# Railway Deployment Guide

> Complete guide to deploying AutoMem on Railway with persistent storage, backups, and zero data loss.

- Skill: `tools-only/railway-deployment-guide` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/railway-deployment-guide`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/railway-deployment-guide/raw
- Safety review: pending (external: skill-scanner PASS, skillspector CAUTION)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-29
- Page: https://skillmd.com/skills/tools-only/railway-deployment-guide

---

# Railway Deployment Guide

Complete guide to deploying AutoMem on Railway with persistent storage, backups, and zero data loss.

## Quick Start (One-Click Deploy)

[![Deploy on Railway](https://railway.app/button.svg)](https://railway.com/deploy/automem-ai-memory-service)

This template automatically sets up:

- ✅ **memory-service** — AutoMem Flask API with health checks
- ✅ **falkordb** — Graph database with **persistent volumes** and password protection
- ✅ **mcp-sse-server** — MCP bridge for cloud AI platforms (ChatGPT, Claude.ai, ElevenLabs)
- ✅ Automatic secret generation and service networking

```mermaid
flowchart TB
    subgraph internet [Public Internet]
        Users[AI Clients<br/>Direct API]
        CloudAI[Cloud AI Platforms<br/>ChatGPT, Claude.ai, ElevenLabs]
        Local[Local Tools<br/>Cursor, Claude Desktop]
    end

    subgraph railway [Railway Project]
        subgraph mcpbridge [MCP Bridge Service]
            MCPServer[MCP Bridge<br/>Streamable HTTP<br/>Port 8080]
            MCPDomain[Public Domain<br/>mcp.up.railway.app]
        end

        subgraph api [memory-service Service]
            FlaskAPI[Flask API<br/>Port 8001]
            APIDomain[Public Domain<br/>automem.up.railway.app]

            Enrichment[Enrichment Pipeline]
            Consolidation[Consolidation Engine]
        end

        subgraph db [falkordb Service]
            FalkorDB[(FalkorDB<br/>Port 6379)]
            Volume[Persistent Volume<br/>/var/lib/falkordb/data]
            TCPProxy[TCP Proxy<br/>Public access]
        end

        subgraph external [External Services]
            QdrantCloud[(Qdrant Cloud<br/>Vector DB)]
            OpenAI[OpenAI API<br/>Embeddings]
        end
    end

    Users -->|HTTPS| APIDomain
    CloudAI -->|Streamable HTTP| MCPDomain
    Local -->|HTTPS| APIDomain

    MCPDomain --> MCPServer
    APIDomain --> FlaskAPI

    MCPServer -->|Internal<br/>memory-service.railway.internal:8001| FlaskAPI

    FlaskAPI -->|Internal<br/>falkordb.railway.internal:6379| FalkorDB
    FlaskAPI --> QdrantCloud
    FlaskAPI --> OpenAI

    Enrichment --> FalkorDB
    Enrichment --> QdrantCloud
    Consolidation --> FalkorDB

    FalkorDB --> Volume

    TCPProxy -.->|Optional<br/>External access| FalkorDB
```

---

## Post-Deploy Checklist

After deploying, complete these steps to fully configure AutoMem:

### Required: Add Your API Keys

1. **Go to `memory-service` → Variables**
2. **Set these variables:**

| Variable         | Required    | How to Get                                                           |
| ---------------- | ----------- | -------------------------------------------------------------------- |
| `OPENAI_API_KEY` | Yes\*       | [platform.openai.com/api-keys](https://platform.openai.com/api-keys) |
| `QDRANT_URL`     | Recommended | See [Qdrant Setup Guide](QDRANT_SETUP.md)                            |
| `QDRANT_API_KEY` | With Qdrant | See [Qdrant Setup Guide](QDRANT_SETUP.md)                            |

\*Without `OPENAI_API_KEY`, semantic search won't work (embeddings skipped).

👉 **First time with Qdrant?** Follow the [Qdrant Setup Guide](QDRANT_SETUP.md) for step-by-step collection setup.

3. **Redeploy** the memory-service after adding variables.

### Verify Deployment

```bash
# Check health (should show falkordb: connected)
curl https://your-automem.up.railway.app/health

# Store a test memory
curl -X POST "https://your-automem.up.railway.app/memory" \
  -H "Authorization: Bearer YOUR_AUTOMEM_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"content": "Test memory from Railway", "tags": ["test"]}'
```

### MCP Bridge (Included)

The template includes **mcp-sse-server**, which exposes AutoMem as an MCP server over HTTPS. This enables cloud AI platforms to access your memories:

| Platform          | How to Connect                                                                                 |
| ----------------- | ---------------------------------------------------------------------------------------------- |
| **ChatGPT**       | Settings → Connectors → Add Server → `https://your-sse.up.railway.app/mcp/sse?api_token=TOKEN` |
| **Claude.ai**     | Settings → MCP → `https://your-sse.up.railway.app/mcp/sse?api_token=TOKEN`                     |
| **Claude Mobile** | Settings → MCP Servers → same URL as above                                                     |
| **ElevenLabs**    | Agent config → MCP → same URL (or use Authorization header)                                    |

👉 **See [MCP_SSE.md](MCP_SSE.md)** for detailed setup per platform.

