buildabot
lmql
| buildabot | lmql | |
|---|---|---|
| 1 | 30 | |
| 70 | 3,375 | |
| - | 4.4% | |
| 6.7 | 9.5 | |
| about 1 year ago | 14 days ago | |
| TypeScript | Python | |
| - | Apache License 2.0 |
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buildabot
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The Problem with LangChain
After running into these issues a few others and I wrote a typescript agent framework that I think significantly improves on LangChain in many ways: https://github.com/sciencecorp/buildabot/
It’s still very early days for software composing AI models and we almost certainly don’t have all the right metaphors yet. And I think there is a lot to be said for strong typing and simple, robust code!
lmql
- Show HN: Fructose, LLM calls as strongly typed functions
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Prompting LLMs to constrain output
have been experimenting with guidance and lmql. a bit too early to give any well formed opinions but really do like the idea of constraining llm output.
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[D] Prompt Engineering Seems Like Guesswork - How To Evaluate LLM Application Properly?
the only time i've ever felt like it was anything other than guesswork was using LMQL . not coincidentally, LMQL works with LLMs as autocomplete engines rather than q&a ones.
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Guidance for selecting a function-calling library?
lqml
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Show HN: Magentic – Use LLMs as simple Python functions
This is also similar in spirit to LMQL
https://github.com/eth-sri/lmql
- Show HN: LLMs can generate valid JSON 100% of the time
- LangChain Agent Simulation – Multi-Player Dungeons and Dragons
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The Problem with LangChain
LLM calls are just function calls, so most functional composition is already afforded by any general-purpose language out there. If you need fancy stuff, use something like Python‘s functools.
Working on https://github.com/eth-sri/lmql (shameless plug, sorry), we have always found that compositional abstractions on top of LMQL are mostly there already, once you internalize prompts being functions.
- Is there a UI that can limit LLM tokens to a preset list?
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Local LLMs: After Novelty Wanes
LMQL is another.
What are some alternatives?
multi-gpt - A Clojure interface into the GPT API with advanced tools like conversational memory, task management, and more
guidance - A guidance language for controlling large language models.
jehuty - Fluent API to interact with chat based GPT model
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
llm-gpt4all - Plugin for LLM adding support for the GPT4All collection of models
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
llm-api - Fully typed & consistent chat APIs for OpenAI, Anthropic, Groq, and Azure's chat models for browser, edge, and node environments.
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
llm - Access large language models from the command-line
guardrails - Adding guardrails to large language models.
gchain - Composable LLM Application framework inspired by langchain
basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.