Context Budget

Context Budget

Measure where an agent's per-turn tokens actually go, then hold the line with a budget.

Overview

Autonomous agents accrete context. One more skill, one more memory note, one more gate module — each cheap alone, together a tax paid on every request. Nobody trims it because there's no ruler. context_budget.py is the ruler: point it at the files loaded into context every turn and it ranks them by token weight, shows each file's share, and returns a non-zero exit code when you blow the budget — so it drops into CI or a pre-commit hook.

Real run against a production agent's gate layer: 214,588 tokens across 83 files, with a single 1,672-line quality gate eating 10.7% of the whole window. You can't fix what you can't see; this makes it visible.

When to use

• Token/$$ cost is climbing on a long-lived agent and you don't know which part.
• You want CI to fail the build when the always-loaded context grows past N tokens.
• You're deciding what to prune and need weights, not vibes.

Not for: estimating one user prompt's cost, or counting a single API call.

The method

1. Enumerate what loads every turn. System prompt, skills dir, memory files, tool schemas, any gate/middleware code injected into context. These are the paths you audit — not your whole repo.
2. Run the audit.

   python scripts/context_budget.py \
     ~/.agent/system_prompt.md ~/.agent/skills ~/.agent/memory \
     --budget 40000 --top 15
   

   Uses tiktoken (cl100k) when installed, else a calibrated chars/4 fallback — never hard-fails on a fresh box.
3. Read the ranking top-down. The heaviest file is your biggest lever. A 10%-share file is where an hour of trimming pays back on every future request.
4. Set a budget and wire it into CI. The tool exits non-zero over budget:

   # .github/workflows/context-budget.yml
   - run: python scripts/context_budget.py PATHS --budget 40000
   

   Now "the agent got fatter" fails the build instead of silently costing money.
5. Trim the top, re-measure. Move detail to on-demand references, delete stale memory, split a mega-gate. Re-run; the number is the scoreboard.

Anti-patterns

• Auditing the whole repo. Only files that enter context every turn count. Test files and docs you never load are noise here.
• Trimming by vibes. Cut the measured top, not the file you happen to dislike.
• Optimizing away autonomy. For agents that need rich context to act on "just handle it", trim redundancy, not the capability. Weight, then cut carefully.
• One-and-done. Bloat regrows. The value is the budget in CI, not a single audit.

Example

$ python scripts/context_budget.py ./skills ./memory --budget 20000
   tokens   share  file
    8,928    4.2%  ./skills/deploy/SKILL.md
    5,517    2.6%  ./memory/MEMORY.md
      ...
   34,110  100.0%  TOTAL  (128,402 chars, 41 files)

budget 20,000  [████████████████████████] 171%  → OVER BUDGET
  14,110 tokens over. Heaviest file is 8,928 tok — start there.
$ echo $?
1