Clean context for each iteration will make the LLM give your better results. Using LLM loop you will full the context faster degrading the LLM responses. Tea supports create a workflow from dot file https://fabceolin.github.io/the_edge_agent/articles/writing-...
Clean context for each iteration will make the LLM give your better results. Using LLM loop you will full the context faster degrading the LLM responses.
Yes, I wrote an article about this: Truth Resolution Agent: A Multi-Source Judicial Framework for Sports Disputes (Senna 1989 Case Study) using llm as a judge and prolog neurosymbolic as a judge
The project started to be a Cyclic State Graph orchestrator, statically defined via YAML, leveraging Neurosymbolic validation (Prolog) to ensure deterministic transitions in edge environments. Langraph also it is, but python and the thread mechanism make not suitable for edge environments.
