Adding Persistent Memory To Local AI Agents With Mem0, OpenClaw, And Ollama

A tutorial describes building a fully local AI coding assistant that retains user preferences and project context across sessions without API keys or external data transfer. The system combines OpenClaw for request orchestration and workspace file operations, Ollama with qwen3:8b for local intent detection and code generation, Mem0 OSS for extracting and managing memories, and Qdrant for persistent semantic vector storage, with Chroma offered as a simpler alternative. It explains configuring the models and vector dimensions, disabling qwen3’s thinking output to prevent Mem0 JSON-parsing failures, filtering messages so only durable preferences and project conventions are saved, and using semantic retrieval to inject relevant memories into code-generation prompts. Requests are classified into actions such as creating files or folders, writing code, opening files, or chatting, with regex fallbacks for unreliable model output. The resulting assistant can generate code aligned with preferences such as type hints, pytest, and documentation styles, although CPU-only operation may be slow; suggested extensions include language-aware generation and project-specific memory isolation.

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