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 inspection UX feels.
- Any real-world workflows where you’d try a memory-focused agent.
I’m excited to see how the community experiments with long-running agents. Let me know what you think, or if you have suggestions for improvements!
Thanks for reading! 🚀
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