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<p align="center">
<img src="https://nginx.gederico.dynu.net/images/CONNPY-resized.png" alt="App Logo">
</p>
# Connpy
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Connpy is a SSH, SFTP, Telnet, kubectl, and Docker pod connection manager and automation module for Linux, Mac, and Docker.
## Installation
pip install connpy
### Run it in Windows using docker
```
git clone https://github.com/fluzzi/connpy
docker compose -f path/to/folder/docker-compose.yml build
docker compose -f path/to/folder/docker-compose.yml run -it connpy-app
```
## Connection manager
### Privacy Policy
Connpy is committed to protecting your privacy. Our privacy policy explains how we handle user data:
- **Data Access**: Connpy accesses data necessary for managing remote host connections, including server addresses, usernames, and passwords. This data is stored locally on your machine and is not transmitted or shared with any third parties.
- **Data Usage**: User data is used solely for the purpose of managing and automating SSH and Telnet connections.
- **Data Storage**: All connection details are stored locally and securely on your device. We do not store or process this data on our servers.
- **Data Sharing**: We do not share any user data with third parties.
### Google Integration
Connpy integrates with Google services for backup purposes:
- **Configuration Backup**: The app allows users to store their device information in the app configuration. This configuration can be synced with Google services to create backups.
- **Data Access**: Connpy only accesses its own files and does not access any other files on your Google account.
- **Data Usage**: The data is used solely for backup and restore purposes, ensuring that your device information and configurations are safe and recoverable.
- **Data Sharing**: Connpy does not share any user data with third parties, including Google. The backup data is only accessible by the user.
For more detailed information, please read our [Privacy Policy](https://connpy.gederico.dynu.net/fluzzi32/connpy/src/branch/main/PRIVATE_POLICY.md).
### Features
- Manage connections using SSH, SFTP, Telnet, kubectl, and Docker exec.
- Set contexts to manage specific nodes from specific contexts (work/home/clients/etc).
- You can generate profiles and reference them from nodes using @profilename so you don't
need to edit multiple nodes when changing passwords or other information.
- Nodes can be stored on @folder or @subfolder@folder to organize your devices. They can
be referenced using node@subfolder@folder or node@folder.
- If you have too many nodes, get a completion script using: conn config --completion.
Or use fzf by installing pyfzf and running conn config --fzf true.
- Create in bulk, copy, move, export, and import nodes for easy management.
- Run automation scripts on network devices.
- Use AI with a multi-agent system (Engineer/Architect) to manage devices.
Supports any LLM provider via litellm (OpenAI, Anthropic, Google, etc.).
Features streaming responses, interactive chat, and extensible plugin tools.
- Add plugins with your own scripts, and execute them remotely.
- Fully decoupled gRPC Client/Server architecture.
- Unified UI with syntax highlighting and theming.
- Much more!
### Usage:
```
usage: conn [-h] [--add | --del | --mod | --show | --debug] [node|folder] [--sftp]
conn {profile,move,mv,copy,cp,list,ls,bulk,export,import,ai,run,api,plugin,config,sync,context} ...
