Practical overview
When it fits
Use Deepmem when an agent or LLM application needs persistent conversation memory and you want a self-hostable service or a managed cloud option. It is also relevant when adapting a Mem0 client using the documented MemoryClient interface.
Who it is for
- Developers adding persistent, searchable memory to AI agents or LLM applications.
- Teams migrating a Mem0 client to a Mem0-compatible interface.
- Operators who want to self-host an AI memory service or use its managed cloud option.
Documented uses
- Store conversation content as memory for later retrieval.
- Search stored memory using vector and BM25 retrieval, with entity boosts and time decay.
- Use LLM fact extraction to turn conversations into memory.
- Connect applications through the HTTP API, Python client, or built-in MCP server.
Requirements & limits
The repository is MIT-licensed and documents self-hosting through Docker or from source. The managed cloud uses an API key. Self-hosted raw storage and retrieval are documented as working without network access; LLM fact extraction requires an LLM key.
- Self-hosting requires configuring an LLM key for fact extraction; the README also documents embedding-provider and Qdrant configuration options.
- The client interface is documented as Mem0-compatible, but graph relations are not replicated.
- Deepmem’s reset operation is scoped per user ID, unlike the account-wide reset described for Mem0.
- The self-hosted setup exposes HTTP on port 8000 and MCP on port 8001.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Run Deepmem with Docker
Start the self-hosted HTTP and MCP services with the documented Docker Compose setup.
Before you start
- The Deepmem repository and Docker Compose
- An LLM key added to the environment configuration
Steps
Copy
.env.exampleto.env.Add an LLM key to
.env.Run the command below.
docker compose up --buildCheck the HTTP service at
http://localhost:8000/health.
Expected outcome
The documented setup starts Deepmem’s HTTP service on port 8000, MCP service on port 8001, and a Qdrant sidecar.
Guide limitations
- The guide documents startup, not a verified deployment or successful runtime test.
- LLM fact extraction requires an LLM key; raw storage and retrieval are documented as working without network access.
Replace a Mem0 client import
Adapt a Mem0-based client to use Deepmem’s documented `MemoryClient` interface.
Before you start
- An application using
mem0.MemoryClient - The
deepmem-clientpackage
Steps
Install the package with the command below.
pip install deepmem-clientReplace
from mem0 import MemoryClientwithfrom deepmem import MemoryClient.Use the documented Deepmem API key when configuring the managed cloud client.
Expected outcome
The README demonstrates retaining calls such as `add` and `search` after changing the import.
Guide limitations
- The compatibility claim covers the documented method signatures, not execution testing.
- Deepmem does not replicate Mem0 graph relations, and
resetis peruser_idrather than account-wide.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- deepmemteam/deepmem · Official homepage title · retained in its source language
Related options
Mem0 — Deepmem documents a Mem0-compatible MemoryClient interface and a migration path that replaces the import. It notes that graph relations and account-wide reset behavior are not repl
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