DeepMem

Drop-in AI memory layer with 2× faster response and 10× lower cost.
Fully compatible with Mem0 API. Migrate in 5 minutes - one import line.

Self-hostable. No auth, no payment, no lock-in. Or use the managed cloud at deepmem.dev.

Cloud · Self-host · Benchmarks · Reproduce them

Migrate from Mem0 in one line - same MemoryClient, same method signatures:

# Before - Mem0
from mem0 import MemoryClient
client = MemoryClient(api_key="m0-...")

# After - DeepMem (only the import changes)
from deepmem import MemoryClient
client = MemoryClient(api_key="dm_live-...")   # get a key at deepmem.dev

pip install deepmem-client

Turn conversations into searchable long-term memory: a FastAPI HTTP API in front of a Qdrant vector store, with LLM fact extraction, hybrid retrieval (vector + BM25 + entity boost + time-decay), semantic caching, async batched distillation, GDPR controls, and a built-in MCP server. It runs in open mode - no API key, no user registration - so you can deploy it for your own agents in minutes. Multi-tenant isolation is driven by user_id in the request body.

Prefer not to self-host? DeepMem Cloud is the managed version of this exact engine at deepmem.dev - same API, no infra. Sign up, grab a key (dm_live_...), point your base URL at https://deepmem.dev, done. The cloud and the open-source server speak the same Mem0-compatible API, so client code is identical.

Migrate from Mem0

Already using Mem0? Switch to DeepMem cloud in one line. The deepmem-client package mirrors mem0.MemoryClient - same class name, same method signatures, same filters={"user_id": ...} style - so everything after the import stays untouched.

pip install deepmem-client

# before (Mem0)
from mem0 import MemoryClient
client = MemoryClient(api_key="m0-...")
client.add(messages, user_id="alex")
client.search("What can Alex cook?", filters={"user_id": "alex"})

# after (DeepMem cloud) - change one import line
from deepmem import MemoryClient
client = MemoryClient(api_key="dm_live_...")        # key at https://deepmem.dev
client.add(messages, user_id="alex")                # identical calls
client.search("What can Alex cook?", filters={"user_id": "alex"})

Pricing

DeepMem Cloud is 10x cheaper than Mem0 at every paid tier - the same shape of plans, a tenth of the price.

Self-host instead and it's $0 - you pay only your own LLM/embedding provider (the same LLM cost Mem0 charges on top of its plan price), with no memory-service markup. Batched distillation also cuts LLM calls ~80%, so even your provider bill is smaller than per-message extractors.

Plans and limits: deepmem.dev · mem0.ai.

Benchmarks

No cherry-picked headline. The scripts and workload ship in /benchmarks - run them yourself. Here's what we measured and the exact config that produced it:

¹ BGE-M3 on a GTX 1070 GPU (2016-era), local file Qdrant, infer=False, 100 ops, concurrency 1. ² Dominated by local-file Qdrant I/O - a Qdrant server cuts this sharply. ³ Mem0 has no raw-store mode; add always runs LLM extraction, so this row isn't apples-to-apples.

• Self-hosted is where intrinsic latency lives - no internet RTT, your embedder, your Qdrant. 73 ms p50 search on an old consumer GPU.
• DeepMem cloud beats Mem0 cloud on search p50 (643 ms vs 653 ms) and returns ~2.3x more candidates per search (195 vs 84 hits across 40 queries).
• Cloud latency is RTT-dominated - both cloud columns were measured through a proxy from mainland China; run-to-run jitter is ~±10%. Run /benchmarks from a low-RTT location for your own numbers.

Why DeepMem

Agent frameworks keep re-discovering that they need persistent, retrievable memory. The hosted options bill per call and send your data to someone else's cloud. DeepMem is the self-hostable alternative: the same Mem0-shaped API you can drop in, but it runs on your box, with your embedder, your LLM key, and your Qdrant - and the code is right here to verify it.

How does DeepMem compare to other Mem0 alternatives? Most are hosted-only or layer memory on top of someone else's vector DB. DeepMem combines three things at once: it's self-hostable (your data stays on your box - $0 beyond your own LLM key), MCP-native (Claude Desktop / Cursor read and write memories directly as tools), and fully open-source - and the managed cloud runs the exact same engine, so cloud and self-host are one API, not two products.

What it is (honestly)

• Hybrid retrieval, not a knowledge graph. Search fuses vector similarity, BM25 keyword match, entity boost, and time-decay scoring. There is no temporal graph layer; if that's what you need, look at Zep.
• Stores preferences, not code dumps. Large fenced code blocks are stripped before LLM extraction, so the store fills with durable user/project facts instead of pasted implementations.
• BYOK, multi-provider. Bring your own LLM (OpenAI / Anthropic / any OpenAI-compatible endpoint) and embedding (BGE-M3 / Google / OpenAI-compatible).
• MCP-native. Ships an MCP server so Claude Desktop / Cursor can read and write memories directly.

Quick start

Three ways to run. All speak the same Mem0-compatible API.

1. Cloud (zero ops)

export DEEPMEM_API_KEY=dm_live_...      # from https://deepmem.dev
curl https://deepmem.dev/v1/memories \
  -H "Authorization: Bearer $DEEPMEM_API_KEY" -H "Content-Type: application/json" \
  -d '{"messages":[{"role":"user","content":"I am Pat, I live in Lisbon."}],"user_id":"pat","infer":false}'
curl https://deepmem.dev/v1/memories/search \
  -H "Authorization: Bearer $DEEPMEM_API_KEY" -H "Content-Type: application/json" \
  -d '{"query":"Where does Pat live?","user_id":"pat"}'

2. Docker (one command)

cp .env.example .env          # add an LLM key
docker compose up --build     # DeepMem (HTTP :8000 + MCP :8001) + Qdrant sidecar
curl http://localhost:8000/health

Or pull the published image:

docker pull langdeepmem/deepmem:latest
docker run -p 8000:8000 -p 8001:8001 -e DEEPSEEK_API_KEY=sk-... langdeepmem/deepmem:latest

The image exposes :8000 (HTTP) and :8001 (MCP). The Dockerfile and docker-compose.yml cover the GPU variant (CUDA torch + BGE_DEVICE=cuda) and BGE-M3 model-download options (HF mirror, proxy, or local mount).

3. From source

git clone https://github.com/deepmemteam/deepmem.git && cd deepmem
pip install -r requirements.txt
cp .env.example .env          # add an LLM key + embedder config
python server/start.py        # HTTP :8000 + MCP :8001

Write and search in three lines:

import httpx
httpx.post("http://localhost:8000/v1/memories",
    json={"messages":[{"role":"user","content":"I'm Pat, I live in Lisbon."}],
          "user_id":"pat"})
print(httpx.post("http://localhost:8000/v1/memories/search",
    json={"query":"Where does Pat live?","user_id":"pat"}).json()["results"])

user_id is optional (defaults to "default"); send different user_ids to isolate end-users. infer: false stores raw text immediately (test-friendly); the default infer: true queues for LLM fact extraction.

Feature highlights