Introducing ODIN: Autonomous Agent Framework with AI Checkpointing – Feedback Requested!
Hi Google Devs 
I’d love your expert feedback on ODIN – an open-source agent framework designed for autonomous, self-correcting LLM behavior using structured prompts, feedback loops (Faux/Parfait), persistent memory (AI_CHECKPOINT.json), and documentation sync.
Why it matters:
ODIN provides a way to stabilize LLM outputs and minimize hallucinations through prompt layering and logic validation. It works with any LLM (GPT, Claude, local models) but I’m now looking to:
- Connect it with TensorFlow Extended (TFX) or LangChain on Vertex AI
- Optimize logic-based agents for inference latency
- Explore compatibility with TF Serving, TF Lite, or Colab Pro workflows
Real-world use cases:
- Self-correcting assistant for game servers (GTA RP)
- Prompt-driven deployment bot (e-commerce infra)
- Blueprint automation agent (Unreal Engine)
I’m particularly looking for:
- Advice on best practices for integrating agent memory and rollback into Google AI pipelines
- Performance tradeoffs in Colab/Vertex AI settings
- How to structure this for scalable deployment with TF + TFX
Thanks so much for your time and insight 
— Julien Gelee (aka Krigs)