Agent Booster

io.github.sseshachalaPublished by GitHub account sseshachalav0.6.4Updated Oct 25, 2026

Cut AI agent token costs 5-15x — routes only relevant code symbols instead of full files.

VerifiedSTDIODesktop onlyKnowledge & MemoryDeveloper Tools

Overview

AI-generated overview

A local MCP server that indexes a codebase's symbols and returns only the functions and classes relevant to a task, cutting token use for AI coding agents.

What you can do
Look up indexed symbols in a file, with names, kinds, line ranges and signatures, without reading the file itself (get_symbols). Search the symbol index semantically by task description, falling back to keyword search when embeddings are missing (search_context). Read only the source lines of symbols matching a task in a given file, with a header showing name and line range (smart_read). Recommend a model tier (haiku, sonnet or opus) for a task based on complexity signals, optionally auto-detecting relevant files (route_model).
When to use it
Useful when an AI coding agent works in a large Python or JavaScript/TypeScript repository and full-file reads are burning tokens. It suits teams that want task-relevant code slices and model-routing hints inside Claude Code, Cursor, Windsurf or Codex.
Requirements
Python 3.10+ and a local install of the agent-booster package from PyPI; the embed extra is recommended for semantic search. Run booster index and booster embed once per project from the project root, then configure the server (booster init or a manual MCP entry running booster serve). No environment variables or credentials are required.
Before you install
booster init writes configuration and hook files into the project and user config directories, including hooks that block Read on indexed files and nudge Grep toward search_context; review the changes before confirming. The repository is archived and development has moved to another project. Embeddings use a local model, so code is not sent off the machine.

Installation

In SourceWeft

  1. Open Agent Booster in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

This repository is archived. Agent Booster now lives in sseshachala/conductai → tools/booster. Install from PyPI (pip install agent-booster); file issues and PRs on conductai.

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.

[Agent Booster demo — fewer tokens sent, lower cost, same result]


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:

LayerWhat it does
Symbol indextree-sitter parses every .py, .ts, .tsx, .js, .jsx file, extracts functions/classes into SQLite
Vector embeddingssentence-transformers encodes each symbol for semantic search
Smart readgiven a file + task, runs per-file vector search and returns only matching symbol line ranges
MCP serverexposes four tools over stdio — any MCP-compatible agent can call them
Platform initone command writes the right config for Claude Code, Cursor, Windsurf, or Codex

Installation

Requires Python 3.10+.

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

Quickstart

Step 1 — Wire up your AI tool

bash
booster init claude     # Claude Codebooster init cursor     # Cursorbooster init windsurf   # Windsurfbooster init codex      # OpenAI Codex CLIbooster init all        # all four

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

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

Example — wiring up OpenAI Codex (booster init codex → booster start → booster gain):

[Agent Booster with OpenAI Codex — init writes ~/.codex/config.json + AGENTS.md, start indexes, gain reports savings]

Run once per project, not once per session. The hooks live in .claude/settings.json and .mcp.json at the project root. Every Claude Code session opened in this directory — any terminal window, same machine — picks them up automatically. No per-session setup needed.

Step 2 — Index your codebase

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

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

The index is stored at .booster/ (gitignored). The index is shared across all sessions.

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

bash
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.

bash
booster init claude     # .mcp.json + CLAUDE.md + .claude/settings.json hookbooster init cursor     # .cursor/mcp.json + .cursorrulesbooster init windsurf   # ~/.windsurf/mcp.json + .windsurfrulesbooster init codex      # ~/.codex/config.json + AGENTS.mdbooster init all        # all four
booster init claude --yes   # skip confirmation (CI/scripts)

booster remove <platform>

Cleanly undoes everything init wrote. No residue.

bash
booster remove claudebooster remove cursorbooster remove windsurfbooster remove codexbooster 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.

bash
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).

bash
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.

bash
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.

bash
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:        3Total reads:        47Tokens served:      12,400Tokens saved:       89,200Savings 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

FileChange
.mcp.jsonAdds agent-booster to mcpServers
CLAUDE.mdAppends booster usage rules block (sentinel markers)
.claude/settings.jsonWires PreToolUse (Read + Grep) and UserPromptSubmit hooks
.claude/hooks/booster-gate.pyBlocks Read on indexed files — forces smart_read
.claude/hooks/booster-grep-nudge.pyNudges semantic Grep patterns toward search_context
.claude/hooks/booster-route.pyAuto 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 readsLayer 2 — RTK               Token compression on CLI/git/build outputLayer 1 — Prompt caching    Stable context reuse (native to Claude Code + API)

Each layer is independent and addable separately.


Common questions

Does it work if I open a second terminal? Yes. The hooks are wired into .claude/settings.json and .mcp.json in your project root — not inside a specific terminal session. Any new Claude Code window you open in this project picks them up automatically.

Do I need to run booster init again after restarting my machine? No. booster init writes files to disk once. They persist across restarts and across sessions. The only time you re-run it is if you delete those files or switch to a new project.

Do teammates need to run booster init too? Yes — once per developer, once per project. If you commit .mcp.json and .claude/settings.json to the repo, teammates get the hooks when they clone. They still need to run pip install agent-booster[embed] and booster index && booster embed locally, since the symbol index is gitignored.

Can I undo it completely? Yes. booster remove claude removes all six files and cleans the settings entries. No residue.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

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

Run booster index && booster embed once from your project root before starting Claude Desktop. No env vars required.


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)

Source: README.md at commit d7c2eeb

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.6.4LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts