Seventeen Sources Your AI Should Read From

Code is half the story

A codebase is the record of decisions, not the reasons for them. The reasons are scattered across the ticket that requested the change, the design doc that argued for it, the incident thread where it broke, and the review comment where someone talked the team out of a worse idea.

An agent that reads only code is reasoning with half the context. It can see that a function guards against a null, but not that the null came from a specific customer bug filed last spring. So it guesses, and its guesses are confident and sometimes wrong.

The insight is that memory has to span sources. The graph is only as good as the corners of your work it can reach. Oynix connects to eighteen sources across every place your team records its thinking, and ties them into one graph so a question about code can be answered with the ticket that explains it.

The source categories and what each adds

How the categories reinforce each other

The value is not in any one source. It is in the edges between them. A function connects to the ticket that requested it, the ticket connects to the design doc that scoped it, the doc connects to the Slack thread where the approach was decided, and an error report connects the whole chain to a real failure in production.

That web is what a lone repository can never give you. Ask who owns a file and the graph knows the author, the reviewers, and the person who has answered every question about it in chat. Ask why a decision was made and the graph walks from the code to the reason. Each source is a thread. The graph is the cloth.

• From code alone. What the system does, structurally. Callers, owners, and paths.
• Add issue trackers. Why each change happened and what problem it was solving.
• Add docs and chat. The reasoning behind the design and the debates that shaped it.
• Add error tracking. Which parts break in the real world, tied to the code and the fix.

Six connect in one tap

Connecting a source should not be a project. For six of the seventeen, it is a single tap. You approve access and the source starts flowing into the graph. No token juggling, no config file, no afternoon lost to setup.

The rest connect with a straightforward credential step, and every source, one-tap or not, respects the same rule. The engine reads it in your infrastructure and writes to your database. Connecting a source adds it to your memory, not to someone else's cloud.

You choose what flows in

More sources does not mean less control. You pick exactly which channels, spaces, and folders sync from each source. The graph reads the archived support channel and skips the meme channel, indexes the engineering space and leaves HR alone. Selection is yours, per source, down to the folder.

That precision keeps the graph relevant and keeps sync cheap, because Oynix only ever scans what you selected. Combined with incremental sync, connecting a broad source costs little, since only your chosen slice of it is ever read, and only the parts that changed are ever reprocessed.

The result is a memory shaped like your actual work. Wide enough to hold the why, narrow enough to stay signal. You decide where the edges are.

Seventeen sources, six in one tap, and you choose every channel, space, and folder that flows in.

Give your agent the whole story

An agent reasoning over code alone is guessing at intent. An agent reasoning over code plus the tickets, docs, threads, and errors around it is working with the same context a senior engineer carries in their head.

That is the point of connecting the sources that matter. Not to hoard data, but to close the gap between what your code does and why it does it. Connect the corners of your work, choose your slices, and let your agent reason over the whole picture instead of a fragment.