Alternatives
Products that do what MaskLLM does
Mask your LLM APIs for secure rotation and logging
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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
- 8OS
Hi HN, Matvey, Ildar, Joey, and Dominik here. If you're building LLM agents that use tools, you're probably worried about prompt injection attacks that can hijack those tools. We were too, and found that solutions like prompt-based filtering or secondary "guard" LLMs can be unreliable. Our thesis is that agent security should be handled at the network level between the agent and the LLM, just like a traditional web application firewall. So we built Archestra Platform: an open-source gateway that acts as a secure proxy for your AI agents. It's designed to be a deterministic firewall against…
Oct 2025 · archestra.ai
- 9RA
Hey, we're excited to share ReLLM! ReLLM provides developers with an API to quickly add permission sensitive context for LLM's such as chatGPT. ReLLM goes a step further and also encrypts all of your plain text data at rest. The Motivation: We built ReLLM, because while developing a different application we realized there was not a great way to provide our users context for their GPT questions that was limited to only the data they are allowed to see. ReLLM is fully functional with 2 API endpoints. One to embed data, and one to invoke chat with GPT.
2023 · rellm.ai
- 10KC
I think in-process key management is the right abstraction for multi-key LLM setups. Not LiteLLM, not a Redis queue, not a custom load balancer. The failure modes are well-understood: a key gets rate-limited, you wait, you try the next one. Billing errors need a longer cooldown than rate limits. This is not a distributed systems problem — it's a state machine that fits in a library. The problem is everyone keeps solving it with infrastructure instead. Spin up LiteLLM, now you have a Python service to maintain. Reach for Redis, now you have a database for a problem that doesn't need one.…
Mar 2026 · github.com
- 11OS
We’re building an open-source tool that makes it easy to expose secure, LLM-optimized APIs on top of your structured data—without manually designing endpoints or worrying about compliance. AI agents and LLM-powered applications need structured access to data, but traditional APIs and databases weren’t built with AI workloads in mind. Our tool automatically generates APIs that: - Filter out PII & sensitive data to comply with GDPR, CPRA, SOC 2, and other regulations. - Provide traceability & auditing, so AI apps aren’t black boxes, and security teams stay in control. - Optimize for AI…
2025 · github.com
- 12OS
Hello HN, I’ve been building AI agents lately and ran into a common "Context Bloat" problem. When an agent has 20+ skills, stuffing every system prompt, reference doc, and tool definition into a single request quickly hits token limits and degrades model performance (the "lost in the middle" problem). To solve this, I built OpenSkills, an open-source SDK that implements a Progressive Disclosure Architecture for agent skills. The Core Concept: Instead of loading everything upfront, OpenSkills splits a skill into three layers: Layer 1 (Metadata): Light-weight tags and triggers (always loaded…
Jan 2026
- 13SB
*Motivation* Hi hackers, I'm Asif. I know we dislike premature standardization, but hear me out. LLM Application development is extremely iterative, more so than most other types of application development. We need a process that allows us to iterate faster. LLM Development is highly iterative due to the activities that come with regular software development, as well as the need to make the LLM Application accurate and reduce hallucination. To improve hallucination, we need to trial and error various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt…
2024 · github.com
- 14GL
I wanted to do a complete audit of my AWS account but was dissatisfied with the existing tools. Many of them are clunky to use, and their verbose scan outputs are difficult to understand. So, I built my own open-source tool that uses LLMs to summarize the scan results.
2024 · guard.dev
- 15LC
Hey, folks here is a peek into Jujutsu. We at Poozle are working with hundreds of APIs and it has been always frustrating to 1. Search the API in the documentation or ask ChatGPT 2. Then copy it to the postman and understand/test the API 3. Generate code to integrate into the codebase We thought how about having all of this at one place. We currently fine-tuned LLM on public REST APIs to reduce hallucination and then combined it with ChatGPT and Postman. I look forward to feedback, feature requests and discussions!
2023 · loom.com
- 16HW
Hello everyone! I’m thrilled to announce the latest feature from Mutahunter.ai, the ultimate tool for finding and fixing weaknesses in your code. We’ve designed Mutahunter to leverage mutation testing powered by advanced LLMs, helping you uncover vulnerabilities and enhance your code quality effortlessly. Introducing our newest feature: Detailed Mutation Testing Reports! After running our mutation tests, Mutahunter now generates comprehensive reports that clearly summarize: • Vulnerable code gaps • Test case gaps These reports significantly reduce the cognitive load on developers by…
2024 · github.com
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Tool for estimating LLM API token usage and cost before execution using heuristics, regression, and research-based methods. Includes CLI, Python API, and CI support.
2025 · github.com
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With the right technique, I was able to break the so-called secure models like Claude and OpenAI. So, I built an open-source tool to automate this and find security holes in any hosted model. I got claude-sonnet-4 to demonstrate the following harmful behavior: - steal data from downstream tool calls using sql injection, code injection and template injection attacks - install spyware or malware using prompt obfuscation to send data to a third-party server Try it yourself with this simple command: pip install compliant-llm && compliant-llm dashboard
2025 · github.com
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Hi HN. I heard you like dev tools and AI, so we wanted to share our project that we’ve been working on. We’re working on Horizon [1] - a higher level abstraction for LLMs so that developers can spend less time trying to grapple with LLMs to make them work and more time with users. This is the starting feature set which takes an auto-ML approach to identify the optimal LLM model, hyperparameters, and prompt - instead of just giving you the tooling to figure it out yourself. You can read more about it in our documentations. Our view is that as LLMs become increasingly commoditized and prompts…
2023 · gethorizon.ai
- 21LR
Hi hacker news, My name is Dillion and I'm the creator of llm.report. A few months ago, I was frustrated by the lack of observability into the OpenAI API. All of us are left in the dark about API performance, latency, cost calculation, cost breakdown, and more. I just wanted to know more about how my AI app is performing in production and make data-driven decisions to improve the product. So I ended up just building it myself. There are three parts to the platform: 1. OpenAI API Dashboard (no-code) - Enter your OpenAI key and get access to detailed insights straight from the OpenAI API…
2023 · github.com
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Long-session LLM memory degradation (entropy) is the silent killer of complex coding projects. Models like Gemini, GPT-4, and Claude all suffer from it, leading to hallucinations and lost context. I've developed an open-source protocol that temporarily "fixes" this issue by structuring the dialogue. It's not the final architectural solution, but it’s a proven patch for developers working right now. Looking for feedback from the community on how we can solve this structurally. https://github.com/robertomisuraca-blip/LLM-Entropy-Fix-Prot...
Nov 2025 · github.com
- 23LG
LLM Globber is a command-line utility written in Rust for collecting files from various locations, filtering them, and outputting their contents to a single text file. This tool is designed to prepare local files for analysis by Language Learning Models (LLMs). Criticism welcome.
2025 · github.com
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