agent-booster 0.1.4

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.

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How it works

Without Booster                    With Booster
─────────────────                  ────────────────────────────
Read executor.py                   Read executor.py + task hint
→ 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 file, extracts functions/classes into SQLite
Semantic diff	only re-indexes files that changed since last run
Smart read	given a file + task description, returns only matching symbol line ranges
MCP server	exposes tools over stdio so any MCP-compatible agent can call them
Platform init	one command writes the right config for Claude Code, Cursor, or Codex

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Installation

Requires Python 3.10+.

pip install -e tools/booster

Verify:

booster --help

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Quickstart

Step 1 — Index your codebase

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

booster index
# Indexed 166 files, 1107 symbols.

The index is stored at .booster/symbols.db (SQLite, gitignored).

Step 2 — Wire up your AI tool

booster init claude    # Claude Code
booster init cursor    # Cursor
booster init codex     # OpenAI Codex CLI
booster init all       # print all three

Each command prints the JSON config snippet to paste into the right file. For Claude Code, copy the output into .mcp.json at your project root (or use the pre-wired .mcp.json already in this repo).

Step 3 — Restart your AI tool

The agent-booster MCP server will be available in every session. No further setup needed.

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CLI reference

booster index

Scans all .py files from the current directory, extracts functions and classes, stores in .booster/symbols.db. Skips node_modules, .venv, __pycache__, .git, .booster.

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booster search "<query>"

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

booster search "workflow execute"
# apps/api/app/runtime/executor.py:1165  function _execute_output  def _execute_output(...)

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booster serve

Starts the MCP server over stdio. Claude Code, Cursor, and Codex connect to this automatically when configured via booster init.

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booster init <platform>

Prints the MCP config snippet for the target platform. platform is one of: claude, cursor, codex, all.

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booster gain

Shows token savings statistics from past sessions. (Full analytics in a future release — currently a stub.)

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MCP tools

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

get_symbols(file)

Returns all indexed symbols for a file — name, kind, line range, and signature.

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)

Keyword search across all symbols in the index. Returns top 10 matches with file locations.

Input:  { "task": "authenticate user token" }
Output: apps/api/app/auth/middleware.py:34 function verify_token — def verify_token(...)
        ...

smart_read(file, task)

Returns only the lines of a file that are relevant to the task. Falls back to full file content if no symbols match.

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, ...):
            ...

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Platform config details

Claude Code

Add to .mcp.json in your project root:

{
  "mcpServers": {
    "agent-booster": {
      "command": "booster",
      "args": ["serve"]
    }
  }
}

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "agent-booster": {
      "command": "booster",
      "args": ["serve"]
    }
  }
}

OpenAI Codex CLI

Add to ~/.codex/config.json:

{
  "mcp": {
    "servers": {
      "agent-booster": {
        "command": "booster",
        "args": ["serve"]
      }
    }
  }
}

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Where it fits

Agent Booster is the third layer in a three-layer token reduction stack — each layer is independent and addable separately:

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

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Project layout

tools/booster/
├── README.md
├── pyproject.toml
└── booster/
    ├── __init__.py
    ├── cli.py          # click commands: index, search, serve, init, gain
    ├── indexer.py      # tree-sitter parser + SQLite symbol store
    ├── retriever.py    # smart_read: task description → relevant line slice
    └── mcp_server.py   # MCP server exposing get_symbols, search_context, smart_read

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Roadmap

• TypeScript/TSX support (tree-sitter-typescript)
• Vector embeddings for semantic (not just keyword) search
• Smart model routing (route_model tool: Haiku / Sonnet / Opus)
• booster gain — real token savings analytics
• Watch mode: auto re-index on file save
• Publish to PyPI as agent-booster