Token optimization
- caveman — terse output
- rtk — CLI-output filtering
- Graph + RAG retrieval instead of reading files
- Model routing — cheap/local for easy work
- Measure first
Context is the budget. aiflow attacks token cost from four directions — the first two are on by
default. Prefer full, unfiltered output instead? Initialise or reconfigure with
aiflow init --no-token-saving / aiflow change-settings --no-token-saving — it switches
caveman and rtk off in one go.
Honest expectation: aiflow’s quality rules (tests, coverage gates, static analysis, architect review) deliberately spend tokens on getting things right — so per-task savings are only partial. The net win is that a requirement implemented production-ready on the first pass needs no re-prompting, no re-sharpening, and no rework: that saves more tokens and time than any filter.
caveman — terse output
A compressed output mode: the agent drops filler and speaks tersely. ~75% fewer output tokens;
code, commits, and security warnings stay in full prose. Toggle in .aiflow/config.json
(caveman.enabled, caveman.mode: full|lite|ultra).
rtk — CLI-output filtering
Verbose command output (installs, test runs, build logs) is filtered/compressed before it enters context — errors and diffs are preserved, noise is trimmed. Typically 60–90% fewer tokens on noisy commands. Enabled per project by aiflow.
Graph + RAG retrieval instead of reading files
The biggest silent cost is re-reading whole files. aiflow routes questions through the code memory: graphify (structure) and cocoindex-code (semantic RAG, ~70% fewer tokens than opening files). The agent locates the few relevant chunks, then opens only those.
Model routing — cheap/local for easy work
Send trivial/background steps to cheaper or local Ollama models via claude-code-router, keeping top Claude models for hard reasoning:
aiflow shell --router
See Models & context7.
Measure first
aiflow cost # ccusage: real token/cost baseline
Optimise what the numbers show, not what you guess. Combined, these routinely cut total token spend by a large multiple on real projects.