positional arguments:
node|folder node[@subfolder][@folder]
Connect to specific node or show all matching nodes
[@subfolder][@folder]
Show all available connections globally or in specified path
options:
-h, --help show this help message and exit
-v, --version Show version
-a, --add Add new node[@subfolder][@folder] or [@subfolder]@folder
-r, --del, --rm Delete node[@subfolder][@folder] or [@subfolder]@folder
-e, --mod, --edit Modify node[@subfolder][@folder]
-s, --show Show node[@subfolder][@folder]
-d, --debug Display all conections steps
-t, --sftp Connects using sftp instead of ssh
--service-mode Set the backend service mode (local or remote)
--remote Connect to a remote connpy service via gRPC
--theme UI Output theme (dark, light, or path)
Commands:
profile Manage profiles
move(mv) Move node
copy(cp) Copy node
list(ls) List profiles, nodes or folders
bulk Add nodes in bulk
export Export connection folder to Yaml file
import Import connection folder to config from Yaml file
ai Make request to an AI
run Run scripts or commands on nodes
api Start and stop connpy api
plugin Manage plugins
config Manage app config
sync Sync config with Google
context Manage contexts with regex matching
```
### Manage profiles:
```
usage: conn profile [-h] (--add | --del | --mod | --show) profile
positional arguments:
profile Name of profile to manage
options:
-h, --help show this help message and exit
-a, --add Add new profile
-r, --del, --rm Delete profile
-e, --mod, --edit Modify profile
-s, --show Show profile
```
### Examples:
```
#Add new profile
conn profile --add office-user
#Add new folder
conn --add @office
#Add new subfolder
conn --add @datacenter@office
#Add node to subfolder
conn --add server@datacenter@office
#Add node to folder
conn --add pc@office
#Show node information
conn --show server@datacenter@office
#Connect to nodes
conn pc@office
conn server
#Create and set new context
conn context -a office .*@office
conn context --set office
#Run a command in a node
conn run server ls -la
```
## Plugin Requirements for Connpy
### Remote Plugin Execution
When Connpy operates in remote mode, plugins are executed **transparently on the server**:
- The client automatically downloads the plugin source code (`Parser` class context) to generate the local `argparse` structure and provide autocompletion.
- The execution phase (`Entrypoint` class) is redirected via gRPC streams to execute in the server's memory, ensuring the plugin runs securely against the server's inventory without passing sensitive data to the client.
- You can manage remote plugins using the `--remote` flag (e.g. `connpy plugin --add myplugin script.py --remote`).
### General Structure
- The plugin script must be a Python file.
- Only the following top-level elements are allowed in the plugin script:
- Class definitions
- Function definitions
- Import statements
- The `if __name__ == "__main__":` block for standalone execution
- Pass statements
### Specific Class Requirements
- The plugin script must define specific classes with particular attributes and methods. Each class serves a distinct role within the plugin's architecture:
1. **Class `Parser`**:
- **Purpose**: Handles parsing of command-line arguments.
- **Requirements**:
- Must contain only one method: `__init__`.
- The `__init__` method must initialize at least one attribute:
- `self.parser`: An instance of `argparse.ArgumentParser`.
2. **Class `Entrypoint`**:
- **Purpose**: Acts as the entry point for plugin execution, utilizing parsed arguments and integrating with the main application.
- **Requirements**:
- Must have an `__init__` method that accepts exactly three parameters besides `self`:
- `args`: Arguments passed to the plugin.
- The parser instance (typically `self.parser` from the `Parser` class).
- The Connapp instance to interact with the Connpy app.
3. **Class `Preload`**:
- **Purpose**: Performs any necessary preliminary setup or configuration independent of the main parsing and entry logic.
- **Requirements**:
- Contains at least an `__init__` method that accepts parameter connapp besides `self`.
### Class Dependencies and Combinations
- **Dependencies**:
- `Parser` and `Entrypoint` are interdependent and must both be present if one is included.
- `Preload` is independent and may exist alone or alongside the other classes.
- **Valid Combinations**:
- `Parser` and `Entrypoint` together.
- `Preload` alone.
- All three classes (`Parser`, `Entrypoint`, `Preload`).
### Preload Modifications and Hooks
In the `Preload` class of the plugin system, you have the ability to customize the behavior of existing classes and methods within the application through a robust hooking system. This documentation explains how to use the `modify`, `register_pre_hook`, and `register_post_hook` methods to tailor plugin functionality to your needs.
#### Modifying Classes with `modify`
The `modify` method allows you to alter instances of a class at the time they are created or after their creation. This is particularly useful for setting or modifying configuration settings, altering default behaviors, or adding new functionalities to existing classes without changing the original class definitions.
- **Usage**: Modify a class to include additional configurations or changes
- **Modify Method Signature**:
- `modify(modification_method)`: A function that is invoked with an instance of the class as its argument. This function should perform any modifications directly on this instance.
