mem0/skills/mem0/SKILL.md

name, description, license, metadata, compatibility

name	description	license	metadata	compatibility
mem0	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).	Apache-2.0	author version category tags mem0ai 3.0.0 ai-memory memory, personalization, ai, python, typescript, vector-search	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. SDK v3 with v2 compatibility mode available.

Mem0 Platform Integration

Skill Graph: This skill is part of the Mem0 skill graph:

• mem0 (this skill) -- Platform Client SDK + OSS (Python + TypeScript)
• mem0-cli (GitHub) -- Command-line interface
• mem0-vercel-ai-sdk (GitHub) -- Vercel AI SDK provider

Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.

Step 1: Install and authenticate

Python:

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

TypeScript/JavaScript:

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

Get an API key at: https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=skill-mem0

Don't have a MEM0_API_KEY? Run mem0 init --agent --json (after pip install mem0-cli or npm install -g @mem0/cli) to mint an evaluation key without email or dashboard. The human can claim later with mem0 init --email <your-email>.

Step 2: Initialize the client

Python:

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

TypeScript:

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

For async Python, use AsyncMemoryClient.

Step 3: Core operations

Every Mem0 integration follows the same pattern: retrieve → generate → store.

Add memories

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")

Search memories

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

Get all memories

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

Update a memory

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

Delete a memory

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

Common integration pattern

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

Common edge cases

• Search returns empty: Memories process asynchronously. Wait 2-3s after add() before searching. Also verify user_id matches exactly (case-sensitive) and use filters={"user_id": "..."} syntax.
• AND filter with user_id + agent_id returns empty: Entities are stored separately. Use OR instead, or query separately.
• Duplicate memories: Don't mix infer=True (default) and infer=False for the same data. Stick to one mode.
• Wrong import: Always use from mem0 import MemoryClient (or AsyncMemoryClient for async). Do not use from mem0 import Memory.
• v3 defaults: top_k=20, threshold=0.1, rerank=False. Adjust as needed for your use case.

v2 Compatibility

If you're using SDK v2.x, note these differences:

• Entity IDs: Pass user_id as top-level kwarg to search() instead of inside filters
• Defaults: top_k=100, no threshold, rerank=True
• Graph memory: Available via enable_graph=True

See the migration guide for details.

Live documentation search

For the latest docs beyond what's in the references, use the doc search tool:

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

No API key needed — searches docs.mem0.ai directly.

Client SDK References

Language-specific deep references (Platform + OSS):

Language	File
Python (MemoryClient + AsyncMemoryClient + Memory OSS)	client/python.md
TypeScript/Node.js (MemoryClient + Memory OSS)	client/node.md
Python vs TypeScript differences	client/differences.md

Platform References

Load these on demand for deeper detail:

Topic	File
Quickstart (Python, TS, cURL)	references/quickstart.md
SDK guide (all methods, both languages)	references/sdk-guide.md
API reference (endpoints, filters, object schema)	references/api-reference.md
Architecture (pipeline, lifecycle, scoping, performance)	references/architecture.md
Platform features (retrieval, graph, categories, MCP, etc.)	references/features.md
Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.)	references/integration-patterns.md
Use cases & examples (real-world patterns with code)	references/use-cases.md

Related Mem0 Skills

Skill	When to use	Link
mem0-cli	Terminal commands, scripting, CI/CD, agent tool loops	local / GitHub
mem0-vercel-ai-sdk	Vercel AI SDK provider with automatic memory	local / GitHub