Tokenade for software teams: cut what your AI agent reads
Software is where AI agents work hardest, and where most of their tokens go to logs, file reads and tool results. Tokenade trims those on the computer where you work.
Who it is for
Indie developers & freelancers
When you pay for your AI agent yourself, every limit message costs you billable time. Tokenade trims what the agent reads so the same plan lasts longer.
CTOs & engineering managers
Spend caps stop the work; budgets alone do not lower the cost of a turn. Tokenade trims what each developer's AI agent reads, locally, across the agents your team already uses.
Web & AI-automation agencies
Agencies spread one AI agent plan over many client projects, and MCP servers for n8n or CMSs fill the context fast. Tokenade folds those results on your machine and masks the keys it recognises.
Software teams use AI agents more than any other trade, and most of what those agents pay for is reading, not writing. Every build log, test run, file read and tool result goes into the context, then gets read again on each following turn. Tokenade shrinks that material on the computer where you work, before the model sees it.
Why software is where agents work hardest
An AI agent in a code repository does what a developer does: it runs the build, reads the failing test, opens five files to find one function, calls a tool to check a ticket. It does this dozens of times per task, without being asked.
The agent is good at this, which is why freelancers, product teams, agencies and studios have all adopted it. The catch is that the agent reads everything in full. A 600-line build log costs the same whether one line matters or all of them do.
Where the tokens go
For software work, the bill grows in four places:
- Command output. Builds, test runners, package managers and
gitprint far more than the agent needs. - File reads. The agent opens whole files when a list of signatures would answer its question.
- Tool results. MCP servers for tickets, workflows or a game editor can return large JSON blobs on a single call.
- Repetition. Anything already in the conversation is re-sent with every turn, so a noisy result keeps costing until the session ends.
None of these is visible in the chat. You see a limit message or an invoice, not the log that caused it. We explain the mechanism in output filtering for command logs.
What these teams have in common
A freelancer on a weekly limit, a CTO watching a monthly invoice, an agency juggling client repos and a studio driving the Unreal editor look like different buyers. Underneath, they share three constraints:
- The agent runs locally, in a terminal or an editor, on code they are responsible for.
- The budget is fixed, whether it is a subscription limit, a spend cap or a client's fee.
- The code is not theirs to leak. It belongs to a client, an employer or an unreleased game.
Tokenade fits that shape. It plugs into the agents you already use (Claude Code, Codex, Cursor, Copilot CLI, OpenCode and others), folds the noisy output, and does the work on your machine. On the public THOL benchmark, sessions with Tokenade came out 39% cheaper on long sessions than the same sessions without it.
Pick your situation
The pages below go into the specific problems of each role, with the sources behind them: what people in that seat actually report, what Tokenade changes for them, and what it does not do.