How the Agent-Reach Doctor Command Detects and Reports Active Backends per Platform

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

The doctor command in Agent-Reach serves as a comprehensive health diagnostic that verifies which backend tools power each supported platform. Understanding how it detects and reports the active backend per platform helps developers debug integration mismatches and confirm which CLI executables are driving their automation workflows.

The Four-Step Detection Pipeline

The backend detection process follows a structured pipeline that spans from individual channel implementations to the central doctor aggregator.

1. Probing Candidates via check()

Each platform channel implements a check() method that probes every possible backend for that platform. For instance, the Twitter channel examines candidates such as twitter-cli, OpenCLI, and bird CLI. The method iterates through ordered_backends(config), testing each candidate's availability and health status.

2. Recording the First Healthy Backend

During the iteration, the channel sets self.active_backend to the first candidate that returns a healthy status ("ok" or "warn"). In agent_reach/channels/twitter.py, this occurs at lines 43-47 inside the findings loop:

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

            return status, message

If no backend responds successfully, self.active_backend remains None.

3. Extracting the Attribute in doctor.check_all

After ch.check(config) completes, the doctor module retrieves the active backend using safe attribute access. In agent_reach/doctor.py (lines 21-23), the code extracts the value:

status, message = ch.check(config)
active = getattr(ch, "active_backend", None)  # safely fetch active backend

This pattern ensures compatibility even if a channel subclass has not yet implemented the attribute.

4. Aggregating Results for the Report

The extracted value is stored under the "active_backend" key in the per-channel results dictionary (lines 27-34 in agent_reach/doctor.py). This data structure powers the final rendered report, displaying which concrete tool (e.g., twitter-cli, yt-dlp, OpenCLI) is currently powering each platform integration.

Code Implementation Walkthrough

The active_backend attribute is defined in the base class at agent_reach/channels/base.py, ensuring all channel instances inherit the field. Individual channels override the check() method to implement platform-specific probing logic.

Here is the simplified internal logic for a channel detecting its active backend:

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 if no healthy backend found

The doctor module then aggregates these values:

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

cfg = Config()                     # loads user configuration

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

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

How to View Active Backends

Command Line Interface

Running the doctor command from the CLI displays active backends in a colored, human-readable 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

You can also access the data programmatically for custom reporting:

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

cfg = Config()
results = check_all(cfg)

# Access specific backend

twitter_backend = results["twitter"]["active_backend"]

# Generate formatted report

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

print(report)

Summary

• Each channel probes multiple backends via the check() method, testing candidates like twitter-cli or yt-dlp in order of preference.
• The first healthy backend is stored in self.active_backend inside the channel instance, as implemented in agent_reach/channels/twitter.py (lines 43-47).
• The doctor extracts this value using getattr(ch, "active_backend", None) in agent_reach/doctor.py (lines 21-23) after invoking the health check.
• Results are aggregated into a dictionary under the "active_backend" key (lines 27-34) and rendered in the final report.
• Key files include agent_reach/doctor.py for aggregation, agent_reach/channels/base.py for the attribute definition, and individual channel files (e.g., twitter.py, youtube.py) for platform-specific probing logic.

Frequently Asked Questions

What happens if no backend is available for a platform?

If all candidate backends fail their health checks, the channel leaves self.active_backend as None. The doctor command reports this absence, indicating that no functional backend was detected for that platform.

Where is the active_backend attribute defined?

The attribute is defined in agent_reach/channels/base.py on the base Channel class, ensuring all platform channels inherit the field. Individual channels set the value during their check() method execution.

Can I force a specific backend to be marked as active?

The detection relies on the actual health probe results from check(). To influence selection, configure the backend priority in your Agent-Reach configuration, as the ordered_backends(config) method typically respects user-defined preferences when iterating candidates.

How does the doctor command handle channels without a check() method?

The doctor iterates over all registered channels from agent_reach/channels/__init__.py. If a channel lacks a check() implementation or the active_backend attribute, the getattr(ch, "active_backend", None) call safely returns None, and the report reflects that no active backend was detected.

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