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We built a persistent agent memory layer on Elasticsearch with 0.89 recall

Agent memory on Elasticsearch: hybrid retrieval and DLS - Elasticsearch Labs

elastic.co

June 18, 2026

21 min read

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51/100

Summary

Agent memory on Elasticsearch utilizes three indices for hybrid recall, incorporating a reranker, supersession, decay, and DLS to create a persistent memory layer for agents. Agent Builder is now generally available, allowing users to start with an Elastic Cloud Trial and access documentation.

Key Takeaways

Agent Builder is now generally available, allowing users to create agent memory systems on Elasticsearch.
The architecture for agent memory includes three types of memory: episodic, semantic, and procedural, each stored in separate Elasticsearch indices.
A hybrid recall system with a reranker and per-user data isolation is implemented to manage user-specific memory without cross-tenant leaks.
The agent memory system is designed to handle temporal queries and maintain an audit trail for contradictions while ensuring efficient retrieval of facts.

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Community Sentiment

Mixed

Positives

Utilizing Elasticsearch for a shared memory layer enhances collaboration across teams, making it easier to connect to remote services and support multiple users effectively.
The approach of integrating advanced features of Elasticsearch can be beneficial for enterprise setups, especially when leveraging its capabilities in cloud environments.

Concerns

Using Elasticsearch for this purpose is seen as overkill, with simpler vector databases like SQLite being more appropriate for basic needs.
There are concerns about the clarity and effectiveness of the custom language used in the implementation, which may complicate understanding for those unfamiliar with the field.