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

> Discover how Agent-Reach's doctor command identifies active backends. It uses platform channel check() methods to find healthy tools, reporting the concrete implementation powering each platform.

- Repository: [Pnant/Agent-Reach](https://github.com/Panniantong/Agent-Reach)
- Tags: how-to-guide
- Published: 2026-06-17

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**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`](https://github.com/Panniantong/Agent-Reach/blob/main/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`](https://github.com/Panniantong/Agent-Reach/blob/main/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`](https://github.com/Panniantong/Agent-Reach/blob/main/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:

```bash
$ 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:

```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:

```python
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`](https://github.com/Panniantong/Agent-Reach/blob/main/agent_reach/doctor.py)**: Collects status from every channel and extracts `active_backend` using `getattr()`
- **[`agent_reach/channels/base.py`](https://github.com/Panniantong/Agent-Reach/blob/main/agent_reach/channels/base.py)**: Defines the `active_backend` attribute on the base Channel class
- **[`agent_reach/channels/twitter.py`](https://github.com/Panniantong/Agent-Reach/blob/main/agent_reach/channels/twitter.py)**: Implements platform-specific probing with lines 43-47 handling the active backend assignment
- **[`agent_reach/channels/youtube.py`](https://github.com/Panniantong/Agent-Reach/blob/main/agent_reach/channels/youtube.py)**: Similar implementation for YouTube-specific backends
- **[`agent_reach/channels/__init__.py`](https://github.com/Panniantong/Agent-Reach/blob/main/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`](https://github.com/Panniantong/Agent-Reach/blob/main/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`](https://github.com/Panniantong/Agent-Reach/blob/main/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`](https://github.com/Panniantong/Agent-Reach/blob/main/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`](https://github.com/Panniantong/Agent-Reach/blob/main/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`](https://github.com/Panniantong/Agent-Reach/blob/main/agent_reach/doctor.py)).