
Agumbe LLM Gateway (and Console)
Guardrails and Provenance for enterprise AI control
What it does
Most LLM guardrails don’t make it to production. Agumbe LLM Gateway lets you define guardrails at the app level and enforces them in your real request path. Detect, redact, or block → Prompt injection (direct + indirect) → PII and secrets detection → Denied topics → Output safety and groundedness → and more.. All while ensuring → Budget enforcement → Cheap models for dev, premium reserved for prod and more Test everything through a console that uses the same headless gateway as production.
Does a similar job
all alternatives →- FGForge – Guardrails take an 8B model from 53% to 99% on agentic tasksMay 2026 · github.com · ▲687
Hi HN, I'm Antoine Zambelli, AI Director at Texas Instruments. I built Forge, an open-source reliability layer for self-hosted LLM tool-calling. What it does: - Adds domain-and-tool-agnostic guardrails (retry nudges, step enforcement, error recovery, VRAM-aware context management) to local models running on consumer hardware - Takes an 8B model from ~53% to ~99% on multi-step agentic workflows without changing the model - just the system around it - Ships with an eval harness and interactive dashboard so you can reproduce every number I wanted to run a handful of always-on agentic systems…

ElevenAgents Guardrails 2.0Apr 2026 · elevenlabs.io · ▲132Configurable safety control for enterprise agent deployment.


- AOAn open-source AI Gateway with integrated guardrails2024 · github.com · ▲21
Hi HN, I've been developing Portkey Gateway, an open-source AI gateway that's now processing billions of tokens daily across 200+ LLMs. Today, we're launching a significant update: integrated Guardrails at the gateway level. Key technical features: 1. Guardrails as middleware: We've implemented a hooks architecture that allows guardrails to act as middleware in the request/response flow. This enables real-time LLM output evaluation and transformation. 2. Flexible orchestration: The gateway can now route requests based on guardrail verdicts. This allows for complex logic like fallbacks…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com


Launched alongside, March 2026
the whole month →

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