DeepClause
A Neurosymbolic AI System Built on WASM and Prolog
Details
- External ID
- 45937480
- Source
- HN
- Company
- —
- Product
- DeepClause
- Website domain
- github.com
- Launched
- Nov. 15, 2025
- Cohort
- —
- Upvotes
- 7
- Upvotes percentile
- 0.37882096069869
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
Hi HN,Today I'd like to present the results of my weekend project of the last year or so. Given there are many posts on HN about LLMs and Prolog, I thought that this would be of interest.DeepClause is my own (possibly misguided :-) attempt at combining LLMs with Logic Programming, ultimately hoping to establish a foundation for building more reliable agents, that produce reproducible and fully traceable result.At the heart of DeepClause is a DSL called "DeepClause Meta Language" (DML) which can be used to encode agent behaviors as executable logic programs. DML is executed by a meta-interpreter implemented in Prolog and thus natively supports things like constraint logic programming, knowledge graphs, symbolic reasoning, ... The DML interpreter itself runs inside the SWI Prolog WASM module, thus allowing for a secure and sandboxed execution environment for AI agents.The project is still rough around a lot of edges, but I'd love to get some feedback and comments.
Enrichment
- Theme
- AI coding agents and developer tools
- Vertical
- Horizontal
- Function
- Model & infra
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Hobby / open-source project
- Normalized one-liner
- neurosymbolic ai system built on wasm and prolog
- Manually corrected
- False
Could you build this?
No Integrating an LLM with a Prolog logic programming engine via WebAssembly is a complex neurosymbolic systems engineering challenge requiring deep compiler and formal logic expertise.
What it would actually take: The system requires compiling an existing Prolog engine (like SWI-Prolog or Scryer) to WebAssembly and creating bidirectional bindings between logic queries and LLM tool calling. The core difficulty is parsing natural language into valid Prolog terms, handling proof searches, backtracking across token generations, and managing deterministic memory/execution boundaries within WASM. This demands expertise in declarative programming, formal logic theory, and WASM runtime interoperability.
Discussion
No comments on this launch.
Competitors
Other products that read as similar to this one — 626 launches clear the similarity bar, closest 8 shown.
Attention rank: #368 of 627 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 16 days after the earliest competitor.
- Model-agnostic cognitive architecture for LLMs · hn · 2025-11-18 · 6 upvotes · similarity 0.51
- Build agents via YAML with Prolog validation and 110 built-in tools · hn · 2026-01-23 · 11 upvotes · similarity 0.50
- GitHub · ph · 2026-09-22 · 1 upvotes · similarity 0.48
- Pacific Slate: a self-hosted, model-agnostic multi-agent AI assistant · hn · 2026-08-09 · 5 upvotes · similarity 0.46
- Mthds · hn · 2026-02-26 · 23 upvotes · similarity 0.46
- LLM agents that write Python to analyze execution traces at scale · hn · 2026-03-07 · 5 upvotes · similarity 0.45
- The Analog I · hn · 2026-01-16 · 29 upvotes · similarity 0.45
- Aiaiai.guide: Plain-English mental model for LLM apps, tools and agents · hn · 2026-04-06 · 7 upvotes · similarity 0.45
Other launches for this product
- No other launches for this product.
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