Alternatives
Products that do what LAPIS does
Lightweight API Specification for Intelligent Systems
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Hey HN, we’re building an open specification that lets agents discover and invoke APIs with natural language, built on the OpenAPI standard. agents.json clearly defines the contract between LLMs and API as a standard that's open, observable, and replicable. Here’s a walkthrough of how it works: https://youtu.be/kby2Wdt2Dtk?si=59xGCDy48Zzwr7ND. There’s 2 parts to this: 1. An agents.json file describes how to link API calls together into outcome-based tools for LLMs. This file sits alongside an OpenAPI file. 2. The agents.json SDK loads agents.json files as tools for an LLM that…
2025 · github.com
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2025 · simonwillison.net
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Hey HN, Nir, Gal and Tomer here. We’re open-sourcing a set of extensions we’ve built on top of OpenTelemetry that provide visibility into LLM applications - whether it be prompts, vector DBs and more. Here’s the repo: https://github.com/traceloop/openllmetry. There’s already a decent number of tools for LLM observability, some open-source and some not. But what we found was missing for all of them is that they were closed-protocol by design, vendor-locking you to use their observability platform or their proprietary framework for running your LLMs. It’s still early in the…
2023 · github.com
- 10AC
api2ai parses OpenAPI Spec to generate an agent that can make API calls. For context, I recently need to explore a handful of API suites and thought LLMs can help expedite this process. After some digging, I found OpenAPI Specs are perfect fit for function calling. Based on a text prompt, api2ai can select the right endpoint and properly parse request params and make api calls. It also handles authentication, currently it supports basic auth, api keys, and bearer token schemes. The tool has helped me explore and see APIs in action without a deep dive into the docs or using postman. It’s open…
2023 · github.com
- 11OC
Hey HN, I’ve built Open Codex, a fully local, open-source alternative to OpenAI’s Codex CLI. My initial plan was to fork their project and extend it. I even started doing that. But it turned out their code has several leaky abstractions, which made it hard to override core behavior cleanly. Shortly after, OpenAI introduced breaking changes. Maintaining my customizations on top became increasingly difficult. So I rewrote the whole thing from scratch using Python. My version is designed to support local LLMs. Right now, it only works with phi-4-mini (GGUF) via…
2025 · github.com
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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
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Trace LLM requests + costs with OpenTelemetry monitoring
Oct 2025
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Docs.codes generates simple markdowns for open-source libraries that you can add to the context of your LLM assistants, helping them generate better code. Here's a quick walkthrough with pypi/mem0ai as example: https://youtu.be/SKZol8G_tIE LLMs struggle with generating correct code when using lesser-known libraries or dealing with major version changes that happen after their training cutoff. With these markdowns, you can ensure that your LLM chat/coding assistants have up-to-date knowledge of the library's API and usage patterns. We built this using the latest…
2024 · docs.codes
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Hi everyone, we’re a small team, supported by Mozilla, who are working on re-imagining a UI for training, tuning and testing local LLMs. Everything is open source. If you’ve been training your own LLMs or have always wanted to, we’d love for you to play with the tool and give feedback on what the future development experience for LLM engineering could look like.
2025 · github.com
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I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com
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Recently featured in a LangChain blog https://blog.langchain.dev/empowering-development-with-flowt... , use LLMs to construct an API first runnable workflow with an IDE experience.
2024 · github.com
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2022 · github.com
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2023 · ragapi.com
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2023 · typeapi.org
- 24LS
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
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