How do I install mem0?

Run these in Claude Code: /plugin marketplace add mem0ai/mem0 then /plugin install mem0@mem0-plugins. Then prompt normally.

Does mem0 auto-invoke its skills?

Not yet. It is indexed on Flowy as a plain plugin. Request auto-invocation on its page and Flowy will route its skills for you as you prompt.

Yes. Flowy is free and open source, with nothing gated. You can read every skill in full before you install.

Data

PopularPlugin

mem0

Mem0 ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions.

FLOW.md

mem0ai@mem0ai

GitHub

Plugin Signal92/100Strong

maintained99

capable74

documented100

adopted100

Install on Claude Code

> /plugin marketplace add mem0ai/mem0
> /plugin install mem0@mem0-plugins

Stats

Python TypeScript Bash Docker Vercel

+6 tags

GitHub Markdown Anthropic OpenAI Hugging Face MCP

62,880

Stars

7,333

Forks

Active

Maintenance

Python

Language

Apache-2.0

License

2d ago

Last commit

3y ago

Created

Repo: mem0ai/mem0

What's inside

Skills

/context-loaderSearches and injects relevant memories into context before starting work on a task. Use when beginning a new task, switching context, or when project history, past decisions, or coding conventions need to be loaded./dreamConsolidates stored memories by merging duplicates, resolving contradictions, and pruning stale entries. Use when memory count is high, search results feel noisy or repetitive, or periodic cleanup is needed to maintain memory quality./exportExports all project memories to a portable Markdown file for backup or migration. Use when backing up memories, migrating to another project, sharing memory state with teammates, or archiving before cleanup./forgetDeletes memories by search query or memory ID with confirmation before removal. Use when removing outdated decisions, incorrect memories, sensitive data, or cleaning up after experiments. Also handles undo of recent additions./healthDiagnoses mem0 connectivity, API key validity, and memory read/write functionality. Use when memory operations fail, searches return empty, add_memory errors occur, MCP connection drops, or to verify the plugin is working correctly./importImports memories from an exported Markdown file or MEMORY.md into the current project. Use when migrating from another project, restoring from backup, importing Claude Code native MEMORY.md content, or setting up a new project with existing knowledge.

