
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.
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.
Installation
In SourceWeft
- Open Agent Booster in the dashboard and add it to a workspace.
- 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
Five layers work together:
Installation
Requires Python 3.10+.
Quickstart
Step 1 — Wire up your AI tool
Each command shows exactly what files will change, asks for confirmation, then writes them. Fully reversible:
Example — wiring up OpenAI Codex (booster init codex → booster start → booster gain):
Run once per project, not once per session. The hooks live in
.claude/settings.jsonand.mcp.jsonat 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.
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
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 remove <platform>
Cleanly undoes everything init wrote. No residue.
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 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 search "<query>"
Keyword search across all indexed symbols. Returns file path, line number, kind, name, and signature.
booster route "<task>"
Recommends haiku, sonnet, or opus based on task complexity — keyword signals, file count, and symbol count.
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.
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.
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.
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.
route_model(task, files?)
Recommends haiku, sonnet, or opus based on task complexity signals. If files is omitted, auto-detects via search_context.
What booster init claude writes
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:
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):
Run booster index && booster embed once from your project root before starting Claude Desktop. No env vars required.
Project layout
Source: README.md at commit d7c2eeb
Tools
0Version history
1- v0.6.4LatestSep 16, 2026
Score
50% of the applicable checks met
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- Reached in the last 30 days25 pts
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- Auth declared10 pts
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- 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