fagent: memory-first AI agent runtime for long-running, interrupted workflows

Hi everyone! 👋 I’m a beginner developer fascinated by AI, neural networks, and agents. I built fagent , a memory-first AI agent runtime, because I noticed that most agent runtimes handle short tasks well, but struggle with long-running or interrupted workflows . Agents often forget context, previous decisions, or dependencies once you restart them. What makes fagent different: Layered memory – multiple memory types (file, vector, graph, task/workflow) working together. Graph-based recall – retrieve not just text, but the relationships between tasks, blockers, and decisions. Workflow repair – a helper layer to fix broken task flows without restarting everything. Multi-channel runtime – works via CLI, terminal, and messaging platforms. Local Graph UI – inspect memory and task graphs visually. fagent is open-source and available here: https://github.com/fresed05/fagent I’d love your feedback on: How useful graph-based memory is compared to vector-only memory. How intuitive the workflow in