Developers using AI coding assistants like Claude Code and Cursor constantly repeat instructions because these agents lack persistent memory. Every session starts from zero, forcing programmers to re-explain project standards, architectural choices, and hard-learned lessons. This memory gap creates frustrating loops where agents forget critical decisions and repeat past mistakes.

To solve this, developer Abian Solved created code-recall, a local server using Model Context Protocol that gives AI agents persistent memory. Built with SQLite, sqlite-vec, and local embeddings, it stores architectural decisions, project patterns, and warnings without sending data outside your machine. The system uses semantic search to retrieve relevant context automatically.

The tool works by letting developers save key decisions like "use Zustand for state management" or "never modify legacyAuth()." Agents can query this memory before writing code and follow automatic guardrails for specific triggers. This maintains consistency across sessions while preventing repeated errors and keeping all project knowledge intact between development cycles.

Early adopters report zero repetition of basic instructions, more consistent code generation, and faster onboarding when returning to projects. code-recall is open source and installs in two minutes using Bun. For teams drowning in AI amnesia, it offers a practical path toward agents that actually learn from experience.