LlamaIndex Agent Memory Exhaustion in Serverless
warningResource ContentionUpdated Feb 23, 2026
In-memory vector indexes in LlamaIndex serverless deployments cause out-of-memory errors on large document sets, especially with batch processing or concurrent requests.
Technologies:
How to detect:
Monitor memory usage during index creation/querying in serverless environments. Alert when memory approaches function limits (e.g., >80% of Lambda/Cloud Function allocation). Track correlation between document batch size and memory spikes.
Recommended action:
Implement batch processing with configurable batch_size (start with 10 documents). Add delays between batches to allow memory cleanup. For production, migrate from in-memory to external vector stores (Pinecone, Weaviate, Chroma). Configure serverless function memory allocation based on observed peak usage plus 30% headroom.