Docker Container OOM Killed — Agent Process Silently Terminated

Symptom

Agent suddenly stops responding with no application error
Container restarts automatically (if restart policy set)
docker logs shows no application error — just stops mid-line
docker inspect <container> shows OOMKilled: true
Happens more frequently under load or with large context windows
All in-progress work is lost

Root Cause

The Docker container’s memory limit was exceeded. The Linux OOM (Out of Memory) killer terminates processes to free memory — it picks the largest memory consumer, which is usually the agent process. No application-level handler can catch this; it’s a kernel-level SIGKILL.

Diagnosis

# Check if container was OOM killed
docker inspect <container_name> --format ''
# → true

# Check container exit code (137 = killed by signal)
docker inspect <container_name> --format ''
# → 137 (128 + 9 for SIGKILL)

# Check system OOM log
dmesg | grep -i "oom\|killed process"
# → [timestamp] Out of memory: Kill process 12345 (node) score 850 ...

# Monitor memory usage live
docker stats <container_name>

Fix

Option 1: Increase container memory limit

# docker-compose.yml
services:
  agent:
    mem_limit: 4g          # Was 512m — increase to what the agent actually needs
    memswap_limit: 4g      # Disable swap (set equal to mem_limit)
    oom_kill_disable: false # Keep enabled — disabling OOM kill can freeze the host

Option 2: Profile actual memory usage first

# Run without limit, measure peak usage
docker run --memory-swap -1 your-agent:latest &
docker stats --no-stream  # Note peak memory

# Set limit to peak × 1.5 (safety margin)

Option 3: Add memory monitoring + graceful degradation

import resource, os

def get_memory_usage_mb():
    """Get current process memory usage in MB"""
    usage = resource.getrusage(resource.RUSAGE_SELF)
    return usage.ru_maxrss / 1024  # Linux returns KB

MEMORY_WARNING_MB = 3000  # Warn at 3GB
MEMORY_LIMIT_MB = 3500    # Start shedding load at 3.5GB

async def check_memory_before_large_op():
    mem = get_memory_usage_mb()
    if mem > MEMORY_LIMIT_MB:
        raise MemoryError(f"Memory limit reached ({mem:.0f}MB) — refusing large operation")
    if mem > MEMORY_WARNING_MB:
        print(f"Warning: High memory usage {mem:.0f}MB")

Option 4: Checkpoint work before OOM can occur

import signal

class OOMProtectedAgent:
    def __init__(self, checkpoint_path):
        self.checkpoint_path = checkpoint_path
        # Save state before potential OOM on SIGUSR1 or periodic
        signal.signal(signal.SIGUSR1, self._emergency_checkpoint)

    def _emergency_checkpoint(self, signum, frame):
        """Called manually or via monitoring when memory is high"""
        self.save_checkpoint()

    async def run_task(self, task):
        try:
            for step in self.plan_steps(task):
                result = await self.execute_step(step)
                self.save_checkpoint()  # Checkpoint after each step
                yield result
        except MemoryError:
            # Re-raise after checkpoint is saved
            self.save_checkpoint()
            raise

Option 5: Set memory limits per operation type

# docker-compose.yml
services:
  agent:
    mem_limit: 2g
  agent-large-context:
    image: your-agent:latest
    mem_limit: 8g          # Separate service for memory-intensive work
    profiles: ["large"]    # Only start when needed

Prevention Checklist

docker inspect checked for OOMKilled after any unexplained restarts
Memory limit set based on profiled peak usage × 1.5
Agent checkpoints work periodically (not only on success)
Memory monitoring alerts configured before OOM limit

Expected Token Savings

Restarting lost work after OOM kill: 10,000–100,000 tokens (task-dependent) Proper memory limits + checkpointing: prevents loss entirely

Environment

Docker deployments of AI agents
Most common: agents with large context windows or file processing
Source: direct experience

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