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fabceolin

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

https://fabceolin.github.io/the_edge_agent/articles/truth-re...

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.
We have checkpoints implemented to save the state in the middle of graph navigation and we can restart from there. It's useful to implement interviews process like https://fabceolin.github.io/the_edge_agent/articles/intellig...

Show HN: Build agents via YAML with Prolog validation and 110 built-in tools

https://fabceolin.github.io/the_edge_agent/index.html

I'm one of the creators of The Edge Agent (TEA). We built this because we needed a way to deploy agents that was verifiable and robust enough for production/edge cases, moving away from loose scripts.The architecture aims to solve critical gaps in deterministic orchestration identified by *Prof. Claudionor Coelho Jr. (Stanford alum, ML/DL Faculty at Santa Clara Univ., and Senior Fellow for AI...

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