Productivity

mem0 - Claude MCP Skill

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).

SEO Guide: Enhance your AI agent with the mem0 tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to mem0 platform sdk for adding persistent memory to ai applications. trigger when: user mentions "mem0... Download and configure this skill to unlock new capabilities for your AI workflow.

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Documentation

SKILL.md
# 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:**
```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?utm_source=oss&utm_medium=skill-mem0

## 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", filters={"user_id": "alice"})
for mem in results.get("results", []):
    print(mem["memory"])
```

### Get all memories
```python
all_memories = client.get_all(filters={"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, 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:

```bash
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.

## Client SDK References

Language-specific deep references (Platform + OSS):

| Language | File |
|----------|------|
| Python (MemoryClient + AsyncMemoryClient + Memory OSS) | [client/python.md](client/python.md) |
| TypeScript/Node.js (MemoryClient + Memory OSS) | [client/node.md](client/node.md) |
| Python vs TypeScript differences | [client/differences.md](client/differences.md) |

## Platform References

Load these on demand for deeper detail:

| Topic | File |
|-------|------|
| Quickstart (Python, TS, cURL) | [references/quickstart.md](references/quickstart.md) |
| SDK guide (all methods, both languages) | [references/sdk-guide.md](references/sdk-guide.md) |
| API reference (endpoints, filters, object schema) | [references/api-reference.md](references/api-reference.md) |
| Architecture (pipeline, lifecycle, scoping, performance) | [references/architecture.md](references/architecture.md) |
| Platform features (retrieval, graph, categories, MCP, etc.) | [references/features.md](references/features.md) |
| Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.) | [references/integration-patterns.md](references/integration-patterns.md) |
| Use cases & examples (real-world patterns with code) | [references/use-cases.md](references/use-cases.md) |

## Related Mem0 Skills

| Skill | When to use | Link |
|-------|-------------|------|
| mem0-cli | Terminal commands, scripting, CI/CD, agent tool loops | [local](../mem0-cli/SKILL.md) / [GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-cli) |
| mem0-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | [local](../mem0-vercel-ai-sdk/SKILL.md) / [GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk) |

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Information

Repository
mem0ai/mem0
Author
mem0ai
Last Sync
5/10/2026
Repo Updated
5/9/2026
Created
3/13/2026

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