mem0Adds persistent, personalized long-term memory to LLM apps and agents with Mem0, the open-source memory layer: it extracts facts from conversations, stores them in a vector database and retrieves them in later sessions. Use when a user asks to make a chatbot or agent remember user preferences across sessions, add memory to an AI app, or search, update and d…
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Mem0 stores what an AI app learns about a user (preferences, facts, history) as small text memories in a vector store, and lets you search them before the next model call. You pass raw conversation messages; an LLM extracts the facts. It runs in three ways: as a Python library (Memory, self-hosted, you bring the LLM and vector store), as a self-hosted server, or as the hosted platform (MemoryClient, needs MEM0_API_KEY from app.mem0.ai).
This page covers the open-source Python library, version 2.x (2.2.1 at the time of writing). Defaults of Memory(): OpenAI gpt-5-mini for extraction, OpenAI text-embedding-3-small embeddings, a local Qdrant store in /tmp/qdrant and a SQLite history at ~/.mem0/history.db. There is also a TypeScript package, npm install mem0ai.
pip install mem0ai # core library
pip install "mem0ai[nlp]" # optional: spaCy-based entity features
pip install "mem0ai[extras]" # optional: fastembed, enables BM25 keyword matching
export OPENAI_API_KEY=... # for the default provider
Set MEM0_TELEMETRY=false to switch off anonymous usage telemetry.
from mem0 import Memory
memory = Memory.from_config({
"llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
"embedder": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
"vector_store": {
"provider": "qdrant",
"config": {"host": "localhost", "port": 6333, "collection_name": "memories"},
},
})
memory.add(
[
{"role": "user", "content": "I'm allergic to peanuts and I'm training for a marathon"},
{"role": "assistant", "content": "Noted. For marathon training, nutrition is key."},
],
user_id="user_42",
)
# Entity IDs go in `filters` for search and get_all. Top-level user_id raises ValueError.
found = memory.search("dietary restrictions", filters={"user_id": "user_42"}, top_k=5)
for item in found["results"]:
print(item["id"], item["memory"], item["score"])
everything = memory.get_all(filters={"user_id": "user_42"})["results"]
memory.update(memory_id=everything[0]["id"], text="User ran their first marathon in March 2026")
memory.delete(memory_id=everything[0]["id"])
memory.delete_all(user_id="user_42") # erase one user
Facts about the 2.x API:
add(messages, *, user_id, agent_id, run_id, metadata, infer=True, ...) takes entity IDs top-level; infer=False stores the text as given, without LLM extraction. It returns {"results": [{"id", "memory", "event": "ADD", ...}]}: only ADD events come back.search(query, *, top_k=20, filters, threshold=0.1, rerank=False) returns {"results": [...]}. Defaults changed from 1.x: top_k 20 (was 100), threshold 0.1, rerank off. limit= is gone, use top_k.update(memory_id, text=..., metadata=...): the old data= argument still works but is deprecated.filters must contain at least one of user_id, agent_id, run_id, and may add metadata keys, operators (eq, ne, in, nin, gt, gte, lt, lte, contains, icontains) and AND / OR / NOT.enable_graph, graph_store) was removed from the open-source library; it exists only on the hosted platform. custom_fact_extraction_prompt is now custom_instructions.AsyncMemory has the same methods as Memory, all awaitable.from mem0 import MemoryClient
client = MemoryClient() # reads MEM0_API_KEY
client.add("Prefers dark mode and vim keybindings", user_id="alice")
client.search("editor preferences", filters={"user_id": "alice"})
from openai import OpenAI
from mem0 import Memory
llm = OpenAI()
memory = Memory()
def chat(user_id: str, user_message: str) -> str:
hits = memory.search(user_message, filters={"user_id": user_id}, top_k=5)["results"]
known = "\n".join(f"- {h['memory']}" for h in hits)
reply = llm.chat.completions.create(
model="gpt-5-mini",
messages=[
{"role": "system", "content": f"You are a support assistant. What you know about the customer:\n{known}"},
{"role": "user", "content": user_message},
],
).choices[0].message.content
memory.add(
[{"role": "user", "content": user_message}, {"role": "assistant", "content": reply}],
user_id=user_id,
)
return reply
chat("cust_1042", "I just moved to Berlin and I love Italian food")
print(chat("cust_1042", "Recommend a restaurant for tonight"))
In the second call the search returns "Lives in Berlin" and "Loves Italian food", so the answer suggests Italian places in Berlin. Search before the model call, add after it.
memory.add("Refunds are allowed within 30 days with receipt",
user_id="support_policies", metadata={"type": "policy"})
memory.add("Customer prefers email over phone for follow-ups",
user_id="cust_1042", agent_id="support_agent")
policy = memory.search("refund window", filters={"user_id": "support_policies", "type": "policy"})
notes = memory.search("contact preference", filters={"user_id": "cust_1042", "agent_id": "support_agent"})
Each search returns only memories whose IDs and metadata match; metadata keys are written flat in filters, not nested under "metadata".
user_id (and agent_id / run_id when needed); one user's memories never appear in another user's search.user_id into filters for search and get_all, rename limit to top_k, and expect lower-scored hits to be dropped by the 0.1 threshold.add with infer=True makes an LLM call, so it costs tokens and latency; batch a whole exchange into one call rather than one per message./tmp/qdrant is wiped by many systems on reboot; for real use set a persistent path, or run a Qdrant server and set host and port.update or delete.delete_all(user_id=...) for erasure requests, and remember extracted text is sent to your LLM provider.