GLM 5.2 + Mem0: Persistent Memory For Long-Horizon Coding Agents

GLM 5.2 is presented as a coding-focused 753B-parameter mixture-of-experts model with up to a 1 million-token context window, High and Max reasoning-effort settings, OpenAI-compatible API access through Z.ai, and strong reported long-horizon coding benchmark results. The material distinguishes this large but temporary context from persistent memory, noting that prompts and cached history disappear after sessions, restarts, or model changes and can become costly to replay at scale. It proposes Mem0 as an external, model-agnostic memory layer that distills durable facts, scopes them by user and project metadata, retrieves relevant information before model calls, and preserves knowledge across services and sessions. A Python implementation demonstrates querying Mem0, incorporating retrieved memories into GLM prompts, using GLM to extract reusable facts from interactions, and saving those facts back to the memory store. The approach is positioned for long-running coding agents, research assistants, and multi-service workflows, while noting that memory quality depends on effective extraction, retention, summarization, pruning, and continued model evaluation.

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