Agent Reach: One CLI to Power AI Agents Across the Web

In the age of LLMs, a model’s usefulness is measured by what it can fetch and synthesize. A single AI agent that can read a YouTube transcript, grab the latest tweets, browse GitHub issues, and pull a Reddit discussion feels surprisingly “human”. Yet every platform hides behind its own API, rate limits, login flow, or an IP block that forces developers to write custom scrapers. This is the problem Agent Reach solves.

What is Agent Reach?

Agent Reach is a free, open‑source CLI that automatically wires an AI agent to the internet. It bundles well‑maintained command‑line tools such as:

• bird – free Twitter/X scraping via cookie authentication
• yt‑dlp – robust video metadata + subtitle extraction for YouTube, Bilibili, and 1,800+ sites
• Exa (via mcp‑porter) – semantic web search that requires no API key
• Jina Reader – lightweight article summarization from any URL
• gh CLI – official GitHub commands for public or authenticated use
• feedparser – RSS/Atom feed parsing
• duck‑in‑mcp‑server – TikTok video analysis without login
• mcp‑servers for Xiaohongshu, LinkedIn, and Boss Zhipin – read, search, and post data, all behind local Docker containers

Instead of learning each platform’s quirks, you install Agent Reach once, let it install system dependencies (Node, Go, Docker, etc.), and your agent gains instant, authenticated access.

Why do you need this?

The result is a minimal footprint for your AI agents and a single source of truth for how they access the web.

Quickstart Guide

1. Install the package – pip works on any environment that can run shell commands:

pip install agent-reach

1. Run the installer – you can choose your environment:

# Normal auto mode – installs everything you need
agent-reach install --env=auto

# Safe mode – lists what it would install, for production servers
agent-reach install --env=auto --safe

# Dry run – preview without making changes
agent-reach install --env=auto --dry-run

1. Configure credentials – for cookie‑based sites, export the cookie through the Chrome extension Cookie‑Editor and feed it:

agent-reach configure twitter-cookies "<cookie string>"
agent-reach configure xiaohongshu-cookies "<cookie string>"

1. Use your agent – in any supported agent (Claude Code, Cursor, OpenClaw, etc.), simply send a natural‑language request and Agent Reach will translate it into a shell command:

Agent: "Help me find the transcript for this YouTube video" ->
Agent: agent-reach youtube "https://youtu.be/abcd1234" --dump-json

1. Check health – run a diagnostic sweep:

agent-reach doctor

It reports which channels are healthy, which need a proxy, or which have missing cookies.

Behind the Scenes

Agent Reach is a scaffolding, not a full framework. It installs the binaries and places a SKILL.md file in your agent’s skill directory. When the agent sees a phrase like "what's on this YouTube link", it looks up SKILL.md, finds yt-dlp --dump-json, and runs it. The heavy lifting happens in the upstream tool—Agent Reach merely stitches them together.

Each platform sits in a dedicated directory (e.g., channels/twitter.py) with a check() method that the agent‑reach doctor command calls. If you prefer another scraper, simply drop it in place of bird.py and edit the check() logic to point at your new executable.

Security & Privacy

• Local storage – All cookies and tokens live in ~/.agent-reach/config.yaml with 600 permissions.
• No data leakage – The CLI never sends your credentials over the network.
• Optional proxy configuration – For IP‑protection, run:

  agent-reach configure proxy http://user:pass@ip:port
  

• Safe mode – Lets you decide what system packages to install.
• Dry‑run – Preview changes before applying.

Because every tool is open source, you can audit the code or replace any component for compliance.

Extending Agent Reach

Adding a new channel is as simple as:

1. Create channels/yourservice.py.
2. Implement a check() method that verifies the tool’s presence.
3. Add a line to SKILL.md mapping a natural phrase to the shell command.
4. Commit and open a PR.

The community already has channels for Twitter, Reddit, YouTube, GitHub, Bilibili, Xiaohongshu, TikTok, LinkedIn, and Boss Zhipin, so you’re likely to find something you need.

Real‑World Use Cases

The potential is limited only by your imagination and your agent’s prompt design.

Get Started

Star on GitHub, clone the repo, or just install via pip. If you’re a prompt engineer, add a simple command:

Help me browse this GitHub repo: https://github.com/panniantong/agent-reach

Your agent, backed by Agent Reach, will automatically read the README, list open issues, and pull the latest commit.

Agent Reach is actively maintained: new channels drop in, bugs are fixed, and the community keeps it up to date. Whether you’re experimenting, building a production bot, or just exploring how AI can read the web, Agent Reach is the bridge you need.