How the Agent-Reach Doctor Command Detects Active Backends Per Platform

The doctor command detects active backends by invoking each platform channel's check() method, which probes available backends and stores the first healthy one in self.active_backend, then extracts this attribute via getattr() to report which concrete tool powers each platform.

The doctor command in the Agent-Reach repository provides a unified health check across all supported platform channels. When executed, it programmatically determines which backend tools are available for operations like Twitter/X interactions or YouTube downloads by inspecting the active_backend attribute set during channel health verification.

How the Doctor Command Detects Active Backends

The detection process follows a three-phase pipeline that consolidates health information from every platform channel.

Phase 1: Channel Health Probing via check()

Each platform channel implements a check() method that probes all possible backends for that platform. For example, the Twitter channel tests candidates like twitter-cli, OpenCLI, and bird CLI to determine availability, while the YouTube channel checks for tools like yt-dlp.

Phase 2: Backend Selection and Attribute Assignment

While iterating through backends, the channel records the first healthy candidate. According to the source code in agent_reach/channels/twitter.py (lines 43-47), the channel executes self.active_backend = backend inside the for backend, status, message in findings loop when encountering a status of "ok" or "warn". If no backends respond successfully, the attribute remains None.

Phase 3: Extraction and Reporting

After ch.check(config) completes, the doctor module extracts the stored value. In agent_reach/doctor.py (lines 21-23), the code retrieves the attribute using active = getattr(ch, "active_backend", None). This value is then stored in the results dictionary under the "active_backend" key (lines 27-34) for inclusion in the final diagnostic report.

Platform-Specific Implementation Examples

Different channels implement the same pattern with platform-specific backend candidates.

Twitter/X Channel Detection

The Twitter channel probes multiple CLI tools and sets self.active_backend to the first working option. The implementation iterates through ordered backends and assigns the active one upon receiving a healthy status response, allowing the system to dynamically identify which Twitter CLI tool is installed.

YouTube and Other Platforms

Similarly, the YouTube channel tests tools like yt-dlp, storing the first functional backend in the same active_backend attribute. This pattern is consistent across all channels defined in the repository, with each channel managing its own prioritized list of candidate tools in agent_reach/channels/__init__.py.

Viewing Active Backends via CLI and Code

You can access this detection data both programmatically and through the command line interface.

Command Line Output

When running the diagnostic tool, the report includes the active backend for each platform:

$ 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 the detection results 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)

Internal Channel Logic

The simplified implementation pattern used by channels like Twitter demonstrates the health check workflow:

class TwitterChannel(Channel):
    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))

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

                    return status, message
        # …fallback handling

Key Source Files

The backend detection system spans several critical files in the repository:

• agent_reach/doctor.py: Collects status from every channel and extracts active_backend using getattr()
• agent_reach/channels/base.py: Defines the active_backend attribute on the base Channel class
• agent_reach/channels/twitter.py: Implements platform-specific probing with lines 43-47 handling the active backend assignment
• agent_reach/channels/youtube.py: Similar implementation for YouTube-specific backends
• agent_reach/channels/__init__.py: Registers all channel singletons used by the doctor command

Summary

• The doctor command relies on each channel's check() method to probe candidate backends and identify working tools.
• Channels store the first healthy backend in self.active_backend, prioritizing statuses of "ok" or "warn".
• The doctor module extracts this attribute via getattr(ch, "active_backend", None) after health checks complete.
• Results are stored in a dictionary under the "active_backend" key and rendered in the final diagnostic report.
• This architecture allows Agent-Reach to dynamically identify which concrete CLI tools (like twitter-cli or yt-dlp) are available on the current system.

Frequently Asked Questions

What happens if no backends are available for a platform?

If none of the probed backends return a healthy status, the channel leaves self.active_backend as None. When the doctor command extracts this attribute from agent_reach/doctor.py, it records None in the results dictionary, indicating that no functional backend was detected for that platform.

Can the doctor command detect multiple active backends per platform?

No, the current implementation selects only the first healthy backend. The check() method iterates through candidates in priority order and assigns self.active_backend to the first backend returning "ok" or "warn" status, then exits the selection loop. Subsequent working backends are not recorded.

Where is the active_backend attribute defined?

The attribute is defined in agent_reach/channels/base.py on the base Channel class. Individual channel implementations inherit this attribute and populate it during their check() method execution, as seen in platform-specific files like agent_reach/channels/twitter.py (lines 43-47).

How does the doctor command access channel check results?

The doctor command invokes check_all() which iterates through registered channels in agent_reach/channels/__init__.py. For each channel, it calls ch.check(config) and then extracts the active_backend attribute using getattr(ch, "active_backend", None) before storing it in the results dictionary under the "active_backend" key (lines 21-23 and 27-34 in agent_reach/doctor.py).

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