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

Multi-provider by config, not code. Both layers switch on env vars:

BGE_DEVICE=auto|cpu|cuda picks GPU when available, else CPU (force cpu on small-VRAM cards to avoid multi-process contention). BYOK overrides the LLM per-request.

Hybrid retrieval. Vector similarity + BM25 keyword + entity boost + time-decay, fused into one score. Over-fetch, re-rank, return.

Async batched distillation. Writes queue behind a silence window and are extracted in batches - ~80% fewer LLM calls than per-message extraction.

Semantic cache. Repeat adds/searches hit a similarity-gated cache and return cached facts without re-embedding or re-querying Qdrant.

Stores preferences, not code. extraction_filter strips large fenced code blocks before LLM extraction, so the store fills with durable facts, not pasted implementations.

MCP server. deepmem_write / deepmem_search / deepmem_delete tools for Claude Desktop, Cursor, and any MCP client.

GDPR. Soft-delete with retention window, hard-delete reset, SHA-256 id masking in logs, export/import for portability.

API

agent_id / run_id optionally scope writes/reads (mirrors Mem0's three-level isolation: user -> agent -> run). Interactive docs at /docs.

MCP integration

The cloud exposes a remote MCP endpoint (streamable HTTP):

URL:    https://deepmem.dev/mcp
Auth:   Authorization: Bearer dm_live_...   (your deepmem.dev API key)
Tools:  deepmem_write / deepmem_search

Works with Claude Code, Claude Desktop, Cursor, and any MCP-compatible client.

Claude Code - one command:

claude mcp add --transport http deepmem https://deepmem.dev/mcp \
  --header "Authorization: Bearer dm_live_..."

Or install the plugin (bundles a /deepmem:setup command that guides API-key configuration):

/plugin marketplace add deepmemteam/deepmem-claude-plugin
/plugin install deepmem@deepmem

Cursor → Settings → MCP → Add new MCP server, or edit ~/.cursor/mcp.json (global) / .cursor/mcp.json (per project). Cursor resolves ${env:NAME} variables in url and headers, so the key can live in an environment variable instead of the config file:

{
  "mcpServers": {
    "deepmem": {
      "url": "https://deepmem.dev/mcp",
      "headers": { "Authorization": "Bearer ${env:DEEPMEM_API_KEY}" }
    }
  }
}

(with DEEPMEM_API_KEY=dm_live_... exported in your shell, or paste the literal key if you prefer). Community MCP listings with one-click "Add to Cursor" live on cursor.directory; official plugins ship in the Cursor Marketplace.

DeepSeek Harness (dsh) - one overlay file, no dsh plugin needed (dsh connects any MCP server through its generic dsh-mcp-client bridge):

export DEEPMEM_API_KEY=dm_live_...
dsh web --patch "$PWD/examples/dsh/deepmem.cordis.yml"

See examples/dsh for details and self-hosted variants.

Self-hosted (stdio, bundled server, no key needed in open mode):

{
  "mcpServers": {
    "deepmem": {
      "command": "python",
      "args": ["server/mcp_server.py"],
      "env": { "DEEPMEMORY_BASE_URL": "http://localhost:8000" }
    }
  }
}

This repo ships a .cursor/mcp.json preconfigured for the self-hosted server - open the repo in Cursor and enable it under Settings → MCP.

Architecture

client (HTTP / MCP)
  -> FastAPI (:8000)
       -> rate-limit middleware (per-IP token bucket on add/search)
       -> SemanticCache.check            (return cached facts on similarity hit)
       -> AsyncBatchDistiller.enqueue    (POST /v1/memories, infer=true)
            ↳ silence-window or max_batch triggers on_batch_ready
                ↳ VectorStore.process_batch  (LLM extraction -> Qdrant upsert)
       -> VectorStore.search             (vector + BM25 + entity + time-decay)
  -> LLM: OpenAI / Anthropic / OpenAI-compatible (fact extraction)
  -> BGE-M3 / Gemini / OpenAI-compatible (embeddings)
  -> Qdrant (vectors)  +  SQLite (audit history)
  -> MCP server (:8001)  deepmem_write / deepmem_search / deepmem_delete

Single Qdrant collection (memories), hard-filtered by user_id payload. TenantValidator NFC-normalizes and enforces a [A-Za-z0-9._:-]{1,256} charset on user_id - never trust the raw request value.

Configuration

Config loads once from env vars (.env, auto-loaded) > config.json > defaults. Key variables:

Backends auto-switch on env vars - no code changes:

• QDRANT_URL set -> remote Qdrant; unset -> local file Qdrant under ./data/qdrant.
• CORS_ORIGINS=* is refused at boot unless DEEPMEMORY_DEBUG=1.

Production (systemd)

systemctl restart deepmem.service     # scripts/start.sh -> uvicorn :8000
journalctl -u deepmem -f

For HTTPS, put Caddy or Nginx in front; scripts/start.sh runs under systemd or any process manager.

Benchmarks

Two reproducible scripts in /benchmarks:

• Cloud vs cloud - DeepMem cloud vs Mem0 cloud. Register keys at deepmem.dev + mem0.ai, then python benchmarks/benchmark_cloud.py.
• Self-hosted - your DeepMem, your hardware (no key, no external service): python benchmarks/run_benchmark.py.

Both ship a self-contained workload and report P50/P95/P99 + throughput. The numbers at the top of this README were produced with these scripts - rerun them and read your own percentiles.

FAQ

Do I need the cloud? No. The open-source server is fully functional on its own. The cloud (deepmem.dev) is the zero-ops option - same API.

Does it work offline? Retrieval and raw-store (infer=false) work with no network. LLM fact extraction (infer=true) needs an LLM key - or run a local OpenAI-compatible model (Ollama / vLLM / LM Studio) and point LLM_BASE_URL at it.

Where is my data? In your Qdrant (local file or server) + a SQLite audit log. Nothing leaves your machine except the LLM/embedding calls you configure.

Multi-tenant? Yes - single Qdrant collection hard-filtered by user_id. agent_id / run_id add agent and session scope.

Is it production-ready? Used in production under systemd with a remote Qdrant. Local file Qdrant is fine for dev/single-worker; use a Qdrant server for multi-worker or high-throughput.

Development

pip install -r requirements.txt
DEEPMEMORY_DEBUG=1 python -m uvicorn server.main:app --reload --host 0.0.0.0 --port 8000
pytest tests/ -x -v

License

MIT.