Show all 32 skills

/list-projectsLists all projects with stored memories for the current user, showing memory counts and last activity dates. Use when checking which projects have memories, comparing memory distribution across repos, or finding a specific project scope./mem0Mem0 SDK reference covering Python and TypeScript APIs, memory client methods, configuration, and framework integrations. Use when writing code that calls mem0 APIs, configuring memory providers, or integrating mem0 into an application./memory-reviewerReviews stored memory quality by detecting duplicates, contradictions, and stale entries with actionable recommendations. Use when search results seem conflicting, before running dream consolidation, or for periodic memory hygiene audits./onboardSets up mem0 for a new project including API key configuration, MCP authentication, project file import, and coding categories. Use on first run in a new project, when API key needs updating, or to re-run initial setup after configuration changes./peekSearches memories and displays compact one-liner results, or looks up a specific memory by ID. Use for quick memory lookups, checking if a decision was recorded, resolving [mem0:id] citations, or browsing memories without full category detail./pinPins or unpins a memory to protect it from pruning during dream consolidation. Use when a memory is critical and must never be removed, such as architecture decisions, security constraints, or immutable team conventions./rememberStores a memory verbatim from user input with appropriate type classification and metadata. Use when the user says remember this, save this, store this, note that, or explicitly asks to record a decision, preference, convention, or learning./statsDisplays memory usage statistics for the current session and project including counts by category, age distribution, and API latency. Use when checking how many memories exist, reviewing session activity, or auditing memory distribution across categories./switch-projectOverrides the auto-detected project scope to read and write memories under a different project ID, or enables global search to access all memories across all users and projects. Use when working across multiple projects, accessing memories from another repo, enabling team-wide /tourBrowses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings./memory-dreamMemory consolidation protocol. Reviews all stored memories, merges duplicates, removes noise and credentials, rewrites unclear entries, and enforces TTL expiration. Use when the user asks to clean up, consolidate, or review their memories. Also triggers automatically after /memory-triagePersistent long-term memory protocol powered by mem0. Evaluate conversations for durable facts worth storing via memory_add. Handles identity, preferences, decisions, configurations, rules, projects, and relationships. Loaded by the openclaw-mem0 plugin when skills mode is /context-loaderSearches and injects relevant memories into context before starting work on a task or topic. Use when beginning a new task, switching context, or when past decisions, preferences, or knowledge need to be loaded./dreamConsolidates stored memories by merging duplicates, resolving contradictions, and pruning stale entries. Use when memory count is high, search results feel noisy or repetitive, or periodic cleanup is needed to maintain memory quality./forgetDeletes memories by search query or memory ID with confirmation before removal. Use when removing outdated information, incorrect memories, sensitive data, or cleaning up after experiments./pinPins or unpins a memory to protect it from pruning during dream consolidation. Use when a memory is critical and must never be removed, such as core preferences, important decisions, or immutable personal facts./rememberStores a memory verbatim from user input with appropriate category classification. Use when the user says remember this, save this, store this, note that, or explicitly asks to record a preference, decision, goal, or lesson./searchSearches memories and displays compact one-liner results, or looks up a specific memory by ID. Use for quick memory lookups, checking if something was recorded, resolving [mem0:id] citations, or browsing memories without full category detail./statusDiagnoses Mem0 connectivity, API key validity, and memory read/write functionality. Use when memory operations fail, searches return empty, or to verify the plugin is working correctly./tourBrowses all stored memories grouped by category with full content display. Use when reviewing all memories, exploring stored knowledge, onboarding to a new session, or getting an overview of what the agent remembers./mem0-cliMem0 CLI -- the command-line interface for mem0 memory operations. TRIGGER when: user mentions "mem0 cli", "mem0 command line", "@mem0/cli", "mem0-cli", "pip install mem0-cli", "npm install -g @mem0/cli", or is running mem0 commands in a terminal/shell (mem0 add, mem0 search,/mem0-integrateIntegrate Mem0 into an existing repository using a goal-driven, TDD pipeline. Detects the repo's language automatically and asks the user to pick between Mem0 Platform (managed) and Mem0 Open Source (self-hosted). Writes failing tests before any implementation. Produces a local /mem0-oss-to-platformPlan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local `Memory` class) to the mem0 Platform / hosted / managed SDK (the `MemoryClient` class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off /mem0-test-integrationVerify a Mem0 integration produced by /mem0-integrate. Runs in the same workspace on the same branch (loose coupling) — installs dependencies, runs the repo's native test suite, then exercises a real end-to-end smoke flow against the user's API key. Produces a scorecard. TRIGGER /mem0-vercel-ai-sdkMem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider). TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider", "createMem0", "retrieveMemories", "addMemories", "getMemories", "searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory", or is using /mem0Mem0 Platform SDK for adding persistent memory to AI applications. TRIGGER when: user mentions "mem0", "MemoryClient", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers

MCP

mem0http

Badges

README

Read on GitHub

New Memory Algorithm (April 2026)

Benchmark	Old	New	Tokens	Latency p50
LoCoMo	71.4	92.5	7.0K	0.88s
LongMemEval	67.8	94.4	6.8K	1.09s
BEAM (1M)	—	64.1	6.7K	1.00s
BEAM (10M)	—	48.6	6.9K	1.05s

All benchmarks run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops) at a top_200 retrieval budget. Scores reflect Mem0's managed platform, which includes proprietary optimizations not available in the open-source SDK; open-source users should expect directionally similar gains but not identical numbers.

What changed:

Single-pass ADD-only extraction -- one LLM call, no UPDATE/DELETE. Memories accumulate; nothing is overwritten.
Agent-generated facts are first-class -- when an agent confirms an action, that information is now stored with equal weight.
Entity linking -- entities are extracted, embedded, and linked across memories for retrieval boosting.
Multi-signal retrieval -- semantic, BM25 keyword, and entity matching scored in parallel and fused.
Temporal Reasoning -- time-aware retrieval that ranks the right dated instance for queries about current state, past events, and upcoming plans.

