Alternatives
Products that do what KeyProbe does
Validate LLM API keys & see model access — free forever
- 1AM
I built this out of curiosity about what Claude Code was actually sending to the API. Turns out, watching your tokens tick up in real-time is oddly satisfying. Sherlock sits between your LLM tools and the API, showing you every request with a live dashboard, and auto-saved copies of every prompt as markdown and json.
Jan 2026 · github.com
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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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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
- 6OC
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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- 8LO
Key management for multiple users and multiple cloud LLM/GenAI APIs is difficult to be both safe and convenient. Sharing keys among users risks leaking the key and makes it difficult to curb the leakage without interruptions. But assigning one key per user per cloud API results in too many keys to keep track of. Meet LlaMa(ster)Key, the secure and easy solution for API key management: * For each user, one master key for multiple APIs. * The master key is unique to each user. Granting and revoking a user's access won't impact other users. * The actual API keys to authenticate with cloud…
2024 · github.com
- 9OS
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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- 13IB
over this weekend myself and two of my friends took part in a hackathon and built this side-project. we have been diving into computer-use recently and developed an sdk to make it easy to implement for devs like us. one feature we were missing though, was the agent being able to log into services. anthropic understandably blocks this capability with their guardrails, and you wouldn't want your credentials to end up in any model context anyways. so we added a keychain service to the vm that the agent is using. it was built using the pass cli (https://www.passwordstore.org/).…
2025 · github.com
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Hi HN, I was once given the advice: Don't waste expensive frontier model credits (GPT/Claude/etc.) on bulk work. Send the boring, repetitive, high-volume jobs to a smaller model, and save the expensive prompts for when you actually need frontier-level reasoning. I complained and told my manager that I shouldnt have to think about using certain models for certain coding tasks, and that one model should handle everything. Well, here we are anyway. If anyone needs a place to absolutely abuse an LLM with high-volume tasks, come beat ours up at https://yolo-auto.com. Here are…
Jul 2026 · yolo-auto.com
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Hey HN! For that last 8 months I've been trying to make agents that can hack web applications to find vulnerabilities in them - An AI Security Tester. The system has 29 agents in total, a custom LLM Orchestration framework which works on the task-subtask architecture (old-school but works amazingly for my use case, and is pretty reliable) with custom agent calling mechanism. No Auo-Gen, Langchain and Crew AI - Everything custom built for pentesting. Each test runs in an isolated Kali linux environment (on AWS Fargate), where the agents have full access to the environment to undertake any…
2025
- 18OS
Hi HN, Hugh and Vince here. LLMonitor helps you record, trace & search your LLM queries and chatbot conversations. You can also capture user feedback on your frontend and correlate it with backend LLM queries then use that to fine-tune your own models. The project started has an internal tool in our previous (failed) AI startup. We’re aware the LLM observability space is very crowded. Apart from being open-source, we differentiate with: - Model-agnostic and minimal lock-in (no MITM of requests). - High focus on DX and dashboard clarity. - Support for complex scenarios: e.g. a chatbot that…
2023 · github.com
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Hey HN, I've been working on something cool that I wanted to share with you all. It's called Viewpoint, an analytics tool for LLMs like OpenAI, Anthropic models, and Gemini. The idea came from the constant flood of new LLM models and the need to figure out which ones work best for my projects without breaking the bank. With viewpoint, I can track token usage, costs, latency(WIP), and traffic over time, making it easier to compare different models and see which ones perform best and save money. The tool works asynchronously, so it doesn't add any latency to your LLM requests, and you have…
2024 · viewpointhq.com
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Scenario: Your company’s IT department says “good news, you have access to azure, aws, openai, mistral, and together AI, here are the API keys”. You think “yippee I can access many models”, but some models like the gpt-oss or Mistral are available on some or all of those platforms? That’s where this app comes in: run it and it will check all the providers that you have configured and then you can search across those providers to see which providers have the model you want available. Built on top of mozilla.ai any-llm library. Check out the link for a GIF showing it in action.
Sep 2025 · github.com
- 24MY
LLM observability is an absolute must-have for anyone running something in prod (or prod-like). While all the observability startups are great, you're essentially sending all your OpenAI usage history - prompts, generations, chats - to a random third party. So this script deploys a basic proxy in your Azure account, catches all incoming OpenAI requests, stores logs in your own resource group, and comes with visualizations premade (charts, timelines, chat history, cost estimation, etc). Thanks for any thoughts and feedback!
2023 · github.com
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