**Don't need the MCP bridge?** If you only use Cursor, Claude Desktop, or direct API access:

1. Go to Railway Dashboard → `mcp-sse-server` service
2. Click the three dots menu → **Delete Service**
3. This saves ~$2-3/month and has no impact on the core memory API

### Optional: Standalone Graph Viewer Service

Deploy `automem-graph-viewer` as a separate Railway service, then configure these on `memory-service`:

| Variable | Value |
|----------|-------|
| `ENABLE_GRAPH_VIEWER` | `true` |
| `GRAPH_VIEWER_URL` | `https://<your-viewer-domain>` |
| `VIEWER_ALLOWED_ORIGINS` | `https://<your-viewer-domain>` |

AutoMem keeps `/viewer` for compatibility and redirects/bootstraps users to the standalone viewer.

### Get Your API Tokens

Your tokens were auto-generated during deployment. Find them in:

- Railway Dashboard → `memory-service` → Variables
- Look for `AUTOMEM_API_TOKEN` and `ADMIN_API_TOKEN`

---

## Manual Setup (Recommended for Production)

### Step 1: Create FalkorDB Service with Persistence

1. **Create new service in Railway**

   - Click "+ New Service"
   - Select "Docker Image"
   - Image: `falkordb/falkordb:latest`

2. **Add persistent volume** (CRITICAL!)

   - Go to service → Settings → Volumes
   - Click "Add Volume"
   - Mount path: `/var/lib/falkordb/data`
   - This ensures data survives restarts

3. **Configure environment variables**:

   ```bash
   PORT=6379
   FALKOR_PASSWORD=${{shared.FALKOR_PASSWORD}}  # Auto-generated secret
   FALKOR_USERNAME=default
   FALKOR_HOST=${{RAILWAY_PRIVATE_DOMAIN}}
   FALKOR_PORT=${{PORT}}
   FALKOR_PUBLIC_HOST=${{RAILWAY_TCP_PROXY_DOMAIN}}
   FALKOR_PUBLIC_PORT=${{RAILWAY_TCP_PROXY_PORT}}
   REDIS_ARGS=--save 60 1 --appendonly yes --appendfsync everysec
   ```

   **Note**: FalkorDB handles authentication via `FALKOR_PASSWORD` - don't add `--requirepass` to `REDIS_ARGS`.

4. **Health check**: Leave blank/disabled (FalkorDB doesn't have HTTP endpoints). Railway monitors container status automatically.

5. **Note the internal URL**: `${{FalkorDB.RAILWAY_PRIVATE_DOMAIN}}`

### Step 2: Deploy AutoMem API

1. **Connect GitHub repo** or **Deploy from Docker**

   - If using GitHub: Connect repository and set root directory
   - If using Docker: Use existing Dockerfile

2. **Configure environment variables**:

   **Option A: Variable References (template style)**

   ```bash
   # Database connections
   FALKORDB_HOST=${{FalkorDB.RAILWAY_PRIVATE_DOMAIN}}
   FALKORDB_PORT=6379
   FALKORDB_PASSWORD=${{FalkorDB.FALKOR_PASSWORD}}
   FALKORDB_GRAPH=memories

   # API authentication (Railway auto-generates secrets)
   AUTOMEM_API_TOKEN=${{shared.AUTOMEM_API_TOKEN}}
   ADMIN_API_TOKEN=${{shared.ADMIN_API_TOKEN}}

   # OpenAI for embeddings (required for semantic search)
   OPENAI_API_KEY=<your-openai-key>

   # Optional: Qdrant Cloud for vector search
   QDRANT_URL=<your-qdrant-cloud-url>
   QDRANT_API_KEY=<your-qdrant-api-key>
   QDRANT_COLLECTION=memories

   # Port (REQUIRED - Flask needs explicit port)
   PORT=8001
   ```

   **Option B: Hardcoded Values (recommended for stability)**

   ```bash
   # Database connections - use actual values from FalkorDB service
   FALKORDB_HOST=falkordb.railway.internal
   FALKORDB_PORT=6379
   FALKORDB_PASSWORD=<copy-from-falkordb-service>
   FALKORDB_GRAPH=memories

   # API authentication - generate or copy from shared variables
   AUTOMEM_API_TOKEN=<your-generated-token>
   ADMIN_API_TOKEN=<your-generated-token>

   # OpenAI for embeddings
   OPENAI_API_KEY=<your-openai-key>

   # Qdrant Cloud
   QDRANT_URL=<your-qdrant-cloud-url>
   QDRANT_API_KEY=<your-qdrant-api-key>
   QDRANT_COLLECTION=memories

   # Port (REQUIRED - Flask needs explicit port)
   PORT=8001
   ```

   **Note**: Hardcoded values (Option B) are more stable and easier to debug, while variable references (Option A) update automatically but can be harder to troubleshoot.

