AxonFlow, governing LLM and agent workflows
Hi HN, we’re building AxonFlow for teams running LLMs or agents in real production systems. Once agent workflows move past demos, failures are rarely model issues. They tend to show up as execution problems during real runs. Short 2-minute technical demo showing execution control and auditability in practice: https://youtu.be/FNgnESo9RtI AxonFlow is a self-hosted, source-available (BSL 1.1) control plane that sits inline in the execution path and governs LLM calls, tool calls, retries, approvals, and policy enforcement step by step. It does not replace your orchestrator and…
AxonFlow, governing LLM and agent workflows launched on January 20, 2026 with 11 votes, #956 of 14,585 launches that month and more than 90% of that year's launches.
In plain words
AxonFlow is a self-hosted control plane for teams running large language models and agents in production. It governs LLM calls, tool calls, retries, approvals, and policy enforcement by sitting inline in the execution path, addressing execution problems that typically emerge only after deployment. The platform works alongside existing orchestrators like LangChain or CrewAI to handle partial failures, prevent unintended side effects from retries, enforce step-level permissions, and provide inspection and intervention capabilities during workflow execution.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, we’re building AxonFlow for teams running LLMs or agents in real production systems. Once agent workflows move past demos, failures are rarely model issues. They tend to show up as execution problems during real runs. Short 2-minute technical demo showing execution control and auditability in practice…
Does a similar job
all alternatives →
TrueFoundry AI GatewayDec 2025 · truefoundry.com · ▲315Many teams connecting LLMs to external tools eventually encounter the same architectural issue: as more tools and agents are added, the integration pattern becomes an N×M mesh of direct connections. Each agent implements its own auth, retries, rate limiting, and logging; each tool needs credentials distributed to multiple places and observability becomes fragmented. We built LLM gateway with this goal to provide a single place to manage authentication, authorization, routing, and observability for MCP servers, with a path toward a more general agent-gateway architecture in the future. The…
Arch2024 · github.com · ▲314Hey HN — I’m Adil from Katanemo (with Salman, Shuguang, and Meiyu) We previously shared an early version of this project as ArchGW. Based on customer feedback, the scope expanded from “LLM routing and model access” into something broader: delivery infrastructure for agentic applications. We renamed it to Plano and reworked the architecture accordingly. The problem On-the-ground AI practitioners will tell you that calling an LLM is not the hard part. The really hard part is delivering agentic applications to production quickly and reliably, then iterating without rewriting system code every…
ControlFlow – open-source AI workflows2024 · github.com · ▲42Hi HN! I’m excited to share ControlFlow, our new open-source framework for building agentic workflows. ControlFlow is built around a core opinion that LLMs perform really well on small, well-defined tasks and run off the rails otherwise. I know that may seem obvious, but the key insight is that if you compose enough of these small tasks into a structured workflow, you can recover the kind of complex behaviors we associate with autonomous AIs, without sacrificing control or observability at each step. It ends up feeling a lot like writing a traditional software workflow. With ControlFlow you:…
Agentflow – Run Complex LLM Workflows from Simple JSON2023 · github.com · ▲51So, it feels like this should exist. But I couldn't find it. So I tried to build it. Agentflow lets you run complex LLM workflows from a simple JSON file. This can be as little as a list of tasks. Tasks can include variables, so you can reuse workflows for different outputs by providing different variable values. They can also include custom functions, so you can go beyond text generation to do anything you want to write a function for. Someone might say: "Why not just use ChatGPT?" Among other reasons, I'd say that you can't template a workflow with ChatGPT, trigger it with different…
Axilla – Open-source TypeScript framework for LLM apps2023 · github.com · ▲161Hi HN, we are Nick and Ben, creators of Axilla - an open source TypeScript framework to develop LLM applications. It’s in the early stages but you can use it today: we’ve already published 2 modules and have more coming soon. Ben and I met while working at Cruise on the ML platform for self-driving cars. We spent many years there and learned the hard way that shipping AI is not quite the same as shipping regular code. There are many parts of the ML lifecycle, e.g., mining, processing, and labeling data and training, evaluating, and deploying models. Although none of them are rocket science,…
Workflow Use – Deterministic, self-healing browser automation (RPA 2.0)2025 · github.com · ▲69Hey HN – Gregor & Magnus here again. A few months ago, we launched Browser Use (https://news.ycombinator.com/item?id=43173378), which let LLMs perform tasks in the browser using natural language prompts. It was great for one-off tasks like booking flights or finding products—but we soon realized enterprises have somewhat different needs: They typically have one workflow with dynamic variables (e.g., filling out a form and downloading a PDF) that they want to reliably run a million times without breaking. Pure LLM agents were slow, expensive, and unpredictable for these…
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 29d ago · world.pixverse.video
Source: Show HN post ↗ · launch text quoted in part
Launched alongside, January 2026
the whole month →- IN
Hey HN! I wanted to share something I built over the last few weeks: isometric.nyc is a massive isometric pixel art map of NYC, built with nano banana and coding agents. I didn't write a single line of code. Of course no-code doesn't mean no-engineering. This project took a lot more manual labor than I'd hoped! I wrote a deep dive on the workflow and some thoughts about the future of AI coding and creativity: http://cannoneyed.com/projects/isometric-nyc
AI · Jan 2026 · cannoneyed.com
Automatic AI-powered code reviews the moment you open a PR
Dev tools · Jan 2026 · kilo.ai
