| 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 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")
Common integration pattern
from mem0 import MemoryClient
from openai import OpenAI
mem0 = MemoryClient()
openai = OpenAI()
def chat(user_input: str, user_id: str) -> str:
memories = mem0.search(user_input, filters={"user_id": user_id})
context = "\n".join([m["memory"] for m in memories.get("results", [])])
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
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.
Client SDK References
Language-specific deep references (Platform + OSS):
Platform 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, OpenAI Agents, etc.) | references/integration-patterns.md |
| Use cases & examples (real-world patterns with code) | references/use-cases.md |
Related Mem0 Skills
| Skill | When to use | Link |
|---|
| mem0-cli | Terminal commands, scripting, CI/CD, agent tool loops | local / GitHub |
| mem0-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | local / GitHub |
