@lukeramsden/pi-context-cap

A pi extension that enforces a context token budget (default 200k) by forcing compaction — including mid-turn during long tool loops — instead of riding a long-context model up to 1M tokens.

Why

Pi compacts when context passes contextWindow - reserveTokens. On a 1M-window model that means ~984k tokens: slow requests, degraded attention, and a large bill before the first compaction.

Two obvious workarounds don't work:

1. Lowering contextWindow (via modelOverrides or an extension) poisons output. Pi clamps every request's max_tokens to contextWindow − estimatedInput − 4096, floor 1 token. As usage nears a lowered cap, the output budget shrinks to nothing and turns die with "Model stopped because it reached the maximum output token limit" — right before compaction would have fired. This extension leaves model.contextWindow untouched.
2. Relying on pi's auto-compaction misses long tool loops. Pi (as of 0.83.0) checks compaction only after a full agent run and before a new user prompt — never between LLM calls inside a tool loop. One long turn can grow unbounded until the provider rejects it (pi issues #2871, #5512, #6879).

How it works

The budget lives only in the extension. It triggers compaction from three hooks:

1. turn_end with tool results — mid-loop backpressure. turn_end fires after every LLM response inside a tool loop, and getContextUsage() includes estimated tokens for trailing tool results (the exact blind spot in pi's own check). Because ctx.compact() aborts the running agent, the extension sends a follow-up prompt after compaction so the task resumes (resume off to disable).
2. agent_settled — the run is done and pi will not continue on its own; compact quietly so the next prompt starts under budget.
3. session_start — a resumed session that is already over budget gets compacted immediately.

Compaction fires when estimated tokens exceed budget − reserve (defaults: 200,000 − 16,384 ≈ 184k). A footer status line shows usage against the budget (cap 132k/200k (66%)), since pi's own percentage is relative to the model's real window.

Guards: no overlapping compactions, a 20k token growth requirement between retries after a failure, and the watcher disables itself for the session after two consecutive compaction failures.

Known limit

The request that crosses the threshold still goes out before its turn_end fires. Overshoot is bounded to roughly one request past the threshold — an extension cannot stop the loop before the next LLM call. Removing that needs a compaction check inside the agent loop itself; the enabling shouldStopAfterTurn hook is tracked in #7299 / PR #7367.

Install

pi install npm:@lukeramsden/pi-context-cap

Or try it for a single run without installing:

pi -e npm:@lukeramsden/pi-context-cap

Configure

CLI flags (set the session defaults):

pi --context-cap 150000 --context-cap-reserve 24000

/context-cap command (changes last for the current session):

Command	Effect
/context-cap or /context-cap status	Show budget, threshold, usage, and state
/context-cap <tokens>	Set the budget (e.g. /context-cap 150000)
/context-cap off / on	Disable / re-enable enforcement
/context-cap resume on|off	Toggle the auto-resume prompt after mid-task compaction

Verify

npm run verify

Type-checks the extension and runs a headless functional test with a mocked pi API — no pi binary, models, or API keys required.

License

MIT