- **Modification Method Signature**:
- **Arguments**:
- `cls`: This function accepts a single argument, the class instance, which it then modifies.
- **Modifiable Classes**:
- `connapp.config`
- `connapp.node`
- `connapp.nodes`
- `connapp.ai`
- ```python
def modify_config(cls):
# Example modification: adding a new attribute or modifying an existing one
cls.new_attribute = 'New Value'
class Preload:
def __init__(self, connapp):
# Applying modification to the config class instance
connapp.config.modify(modify_config)
```
#### Implementing Method Hooks
There are 2 methods that allows you to define custom logic to be executed before (`register_pre_hook`) or after (`register_post_hook`) the main logic of a method. This is particularly useful for logging, auditing, preprocessing inputs, postprocessing outputs or adding functionalities.
- **Usage**: Register hooks to methods to execute additional logic before or after the main method execution.
- **Registration Methods Signature**:
- `register_pre_hook(pre_hook_method)`: A function that is invoked before the main method is executed. This function should do preprocessing of the arguments.
- `register_post_hook(post_hook_method)`: A function that is invoked after the main method is executed. This function should do postprocessing of the outputs.
- **Method Signatures for Pre-Hooks**
- `pre_hook_method(*args, **kwargs)`
- **Arguments**:
- `*args`, `**kwargs`: The arguments and keyword arguments that will be passed to the method being hooked. The pre-hook function has the opportunity to inspect and modify these arguments before they are passed to the main method.
- **Return**:
- Must return a tuple `(args, kwargs)`, which will be used as the new arguments for the main method. If the original arguments are not modified, the function should return them as received.
- **Method Signatures for Post-Hooks**:
- `post_hook_method(*args, **kwargs)`
- **Arguments**:
- `*args`, `**kwargs`: The arguments and keyword arguments that were passed to the main method.
- `kwargs["result"]`: The value returned by the main method. This allows the post-hook to inspect and even alter the result before it is returned to the original caller.
- **Return**:
- Can return a modified result, which will replace the original result of the main method, or simply return `kwargs["result"]` to return the original method result.
- ```python
def pre_processing_hook(*args, **kwargs):
print("Pre-processing logic here")
# Modify arguments or perform any checks
return args, kwargs # Return modified or unmodified args and kwargs
def post_processing_hook(*args, **kwargs):
print("Post-processing logic here")
# Modify the result or perform any final logging or cleanup
return kwargs["result"] # Return the modified or unmodified result
class Preload:
def __init__(self, connapp):
# Registering a pre-hook
connapp.ai.some_method.register_pre_hook(pre_processing_hook)
# Registering a post-hook
connapp.node.another_method.register_post_hook(post_processing_hook)
```
### Executable Block
- The plugin script can include an executable block:
- `if __name__ == "__main__":`
- This block allows the plugin to be run as a standalone script for testing or independent use.
### Command Completion Support
Plugins can provide intelligent **tab completion** by defining autocompletion logic. There are two supported methods, with the tree-based approach being the most modern and recommended.
#### 1. Tree-based Completion (Recommended)
Define a function called `_connpy_tree` that returns a declarative navigation tree. This method is highly efficient, supports complex state loops, and is very simple to implement for most use cases.
```python
def _connpy_tree(info=None):
nodes = info.get("nodes", [])
return {
"__exclude_used__": True, # Filter out words already typed
"__extra__": nodes, # Suggest nodes at this level
"--format": ["json", "yaml", "table"], # Fixed suggestions
"*": { # Wildcard matches any positional word
"interface1": None,
"interface2": None,
"--verbose": None
}
}
```
- **Keys**: Literal completions (exact matches).
- **`*` Key**: A wildcard that matches any positional word typed by the user.
- **`__extra__`**: A list or a callable `(words) -> list` that adds dynamic suggestions.
