Build agents via YAML with Prolog validation and 110 built-in tools
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 at Majestic Labs)* during our work on the Kiroku project. *Key Technical Features:* * *Neurosymbolic Native:* We integrated Prolog to logically validate LLM outputs. This combines neural…
Build agents via YAML with Prolog validation and 110 built-in tools launched on January 23, 2026 with 11 votes, #956 of 14,584 launches that month and more than 90% of that year's launches.
In plain words
The Edge Agent is a platform for building and deploying AI agents defined in YAML with Prolog-based validation of language model outputs. It includes 110 built-in tools and uses neurosymbolic reasoning to reduce hallucinations by combining neural processing with logical validation. The system supports configuration overlays for environment-specific deployments without code duplication. It is designed for developers and teams who need production-grade agent orchestration with deterministic behavior and verifiable outputs.
written from the facts on this page · September 2026
From the sources
One binary. No cloud. Neurosymbolic AI that actually reasons. TEA combines LLMs with symbolic reasoning (Prolog) to create AI agents that can prove their conclusions, not just generate plausible-sounding text. Perfect for small/local models (Llama, Mistral, Phi, Ollama) where symbolic reasoning compensates for limited model capacity. # LLM extracts facts, Prolog derives relationships via temporal reasoning name : hero-family-reasoning nodes : - name : extract uses : llm.call # LLM extracts: mother(alice, bob). affair(alice, dave, 1980, 1990). with : model : "gemma3n:e4b" messages : - role : user content : "Extract family relationships as Prolog facts from: {{ state.text }}" output :…from fabceolin.github.io
In the maker’s words, at launch
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…
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