Laravel AI Agent Memory: Persisting Context Across Conversations and Sessions
Dev.to / 6/13/2026
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Key Points
- The article argues that most Laravel AI agent tutorials default to stateless request/response designs, which causes real failures like repeated tool calls and redundant clarifying questions in production.
- It explains that “agent memory” is not one pattern but three separate layers, each requiring different storage backends, TTL strategies, and points of injection into the agent workflow.
- The piece details a full approach to implementing all three memory layers from scratch in Laravel using Eloquent for persistence across HTTP boundaries, Redis for faster session/work state, and pgvector for long-term semantic memory.
- It emphasizes that these are architectural gaps rather than model prompting issues, and that a coherent agent over time needs explicit memory plumbing beneath the tool-calling layer.
- The article points readers to related resources (a broader Laravel AI architecture overview and Prism PHP agentic app guidance) to understand how memory integrates with the overall production system.
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