Dev tools · alternatives · 2026

24 alternatives to BAML - A new programming language to get structured data from LLMs

The programming language for agents. Contribute to BoundaryML/baml development by creating an account on GitHub.

BAML is a programming language designed to extract structured data from large language models. It provides developers with tools to define schemas and reliably parse LLM outputs into organized formats. The language… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. BAML - A new programming language to get structured data from LLMs launched in 2024; newer entries below may have overtaken it.

1
I built a tiny LLM to demystify how language models work▲915
Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.
Apr 2026 · github.com · its alternatives →
2
LLMs can generate valid JSON 100% of the time▲854
Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…
2023 · github.com · its alternatives →
3
Structured output from LLMs without reprompting▲174
Built a tool for transforming unstructured data into structured outputs using language models (with 100% adherence). If you're facing problems getting GPT to adhere to a schema (JSON, XML, etc.) or regex, need to bulk process some unstructured data, or generate synthetic data, check it out. We run our own tuned model (you can self-host if you want), so, we're able to have incredibly fine grained control over text generation. Repository: https://github.com/automorphic-ai/trex Playground: https://automorphic.ai/playground
2023 · automorphic.ai · its alternatives →
4
ContextGem▲120
Effortless LLM extraction from documents
2025 · github.com · its alternatives →
5
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 · its alternatives →
6DS
DBML – simple DSL language to document database schemas▲57
2019 · dbml-lang.org · its alternatives →
7
DB-GPT, an LLM tool for database▲156
open-source agentic AI data assistant for the next generation of AI + Data products. - eosphoros-ai/DB-GPT
2023 · github.com · its alternatives →
8
LLMs following grammar for general-purpose PLs▲11
Efficient and general syntactical decoding for Large Language Models - structuredllm/syncode
2024 · github.com · its alternatives →
9
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 · its alternatives →
10
An online collaborative UML editor using PlantUML▲116
TAKTIK88 menghadirkan perpaduan strategi, gaya bermain, dan nuansa sultan paling prestisius. Temukan informasi menarik, inspirasi taktik, serta pengalaman digital berkelas dalam satu tempat.
2020 · collabuml.com · its alternatives →
11
RAML▲62
A Standard for API modeling, documentation and exploration
2014 · raml.org · its alternatives →
12
AGL a toy language that compiles to Go▲83
I spent the past 2 weeks making this toy programming language. I basically forked the Go scanner/parser, and changed the syntax to have functions return a single value. This enable proper Result/Option type to be used as well as propagating errors with an operator. I also wanted to have short "type inferred" anonymous functions to be able to use functions like Map/Reduce/Filter, without having to use 100 characters to specify the types.
2025 · github.com · its alternatives →
13
A new programming language inspired by Go, no LLVM▲82
This is an introduction to the "Nature" project: https://nature-lang.org/docs/getting-started/meet-nature I am the author of the Nature language, and today I have released the 0.4.0-beta version. For detailed updates, please see https://nature-lang.org/docs/prologue/release-notes Now, Nature can easily interact with C language. It is anticipated that in about six months, Nature will release a version available to the community. By then, there will be stable syntax API, a complete standard library, and support for development tools. Moreover,…
2025 · github.com · its alternatives →
14DA
DTL: a language and JavaScript lib to transform and manipulate data▲63
DTL is a project that began it's life as part of a another piece of software for a startup I founded a few years ago. For that project I needed a way to describe how to rewrite data in a portable way. I needed to be able to create the rules on the fly and store them in a database, I needed them to be able to describe transformations I hadn't thought about, but also needed them to be safe and predictable. Though the startup didn't survive, the language I made was so useful to me I felt I had to extract it and make it usable to everyone. DTL is the result. Though the npm module is relatively…
2022 · getdtl.org · its alternatives →
15
Fine-grained stylistic control of LLMs using model arithmetic▲85
We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
2023 · github.com · its alternatives →
16
Improve Text-to-SQL Accuracy with LLM▲17
A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL - eosphoros-ai/DB-GPT-Hub
2023 · github.com · its alternatives →
17
B-IR – An LLM-optimized programming language▲62
Contribute to imjasonh/imjasonh development by creating an account on GitHub.
Jan 2026 · github.com · its alternatives →
18
CodeRLM – Tree-sitter-backed code indexing for LLM agents▲81
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 · its alternatives →
19
A new benchmark for testing LLMs for deterministic outputs▲60
When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…
Apr 2026 · interfaze.ai · its alternatives →
20
Tiger – Function Hub for LLM Agents▲28
Neuralink for your AI Agents - LangChain - Autogen - CrewAI - Upsonic/Tiger
2024 · github.com · its alternatives →
21
MOL – A programming language where pipelines trace themselves▲38
Hi HN, I built MOL, a domain-specific language for AI pipelines. The main idea: the pipe operator |> automatically generates execution traces — showing timing, types, and data at each step. No logging, no print debugging. Example: let index be doc |> chunk(512) |> embed("model-v1") |> store("kb") This auto-prints a trace table with each step's execution time and output type. Elixir and F# have |> but neither auto-traces. Other features: - 12 built-in domain types (Document, Chunk, Embedding, VectorStore, Thought, Memory, Node) - Guard assertions: `guard answer.confidence > 0.5 : "Too low"` -…
Feb 2026 · github.com · its alternatives →
22
Semlib – Semantic Data Processing▲63
Build data processing and data analysis pipelines that leverage the power of LLMs 🧠 - anishathalye/semlib
2025 · github.com · its alternatives →
23
Data Bonsai: a Python package to clean your data with LLMs▲47
I've been doing some data cleaning for my fine tuning projects using LLMs, and decided to just build a package for it as a side project. Check it out here: https://github.com/databonsai/databonsai Some features: - categorization (labelling), transformation and decomposition (text into structured format) - validates llm outputs - batch mode batches up the inputs/outputs so you don't send the prompt (schema, fewshot examples) for every row of data, saving a significant amount of tokens There are some similarities to the Instructor repo, but this is simpler and made for…
2024 · github.com · its alternatives →
24
I made a state machine framework for guided conversations with LLMs▲19
It’s written in Python and I call it GoalChain. It lets you build a conversation flow graph that the user traverses. When there’s enough input it spits out a dictionary with the defined fields. Otherwise it will jump state to state as led by the user. It was fun to write, and it’s surprisingly effective if you keep in mind you’re prompt-engineering every string and field name. README.md has a mini-tutorial. Would be cool to get some ideas for how to build it further and what improvements I could make.
2024 · github.com · its alternatives →

Also compare

Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →