Git tells you what changed. Causari tells you why. A developer created Causari, a local CLI tool that records AI agent actions as causal events to track intent and provenance behind code changes. Unlike Git, which tracks what changed, Causari answers why an AI agent made a change by capturing prompts, models, reads, writes, diffs, and reasoning. The tool supports multiple capture paths including an LLM proxy, filesystem watching, and native agent hooks. AI coding agents are becoming good enough to touch real codebases. They can refactor files, write tests, change architecture, move logic around, and sometimes modify more code in ten minutes than a human would in an afternoon. That is powerful. But it creates a new debugging problem. Git can tell you what changed . When an AI agent was involved, you often need to know something deeper: That is the problem I wanted to solve with Causari . Causari is a local CLI for intent-addressable code . It records AI agent actions as causal events: prompts, models, reads, writes, diffs, reasoning, cost, and relationships between actions. The goal is simple: Git tracks bytes. Causari tracks intent and causality. Repository: https://github.com/croviatrust/causari https://github.com/croviatrust/causari Website: https://causari.dev https://causari.dev When a human developer changes code, there is usually some context. A commit message. A pull request. A ticket. A discussion. A design decision. With AI coding agents, the workflow is different. You ask something like this: Refactor the auth flow and add JWT refresh logic. The agent reads files, makes assumptions, writes code, maybe fixes tests, maybe changes something unrelated, then moves on. At the end, you have a diff. But the diff does not tell the full story. A suspicious line appears in auth.ts . Git can show when the line appeared. But Git cannot answer: which prompt produced this exact line? what completion did it come from? did the agent read the right files first? was this part of the original request or an accidental side effect? if I revert this, what downstream work am I also undoing? That gap becomes bigger as agents become more autonomous. The more work agents do, the more we need provenance. Not only code provenance. Intent provenance. Causari treats an AI agent action as something that should be traceable. Not just as a Git diff. But as an event with cause and effect. An event can include: So instead of only asking: what changed in this file? You can ask: why does this line exist? Example: re why src/auth.ts:42 Or: re trace src/auth.ts:42 Or: re impact