See the migration guide for upgrade instructions. The evaluation framework is open-sourced so anyone can reproduce the numbers.

Research Highlights

92.5 on LoCoMo -- +21 points over the previous algorithm
94.4 on LongMemEval -- +27 points, with 98.2 on assistant memory recall
64.1 on BEAM (1M) -- production-scale memory evaluation at 1M tokens
Read the full paper

Introduction

Mem0 ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. It remembers user preferences, adapts to individual needs, and continuously learns over time—ideal for customer support chatbots, AI assistants, and autonomous systems.

Key Features & Use Cases

Core Capabilities:

Multi-Level Memory: Seamlessly retains User, Session, and Agent state with adaptive personalization
Developer-Friendly: Intuitive API, cross-platform SDKs, and a fully managed service option

Applications:

AI Assistants: Consistent, context-rich conversations
Customer Support: Recall past tickets and user history for tailored help
Healthcare: Track patient preferences and history for personalized care
Productivity & Gaming: Adaptive workflows and environments based on user behavior

🚀 Quickstart Guide

Sign up as an agent

AI agents can mint a working Mem0 API key in under five seconds — no email, no dashboard, no OTP. Four commands end-to-end:

# 1. Install
npm install -g @mem0/cli      # or: pip install mem0-cli

# 2. Sign up as an agent (replace `claude-code` with your name)
mem0 init --agent --agent-caller claude-code

# 3. Add a memory
mem0 add "I am using mem0"

# 4. Search
mem0 search "am I using mem0"

The human owner can claim the account later with mem0 init --email <their-email> — same key, memories preserved. Full guide: Sign up as an agent.

	Library	Self-Hosted Server	Cloud Platform
Best for	Testing, prototyping	Teams running on their own infrastructure	Zero-ops production use
Setup	`pip install mem0ai`	`docker compose up`	Sign up at app.mem0.ai
Dashboard	--	Yes	Yes
Auth & API Keys	--	Yes	Yes
Advanced Features	--	Teasers	All included

Just testing? Use the library. Building for a team? Self-hosted. Want zero ops? Cloud.

Library (pip / npm)

pip install mem0ai

For enhanced hybrid search with BM25 keyword matching and entity extraction, install with NLP support:

pip install mem0ai[nlp]
python -m spacy download en_core_web_sm

Install sdk via npm:

npm install mem0ai

Self-Hosted Server

Note: Self-hosted auth is on by default. Upgrading from a pre-auth build? Set ADMIN_API_KEY, register an admin through the wizard, or AUTH_DISABLED=true for local dev only. See upgrade notes.

# Recommended: one command — start the stack, create an admin, issue the first API key.
cd server && make bootstrap

# Manual: start the stack and finish setup via the browser wizard.
cd server && docker compose up -d    # http://localhost:3000

See the self-hosted docs for configuration.

Cloud Platform

Sign up on Mem0 Platform
Embed the memory layer via SDK or API keys
Using hosted Qdrant vectors? See the Platform migration guide to import them into Mem0 Platform.

CLI

Manage memories from your terminal:

npm install -g @mem0/cli   # or: pip install mem0-cli

mem0 init
mem0 add "Prefers dark mode and vim keybindings" --user-id alice
mem0 search "What does Alice prefer?" --user-id alice

See the CLI documentation for the full command reference.

Agent Skills

Teach your AI coding assistant (Claude Code, Codex, Cursor, Windsurf, OpenCode, OpenClaw, and any tool that supports the skills standard) how to build with Mem0. Two categories:

Reference skills — always on (SDK knowledge loaded into the assistant's context):

npx skills add https://github.com/mem0ai/mem0 --skill mem0
npx skills add https://github.com/mem0ai/mem0 --skill mem0-cli
npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk

Pipeline skills — run on demand (execute an end-to-end workflow in an existing repo):

npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform

Use /mem0-integrate to wire Mem0 into an existing repo via a test-first pipeline, then /mem0-test-integration to verify. Use /mem0-oss-to-platform to migrate an existing project from Mem0 OSS to the hosted Platform SDK. See the skills catalog or Vibecoding with Mem0 for the full picture.

