Context

Users running LangGraph deployments may encounter Redis memory-related issues, particularly in environments with high load or limited resources. These issues can manifest as unexplained latencies, failed runs, or Out of Memory (OOM) errors when Redis reaches its maximum memory limit.

Answer

There are several steps you can take to resolve Redis memory issues in your LangGraph deployment:

1. Check Deployment Type: Ensure you're using the appropriate deployment type:

   • Dev deployments have hard compute and memory constraints

   • Production deployments auto-scale database size based on needs and provide higher max resources

2. Redis Configuration:

   • For non-clustered Redis: Use the environment variable REDIS_CLUSTER=false

   • For enterprise/production use: Enable clustering with REDIS_CLUSTER=true

3. Memory Management:

   • Monitor Redis memory usage through your monitoring dashboard

   • Implement TTL (Time To Live) settings in your langgraph.json to manage data retention

   • Consider increasing Redis memory limits if you consistently hit capacity

Immediate Resolution Steps for Memory Issues:

1. If possible, restart your Redis instance as a temporary solution

2. Monitor memory usage patterns to identify potential memory leaks or usage spikes

3. Consider implementing async calls for API operations to reduce memory pressure

Advanced Resource Management and Compatibility

Understanding LangGraph Container Resource Constraints

• 2 CPU cores and 2GB RAM per container

• Autoscaling up to 10 containers based on 75% CPU/memory utilization targets

• Monitor these metrics at Deployments → Monitoring to review CPU, memory, and pending runs

Preventing Memory Issues Through Workload Management

Configure Concurrent Operations:

• Set the N_JOBS_PER_WORKER environment variable to limit concurrent runs per worker (default is 10)

• Reduce this value if experiencing resource pressure from parallel operations

• Add delays between task submissions when running many parallel tasks

Critical Compatibility Requirements

• Use Redis instead of Valkey – AWS ElastiCache with Valkey can cause hanging requests and connection issues

• If experiencing unexplained hangs with Valkey, switch to Redis for reliable operation

• This compatibility issue can manifest as silent failures that are difficult to diagnose

Proactive Monitoring and Prevention

Resource Pattern Analysis:

• Track correlation between pending runs and memory spikes

• Monitor autoscaling triggers to understand when you're approaching limits

• Identify optimal N_JOBS_PER_WORKER values based on your workload patterns