   **⚠️ Important**: `PORT=8001` is **required** for the memory-service. Without it, Flask defaults to port 5000, causing connection failures from other services.

3. **Set health check**:

   - Path: `/health`
   - Timeout: 100s

4. **Generate public domain**:
   - Settings → Networking → Generate Domain
   - Save your URL: `https://your-automem.up.railway.app`

### Step 3: Verify Deployment

```bash
# Check health
curl https://your-automem.up.railway.app/health

# Expected response:
{
  "status": "healthy",
  "falkordb": "connected",
  "qdrant": "connected",
  "memory_count": 1234,  # Added in recent versions
  "enrichment": {
    "status": "running",
    "queue_depth": 0,
    "pending": 0,
    "inflight": 0,
    "processed": 0,
    "failed": 0
  },
  "graph": "memories",
  "timestamp": "2025-10-20T03:47:39+00:00"
}
```

**Note**: `memory_count` field requires AutoMem commit from Oct 20, 2025 or later. For detailed analytics, use `/analyze` endpoint.

```bash
# Check detailed memory analytics
curl "https://your-automem.up.railway.app/analyze?api_key=YOUR_API_TOKEN"

# Shows:
# - Total memories by type (Context, Decision, Insight, etc.)
# - Entity frequency (projects, tools)
# - Confidence distribution
# - Temporal insights (activity by hour)
```

If you get `503`:

- Check FalkorDB is running and healthy
- Verify `FALKORDB_HOST` is set to private domain (use `falkordb.railway.internal`, not `${{...}}` syntax)
- Confirm `FALKORDB_PASSWORD` matches between services
- Test connection: `railway logs --service memory-service | grep -i falkordb`

### Step 4: Store First Memory

```bash
export AUTOMEM_URL="https://your-automem.up.railway.app"
export AUTOMEM_TOKEN="your-api-token"

curl -X POST "$AUTOMEM_URL/memory" \
  -H "Authorization: Bearer $AUTOMEM_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "First memory from Railway!",
    "tags": ["test", "deployment"],
    "importance": 0.8
  }'
```

---

## Data Persistence & Backups

### Persistent Volumes (Required)

Railway volumes ensure data survives:

- Service restarts
- Deployments
- Platform maintenance

**Volume Configuration**:

- Mount path: `/var/lib/falkordb/data`
- Minimum size: 1GB (adjust based on needs)
- Backed up automatically by Railway

### Automated Backups

Railway provides built-in volume backups for FalkorDB (automatic, one-click restore).

For comprehensive backups covering both FalkorDB and Qdrant with S3 upload:

👉 **See [MONITORING_AND_BACKUPS.md](MONITORING_AND_BACKUPS.md)** for complete backup setup including:

- Railway volume backups (built-in)
- GitHub Actions automated backups (recommended, free)
- Manual backup scripts

### Disaster Recovery

If FalkorDB data is lost but Qdrant is intact:

```bash
# SSH into AutoMem service
railway run

# Run recovery script
python scripts/recover_from_qdrant.py

# This will:
# - Read all 636+ memories from Qdrant
# - Rebuild FalkorDB graph structure
# - Restore all relationships
```

---

## Optional: FalkorDB Browser

For visual graph exploration:

1. **Create new service**:

   - Image: `falkordb/falkordb-browser:latest`
   - Port: 3000

2. **Configure connection**:

   ```bash
   FALKORDB_URL=redis://default:${{FalkorDB.FALKOR_PASSWORD}}@${{FalkorDB.RAILWAY_PRIVATE_DOMAIN}}:6379
   ```

3. **Access**:
   - Generate public domain
   - Open in browser
   - Visual query builder included

---

## Monitoring & Health Checks

### Built-in Health Endpoint

```bash
# Check system health
curl https://your-automem.up.railway.app/health

# Response includes:
# - FalkorDB connection status
# - Qdrant connection status
# - Memory count
# - Timestamp
```

### Automated Health Monitoring

Deploy health monitor as separate service:

```bash
# In new Railway service
docker run automem/health-monitor \
  -e AUTOMEM_API_URL=${{AutoMemAPI.RAILWAY_PUBLIC_DOMAIN}} \
  -e CHECK_INTERVAL=300
```

This will:

- Monitor FalkorDB/Qdrant health every 5min
- Check memory count consistency
- Auto-trigger recovery if >5% drift detected
- Send alerts via webhook (configure ALERT_WEBHOOK_URL)

