Alternatives
Products that do what prolog-reasoner does
Give your LLM a logic engine — SWI-Prolog as an MCP server
- 1AS
2019 · github.com
- 2
- 3
- 4PS
I got tired of playwright-mcp eating through Claude's 200K token limit, so I built this using the new Claude Skills system. Built it with Claude Code itself. Instead of sending accessibility tree snapshots on every action, Claude just writes Playwright code and runs it. You get back screenshots and console output. That's it. 314 lines of instructions vs a persistent MCP server. Full API docs only load if Claude needs them. Same browser automation, way less overhead. Works as a Claude Code plugin or manual install. Token limit issue:…
Oct 2025 · github.com
- 5LV
This is a weekend hack that I'd like to further develop as it's working surprisingly well. Using MCTS, we can explore a space of possible verified programs with an LLM. We check the partial programs at each step, and so steer towards programs that pass the verifier. https://github.com/namin/llm-verified-with-monte-carlo-tree-...
2023 · github.com
- 6AE
2021 · github.com
- 7CT
I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into…
Feb 2026 · github.com
- 8AC
2019 · github.com
- 9EP
2021 · github.com
- 102C
Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com
- 11
- 12IB
Hi! My name is Herve Kom, a computer science student that is interested in learning new things everyday! As one of my graduation project, I have developed a Claude Code -like Coding CLI, but with enhancement for API Testing: - Auto-generate & run tests (unit, e2e, Playwright, CI/CD, etc.) - Say bye-bye to hallucinations with built-in MCP Server to let LLM directly read from API Docs - Adding Agent.md support for better context persistence across your whole codebase - Automatic bug & security scans (logic is kind of basic but works great!) - Vibes, I want it to feel less "enterprise" but…
2025 · github.com
- 13EA
2021 · github.com
- 14FL
Hi HN! We just launched Codacy Guardrails, an IDE extension with a CLI for code analysis and MCP server that enforces security & quality rules on AI-generated code in real-time. It hooks into AI coding assistants (like VS Code Agent Mode, Cursor, Windsurf), silently scanning and fixing AI-suggested code that has vulnerabilities or violates your coding standards, while the code it’s being generated. We built this because coding agents can be a double-edged sword. They do boost productivity, but can easily introduce insecure or non-compliant code. One recent research team at NYU found that 40%…
2025
- 15PC
2015 · prolog.jldupont.com
- 16AC
We built a reference MCP server that lets your editor/agent learn a codebase directly from source (signatures, types, AST, comments). Docs are optional. The reference impl runs on our open-source project’s codebase. Why we built it Docs are important, but they add another abstraction layer between your code and your users. Keeping them at the right quality is hard (especially at a startup), and LLM-generated docs are often mediocre until you invest real polish. Exposing code to the model in a structured way keeps answers grounded and current, and it’s always available. You can even…
Sep 2025 · github.com
- 17OD
I’d like to use LLMs for remembering all kinds of things: fitness, to-do lists, contacts, bug reports, research links, whatever. But there is no way to do that now. For example, if I find a great coding tutorial in chat, or tell it how much I ran yesterday, it forgets that when I close the chat. Even if I keep the chat history, I still need to scour through lots of messages to find the data I want. Ideally, Claude would remember all this, and I’d be able to find it later with ease. This is what my team built. It is a collaborative database you add to any LLM that supports MCP. (Claude Code,…
2025 · dry.ai
- 18DA
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…
Nov 2025 · github.com
- 19AL
2025 · donsir.com
- 20IO
Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com
- 21
- 22
- 23AT
We kept shipping “simple” LLM features that were fluent-but-wrong. After too many postmortems we wrote down the failure patterns and added a small reasoning layer in front of the model. It’s model-agnostic, sits beside your existing stack, and you can implement it from a single PDF (MIT). What’s inside the PDF A problem map of 16 failure modes we kept hitting in real systems (OCR/layout drift, table-to-question mismatches, embedding≠meaning, pre-deploy collapse, etc.). Four lightweight gates you can add today: Knowledge-boundary canaries (empty/adversarial/known-fact probes).…
2025 · github.com
- 24
Ranked by how close each launch is in meaning, then by votes. Refine with a description →