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Long-running coding and repository sessions burn tokens on output the model rarely needs in full — sprawling diffs, directory listings, search results, and build logs. Synti can hand that output to a local optimizer that compresses it before it ever reaches the model, so more of your context budget stays available for code and reasoning.

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How token optimization works

The optimizer is a separate command-line tool that runs on your machine. When enabled, Synti routes eligible command output through it: the tool strips repetitive noise and condenses verbose text, then passes the compact result to the model. It changes what the model reads, not how fast the command runs — execution time is unaffected.
Optimization only touches the output path. Synti still shows you the original command and asks for permission on the original command, so nothing about the security review changes. See Permissions.

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What it optimizes

The tool is most effective on commands that produce bulky, structured output:
  • Diffs
  • Directory listings
  • Search results
  • Test logs
  • Package-manager output
  • Build output
It offers little benefit for commands that are already concise, so the wins concentrate in coding and repo-maintenance work.

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Requirements

  • The optimizer installed as an executable on your system PATH.
  • A current Synti release (the 0.11.x line).
The optimizer is not bundled with Synti. Synti detects an existing installation and invokes it — there’s no separate initialization step.
On Windows, place the executable on the Windows PATH, not on a Git Bash-only PATH, or Synti won’t find it.

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Enabling it

1

Install the optimizer

Install the command-line tool for your operating system (macOS, Linux, or Windows).
2

Let Synti detect it

Open Settings → AI → Performance and click Re-check so Synti finds the executable on your PATH.
3

Turn on Token Optimization

Enable the Token Optimization toggle.
4

Run a command-heavy session

Work as usual on a coding or repository task so the tool has output to compress.
5

Review the savings

Return to Settings → AI → Performance to see saved-token counts and efficiency statistics.

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Reading the statistics

After the optimizer has processed some commands, the Performance settings show how many tokens it saved and an overall efficiency figure. Use those numbers to decide whether a given kind of session benefits enough to keep the feature on.

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Other ways to save context

Token optimization is one lever. You can also:
  • Choose the right model. Match the model to the task — see LLM connections.
  • Keep sessions focused. Start a fresh session for a new task rather than letting one grow unbounded.
  • Write large results to files. Ask the agent to save big datasets to disk instead of printing them inline.

LLM connections

Pick the model that fits the task.

Rich output

Large results render as files, not inline dumps.