Agent Reach
Set up and diagnose multi-platform internet access for shell-capable AI assistants.
Per-dimension scores and reasoning
Evidence shows default install does not modify system, requires explicit --system; cookies stored locally with 600 permissions; dry-run and uninstall available. But dependency security only claims open source, no audit or version pinning; external effects have account ban risk warning but no mitigation.
Docs and tests consistent, channel registry has contract tests; but dependency availability relies on external tools (yt-dlp, bili-cli) without version pinning or mirrors; failure messages have doctor diagnostics but no specific error handling examples.
Targets multiple agents and platforms, scenarios clear; capability boundaries clear (zero-config vs config required); trigger words explicit (install/update/configure); environment fit has local/server distinction but no detailed Windows/macOS instructions.
Information architecture clear with README, docs, guides; install notes detailed; naming stable (agent-reach); examples and FAQ present; known limitations documented; MIT license; version number exists but no changelog; maintenance responsibility declared but no contribution guide.
Output usability high, provides structured results; marginal value high, solves Agent internet access pain point; cost-benefit good, free and open source, but external services may incur costs.
Claims supported by tests, but no test results provided; cross-source corroboration insufficient, relies on single repo; fact/inference separation, docs distinguish current selection and design philosophy.
- External tools (yt-dlp, bili-cli) may become unavailable, requiring regular updates;
- Cookie usage carries account ban risk, recommend using secondary accounts;
- No dependency version pinning, supply chain security needs attention.
What does this agent do, and when should you use it?
Agent Reach is a Python 3.10+ command-line capability layer that installs, configures, and diagnoses internet-access tooling for AI assistants that can run shell commands. Rather than wrapping platform data access behind its own API, it selects usable backends from ordered channel routes and has the agent invoke upstream tools directly. Its documented channels include web pages, YouTube, RSS, GitHub, X, Reddit, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, V2EX, Xueqiu, and web-scale semantic search. Installation can configure Exa through MCP, register a SKILL.md, and use agent-reach doctor to report each channel’s active backend and remediation guidance. It fits users who want one command-line agent environment for public web content, video transcripts, code-hosting content, and selected login-gated social platforms.
When agent-reach install runs, the project installs the agent-reach CLI and detects or installs Node.js, gh CLI, and mcporter; the README also says it connects Exa through mcporter MCP without an API key and registers a SKILL.md in the agent’s skills directory. channels/web.py uses Jina Reader for web pages, youtube.py uses yt-dlp for transcripts, rss.py uses feedparser for RSS/Atom, and github.py uses gh CLI for GitHub reading and search. doctor actively probes channel candidates in order, including twitter-cli, OpenCLI, rdt-cli, bili-cli, xiaohongshu-mcp, and linkedin-scraper-mcp, then reports the selected backend or a repair prescription. Search, reading, and extraction are performed by the agent calling those upstream tools directly, not by a unified Agent Reach data API.
- A Claude Code developer wants quick access to public GitHub repositories and search, then authenticated access to private repositories, issues, pull requests, or forks.
- A researcher needs to extract YouTube subtitles, search videos, or turn ordinary web pages into readable content with Jina Reader.
- An operator working on a desktop with an existing login session needs to search or read X, Reddit, Facebook, Instagram, or Xiaohongshu.
- A user tracking RSS or Atom sources wants an agent to read feeds through feedparser.
- A team deploying command-line agents on workstations or servers needs agent-reach doctor to check channel availability and backend selection.
What are this agent's strengths and limitations?
- It models each channel as an ordered primary-and-fallback route and has agent-reach doctor actively test candidates instead of merely checking whether a command exists.
- It brings web reading, YouTube transcripts, RSS, GitHub, and Exa semantic search into one installation and diagnostic workflow for command-line agents.
- It documents distinct paths for platforms requiring login state, including desktop Chrome sessions, manually exported Cookie-Editor cookies, and server-oriented backends.
- It is a capability layer, not a unified reading API: the agent still invokes upstream tools such as Jina Reader, gh, yt-dlp, and OpenCLI.
- X, Reddit, Xiaohongshu, Facebook, and Instagram depend on user login state or cookies, and the README warns that scripted or API access can risk account restrictions or bans.
- Adoption can require Python, Node.js, gh CLI, mcporter, and channel-specific upstream tools; server deployments may also need a proxy.
- Some channels have no zero-configuration route; the README explicitly states that Reddit’s anonymous interface is blocked and a logged-in path is required.
How do you install or deploy this agent?
The documented runtime is Python 3.10+, and the agent needs permission to execute shell commands. Run: agent-reach install --env=auto. For safe mode, run: agent-reach install --env=auto --safe. To preview actions without changes, run: agent-reach install --env=auto --dry-run. The README also documents giving this instruction to Claude Code, OpenClaw, Cursor, or similar agents: “帮我安装 Agent Reach：https://raw.githubusercontent.com/Panniantong/agent-reach/main/docs/install.md”. OpenClaw users must enable exec permissions first, for example with openclaw config set tools.profile "coding", then restart the Gateway and begin a new conversation.
How do you use this agent?
After installation, run agent-reach doctor to inspect channel status, the active backend, and repair guidance. The documented no-extra-configuration paths include Jina Reader for web pages, yt-dlp for YouTube subtitles, bili search for Bilibili, gh repo view owner/repo for public GitHub repositories, and feedparser for RSS. For login-dependent services, ask the agent to configure the relevant platform, such as Twitter, Reddit, or Xiaohongshu. X cookies must be manually exported by the user with Cookie-Editor; direct use of the upstream twitter command also requires TWITTER_AUTH_TOKEN and TWITTER_CT0 to be explicitly set in the current process environment.
How does this agent compare with similar options?
Compared with installing Jina Reader, yt-dlp, gh CLI, feedparser, twitter-cli, or OpenCLI separately, Agent Reach is positioned as the layer that selects, installs, health-checks, and switches among those upstream tools. It does not replace them for the underlying reads.