Reduce AI Coding Agent Token Costs: Claude Code and Codex Budget Control¶
Quick Answer: Coding agent token bills grow from three silent leaks: injected context that is never used, conversation history that repeats itself, and tool output that floods the model window. You reduce cost with compression boundaries: pull-based context instead of injection, local output compression (RTK / Caveman), explicit retrieval instead of full history (Headroom MCP), and project memory that survives sessions without being re-read. AIOS wires all four locally — no data leaves the machine.
Where the tokens actually go¶
A typical coding session spends tokens on things you never asked for:
- Injected context — every prompt carries a project preamble whether the current task needs it or not.
- Repeated history — the agent re-reads the same files because nothing remembers the previous answer.
- Tool output — a
git diff, log file, or browser snapshot lands in the window in full, crowding out the decision the model actually needs to make.
Cut those three and the bill drops without reducing the quality of the work.
The four local compression boundaries¶
| Boundary | Tool | What it does |
|---|---|---|
| Context is pull-based | ContextDB | The agent searches or recalls relevant memory instead of receiving the whole project on every prompt. |
| Output compression | RTK / Caveman | Filter and compress command output in-process, locally — 60–90% smaller tool results. |
| Explicit retrieval | Headroom MCP | Compress/retrieve tools when a later step needs the content — no transparent interception of every request. |
| Model tiering | Model Router | Bounded, repetitive work (extraction, classification) runs on a cheaper model; judgment-heavy nodes keep the strong model. |
What you can do today¶
# Install AIOS locally
curl -fsSL https://github.com/rexleimo/aios/releases/latest/download/aios-install.sh | bash
source ~/.zshrc
aios init --all
# Verify compression boundaries and token config
aios doctor --native --verbose
Then measure: run the same task with and without AIOS and compare the token usage reported by your provider. The token intelligence documentation has the architecture; the cost-crisis post has the field numbers.
FAQ¶
Will compression hurt answer quality? No — it removes noise, not signal. Pull-based context and output compression keep the decisions in the window and drop the boilerplate.
Is RTK or Caveman a cloud service? No. Both run in-process on your machine. RTK filters command output locally; Caveman compresses agent output style. Data does not leave the machine.
Can I keep using raw codex or claude CLI? Yes. AIOS sits underneath your existing clients. You keep the same commands; the compression boundaries work around them.
Next step¶
Read Token Intelligence and Compression for the full architecture, or start with the Quick Start.