Show HN: Build agents via YAML with Prolog validation and 110 built-in tools(fabceolin.github.io)
11 pointsby fabceolin15 days ago4 comments
  • raphaelcangucu15 days ago
    Hi Fabricio, can I use this as a Judge?

    Let me put the scenario here:

    I need a truth resolution mechanism, for example who won some sports match.

    I input the sources, news , data, etc and the this agent you handle the judging process.

  • pisrael15 days ago
    What is the main difference in results of a pure LLM loop?
    • fabceolin14 days ago
      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-...
    • fabceolin15 days ago
      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.
  • thalesac15 days ago
    can you elaborate more on the human in the loop? would be nice a more comprehensive example
    • fabceolin15 days ago
      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...
    • thalesac15 days ago
      also I didn't get the name, why edge agent? seems like this is an orchestrator, not edge. seems very useful tho
      • fabceolin15 days ago
        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.
  • johnjr15 days ago
    Very nice job!