Mem0 Platform SDK

What it does

When your agent uses it

• Adding long-term memory to a chatbot or agent
• Remembering user preferences across sessions
• Wiring Mem0 into LangChain, CrewAI or LlamaIndex
• Choosing between the hosted platform and self-hosted memory

Example prompts

• “Add Mem0 to my support chatbot so it remembers each customer's preferences between sessions.”
• “Store this conversation as memories for user alice and then search for her dietary preferences.”
• “Wire Mem0 memory into my CrewAI agent in Python.”

Requirements

• Python 3.10 or newer, or Node.js 18 or newer
• The mem0ai package
• A MEM0_API_KEY for the Platform
• Internet access to api.mem0.ai
• Compatibility (from SKILL.md): Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai. Targets the v3 API (Python mem0ai 2.x, TypeScript mem0ai 3.x).

Workflow steps

3 steps, taken from the step headings in SKILL.md.

1. 1Install and authenticate
2. 2Initialize the client
3. 3Core operations

What it can do on your machine

Read from SKILL.md and the folder at commit b7ad69a. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 1 file in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python
  • pip
  • npm

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • docs.mem0.ai

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • MEM0_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai. Targets the v3 API (Python mem0ai 2.x, TypeScript mem0ai 3.x).

  From compatibility in the SKILL.md frontmatter.

Context cost

Mem0 Platform SDK loads about 2.2k tokens when it runs, and up to ~23k if it reads all its reference files. Until then it costs about 182 tokens; SKILL.md has 564 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~182

When it runs · the whole SKILL.md, loaded when a task matches

~2.2k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~23k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mem0ai/mem0 at commit b7ad69a, republished under its Apache-2.0 licence (© mem0ai). 564 words, ~2,199 tokens.

name

mem0

description

Mem0 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 Python SDK (mem0ai), TypeScript SDK (mem0ai), and framework integrations (LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph). Also covers the open-source self-hosted Memory class. This is the DEFAULT mem0 skill for ambiguous queries. DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell scripts (use mem0-cli), or Vercel AI SDK / @mem0/vercel-ai-provider / createMem0 (use mem0-vercel-ai-sdk).

compatibility

Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai. Targets the v3 API (Python mem0ai 2.x, TypeScript mem0ai 3.x).

license

Apache-2.0

metadata.author

mem0ai

metadata.version

3.1.0

metadata.category

ai-memory

metadata.tags

memory, personalization, ai, python, typescript, vector-search

metadata.mem0_tested_versions

mem0ai (PyPI) >=2.0.0,<3.0.0; mem0ai (npm) >=3.0.0,<4.0.0

pip install mem0ai
export MEM0_API_KEY="m0-your-api-key"

npm install mem0ai
export MEM0_API_KEY="m0-your-api-key"

from mem0 import MemoryClient
client = MemoryClient(api_key="m0-xxx")

import MemoryClient from 'mem0ai';
const client = new MemoryClient({ apiKey: 'm0-xxx' });

messages = [
    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
    {"role": "assistant", "content": "Got it! I'll remember that."}
]
client.add(messages, user_id="alice")

results = client.search("dietary preferences", filters={"user_id": "alice"})
for mem in results.get("results", []):
    print(mem["memory"])

all_memories = client.get_all(filters={"user_id": "alice"})

client.update("memory-uuid", text="Updated: vegetarian, nut allergy, prefers organic")

client.delete("memory-uuid")
client.delete_all(user_id="alice")  # delete all for a user

from mem0 import MemoryClient
from openai import OpenAI

mem0 = MemoryClient()
openai = OpenAI()

def chat(user_input: str, user_id: str) -> str:
    # 1. Retrieve relevant memories
    memories = mem0.search(user_input, filters={"user_id": user_id})
    context = "\n".join([m["memory"] for m in memories.get("results", [])])

    # 2. Generate response with memory context
    response = openai.chat.completions.create(
        model="gpt-5-mini",
        messages=[
            {"role": "system", "content": f"User context:\n{context}"},
            {"role": "user", "content": user_input},
        ]
    )
    reply = response.choices[0].message.content

    # 3. Store interaction for future context
    mem0.add(
        [{"role": "user", "content": user_input}, {"role": "assistant", "content": reply}],
        user_id=user_id
    )
    return reply

python ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.py --query "topic"
python ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.py --page "/platform/features/graph-memory"
python ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.py --index

© mem0ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 13 other files (scripts, references) in skills/mem0 of mem0ai/mem0.

• SKILL.md
• LICENSE
• README.md
• client/differences.md
• client/node.md
• client/python.md
• references/api-reference.md
• references/architecture.md
• references/features.md
• references/integration-patterns.md
• references/quickstart.md
• references/sdk-guide.md
• references/use-cases.md
• scripts/mem0_doc_search.py

Open the folder on GitHubat commit b7ad69a

Used in 2 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in mem0ai/mem0, which our catalogue first saw on October 7, 2026.

• KartikLabhshetwar/mind-mentor.agents/skills/mem0.claude/skills/mem0
• majiayu000/claude-skill-registryskills/bash/mem0skills/bash/mem0-om-scogo-skillsh-scraper