ManimAgent

ManimAgent: Self-evolving multimodal agent with cross-task episodic memory for code generation

ManimAgent is a multimodal agent system that accumulates reflection experience across tasks via a dual-channel Episodic Memory Bank, without weight updates or human-curated seeds. The agent generates Python/Manim animations from scientific paper sections, and a vision-language model scores rendered keyframes to populate positive (success rationales) and negative (failure patterns) memory channels. On a fixed-probe evaluation, Pass@1 improves and reflection rounds decrease as memory grows, outperforming no-memory, RAG, and shuffled-memory baselines. The work addresses a known limitation of single-episode reflection in LLM agents by enabling persistent, self-generated learning across task boundaries.