid search, the optional NLP dependencies and spaCy English model.

Steps

  1. Install the library with the command below.

    Documented command
    pip install mem0ai

  2. For enhanced hybrid search, install the command below.

    Documented command
    pip install mem0ai[nlp]

  3. Install the documented spaCy model with the command below.

    Documented command
    python -m spacy download en_core_web_sm

Expected outcome

The MEM0 library is installed, with optional NLP support available for BM25 keyword matching and entity extraction.

Guide limitations

  • This setup recipe documents installation only; it does not establish a complete agent integration or deployment.
  • The optional NLP path is specifically documented with the English spaCy model.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
30 September 2026
Original website title
mem0ai/mem0 · Official homepage title · retained in its source language
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