mem0/skills/mem0/SKILL.md

name, description, license, metadata, compatibility

name	description	license	metadata	compatibility
mem0	Integrate Mem0 Platform into AI applications for persistent memory, personalization, and semantic search. Use this skill when the user mentions "mem0", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python and TypeScript SDKs, framework integrations (LangChain, CrewAI, Vercel AI SDK, OpenAI Agents SDK, Pipecat), and the full Platform API. Use even when the user doesn't explicitly say "mem0" but describes needing conversation memory, user context retention, or knowledge retrieval across sessions.	Apache-2.0	author version category tags mem0ai 1.0.0 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, and internet access to api.mem0.ai

Mem0 Platform Integration

Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy.

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

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", user_id="alice")
for mem in results.get("results", []):
    print(mem["memory"])

Get all memories

all_memories = client.get_all(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, 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-4.1-nano-2025-04-14",
        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 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.
• Immutable memories: Cannot be updated or deleted once created. Use client.history(memory_id) to track changes over time.

Live documentation search

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

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

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

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, Vercel AI, etc.)	references/integration-patterns.md
Use cases & examples (real-world patterns with code)	references/use-cases.md