Pre-load relevant memories for current task
Consolidate memories — merge duplicates, resolve contradictions, prune stale
Export project memories to a portable Markdown file
Delete memories by search or ID — confirms before deleting
Diagnose mem0 connection, API key, and memory read/write
Import memories from an export file into this project
| name | mem0 |
| description | Mem0 SDK reference — Python, TypeScript, and integrations |
| license | Apache-2.0 |
| metadata | {"author":"mem0ai","version":"0.1.1","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. Uses Mem0 v3 API. |
Skill Graph: This skill is part of the Mem0 skill graph:
- mem0 (this skill) -- Platform Client SDK + OSS (Python + TypeScript)
- 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.
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=mem0-plugin-skill
Don't have a
MEM0_API_KEY? Sign up at https://app.mem0.ai and create one from the dashboard. Keys start withm0-.
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.
Every Mem0 integration follows the same pattern: retrieve → generate → store.
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")
results = client.search("dietary preferences", filters={"user_id": "alice"})
for mem in results.get("results", []):
print(mem["memory"])
all_memories = client.get_all(filters={"user_id": "alice"})
client.update("memory-uuid", text="Updated: vegetarian, nut allergy, prefers organic")
client.delete("memory-uuid")
client.delete_all(user_id="alice") # delete all for a user
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
add() asynchronously — returns an event ID immediately. Wait 2-3s before searching. Also verify user_id matches exactly (case-sensitive) and use filters={"user_id": "..."} syntax.{"AND": [{"user_id": "alice"}, {"agent_id": "bot"}]} returns nothing. Use OR instead, or query each separately.infer=True (default) and infer=False for the same data. infer=True extracts facts via LLM with dedup. infer=False stores raw — same text can be stored twice.filters={"user_id": "alice"} only returns memories where agent_id, app_id, run_id are ALL null. Wrap in {"OR": [...]} to include memories with non-null scoping fields.from mem0 import MemoryClient. OSS: from mem0 import Memory. Don't mix them — MemoryClient talks to api.mem0.ai, Memory runs locally.top_k=20, threshold=0.1, rerank=False. Adjust as needed.Mem0 v3 uses single-pass extraction, entity linking, and multi-signal retrieval.
Key v3 changes from v2:
POST /v3/memories/add/, POST /v3/memories/search/, POST /v3/memories/ (paginated list)add(), no config needed. Remove enable_graph and graph_store from any old config.top_k=20, threshold=0.1, rerank=Falseorg_id, project_id, enable_graph — all removed from SDKuserId, agentId, appId, topK)GET /v1/event/{event_id}/See the migration guide for details.
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.
Language-specific deep references (Platform + OSS):
| Language | File |
|---|---|
| Python (MemoryClient + AsyncMemoryClient + Memory OSS) | client/python.md |
| TypeScript/Node.js (MemoryClient + Memory OSS) | client/node.md |
| Python vs TypeScript differences | client/differences.md |
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 |
| Skill | When to use | Link |
|---|---|---|
| mem0-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | GitHub |