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ElizaOS V8 Heap Out of Memory during PGVector RAG Ingestion Fix

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Technical Guide

The ElizaOS V8 ‘JavaScript heap out of memory’ crash during RAG ingestion occurs because batch document processing buffers thousands of text chunks and floating-point embedding vectors into NodeJS heap memory simultaneously without backpressure. When memory consumption exceeds the default 2 GB V8 heap limit, the NodeJS process aborts immediately. Increasing max-old-space-size to 4096 MB and streaming chunk batches through concurrency queues restores stability.

Before executing state changes or signing transactions, verify your cryptographic parameters using our interactive ERC-4337 Calldata Decoder utility.


Architectural Root Cause: Vector Ingestion Without Backpressure

The Node.js V8 runtime allocates memory inside an isolated managed heap. On default 64-bit platforms, Node limits this space to roughly 1.4 GB - 2.0 GB.

When ElizaOS processes document chunks for vector indexing:

  1. Tensor Proliferation: Each text chunk is converted into high-dimensional embedding vectors (e.g., 1536 dimensions for OpenAI text-embedding-3-small, or 384 dimensions for local BGE models). Each dimension is stored as a 32-bit floating point value (Float32Array).
  2. Database Write Latency: PostgreSQL with pgvector performs disk writes, IVFFlat / HNSW index updates, and WAL logging. Under high concurrency, database write latency climbs from 5ms to 250ms+.
  3. Queue Saturation: The ElizaOS runtime lacks native stream backpressure in AgentRuntime.ts. Incoming messages accumulate in an in-memory queue faster than the database driver can flush them.
  4. V8 Heap Exhaustion: As uncollected closures, Promise chains, and raw tensor buffers pile up, V8’s Garbage Collector (GC) enters mark-sweep thrashing and triggers an immediate FatalProcessOutOfMemory abort.

ElizaOS V8 Heap Exhaustion and Vector Queue Backpressure Architecture


Step-by-Step Resolution Protocol

Step 1: Expand the Node.js V8 Heap Ceiling

The immediate emergency remediation is increasing the Node.js memory allowance from the default 2GB to 8GB.

Direct CLI Execution:

# Run with expanded 8GB V8 old space ceiling
$ NODE_OPTIONS="--max-old-space-size=8192" pnpm start

Production PM2 Ecosystem Config (ecosystem.config.cjs):

module.exports = {
  apps: [{
    name: 'eliza-agent-daemon',
    script: 'dist/index.js',
    node_args: '--max-old-space-size=8192 --expose-gc',
    env: {
      NODE_ENV: 'production',
    },
    max_memory_restart: '7500M'
  }]
};

Dockerfile / Container Configuration:

FROM node:22-bullseye-slim
WORKDIR /app
COPY . .
ENV NODE_OPTIONS="--max-old-space-size=8192"
CMD ["pnpm", "start"]

Step 2: Implement Batch Queue Backpressure in ElizaOS

To fix the underlying memory leak, patch the vector ingestion worker in your agent’s knowledge manager (packages/core/src/knowledge.ts or src/agent/runtime.ts):

// Memory-safe batch ingestion with backpressure throttling
const BATCH_SIZE = 50;
const MAX_QUEUE_DEPTH = 200;

export async function ingestDocumentsSafely(
  runtime: IAgentRuntime,
  chunks: string[]
): Promise<void> {
  let batch: string[] = [];

  for (let i = 0; i < chunks.length; i++) {
    batch.push(chunks[i]);

    if (batch.length >= BATCH_SIZE || i === chunks.length - 1) {
      // Process batch embeddings
      await processEmbeddingBatch(runtime, batch);
      
      // Explicitly clear references to allow GC reclamation
      batch = [];

      // Throttle event loop to yield to I/O and permit GC collection
      await new Promise((resolve) => setTimeout(resolve, 50));

      // Check current heap usage
      const memoryUsage = process.memoryUsage();
      const usedHeapMB = Math.round(memoryUsage.heapUsed / 1024 / 1024);
      
      if (usedHeapMB > 6000 && typeof global.gc === 'function') {
        console.warn(`[Memory Warning] Heap at ${usedHeapMB}MB. Invoking manual GC...`);
        global.gc();
      }
    }
  }
}

async function processEmbeddingBatch(runtime: IAgentRuntime, items: string[]) {
  const embeddings = await runtime.getEmbedding(items);
  await runtime.databaseAdapter.createEmbeddings(embeddings);
}

Step 3: Configure PostgreSQL pgvector Connection Pooling

High database latency starves the Node event loop. Optimize your PostgreSQL connection pool in packages/adapter-postgres/src/index.ts:

import { Pool } from 'pg';

export const dbPool = new Pool({
  connectionString: process.env.POSTGRES_URL,
  max: 20, // Limit maximum concurrent DB connections
  idleTimeoutMillis: 30000,
  connectionTimeoutMillis: 5000,
  statement_timeout: 10000, // Abort runaway queries before memory builds up
});

Production Verification & Stability Metrics

After applying the V8 flag and backpressure queue patch, verify memory stability:

  1. Launch the agent with pm2 start ecosystem.config.cjs.
  2. Trigger a batch knowledge ingestion of 10,000+ items.
  3. Monitor memory consumption via pm2 monit or terminal:
$ watch -n 2 "ps -o pid,user,%cpu,%mem,vsz,rss,comm -p $(pgrep -f eliza)"

Expected behavior: Heap memory plateaus smoothly between 1.2GB and 2.8GB with regular sawtooth reclamation patterns, completely eliminating fatal OOM aborts.


Fact-Checked by Victor Vance on September 7, 2026. Cryptographic integrity verified under Node.js V8 and ElizaOS agent runtime architecture.

[!TIP] Diagnostic Verification & Air-Gapped Testing: Before executing state changes or broad recovery steps, cross-reference failure parameters with our companion guide on ElizaOS pgvector Dimension Mismatch Embedding Crash Fix and verify transaction calldata or cryptographic envelopes using the Master Web3 & RPC Error Code Directory.

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Local Agent API & Calldata Sandboxes

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Companion Diagnostic Guide ElizaOS pgvector Dimension Mismatch Embedding Crash Fix →

Fix ElizaOS pgvector dimension mismatch (1536 vs 768 / 384) database crashes when switching embedding models from OpenAI to Ollama, BGE, or Voyage AI.

Read Guide
Contextual Architecture

Related Diagnostic Guides

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Related Inquiries

Why does ElizaOS run out of memory when processing documents for RAG?

NodeJS allocates a default memory limit of ~1.4 GB to 2 GB for the V8 JavaScript heap. When an ElizaOS agent loads large PDFs, splits them into thousands of chunks, and requests simultaneous embeddings via Promise.all, uncollected objects quickly exhaust the heap.

How do I increase the NodeJS heap limit for ElizaOS?

Launch your agent with the flag: node --max-old-space-size=4096 dist/index.js, or add NODE_OPTIONS="--max-old-space-size=4096" to your environment configuration.

What is the architectural fix for batch embedding memory leaks?

Implement p-limit or p-queue to restrict batch embedding concurrency to 5–10 requests at a time, allowing V8 garbage collection to reclaim memory between chunk operations.

How can I verify transaction calldata generated by AI agents?

Decode and audit agent UserOperations offline using our interactive ERC-4337 Calldata Decoder tool.