- **`__exclude_used__`**: (Boolean) If True, automatically filters out words already present in the command line.
#### 2. Legacy Function-based Completion
For backward compatibility or highly custom logic, you can define `_connpy_completion`.
```python
def _connpy_completion(wordsnumber, words, info=None):
if wordsnumber == 3:
return ["--help", "--verbose", "start", "stop"]
elif wordsnumber == 4 and words[2] == "start":
return info["nodes"] # Suggest node names
return []
```
| Parameter | Description |
|----------------|-------------|
| `wordsnumber` | Integer indicating the total number of words on the command line. For plugins, this typically starts at 3. |
| `words` | A list of tokens (words) already typed. `words[0]` is always the name of the plugin. |
| `info` | A dictionary of structured context data (`nodes`, `folders`, `profiles`, `config`). |
> In this example, if the user types `connpy myplugin start ` and presses Tab, it will suggest node names.
### Handling Unknown Arguments
Plugins can choose to accept and process unknown arguments that are **not explicitly defined** in the parser. To enable this behavior, the plugin must define the following hidden argument in its `Parser` class:
```
self.parser.add_argument(
"--unknown-args",
action="store_true",
default=True,
help=argparse.SUPPRESS
)
```
#### Behavior:
- When this argument is present, Connpy will parse the known arguments and capture any extra (unknown) ones.
- These unknown arguments will be passed to the plugin as `args.unknown_args` inside the `Entrypoint`.
- If the user does not pass any unknown arguments, `args.unknown_args` will contain the default value (`True`, unless overridden).
#### Example:
If a plugin accepts unknown tcpdump flags like this:
```
connpy myplugin -nn -s0
```
And defines the hidden `--unknown-args` flag as shown above, then:
- `args.unknown_args` inside `Entrypoint.__init__()` will be: `['-nn', '-s0']`
> This allows the plugin to receive and process arguments intended for external tools (e.g., `tcpdump`) without argparse raising an error.
#### Note:
If a plugin does **not** define `--unknown-args`, any extra arguments passed will cause argparse to fail with an unrecognized arguments error.
### Script Verification
- The `verify_script` method in `plugins.py` is used to check the plugin script's compliance with these standards.
- Non-compliant scripts will be rejected to ensure consistency and proper functionality within the plugin system.
### Example Script
For a practical example of how to write a compatible plugin script, please refer to the following example:
[Example Plugin Script](https://github.com/fluzzi/awspy)
This script demonstrates the required structure and implementation details according to the plugin system's standards.
## Automation module usage
### Standalone module
```
import connpy
router = connpy.node("uniqueName","ip/host", user="username", password="password")
router.run(["term len 0","show run"])
print(router.output)
hasip = router.test("show ip int brief","1.1.1.1")
if hasip:
print("Router has ip 1.1.1.1")
else:
print("router does not have ip 1.1.1.1")
```
### Using manager configuration
```
import connpy
conf = connpy.configfile()
device = conf.getitem("router@office")
router = connpy.node("unique name", **device, config=conf)
result = router.run("show ip int brief")
print(result)
```
### Running parallel tasks on multiple devices
```
import connpy
conf = connpy.configfile()
#You can get the nodes from the config from a folder and fitlering in it
nodes = conf.getitem("@office", ["router1", "router2", "router3"])
#You can also get each node individually:
nodes = {}
nodes["router1"] = conf.getitem("router1@office")
nodes["router2"] = conf.getitem("router2@office")
nodes["router10"] = conf.getitem("router10@datacenter")
#Also, you can create the nodes manually:
nodes = {}
nodes["router1"] = {"host": "1.1.1.1", "user": "user", "password": "password1"}
nodes["router2"] = {"host": "1.1.1.2", "user": "user", "password": "password2"}
nodes["router3"] = {"host": "1.1.1.2", "user": "user", "password": "password3"}
#Finally you run some tasks on the nodes
mynodes = connpy.nodes(nodes, config = conf)
result = mynodes.test(["show ip int br"], "1.1.1.2")
for i in result:
print("---" + i + "---")
print(result[i])
print()
# Or for one specific node
mynodes.router1.run(["term len 0". "show run"], folder = "/home/user/logs")
```
### Using variables
```
import connpy
config = connpy.configfile()
nodes = config.getitem("@office", ["router1", "router2", "router3"])
commands = []
commands.append("config t")
commands.append("interface lo {id}")
commands.append("ip add {ip} {mask}")
commands.append("end")
variables = {}
variables["router1@office"] = {"ip": "10.57.57.1"}
variables["router2@office"] = {"ip": "10.57.57.2"}
variables["router3@office"] = {"ip": "10.57.57.3"}
variables["__global__"] = {"id": "57"}
variables["__global__"]["mask"] = "255.255.255.255"
expected = "!"