---

## Cost Optimization

**Recommended Railway Plan**: Pro ($20/mo)

**Service Sizing**:

- **memory-service**: 512MB RAM, 0.5 vCPU (~$5/mo)
- **FalkorDB**: 1GB RAM, 1 vCPU + 2GB volume (~$10/mo)
- **mcp-sse-server**: 256MB RAM, 0.25 vCPU (~$2-3/mo)
- **Qdrant Cloud**: Free tier (1GB) or $25/mo (10GB)

**Total**: ~$17-40/month depending on usage

**Cost Saving Tips**:

- Use Qdrant Cloud free tier initially
- Start with smaller FalkorDB volume (1GB)
- Use Railway's usage-based pricing (scales down when idle)
- **Remove mcp-sse-server** if you only use Cursor/Claude Desktop (saves ~$2-3/mo)

---

## Troubleshooting

### Connection Issues

**Problem**: API can't connect to FalkorDB

**Solution**:

```bash
# Check internal networking
railway logs --service memory-service | grep FalkorDB

# Verify private domain
echo $FALKORDB_HOST  # Should be: falkordb.railway.internal

# Test connection
railway run --service memory-service
> redis-cli -h $FALKORDB_HOST -p 6379 -a $FALKORDB_PASSWORD ping
```

### Service Connection Refused (ECONNREFUSED)

**Problem**: SSE or other services get "fetch failed" or "ECONNREFUSED" when connecting to memory-service

**Symptoms**:

```
Error: connect ECONNREFUSED fd12:ca03:42be:0:1000:50:1079:5b6c:8001
```

**Causes & Solutions**:

1. **Missing PORT variable** (most common):

   - Check memory-service variables: `PORT` must be set to `8001`
   - Without it, Flask defaults to port 5000
   - **Fix**: Add `PORT=8001` to memory-service environment variables and redeploy

2. **IPv6 binding issue** (fixed in latest code):

   - Railway internal networking uses IPv6
   - Older AutoMem versions bound to IPv4 only (`0.0.0.0`)
   - **Fix**: Update to latest code (Flask now binds to `::` for IPv6 dual-stack)
   - Check startup logs should show: `* Running on http://[::1]:8001`

3. **Wrong internal hostname**:
   - Verify `AUTOMEM_API_URL` in SSE service matches memory-service's `RAILWAY_PRIVATE_DOMAIN`
   - Should be: `http://memory-service.railway.internal:8001`

### Variable Reference Issues

**Problem**: Variables using `${{...}}` syntax not resolving (showing literal `${{...}}` in logs)

**Cause**: Railway variable references only work in templates, not manual service configuration

**Solution**: Use hardcoded values instead

```bash
# ❌ Don't use in manual setup:
FALKORDB_HOST=${{FalkorDB.RAILWAY_PRIVATE_DOMAIN}}

# ✅ Do use in manual setup:
FALKORDB_HOST=falkordb.railway.internal
FALKORDB_PASSWORD=<copy-exact-value-from-falkordb-service>
```

**Benefit**: Hardcoded values are more stable, easier to debug, and work consistently across redeployments.

### Data Loss

**Problem**: FalkorDB restarted and lost data

**Cause**: No persistent volume configured

**Solution**:

1. Add volume to FalkorDB service (Settings → Volumes)
2. Run recovery: `python scripts/recover_from_qdrant.py`
3. Redeploy FalkorDB service

### High Memory Usage

**Problem**: FalkorDB using too much RAM

**Solution**:

```bash
# Optimize Redis memory
REDIS_ARGS=--maxmemory 512mb --maxmemory-policy allkeys-lru
```

---

## Security Best Practices

1. **Always set FALKOR_PASSWORD** (Railway auto-generates)
2. **Use Railway's private networking** for service-to-service
3. **Don't expose FalkorDB publicly** (use private domain only)
4. **Rotate API tokens** periodically via Railway dashboard
5. **Enable Railway's Audit Logs** (Enterprise plan)

**Note on Service Naming**: Railway's internal DNS is based on the service name (e.g., `memory-service.railway.internal`). If you rename a service, its `RAILWAY_PRIVATE_DOMAIN` updates automatically, but you'll need to update any hardcoded hostnames in other services' environment variables.

---

## Next Steps

- [ ] Set up monitoring alerts (see [MONITORING_AND_BACKUPS.md](MONITORING_AND_BACKUPS.md))
- [ ] Configure automated backups (see [MONITORING_AND_BACKUPS.md](MONITORING_AND_BACKUPS.md))
- [x] Add Remote MCP server integration — see docs/MCP_SSE.md
- [ ] Deploy FalkorDB Browser
- [ ] Set up staging environment

**Questions?** Open an issue: https://github.com/verygoodplugins/automem/issues