Vexor Search

Vexor

Vexor is a semantic search engine that builds reusable indexes over files and code. It supports configurable embedding and reranking providers, and exposes the same core through a Python API, a CLI tool, and an optional desktop frontend.

Featured In

Vexor has been recognized and featured by the community:

• Ruan Yifeng's Weekly (Issue #379) - A leading tech newsletter in the Chinese developer community.
• Awesome Claude Skills - Curated list of best-in-class skills for AI agents.

Why Vexor?

When you remember what a file does but forget its name or location, Vexor finds it instantly—no grep patterns or directory traversal needed.

Designed for both humans and AI coding assistants, enabling semantic file discovery in autonomous agent workflows.

Install

Download standalone binary from releases (no Python required), or:

pip install vexor  # also works with pipx, uv

Quick Start

0. Guided Setup (Recommended)

vexor init

The wizard also runs automatically on first use when no config exists.

1. Search

vexor "api client config"  # defaults to search current directory
# or explicit path:
vexor search "api client config" --path ~/projects/demo --top 5
# in-memory search only:
vexor search "api client config" --no-cache 

Vexor auto-indexes on first search. Example output:

Vexor semantic file search results
──────────────────────────────────
#   Similarity   File path                       Lines   Preview
1   0.923        ./src/config_loader.py          -       config loader entrypoint
2   0.871        ./src/utils/config_parse.py     -       parse config helpers
3   0.809        ./tests/test_config_loader.py   -       tests for config loader

2. Explicit Index (Optional)

vexor index  # indexes current directory
# or explicit path:
vexor index --path ~/projects/demo --mode code

Useful for CI warmup or when

auto_index

Desktop App (Experimental)

The desktop app is experimental and not actively maintained. It may be unstable. For production use, prefer the CLI.

Download the desktop app from releases.

Python API

Vexor can also be imported and used directly from Python:

from vexor import index, search

index(path=".", mode="head")
response = search("config loader", path=".", mode="name")

for hit in response.results:
    print(hit.path, hit.score)

By default it reads

~/.vexor/config.json

docs/api/python.md

AI Agent Skill

This repo includes a skill for AI agents to use Vexor effectively:

vexor install --skills claude  # Claude Code
vexor install --skills codex   # Codex

Skill source:

plugins/vexor/skills/vexor-cli

Configuration

vexor config --set-provider openai          # default; also supports gemini/custom/local
vexor config --set-model text-embedding-3-small
vexor config --set-batch-size 0             # 0 = single request
vexor config --set-embed-concurrency 4       # parallel embedding requests
vexor config --set-extract-concurrency 4     # parallel file extraction workers
vexor config --set-extract-backend auto      # auto|thread|process (default: auto)
vexor config --set-auto-index true          # auto-index before search (default)
vexor config --rerank bm25                  # optional BM25 rerank for top-k results
vexor config --rerank flashrank             # FlashRank rerank (requires optional extra)
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