searchts

searchts

Enables AI agents to read any URL by using an escalating open-source unlocker to bypass common bot walls, returning clean Markdown content.

Category
Visit Server

README

searchts

Give your AI agent eyes on the open web. searchts is a Python CLI and library that lets an AI agent read and search the internet, fronted by a fully open-source "unlocker" that gets through common bot-walls without any paid proxy or unlocker service.

License: MIT. Python 3.10+.

Fun fact: "searchts" doesn't officially abbreviate anything. Off the record, it stands for "search this shit". 🥀

Why

AI agents constantly need to read web pages, but the naive way they fetch is trivially blocked by modern anti-bot systems (Cloudflare, PerimeterX, DataDome). Paid unlocker services solve this, but the thing they really charge for is a large pool of clean residential IP addresses. searchts runs on your own machine, from your own connection, at personal volume, so it sidesteps that cost and gets through most of those walls for free.

The unlocker

searchts reads any URL through an escalating ladder and stops at the first tier that returns real content:

  1. curl_cffi: a fetch that impersonates a real Chrome's TLS/JA3 and HTTP2 fingerprint. Beats user-agent and fingerprint filters. Fast, local, private.
  2. Jina Reader: a JavaScript-rendering relay, for pages that only fill in content after running JS.
  3. stealth browser: an undetected headless Chromium (patchright), launched lazily only when the cheaper tiers fail, for live JS / Cloudflare managed challenges.

If every tier is defeated by an interactive CAPTCHA, an optional human-in-the-loop step opens a real browser so you can solve it once and continue. Block detection is phrase-based (not vendor-name based), so legitimate pages that merely embed a bot-sensor script are not falsely rejected. Content is extracted to clean Markdown with trafilatura.

Install

pipx install searchts            # recommended: global, isolated CLI
# or
pip install searchts

# optional extras
pip install "searchts[browser]" && patchright install chromium   # stealth-browser tier
pip install "searchts[mcp]"                                       # MCP server for agents

Quickstart

searchts read https://example.com          # fetch any page as clean Markdown
searchts search "open source vector db"    # multi-provider web search (keyless by default)
searchts transcribe https://youtu.be/...   # transcript of a YouTube/TikTok/Instagram/Reddit video
searchts doctor                            # see what is configured and working

read flags: --json, --backend <tier>, --human (CAPTCHA handoff), --scrub (redact injection). search flags: -n <count>, --json, --provider <name>. Content goes to stdout (pipeable); status to stderr.

Use it from your AI agent

Two ways, both one command:

# 1) MCP: gives the agent always-on read_url + web_search tools
pip install "searchts[mcp]"
searchts mcp install          # prints the wiring, e.g. for Claude Code:
                              #   claude mcp add searchts -- searchts mcp serve

# 2) Slash command: type /searchts <url-or-query> in Claude Code
searchts skill install        # writes ~/.claude/commands/searchts.md

Features

  • Escalating open-source unlocker: curl_cffi, then Jina Reader, then a stealth browser.
  • Multi-provider search with rank fusion: DuckDuckGo (keyless default), plus SearXNG, Exa, Brave, and Tavily when configured; results merged with reciprocal rank fusion and de-duplicated.
  • Video transcription: yt-dlp audio plus Whisper for YouTube, TikTok, Instagram, and Reddit videos.
  • Prompt-injection scrubbing: strips invisible/bidi characters, flags injection indicators, optional redaction, so untrusted page content is safer to feed a model.
  • Per-domain backend memory: remembers which tier worked per domain and tries it first (SEARCHTS_NO_MEMORY=1 to disable).
  • Surfaces: a CLI, an MCP server (read_url, web_search), and a Python library.

Use as a library

from searchts import unlocker
r = unlocker.fetch("https://example.com")
print(r.backend, r.status, r.text)

from searchts.search import search
for hit in search("open source vector db", max_results=5):
    print(hit.title, hit.url)

How it works, and its limits

  • It runs from your own residential IP at personal volume, which is why it needs no paid proxy pool. It is a personal-grade research tool, not a mass-scraping system.
  • Interactive CAPTCHAs (DataDome / Turnstile press-and-hold) are the honest ceiling. Use --human for those.
  • Some platforms (notably Instagram, and YouTube in 2026) may need your browser cookies or fail intermittently; that is platform-side.
  • Anti-bot systems evolve; this is an arms race and the techniques may need occasional updates. Respect each site's terms of service and use responsibly.

Configuration

Search works with no keys (DuckDuckGo). Everything else is optional, via searchts configure or a .env (see .env.example):

  • Search providers: Exa, Brave, Tavily API keys, or a self-hosted SEARXNG_URL, for more and better results.
  • Transcription: a Groq or OpenAI (Whisper) key, plus ffmpeg and yt-dlp.
  • GitHub token for higher rate limits.

Run searchts doctor to check what is configured and working.

Credits

searchts builds on and extends Agent-Reach (MIT), reusing its channel, installer, and diagnostics architecture. The escalating open-source unlocker, multi-provider search with rank fusion, prompt-injection scrubbing, per-domain backend memory, the human-in-the-loop CAPTCHA flow, the video transcript channels, the read_url / web_search MCP tools, and the read / search CLI commands are additions in searchts. Thanks to the original authors.

License

MIT. See LICENSE. Original portions Copyright (c) 2025 Agent Eyes; modifications and additions Copyright (c) 2026 capad-xyz.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured