How the Agent-Reach Doctor Command Detects the Active Backend for Each Platform

The doctor command detects the active backend for each platform by invoking each channel's check() method, which probes all available backends and sets self.active_backend to the first healthy candidate, then aggregates these values into a final health report.

The doctor command in the Panniantong/Agent-Reach repository serves as a comprehensive health diagnostic tool that identifies which concrete CLI tools are currently powering each social media platform. Understanding exactly how it detects active backends helps developers debug connectivity issues and verify configuration across channels including Twitter/X, YouTube, and others.

The Backend Detection Pipeline

The detection process follows a three-phase pipeline where each platform channel independently evaluates its available backends before the doctor aggregates the results.

Step 1: Channel-Level Health Probing

Each platform channel implements a check() method that systematically probes every possible backend for that platform. For example, the Twitter channel examines candidates such as twitter-cli, OpenCLI, and bird CLI, while the YouTube channel checks for yt-dlp and other supported tools.

The probing occurs in agent_reach/channels/twitter.py within the check() method, where the channel iterates through backends returned by self.ordered_backends(config):

def check(self, config=None):
    self.active_backend = None
    findings = []
    for backend in self.ordered_backends(config):
        # probe each candidate [...]

        if result is not None:
            findings.append((backend, *result))

Step 2: Recording the First Healthy Backend

While iterating through findings, the channel selects the first backend reporting a healthy status—either "ok" or "warn"—and assigns it to self.active_backend. If no backends respond successfully, the attribute remains None.

In agent_reach/channels/twitter.py at lines 43-47, this logic appears as:

for wanted in ("ok", "warn"):
    for backend, status, _ in findings:
        if status == wanted:
            self.active_backend = backend   # ← set active backend

            return status, message

The base class in agent_reach/channels/base.py defines the active_backend attribute, ensuring all channels maintain consistent state for the doctor to retrieve.

Step 3: Aggregation in the Doctor Module

After channel checking completes, the doctor module extracts the recorded backend using getattr() and stores it in the results dictionary. In agent_reach/doctor.py at lines 21-23, the code retrieves the attribute:

active = getattr(ch, "active_backend", None)

The value is then stored under the "active_backend" key for that channel's entry at lines 27-34, creating a mapping of platforms to their currently active tools.

Implementation Details by File

Understanding the file structure clarifies how the detection mechanism spans multiple modules.

Channel Implementations

Individual channel files such as agent_reach/channels/twitter.py and agent_reach/channels/youtube.py contain platform-specific logic for probing their respective backend candidates. Each implements the check() method following the pattern of testing backends in priority order and setting self.active_backend upon finding the first healthy response.

Doctor Check Logic

The agent_reach/doctor.py file implements the check_all() function that orchestrates the health check across all registered channels. It imports channel singletons from agent_reach/channels/__init__.py and invokes ch.check(config) for each, then extracts the active_backend attribute to build the comprehensive report.

Practical Usage Examples

You can interact with backend detection both through the command line and programmatically.

CLI Output

Running the doctor command from the terminal displays active backends in a colored report:

$ agent-reach doctor
✅ 装好即用：
  ✅ Twitter/X — Twitter CLI 可用（搜索、读推文…） (当前后端：twitter-cli)
  ✅ YouTube — yt-dlp (当前后端：yt-dlp)
...

Programmatic Access

Access active backend information directly in Python:

from agent_reach.doctor import check_all, format_report
from agent_reach.config import Config

cfg = Config()                     # loads user config

results = check_all(cfg)           # dict of per‑channel status

print(results["twitter"]["active_backend"])   # → "twitter-cli" (or None)

report = format_report(results)    # human‑readable Rich markup

print(report)

Summary

• The doctor command aggregates health data from all platform channels to identify active backends.
• Each channel's check() method probes available backends and sets self.active_backend to the first healthy candidate found in agent_reach/channels/twitter.py and similar files.
• The doctor retrieves this attribute via getattr(ch, "active_backend", None) in agent_reach/doctor.py and includes it in the final report under the "active_backend" key.
• Failed backends result in a None value, indicating no working tool is available for that platform.

Frequently Asked Questions

What happens if no backend is available for a platform?

If all backend candidates fail their health checks, the channel's active_backend attribute remains None. The doctor report will display this as the active backend for that platform, indicating that no functional tool is currently available for operations.

How does the doctor command prioritize which backend to select?

The channel's check() method iterates through backends in a specific order determined by ordered_backends(config), selecting the first candidate that returns either "ok" or "warn" status. This allows preferred tools to take precedence when multiple options are installed on the system.

Can I see which backends were tried but not selected?

The current implementation in agent_reach/doctor.py only exposes the final active_backend value in the report. While channels internally collect findings into a list during probing, the doctor does not expose attempted backends that failed or were skipped in favor of a higher-priority working tool.

Where is the active backend stored after detection?

The active backend is stored as the active_backend attribute on the channel instance itself, defined in the base class at agent_reach/channels/base.py, then extracted by the doctor in agent_reach/doctor.py and placed into the results dictionary under the "active_backend" key for programmatic access.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}