Vector Retrieval and RAG · Public

mem0

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

Vector databaseRAGEmbeddingsSemantic searchData boundaries

Best fitDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Check whether this project matches your task before installing it.

What it can doVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot58k stars

6.6k forks · 316 contributors

Doramagic.ai Last verification date: 2026-06-29 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-06-29

What is mem0?

mem0 is a vector database, retrieval, or RAG storage component for AI applications.
Best fit: Developers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.
Not for: Not for one-off model API calls or environments that cannot isolate indexed data, credentials, and persistence paths.
Capability added to an AI workflow: Vector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance
First safe verification step: Verify create, query, delete, and rollback with a small public text sample before using real data.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: May increase setup, validation, or first-run risk for the user.
Evidence base: https://github.com/mem0ai/mem0, https://github.com/mem0ai/mem0#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Match the project to your task before installing it.

CapabilityVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

Repositorymem0ai/mem0

58k stars · 6.6k forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Overview & V3 Memory Algorithm

Related topics: SDK Quickstart & Memory Operations, Self-Hosting, Server Stack & OpenMemory

Source: https://github.com/mem0ai/mem0 / Human Manual

SDK Quickstart & Memory Operations

Related topics: Overview & V3 Memory Algorithm, Integrations, Extensibility & Common Failure Modes

Source: https://github.com/mem0ai/mem0 / Human Manual

Self-Hosting, Server Stack & OpenMemory

Related topics: Overview & V3 Memory Algorithm, Integrations, Extensibility & Common Failure Modes

Source: https://github.com/mem0ai/mem0 / Human Manual

Integrations, Extensibility & Common Failure Modes

Related topics: Overview & V3 Memory Algorithm, SDK Quickstart & Memory Operations, Self-Hosting, Server Stack & OpenMemory

Source: https://github.com/mem0ai/mem0 / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

Sources: https://github.com/mem0ai/mem0, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars58k stars

Forks6.6k forks

Contributors316 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using mem0 with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
Silent memory loss when batch embedding partially fails in V3 add pipeli
github / github_issue
02
Proposal: Make Mem0 OSS Storage Fully Pluggable for Cloud-Native Backend
github / github_issue
03
Qdrant vector store uses HTTPS unexpectedly with host/port/api_key for s
github / github_issue
04
Workaround: Solving time-blindness, semantic conflicts, and missing CRUD
github / github_issue
05
Bug: OpenAIEmbedder in mem0-ts fails with OpenAI-compatible proxies (enc
github / github_issue
06
[Bug] Platform v3 API: add() accepts user_id/agent_id but doesn't store
github / github_issue
07
🚨 Malicious impersonator package 'mem0-mcp-server' on npm
github / github_issue
08
Self-hosting build fail
github / github_issue
09
Vercel AI SDK Provider (v2.4.6)
github / github_release
10
Mem0 Node SDK (v3.0.6)
github / github_release
11
Mem0 OpenClaw Plugin (v1.0.12)
github / github_release
12
Mem0 Node CLI (v0.2.8)
github / github_release

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install mem0-cli

Official start command · https://github.com/mem0ai/mem0#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

mem0 Manual

Universal memory layer for AI Agents

Open the full manual

https://github.com/mem0ai/mem0 Project Manual
Table of Contents
Overview & V3 Memory Algorithm
Related Pages
1. Project Overview
2. The V3 Memory Algorithm
Extraction Prompt Contract
Hybrid Retrieval Scoring

Overview & V3 Memory Algorithm

Related topics: SDK Quickstart & Memory Operations, Self-Hosting, Server Stack & OpenMemory

Source: https://github.com/mem0ai/mem0 / Human Manual

SDK Quickstart & Memory Operations

Related topics: Overview & V3 Memory Algorithm, Integrations, Extensibility & Common Failure Modes

Source: https://github.com/mem0ai/mem0 / Human Manual

Self-Hosting, Server Stack & OpenMemory

Related topics: Overview & V3 Memory Algorithm, Integrations, Extensibility & Common Failure Modes

Source: https://github.com/mem0ai/mem0 / Human Manual

Integrations, Extensibility & Common Failure Modes

Related topics: Overview & V3 Memory Algorithm, SDK Quickstart & Memory Operations, Self-Hosting, Server Stack & OpenMemory

Source: https://github.com/mem0ai/mem0 / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start EvidenceREPO_INSPECTION.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Security or permission risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Installation risk requires verification

Developers may fail before the first successful local run: Bug: OpenAIEmbedder in mem0-ts fails with OpenAI-compatible proxies (encoding_format default base64) — port #4058 to TS SDK

medium

Installation risk requires verification

Upgrade or migration may change expected behavior: Mem0 Node CLI (v0.2.8)

medium

Installation risk requires verification

Upgrade or migration may change expected behavior: Mem0 Node SDK (v3.0.6)