agent-booster 0.2.4

AST+vector context router for AI coding agents — cut token costs 5-15x

Agent Booster

Cut AI coding agent token costs 5–15x by routing only the code that matters.

Instead of sending full source files to the model on every read, Agent Booster builds a symbol index of your codebase and returns only the functions and classes relevant to the current task.

---

How it works

Without Booster                    With Booster
─────────────────                  ────────────────────────────
Read executor.py                   smart_read executor.py + task
→ 1,881 lines (~6k tokens)         → 3 matching functions (~200 tokens)

Five layers work together:

Layer	What it does
Symbol index	tree-sitter parses every .py, .ts, .tsx, .js, .jsx file, extracts functions/classes into SQLite
Vector embeddings	sentence-transformers encodes each symbol for semantic search
Smart read	given a file + task, runs per-file vector search and returns only matching symbol line ranges
MCP server	exposes four tools over stdio — any MCP-compatible agent can call them
Platform init	one command writes the right config for Claude Code, Cursor, Windsurf, or Codex

---

Installation

Requires Python 3.10+.

pip install agent-booster          # symbol index + MCP tools
pip install agent-booster[embed]   # + semantic vector search (recommended)

---

Quickstart

Step 1 — Wire up your AI tool

booster init claude     # Claude Code
booster init cursor     # Cursor
booster init windsurf   # Windsurf
booster init codex      # OpenAI Codex CLI
booster init all        # all four

Each command shows exactly what files will change, asks for confirmation, then writes them. Fully reversible:

booster remove claude   # or cursor, windsurf, codex, all

Step 2 — Index your codebase

Run once from your project root. Re-run after large refactors.

booster index && booster embed
# Indexed 260 files, 1800 symbols.
# Built embeddings for 1800 symbols.

The index is stored at .booster/ (gitignored).

Step 3 — Restart your AI tool

The agent-booster MCP server is now available in every session. Claude Code (and other tools) will use smart_read and search_context automatically on indexed files.

Step 4 — Track savings

booster gain

---

CLI reference

booster init <platform>

Writes the MCP server config and rules file for the target platform. Asks for confirmation before making any changes.

booster init claude     # .mcp.json + CLAUDE.md + .claude/settings.json hook
booster init cursor     # .cursor/mcp.json + .cursorrules
booster init windsurf   # ~/.windsurf/mcp.json + .windsurfrules
booster init codex      # ~/.codex/config.json + AGENTS.md
booster init all        # all four

booster init claude --yes   # skip confirmation (CI/scripts)

booster remove <platform>

Cleanly undoes everything init wrote. No residue.

booster remove claude
booster remove cursor
booster remove windsurf
booster remove codex
booster remove all

booster index

Scans all .py, .ts, .tsx, .js, .jsx files from the current directory. Extracts functions, classes, methods, and interfaces into .booster/symbols.db. Skips node_modules, .venv, __pycache__, .git, .booster, .next, dist, build.

booster index
# Indexed 260 files, 1800 symbols.

booster embed

Builds sentence-transformer vector embeddings for all indexed symbols. Required for semantic search_context calls. Uses all-MiniLM-L6-v2 (local, no data leaves your machine).

booster embed
# Built embeddings for 1800 symbols.

booster search "<query>"

Keyword search across all indexed symbols. Returns file path, line number, kind, name, and signature.

booster search "guard install"
# apps/web/src/app/settings/modules/page.tsx:99  function handleInstall  function handleInstall() {

booster route "<task>"

Recommends haiku, sonnet, or opus based on task complexity — keyword signals, file count, and symbol count.

booster route "fix the guard SSE role check"
# haiku  (narrow task — 0 symbol(s) in 1 file)

booster route "refactor the entire runtime compiler and DSL layer"
# opus  (matches complexity keywords)

booster serve

Starts the MCP server over stdio. Called automatically by Claude Code, Cursor, Windsurf, and Codex when configured via booster init. You rarely need to run this directly.

booster gain

Shows token savings from past smart_read calls — total reads, tokens served vs. tokens saved, savings rate, and top files by savings.

Agent Booster — Token Savings Report
─────────────────────────────────────
Active days:        3
Total reads:        47
Tokens served:      12,400
Tokens saved:       89,200
Savings rate:       88%

Top files by savings:
  executor.py              18,400 tokens saved  (12 reads)
  guard.py                 14,200 tokens saved  (8 reads)

---

MCP tools

Once booster serve is running, these four tools are available to the agent:

get_symbols(file)

Returns all indexed symbols for a file — name, kind, line range, and signature. No file read required.

Input:  { "file": "apps/api/app/runtime/executor.py" }
Output: function run_workflow (lines 42-89): def run_workflow(...)
        class WorkflowState (lines 91-140): class WorkflowState:

search_context(task)

Semantic vector search across all symbols in the index. Returns top 10 matches by cosine similarity. Falls back to keyword search if embeddings haven't been built.

Input:  { "task": "guard install uninstall" }
Output: apps/web/src/app/settings/modules/page.tsx:99 function handleInstall — function handleInstall() {
        apps/web/src/components/guard/GuardNav.tsx:13 function GuardNav — function GuardNav() {
        ...

smart_read(file, task)

Runs per-file vector search and returns only the source lines for matching symbols, with a header showing name and line range. If no symbols match, returns an explicit message so the model knows to fall back to a full Read rather than silently receiving the whole file.

Input:  { "file": "apps/api/app/runtime/executor.py", "task": "execute output block" }
Output: # function _execute_output (lines 1165-1210)
        def _execute_output(block, state, credentials, ...):
            ...

route_model(task, files?)

Recommends haiku, sonnet, or opus based on task complexity signals. If files is omitted, auto-detects via search_context.

Input:  { "task": "fix the login redirect bug" }
Output: { "model": "haiku", "reason": "narrow task — 1 symbol in 1 file" }

---

What booster init claude writes

File	Change
.mcp.json	Adds agent-booster to mcpServers
CLAUDE.md	Appends booster usage rules block (sentinel markers)
.claude/settings.json	Wires PreToolUse (Read + Grep) and UserPromptSubmit hooks
.claude/hooks/booster-gate.py	Blocks Read on indexed files — forces smart_read
.claude/hooks/booster-grep-nudge.py	Nudges semantic Grep patterns toward search_context
.claude/hooks/booster-route.py	Auto route_model — recommends haiku/sonnet/opus on every user turn

booster remove claude removes all six, cleanly. No residue.

---

Where it fits

Agent Booster is the third layer in a three-layer token reduction stack:

Layer 3 — Agent Booster     AST+semantic routing, smart file reads
Layer 2 — RTK               Token compression on CLI/git/build output
Layer 1 — Prompt caching    Stable context reuse (native to Claude Code + API)

Each layer is independent and addable separately.

---

Project layout

tools/booster/
├── README.md
├── pyproject.toml
└── booster/
    ├── cli.py          # click commands: index, embed, search, serve, init, remove, route, gain
    ├── indexer.py      # tree-sitter parser + SQLite symbol store + vector search
    ├── retriever.py    # smart_read: per-file vector search → relevant line slice
    ├── mcp_server.py   # MCP server: get_symbols, search_context, smart_read, route_model
    └── stats.py        # token savings tracking (booster gain)