---
title: "Deploy Mem0 | AI Memory Layer, Built From Source for amd64"
description: "AI memory layer, built from source since the image has no amd64 build"
category: "Starters"
url: https://railway.com/deploy/mem0-or-ai-memory--1
---

# Deploy Mem0 | AI Memory Layer, Built From Source for amd64

AI memory layer, built from source since the image has no amd64 build

**[Deploy Mem0 | AI Memory Layer, Built From Source for amd64 on Railway](https://railway.com/template/mem0-or-ai-memory--1)**

Machine-readable deploy manifest (JSON, validated by TemplateCI): https://railway.com/deploy/mem0-or-ai-memory--1/manifest.json

- **Creator:** Templates Guru
- **Category:** Starters

## Template content

### pgvector

- **Image:** pgvector/pgvector:pg17

### mem0-src

- **Source:** https://github.com/ak40u/mem0
- **Start command:** `python3 -c "import base64;exec(base64.b64decode('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').decode())"`
- **Health check:** /docs
- **Public domain:** Yes

## Documentation

# Deploy and Host Mem0 on Railway

Mem0 is an open-source memory layer for AI agents - it stores and recalls
facts across conversations so an LLM application does not start from zero
every session. This template builds the server from source
(`mem0ai/mem0`, `/server`) instead of using the catalog's published image,
because that image has no `amd64` build at all.

## About Hosting Mem0

`mem0/mem0-api-server:latest` on Docker Hub ships only an `arm64` manifest
plus an attestation layer - no `amd64`. Railway builds and runs containers
on `amd64`, so a straight `docker pull` of that image fails outright; a live
test deploy of it on Railway confirmed this, failing with an empty build log
in the exact pattern of a platform mismatch. The fix is to have Railway
build the image itself from the same Dockerfile the maintainers publish
(`server/Dockerfile`), which runs on Railway's own `amd64` build
infrastructure instead of pulling a pre-built image.

Getting a clean build to actually boot took three more fixes beyond the
platform switch:

- **Migrations never ran.** The stock `Dockerfile`'s `CMD` is just
  `uvicorn main:app --reload` - no `alembic upgrade head`. Without it,
  Postgres never gets its tables. This template's start command runs
  migrations before starting the server.
- **The app's own database doesn't exist.** `server/db.py` connects to
  `APP_DB_NAME` (`mem0_app`), a *separate* database from `POSTGRES_DB`.
  Upstream creates it via `init-db.sh`, mounted into Postgres's
  `docker-entrypoint-initdb.d` on first container init - a mechanism that
  isn't available on a stock `pgvector/pgvector:pg17` image. The start
  command creates `mem0_app` itself (via `psycopg`, autocommit) before
  migrations run.
- **`psycopg` has no working backend.** `requirements.txt` pins bare
  `psycopg>=3.2.8` - no `[binary]` extra - and the `Dockerfile` never
  installs system `libpq`. The result: `ImportError: no pq wrapper
  available`, reproduced on a live deploy. This isn't a bug in this
  template's config; it's the same import `db.py` itself makes, so the
  *official* Dockerfile hits it too. The start command installs
  `psycopg[binary]` (version-matched to what's already installed) before
  anything touches Postgres.

## Why Deploy Mem0 on Railway?

Railway is a singular platform to deploy your infrastructure stack. Railway
will host your infrastructure so you don't have to deal with configuration,
while allowing you to vertically and horizontally scale it.

By deploying Mem0 on Railway, you are one step closer to supporting a
complete full-stack application with minimal burden. Host your servers,
databases, AI agents, and more on Railway.

Specific to this template:

- **Actually runs on Railway's infrastructure.** Built from source for
  `amd64` instead of pulling an image that only exists for `arm64`.
- **Migrations and database creation happen automatically** on first
  deploy - not a manual step you have to remember.
- **A persistent volume backs `/app/history`**, where Mem0 keeps a SQLite
  log of memory operations; the catalog's leading template has no volume
  at all, so that history is lost on every redeploy.

## Common Use Cases

- Giving a chatbot or agent long-term memory of user facts and preferences
  across sessions, instead of relying only on a context window.
- A shared memory layer for multiple AI agents or tools that need to read
  and write the same facts about a user or project.
- Self-hosting Mem0's dashboard and API for teams that want their memory
  data on infrastructure they control, not a third-party SaaS.

## Dependencies for Mem0 Hosting

- pgvector (Postgres 17 with the pgvector extension) for embedding storage

### Deployment Dependencies

- [Mem0 on GitHub](https://github.com/mem0ai/mem0)
- [Mem0 documentation](https://docs.mem0.ai)
- [server/db.py - where APP_DB_NAME is used](https://github.com/mem0ai/mem0/blob/main/server/db.py)

### Implementation Details

`JWT_SECRET`, `ADMIN_API_KEY`, and both Postgres passwords are generated per
deployment. `APP_DB_NAME`, `POSTGRES_PORT`, and the Python/Mem0 defaults
(`MEM0_DEFAULT_LLM_MODEL`, `MEM0_DEFAULT_EMBEDDER_MODEL`, etc.) are
pre-filled to match the official `.env.example`.

`OPENAI_API_KEY` is required, not optional - fill it in before deploying.
Mem0 builds its default embedder client at import time
(`server/main.py`), before any request arrives, so an empty key crashes the
container on boot rather than failing gracefully on first use. Get a key at
[platform.openai.com/api-keys](https://platform.openai.com/api-keys).

Verified on this template: full build succeeded for `amd64`, migrations ran
cleanly against a fresh `mem0_app` database, and the deployed API answered
real requests - user registration and login through `/auth/register` and
`/auth/login`, then a `POST /memories` call that reached the configured LLM
provider and returned a clean `provider_auth_failed` error (proving the
whole request path works; that specific error is expected with a
placeholder key).


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