routers = connpy.nodes(nodes, config = config)
routers.run(commands, variables)
routers.test("ping {ip}", expected, variables)
for key in routers.result:
print(key, ' ---> ', ("pass" if routers.result[key] else "fail"))
```
### Using AI
The AI module uses a multi-agent architecture with an **Engineer** (fast execution) and an **Architect** (strategic reasoning). It supports any LLM provider through [litellm](https://github.com/BerriAI/litellm).
```python
import connpy
conf = connpy.configfile()
# Uses models and API keys from config, or override them:
myai = connpy.ai(conf, engineer_model="gemini/gemini-2.5-flash", engineer_api_key="your-key")
result = myai.ask("go to router1 and show me the running configuration")
print(result["response"])
# Streaming is enabled by default for CLI, disable for programmatic use:
result = myai.ask("show interfaces on all routers", stream=False)
print(result["response"])
```
#### AI Plugin Tool Registration
Plugins can extend the AI system by registering custom tools via the `Preload` class:
```python
def _register_my_tools(ai_instance):
tool_def = {
"type": "function",
"function": {
"name": "my_custom_tool",
"description": "Does something useful.",
"parameters": {
"type": "object",
"properties": {"query": {"type": "string"}},
"required": ["query"]
}
}
}
ai_instance.register_ai_tool(
tool_definition=tool_def,
handler=my_handler_function,
target="engineer", # or "architect" or "both"
engineer_prompt="- My tool: does X.",
architect_prompt=" * My tool (my_custom_tool)."
)
class Preload:
def __init__(self, connapp):
connapp.ai.modify(_register_my_tools)
```
## gRPC Service Architecture
Connpy features a completely decoupled gRPC Client/Server architecture. You can run Connpy as a standalone background service and connect to it remotely via the CLI or other clients.
### 1. Start the Server
Start the gRPC service by running:
```bash
connpy api -s 50051
```
The server will handle all configurations, connections, AI sessions, and plugin execution locally on the machine it runs on.
### 2. Connect the Client
Configure your local CLI client to connect to the remote server:
```bash
connpy config --service-mode remote
connpy config --remote-host localhost:50051
```
Once configured, all commands (`connpy node`, `connpy list`, `connpy ai`, etc.) will execute transparently on the remote server via thin-client proxies. You can revert back to standalone execution at any time by running `connpy config --service-mode local`.
### Programmatic Access (gRPC & SOA)
If you wish to build your own application (Web, Desktop, or Scripts) using the Connpy backend, you can use the `ServiceProvider` to interact with either a local or remote service transparently.
```python
import connpy
from connpy.services.provider import ServiceProvider
# Initialize local config
config = connpy.configfile()
# Connect to the remote gRPC service
services = ServiceProvider(
config,
mode="remote",
remote_host="localhost:50051"
)
# Use any service (the logic is identical to local mode)
nodes = services.nodes.list_nodes()
for name in nodes:
print(f"Found node: {name}")
# Run a command remotely via streaming
for chunk in services.execution.run_commands(nodes=["server1"], commands=["uptime"]):
print(chunk["output"], end="")
```
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