Disclosure: I maintain Tura.
Here is the simple idea. A normal coding agent may use five separate LLM turns for one predictable workflow.
Turn 1 — inspect
rg -n "TODO|command_run|handler" crates/
rg --files crates/runtime/src crates/tools/src
Turn 2 — apply the patch
- // old command handler logic
+ // patched command handler logic
Turn 3 — build
cargo build -p runtime
Turn 4 — test
cargo test -p runtime --lib
Turn 5 — lint
cargo clippy -p runtime --all-targets
The overhead is not only the shell commands. The model wakes up five times and receives the growing conversation again.
Tura exposes one macro tool called command_run, so the agent can send the same workflow once:
{
"name": "command_run",
"arguments": {
"commands": [
{ "step": 1, "command_type": "shell_command", "command_line": "rg -n \"TODO|command_run|handler\" crates/" },
{ "step": 1, "command_type": "shell_command", "command_line": "rg --files crates/runtime/src crates/tools/src" },
{ "step": 2, "command_type": "apply_patch", "command_line": "*** Begin Patch\n*** Update File: crates/tools/src/command_run/handler.rs\n@@\n- // old command handler logic\n+ // patched command handler logic\n*** End Patch" },
{ "step": 3, "command_type": "shell_command", "command_line": "cargo build -p runtime" },
{ "step": 4, "command_type": "shell_command", "command_line": "cargo test -p runtime --lib" },
{ "step": 4, "command_type": "shell_command", "command_line": "cargo clippy -p runtime --all-targets" }
]
}
}
Build, test, and lint still run. The model simply does not need a new turn between predictable steps.
In the full DeepSWE comparison, Balanced used 35.8% fewer turns and 31.1% fewer tokens than Codex CLI. Direct used 69.1% fewer turns and 77.5% fewer tokens.
GitHub: https://github.com/Tura-AI/tura
Benchmark: https://turaai.net/benchmark