We built a persistent agent memory layer on Elasticsearch with 0.89 recall
elastic.co
June 18, 2026
21 min read
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47/100
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.
Community Sentiment
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.
