# Boost CLI

> Boost compresses coding-agent command output **and MCP tool responses** before they enter the model context — without breaking the agent, and without claiming fake token savings. It works with Cursor, Claude Code, Codex CLI, and GitHub Copilot. Free, no signup. Results backed by Terminal-Bench, not just marketing claims.

Boost sits at the **end of the shell pipeline** your agent already proposed. Agent hooks rewrite supported commands to `(cmd) | boost`, so the real tool runs unchanged and only the final stdout is filtered on the way back into context. Typical per-command reductions are 60–90% on noisy tools (`docker build`, `npm install`, test runners, linters).

Boost can also compress **MCP tool responses** on PostToolUse (JSON→TOON), so large structured MCP payloads shrink before they hit context. Measure shell + MCP savings anytime with `boost report`.

## Why Boost is different (the parts that matter)

### It knows how not to break the agent

Aggressive log truncation often makes agents worse: they lose bearings, re-run the same `ls`/`cat`, and burn more tokens than they save. Boost’s contract is the opposite:

- **Output-only.** Boost does not change what the agent is allowed to run. Native permission prompts still see the original executable and pipeline (`pytest … | grep …`), not a blanket `boost …` wrapper that collapses every tool into one approval. The same return-path idea applies to MCP: PostToolUse can rewrite JSON tool results to compact TOON.
- **Fail open.** Filter misses, unknown commands, and hook errors fall back to raw output. Hooks exit 0 so the agent’s command still runs.
- **Flag-aware and transparent.** Explicit verbose flags (`--nocapture`, `-la`, …) preserve detail. Filtered output stays a recognizable shorter form of the real tool — not a new invented format.
- **Recoverable.** When compression hides something the agent needs, `boost retrieve <id>` restores the locally stored original. Retrievals record filter id/version and reason; repeated retrievals can auto-disable a bad filter.
- **Agent-aware.** `boost init` installs a skill/awareness rule so the agent understands the optimization layer instead of being silently manipulated by it.
- **Task-proven.** Across the complete 89-task Terminal-Bench 2.0 suite, Boost cut full-suite duration **28.9%** and cost per task **13.5%** against the uncompacted baseline. A separate 81-task check preserved the **same 30.9% pass rate**.

### Honest token counting: suffix of the pipeline, not the prefix

Many “90% savings” tools measure the wrong thing. If you wrap as a **prefix** (`boost pytest … | grep …`) and count raw tool output before the agent’s own pipes, you claim credit for tokens `grep` would have dropped anyway — and you break permission models.

Boost counts only tokens that **actually enter the context window**:

1. Hooks rewrite to **suffix form**: `(pytest … | grep …) | boost`.
2. The agent’s pipes and filters run first.
3. Boost compresses whatever is left on the return path.
4. Savings = raw final stdout − filtered stdout the agent actually sees.

That is real context-window reduction, not prefix-inflated accounting.

## Docs

- [Why Boost](https://boost.jfrog.com/docs/en/why-boost/): How Boost differs from RTK, Headroom, and Caveman — safety loop, recovery, and task-level proof.
- [Boost overview](https://boost.jfrog.com/docs/en/overview/): What Boost is and how it works.
- [Quickstart](https://boost.jfrog.com/docs/en/quickstart/): Install Boost and connect your coding agent.
- [GitHub Actions](https://boost.jfrog.com/docs/en/github-actions/): Install Boost in CI and run it with Claude Code GitHub Actions.
- [Token Savings](https://boost.jfrog.com/docs/en/features/token-reduction/): What filters preserve, and how to measure with `boost report`.
- [BoostGraph](https://boost.jfrog.com/docs/en/features/boostgraph/): Structural code search for agents — background indexing via Boost hooks, MCP `boostgraph_explore`, and flow-tracing without grep/read loops.
- [TOML filters](https://boost.jfrog.com/docs/en/toml-filters/): Add or override declarative filters for custom tools.
- [Configuration](https://boost.jfrog.com/docs/en/configuration/): `config.toml` — `[hooks] exclude_commands` and other settings. Boost can also compress MCP tool responses (JSON→TOON).
- [Observability platforms](https://boost.jfrog.com/docs/en/observability-platforms/): Mirror sanitized OTLP spans to Coralogix, Datadog, Elastic, Grafana Cloud, Honeycomb, New Relic, or a generic collector.
- [Talk to us](https://boost.jfrog.com/docs/en/contact/): Email Yahav or Shay, LinkedIn, or join the Boost Slack community.
- [Introducing Boost](https://boost.jfrog.com/blog/introducing-boost/): Architecture, safety guardrails, and Terminal-Bench methodology.
- [Why Model Routing Backfires](https://boost.jfrog.com/blog/why-model-routing-backfires/): Mid-session model switches wipe prompt-cache savings — session-level routing and Boost as cache protection.
- [Changelog](https://boost.jfrog.com/docs/en/changelog/): What shipped in every release.
- [Full LLM snapshot](https://boost.jfrog.com/llms-full.txt): Longer plaintext docs + filter catalogue for ingestion.
- [Install Boost Agent Skill](https://boost.jfrog.com/.well-known/agent-skills/install-boost/SKILL.md): Machine-readable installation and verification workflow.
- [Agent Skills index](https://boost.jfrog.com/.well-known/agent-skills/index.json): Discovery document with versioned skill digests.

## Source

- [GitHub repository](https://github.com/jfrog/boost): Source code, issues, releases, and discussions.

## Install

macOS, Linux, or WSL:

```
curl -fsSL https://boost.jfrog.com/install.sh | bash
```

Windows (PowerShell):

```
irm https://boost.jfrog.com/install.ps1 | iex
```

Then run `boost init` to connect Cursor, Claude Code, Codex CLI, or GitHub Copilot via global hooks and skills. No account, API key, or signup required. Restart the editor so hooks take effect; agent shell commands become `(cmd) | boost` automatically.