fruxon-ai/fruxon
Build, run, and debug Fruxon agents through the fruxon CLI
How to drive the fruxon CLI from an AI-agent / LLM driver (Claude Code, Cursor, custom orchestrators) and from CI. Documents the agent-mode contract end-to-end: JSON outputs, NDJSON streams, typed exit codes, structured error envelopes, and the schema → validate → run authoring loop. Load this first when an agent is driving the CLI.
Author a working Fruxon agent revision end-to-end via the CLI: discover, draft, test, create, deploy. Load when the user says "build a Fruxon agent", "create a revision", or starts authoring agent JSON.
Bootstrap a brand-new integration (with API tools or Python code tools) end-to-end via the CLI: schema discovery, body authoring, tools, then a clean handoff to the user for credentials. Load when the user asks to add an integration; the flow first checks whether one already exists and only builds a new one if nothing suitable is found.
Work out why a run did what it did, from its execution trace: find the run, read the step tree, drill into a tool call's parameters and result, and reconstruct the state the agent decided from. Load when a run failed, hung, or produced a surprising result — including the common case of a run that reports COMPLETED but did the wrong thing.
Orientation to Fruxon — what an agent / revision / definition / integration is, and the fruxon CLI's main entry points. Load when starting any task that involves authoring or operating Fruxon agents.
Wire integration tools into an agent: discover what's connected, attach the right tool, point it at a credential slot. Load when adding tools (Slack, GitHub, Jira, Drive…) to a Fruxon agent.