/mem0

name: mem0
description: >
  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).
license: Apache-2.0
metadata:
  author: mem0ai
  version: "3.0.0"
  category: ai-memory
  tags: "memory, personalization, ai, python, typescript, vector-search"
compatibility: 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](../mem0-cli/SKILL.md)** ([GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-cli)) -- Command-line interface > - **[mem0-vercel-ai-sdk](../mem0-vercel-ai-sdk/SKILL.md)** ([GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk)) -- 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 --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](https://docs.mem0.ai/migration/oss-v2-to-v3) 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

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