Memory Systems for Long-Running Agents: Episodic to Procedural

2026-06-19 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, quick

Summary

Long-running agents, unlike single-session chatbots, cannot rely on a flat context window due to its size, lack of differentiation, and volatility. Production agents require structured memory systems, which cognitive science categorizes into episodic (interaction logs), semantic (fact stores), and procedural (action-outcome stores). This article details building all three memory types from scratch in pure Python, demonstrating their composition into a unified agent context builder. Economically, loading all past memory into context costs approximately \$0.61 per turn for 200K tokens with Claude Sonnet 4.6 pricing, whereas retrieval-based memory access costs only \$0.019. This makes retrieval-based memory the only scalable and economically viable approach for agents with more than a few dozen persistent memories.

Key takeaway

For AI Engineers developing long-running agents, relying solely on a flat context window is unsustainable. You must implement structured, retrieval-based memory systems—episodic, semantic, and procedural—to ensure scalability and cost-efficiency. This approach reduces per-turn costs significantly, from \$0.61 to \$0.019 for 200K tokens, making complex, persistent agent behaviors economically viable. Prioritize building these memory components to enhance agent performance and reduce operational expenses.

Key insights

Long-running agents require structured, retrieval-based memory systems to overcome context window limitations and scale economically.

Principles

• Cognitive science memory types (episodic, semantic, procedural) map to agent engineering requirements.
• Retrieval-based memory is essential for scaling long-running agents economically.

Method

Build episodic, semantic, and procedural memory systems from scratch in pure Python, then compose them into a unified agent context builder for long-running agents.

In practice

• Implement episodic memory for agent interaction logs.
• Implement semantic memory for agent fact storage.
• Implement procedural memory for agent action-outcome storage.

Topics

• Long-Running Agents
• Memory Systems
• Episodic Memory
• Semantic Memory
• Procedural Memory
• Retrieval-Based Memory

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.