Basic Usage

Mem0 requires an LLM to function, with gpt-5-mini from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our Supported LLMs documentation.

Mem0 uses text-embedding-3-small from OpenAI as the default embedding model. For best results with hybrid search (semantic + keyword + entity boosting), we recommend using at least Qwen 600M or a comparable embedding model. See Supported Embeddings for configuration details.

First step is to instantiate the memory:

from openai import OpenAI
from mem0 import Memory

openai_client = OpenAI()
memory = Memory()

def chat_with_memories(message: str, user_id: str = "default_user") -> str:
    # Retrieve relevant memories
    relevant_memories = memory.search(query=message, filters={"user_id": user_id}, top_k=3)
    memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories["results"])

    # Generate Assistant response
    system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}"
    messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}]
    response = openai_client.chat.completions.create(model="gpt-5-mini", messages=messages)
    assistant_response = response.choices[0].message.content

    # Create new memories from the conversation
    messages.append({"role": "assistant", "content": assistant_response})
    memory.add(messages, user_id=user_id)

    return assistant_response

def main():
    print("Chat with AI (type 'exit' to quit)")
    while True:
        user_input = input("You: ").strip()
        if user_input.lower() == 'exit':
            print("Goodbye!")
            break
        print(f"AI: {chat_with_memories(user_input)}")

if __name__ == "__main__":
    main()

For detailed integration steps, see the Quickstart and API Reference.

🔗 Integrations & Demos

ChatGPT with Memory: Personalized chat powered by Mem0 (Live Demo)
Browser Extension: Store memories across ChatGPT, Perplexity, and Claude (Chrome Extension)
Langgraph Support: Build a customer bot with Langgraph + Mem0 (Guide)
CrewAI Integration: Tailor CrewAI outputs with Mem0 (Example)

📚 Documentation & Support

Full docs: https://docs.mem0.ai
Community: Discord · X (formerly Twitter)
Contact: founders@mem0.ai

Citation

We now have a paper you can cite:

@article{mem0,
  title={Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory},
  author={Chhikara, Prateek and Khant, Dev and Aryan, Saket and Singh, Taranjeet and Yadav, Deshraj},
  journal={arXiv preprint arXiv:2504.19413},
  year={2025}
}

⚖️ License

Apache 2.0 — see the LICENSE file for details.

