MCP server de DeepMem

---

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"})

• add(infer=True) (the default) is asynchronous on DeepMem cloud - it returns pending=True with results=[] and extracted facts land a few seconds later. (Mem0 cloud's add is async too - it returns PENDING.) Pass infer=False for synchronous raw-text storage that's immediately searchable.
• No graph relations - DeepMem uses hybrid vector retrieval (vector + BM25
  • time-decay), so relations is always []. Mem0's graph features aren't replicated.
• reset differs - Mem0's is account-wide; DeepMem's is per-user_id with a confirm guard.

Pricing

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

Tier	DeepMem	Mem0 cloud
Hobby	Free	Free
Starter	$1.9/mo	$19/mo
Growth	$7.9/mo	$79/mo
Professional	$24.9/mo	$249/mo

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:

Metric	DeepMem self-hosted ¹	DeepMem cloud	Mem0 cloud
Search p50	73 ms	643 ms	653 ms
Search p95	86 ms	811 ms	710 ms
Search hits (40 queries)	-	195	84
Add p50 (raw store)	899 ms ²	792 ms	695 ms ³

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.

Without DeepMem	With DeepMem
Re-explain who you are and what you're working on every session	The agent recalls identity, projects, and preferences automatically
Lose debugging and research context between sessions	Past root causes, dead ends, and findings are recalled, so work isn't repeated
Manually restate preferences every session	Preferences persist across sessions, agents, and projects
Hosted memory services that bill per call and hold your data	Self-host on your infra, or use the cloud - your call, same API

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:

Layer	Options	Selector
LLM (fact extraction)	OpenAI · Anthropic (native SDK) · any OpenAI-compatible (DeepSeek / vLLM / Ollama / Groq / LM Studio)	LLM_PROVIDER + LLM_API_KEY / ANTHROPIC_API_KEY / DEEPSEEK_API_KEY
Embeddings	BGE-M3 (local, GPU/CPU) · Google Gemini · any OpenAI-compatible	EMBEDDING_PROVIDER + BGE_M3_PATH / GOOGLE_API_KEY / OPENAI_API_KEY

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

Method	Path	Description
POST	/v1/memories	Write messages; LLM-extract facts (infer=false stores raw)
POST	/v1/memories/search	Semantic search (vector + BM25 + entity + time-decay)
GET	/v1/memories	List all for a user_id (paginated)
GET	/v1/memories/{id}	Get one by ID
PUT	/v1/memories/{id}	Update one memory's text
DELETE	/v1/memories/{id}	Soft-delete one
DELETE	/v1/memories	Soft-delete all for a user_id (GDPR)
GET	/v1/memories/{id}/history	ADD/UPDATE/DELETE audit log
POST	/v1/reset	Hard-delete all + history (needs confirm_user_id)
GET	/v1/export · POST /v1/import	Portable JSON export / import
GET	/health · /ready	Liveness / readiness probes

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:

Variable	Required	Description
DEEPSEEK_API_KEY / LLM_API_KEY / ANTHROPIC_API_KEY	one LLM key	LLM for fact extraction
LLM_PROVIDER	no	auto / openai / anthropic / openai_compatible (default auto)
EMBEDDING_PROVIDER	no	bge-m3 / google / openai (default bge-m3)
BGE_M3_PATH	no	local BGE-M3 dir or HF model id (default BAAI/bge-m3)
BGE_DEVICE	no	auto / cpu / cuda (default auto)
QDRANT_URL / QDRANT_API_KEY	no	remote Qdrant; omit for local file store
CORS_ORIGINS	yes	comma-separated allowed origins (no * in prod)
RATE_LIMIT_ADD / RATE_LIMIT_SEARCH	no	per-minute limits (default 30 / 60)

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.