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

Details

External ID
46731256
Source
HN
Company
—
Product
Build agents via YAML with Prolog validation and 110 built-in tools
Website domain
github.io
Launched
Jan. 23, 2026
Cohort
—
Upvotes
11
Upvotes percentile
0.5335968379446641
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Description

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 flexibility with symbolic reasoning to help mitigate hallucinations.* *YAML + Overlays:* Agents are defined in YAML with overlay support (similar to the Kustomize pattern in Kubernetes), making configs testable and reproducible across environments (Dev/Prod) without code duplication.* *Hybrid Scripting:** *Lua:* Embedded in all binaries (Python, Rust, Wasm) for secure, lightweight logic at the Edge.* *Python:* Full integration for data science workloads.* *Batteries Included:* We implemented 110+ tools based on Sarwar Alam’s Agentic Design Patterns. https://github.com/sarwarbeing-ai/Agentic_Design_Patterns* *Polyglot:* Core written in Rust/Python with Wasm support (runs in browser, Docker, or embedded).* *Observability:* Native hooks for Comet (Opik) to track execution/cost.The goal is to provide a solid engineering foundation for agents. I’d love to hear your feedback on the Prolog integration and the YAML-based architecture.Repo: https://github.com/fabceolin/the_edge_agentDemo (Wasm): https://fabceolin.github.io/the_edge_agent/wasm-demo

Enrichment

Theme
AI agent frameworks and developer tools
Vertical
Horizontal
Function
Agent / copilot
Audience
Developer
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
yaml-based agent builder with built-in tools
Manually corrected
False

Could you build this?

No Creating a neurosymbolic agent framework with native Prolog validation, formal verification, and compiled runtimes requires deep academic expertise in formal logic and compiler/language implementation.

What it would actually take: The platform requires embedding or compiling a Prolog inference engine (such as Scryer or SWI-Prolog) into a Rust runtime, writing a formal logic bridge to validate non-deterministic LLM JSON/text outputs against formal Prolog constraints, and implementing a directed workflow engine. The hard part is formalizing constraint logic programming (CLP) guarantees on dynamic agent actions and edge deployment in single binaries without cloud dependencies. This demands advanced knowledge of neurosymbolic AI, logic programming, and low-level systems engineering.

Discussion

11 comments analyzed.

Competitors mentioned: Langraph, Prolog neurosymbolic systems

Concerns raised: LLM loops degrade response quality by filling context faster, Python and thread mechanisms unsuitable for edge environments, Naming confusion - why called 'edge agent' if it's an orchestrator

Feature requests: More comprehensive human-in-the-loop examples, Better documentation of interview process workflows

Competitors

Other products that read as similar to this one — 438 launches clear the similarity bar, closest 8 shown.

Attention rank: #214 of 439 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 86 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a agent / copilot tool for Agriculture yet.