File tree

1675

mem0ai/mem0View on GitHub

.agents/
  plugins/
    marketplace.json
.claude-plugin/
  marketplace.json
.codex-plugin/
  marketplace.json
.cursor-plugin/
  marketplace.json
.github/
  advanced-issue-labeler.yml
  component-keywords.json
  ISSUE_TEMPLATE/
    bug_report.yml
    config.yml
    documentation_issue.yml
    feature_request.yml
  labeler.yml
  PULL_REQUEST_TEMPLATE.md
  scripts/
    infer-component-labels.js
    infer-component-labels.test.js
  workflows/
    cd.yml
    ci-gate.yml
    ci.yml
    cli-node-cd.yml
    cli-node-ci.yml
    cli-python-cd.yml
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    docs-llms-txt-check.yml
    issue-labeler.yml
    mem0-plugin-checks.yml
    n8n-nodes-mem0-cd.yml
    n8n-nodes-mem0-checks.yml
    openclaw-cd.yml
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    opencode-plugin-cd.yml
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    pr-labeler.yml
    release.yml
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    vercel-ai-cd.yml
    zapier-mem0-cd.yml
    zapier-mem0-checks.yml
.gitignore
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.pre-commit-config.yaml
AGENTS.md
CLAUDE.md
cli/
  CLI_SPECIFICATION.md
  cli-spec.json
  node/
    development.md
    package.json
    pnpm-lock.yaml
    pnpm-workspace.yaml
    README.md
    src/
      agent-detect.ts
      backend/
        base.ts
        index.ts
        platform.ts
      branding.ts
      commands/
        agent-mode.ts
        agent-rush.ts
        config.ts
        entities.ts
        events.ts
        identify.ts
        init.ts
        memory.ts
        utils.ts
        whoami.ts
      config.ts
      globals.d.ts
      help.ts
      index.ts
      output.ts
      plugin-sync.ts
      state.ts
      telemetry.ts
      version.ts
    telemetry-sender.cjs
    tests/
      agent-mode.test.ts
      branding.test.ts
      cli-integration.test.ts
      commands.test.ts
      config.test.ts
      init-internals.test.ts
      option-parity.test.ts
      output.test.ts
      platform-backend.test.ts
      setup.ts
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    tsconfig.json
    tsup.config.ts
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  python/
    development.md
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    README.md
    src/
      mem0_cli/
        __init__.py
        __main__.py
        agent_detect.py
        app.py
        backend/
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          base.py
          platform.py
        branding.py
        commands/
          __init__.py
          agent_mode_cmd.py
          agent_rush_cmd.py
          config_cmd.py
          entities.py
          events_cmd.py
          identify_cmd.py
          init_cmd.py
          memory.py
          utils.py
          whoami_cmd.py
        config.py
        output.py
        plugin_sync.py
        state.py
        telemetry_sender.py
        telemetry.py
    tests/
      __init__.py
      conftest.py
      test_agent_mode.py
      test_branding.py
      test_cli_integration.py
      test_commands.py
      test_config.py
      test_init_internals.py
      test_option_parity.py
      test_output.py
      test_platform_backend_options.py
      test_platform_backend_paths.py
      test_platform_backend.py
      test_telemetry.py
  README.md
CONTRIBUTING.md
docs/
  _snippets/
    async-memory-add.mdx
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      delete-user.mdx
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    events/
      get-event.mdx
      get-events.mdx
    memory/
      add-memories.mdx
      batch-delete.mdx
      batch-update.mdx
      create-memory-export.mdx
      delete-memories.mdx
      delete-memory.mdx
      feedback.mdx
      get-memories.mdx
      get-memory-export.mdx
      get-memory.mdx
      history-memory.mdx
      search-memories.mdx
      update-memory.mdx
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      add-org-member.mdx
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      config.mdx
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        aws_bedrock.mdx
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      overview.mdx
  contributing/
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      ai-tutor.mdx
      local-companion-ollama.mdx
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      youtube-research.mdx
    essentials/
      building-ai-companion.mdx
      controlling-memory-ingestion.mdx
      entity-partitioning-playbook.mdx
      exporting-memories.mdx
      tagging-and-organizing-memories.mdx
    frameworks/
      eliza-os-character.mdx
      gemini-3-with-mem0-mcp.mdx
      llamaindex-multiagent.mdx
      llamaindex-react.mdx
      multimodal-retrieval.mdx
    integrations/
      agents-sdk-tool.mdx
      aws-bedrock.mdx
      healthcare-google-adk.mdx
      mastra-agent.mdx
      openai-tool-calls.mdx
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    operations/
      content-writing.mdx
      deep-research.mdx
      email-automation.mdx
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  core-concepts/
    how-it-works.mdx
    memory-evaluation.mdx
    memory-operations/
      add.mdx
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... 1580 more

FAQ

mem0 is a Claude Code plugin with 32 hand-picked skills for data work, indexed on Flowy. Install it with the command on its page. It includes context-loader, dream, export. Its skills do not fire on their own yet. Request auto-invocation to have Flowy route them as you prompt. Free and open source.

What is mem0?: Mem0 ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions.
How do I install mem0?: Run these in Claude Code: /plugin marketplace add mem0ai/mem0 then /plugin install mem0@mem0-plugins. Then prompt normally.
Does mem0 auto-invoke its skills?: Not yet. It is indexed on Flowy as a plain plugin. Request auto-invocation on its page and Flowy will route its skills for you as you prompt.
Is mem0 free?: Yes. Flowy is free and open source, with nothing gated. You can read every skill in full before you install.