SKILL.md

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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&#x26;utm_medium=skill-mem0

**Don't have a MEM0_API_KEY?** Run mem0 init --agent --agent-caller <your-name> --json (after pip install mem0-cli or npm install -g @mem0/cli), substituting your agent identity (e.g. claude-code, cursor). If you forgot to pass --agent-caller, run mem0 identify <your-name> after init. 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.