Alternatives

Products that do what Artisan: Symbolic DSL for LLM Governance does

Deterministic execution & safety protocol for LLMs.

1PD
Pipelex – Declarative language for repeatable AI workflows▲122
We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them. Why this instead of yet another workflow builder? - Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open…
Oct 2025 · github.com
2LA
LLM, a Rust Crate/CLI for CPU Inference of LLMs (LLaMA, GPT-NeoX, etc.)▲45
G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
2023 · github.com
3DS
DBML – simple DSL language to document database schemas▲57
2019 · dbml-lang.org
4LC
LLMs can be susceptible to a new kind of malware▲17
2023 · github.com
5LS
LLMWare – Small Specialized Function Calling 1B LLMs for Multi-Step RAG▲51
Hi, I was a corporate lawyer for many years working with a lot of financial services and insurance companies. In practicing law, I noticed there was a lot of repetition in the tasks I was working on even as a highly paid attorney that could be automated. I wanted to solve the problem of dealing with a lot information and data in a practical way, using AI. This motivated me to start AI Bloks/LLMWare with my husband, who had a deep background in software and is a very early adopter of AI. We have been on this journey with our open source project LLMWare for the past 4 months, producing a…
2024 · github.com
6BA
BAML - A new programming language to get structured data from LLMs▲18
2024 · github.com
7MB
Mthds – Beyond skills: a typed DSL for executable AI methods▲23
Feb 2026 · mthds.ai
8LT
LLM-Term – Simple Rust-based CLI assist tool▲32
Current AI-assisted CLI tools are often part of larger systems and work better on Linux. I built llm-term to address these. It's a Rust-based tool that compiles into a single binary file. You only need to download the binary, add it to your PATH, and configure your OpenAI key to get started. While llm-term offers an option for gpt-4o, it works great with gpt-4o-mini. So it's not costly. I appreciate any feedback or suggestions.
2024 · github.com
9OB
Offeryn – Build Tools for LLMs in Rust, with Model Context Protocol▲7
2025 · github.com
10BP
Best Practices for Using Structured Output from LLMs▲21
2024 · instill.tech
11
LLM Governance Engine▲4
Self-hosted PII firewall for LLMs — policies, audit trail
Jun 2026 · github.com
12
Conduct, open-source guardrails for LLM and MCP tool calls▲22
Runtime governance for AI agents. Allow, warn, or block every model and tool call before it commits. Hash-chained audit for every decision. Compliance packs for SOC 2, HIPAA, PCI DSS, EU AI Act, SR 11-7, and FDA CSA. Apache 2.0. - sseshachala/conductai
9d ago · github.com
13IM
I made a library for LLM prompt injection/exploit/jailbreak detection▲11
2024 · github.com
14OS
Open-source project that use LLM as deception system▲10
Hello everyone I wanted to share a project I've been working on that I think you'll find really interesting. It's called Beelzebub, an open-source honeypot framework that uses LLMs to create incredibly realistic and dynamic deception environments. By integrating LLMs, it can mimic entire operating systems and interact with attackers in a super convincing way. Imagine an SSH honeypot where the LLM provides plausible responses to commands, even though nothing is actually executed on a real system. The goal is to keep attackers engaged for as long as possible, diverting them from your real…
2025
15AG
Aiaiai.guide: Plain-English mental model for LLM apps, tools and agents▲7
I’ve been building LLM tooling for a small VC fund and found myself explaining the same mental model over and over to non-technical people around me: how a stateless LLM becomes a chatbot, how tool use works, what an agent is mechanically, and why context windows shape all of it. I never found a guide that covered that full chain at the level I wanted, so I wrote one. It’s nine short chapters, each building on the last. Deliberately simplified: the goal is a useful mental model, not a textbook. Feedback, corrections, and contributions welcome: github.com/ymyke/aiaiai
Apr 2026 · aiaiai.guide
16RL
React-like Declarative DSL for building synthetic LLM datasets▲10
Nov 2025 · github.com
17CR
ChainFactory – Run Structured LLM Inference with Easy Parallelism▲8
hi everyone. how does moving llm call prompts and output structure definitions away from code into configuration land sound? would you use something like this if it was stable and well documented enough? please don't hold back the criticism. i appreciate all feedback (constructive & otherwise).
2024 · github.com
18CA
Curator – an open-source library for synthetic data generation▲13
Synthetic data generation is an essential step in training and evaluating LLMs/Agents/RAG pipelines, but tooling around this is still lacking. We're introducing Curator, an open-source library designed to streamline the data curation process. While there are many libraries to prompt LLMs, the semantics of generating synthetic data is different from prompting. For example, we need to process a large number of prompts (sometimes in millions or more) while accepting some failures, utilize several stages of prompting, incorporate human feedback, and filter out bad data using verifiers…
2025 · github.com
19TO
Tired of building agents? throw an LLM at this framework▲14
2025 · github.com
20SA
SOSML – An online interpreter for SML▲6
2019 · sosml.discourse.ps.uni-saarland.de
21GB
Graph-Based Editor for LLM Workflows▲8
Hey HN, We’re excited to share PySpur, an open-source tool that provides a graph-based interface for building, debugging, and evaluating LLM workflows. Why we built this: Before this, we built several LLM-powered applications that collectively served thousands of users. The biggest challenge we faced was ensuring reliability: making sure the workflows were robust enough to handle edge cases and deliver consistent results. In practice, achieving this reliability meant repeatedly: 1. Breaking down complex goals into simpler steps: Composing prompts, tool calls, parsing steps, and branching…
2024 · github.com
22
LLMSafe — Zero-Trust AI Security Gateway▲8
A zero-trust security layer between your apps and LLMs
Jan 2026
23
L4: AI Agent Programming Language▲2
Program your AI Agents with Pure Declaration
Jul 2026 · l4.ssww.one
24AA
An application stack Claude coded directly in LLVM IR▲10
This repo is the result of a debate about what kind of programming language might be appropriate if humans are no longer the primary authors. Initially the thought was "LLMs can just generate binaries directly" (this was before a more famous person had the same idea). But that on reflection seems like a bad approach because languages exist to capture program semantics that are elided by translation to machine code. The next step was to wonder if an existing "machine readable" program representation can be the target for LLM code generation. It turns out yes. This project is the result of…
Mar 2026 · github.com

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