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AI · January 20, 2026

AG

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…

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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: 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 can run alongside LangChain, CrewAI, or custom systems. The problems we focus on are usually discovered only after going to production: - retries that accidentally repeat side effects - partial failures mid-workflow - permissions that differ per step - limited ability to inspect or intervene during execution This is not aimed at early demos or hobby projects. It’s for teams already operating under real production constraints. GitHub: https://github.com/getaxonflow/axonflow Docs: https://docs.getaxonflow.com I’d value feedback from folks running LLM or agent workflows in production.

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