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.
Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy.
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
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.
Every Mem0 integration follows the same pattern: retrieve → generate → store.
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", user_id="alice")
for mem in results.get("results", []):
print(mem["memory"])
all_memories = client.get_all(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, 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
add() before searching. Also verify user_id matches exactly (case-sensitive).OR instead, or query separately.infer=True (default) and infer=False for the same data. Stick to one mode.from mem0 import MemoryClient (or AsyncMemoryClient for async). Do not use from mem0 import Memory.client.history(memory_id) to track changes over time.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.
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 |