mem0

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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.

3installs

Sourcemem0ai/mem0

Added on2026-03-13

NPX Install

npx skill4agent add mem0ai/mem0 mem0

SKILL.md Content

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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:

bash

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

TypeScript/JavaScript:

bash

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:

python

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

TypeScript:

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

python

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

python

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

Get all memories

python

all_memories = client.get_all(user_id="alice")

Update a memory

python

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

Delete a memory

python

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

Common integration pattern

python

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:

bash

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

mem0

NPX Install

Tags

SKILL.md Content

Mem0 Platform Integration

Step 1: Install and authenticate

Step 2: Initialize the client

Step 3: Core operations

Add memories

Search memories

Get all memories

Update a memory

Delete a memory

Common integration pattern

Common edge cases

Live documentation search

References