Creating a Matrix bridge involves connecting the Matrix ecosystem with another chat service, enabling users to communicate across different platforms. In this guide, we'll develop a simple Matrix bridge using Python 3.12 and the Matrix Application Service (AppService) API with Synapse.
- How to develop a Matrix Bridge in Python
Before diving into the development of a Matrix bridge, you'll need to ensure that you have the following prerequisites in place:
- A running Matrix server: You'll need access to a Matrix homeserver, and Synapse is the reference homeserver implementation for Matrix. If you don't have it set up yet, follow the installation instructions provided by the Matrix.org team.
- Python: Ensure that you have Python installed on your system. You can download it from the official Python website, and for the purpose of this guide I am using Python 3.12.
- Familiarity with async/await: A good understanding of Python's asynchronous programming features, specifically
asyncandawait, is required as we'll be dealing with asynchronous APIs. - Basic understanding of the Matrix protocol: Familiarise yourself with the basics of the Matrix protocol, which is the underlying technology for real-time communication.
To get started, you'll need to set up a dedicated project directory and a virtual environment for your bridge. Follow these steps:
# Create a project directory for your Matrix bridge
mkdir my-matrix-bridge
cd my-matrix-bridge
# Create a virtual environment using Python 3.12
python3.12 -m venv venv
# Activate the virtual environment
source venv/bin/activateOnce your virtual environment is activated, you'll want to install the necessary Python packages. For Matrix communication, we'll use matrix-nio, which is a Python Matrix client library that supports AsyncIO.
# Install matrix-nio with end-to-end encryption support
pip install "matrix-nio[e2e]"With the virtual environment set up and the necessary packages installed, you're ready to begin developing your Matrix bridge.
The Matrix Application Service API is a powerful interface that allows third-party services (like the bridge you're about to build) to integrate with Matrix homeservers. It's important to understand how this API works and the concepts of "virtual users" and "namespaces" that you'll be using to map users and rooms between Matrix and the external chat service.
Take some time to read through the Application Service API specification to familiarise yourself with its capabilities and requirements.
To have your bridge communicate with the Matrix homeserver, you must first register it as an Application Service (AppService). This section guides you through creating a registration file for Synapse, which includes necessary details about your bridge and its permissions.
The registration file is a YAML document that Synapse uses to recognise and interact with your AppService. Create a file named bridge-registration.yaml with the following structure:
# bridge-registration.yaml
id: "example-bridge"
url: "http://localhost:5000"
as_token: "YOUR_AS_TOKEN"
hs_token: "YOUR_HS_TOKEN"
sender_localpart: "example-bridge-bot"
namespaces:
users:
- exclusive: true
regex: "@example-bridge_.*:your-homeserver.com"
aliases:
- exclusive: true
regex: "#example-bridge_.*:your-homeserver.com"
rooms: []Replace your-homeserver.com with your Matrix homeserver's domain to ensure the namespaces match correctly.
Here's a breakdown of the keys:
id: A unique identifier for your AppService.url: The callback URL where Synapse will send incoming events.as_token: A token for the AppService to authenticate with Synapse.hs_token: A token for Synapse to authenticate with the AppService.sender_localpart: The localpart for the user ID that the bridge will use to send messages.namespaces: Defines the users, aliases, and rooms that your bridge will manage.
You'll need to generate secure tokens for YOUR_AS_TOKEN and YOUR_HS_TOKEN. These should be unique, random strings. You can use openssl to generate them:
# Generate the AS token
openssl rand -base64 32
# Generate the HS token
openssl rand -base64 32Run the command twice and replace YOUR_AS_TOKEN and YOUR_HS_TOKEN in the bridge-registration.yaml with the generated strings.
Copy the bridge-registration.yaml to Synapse's application services directory. The default location is /etc/matrix-synapse/appservices, but this may vary:
# Copy the registration file to the Synapse directory
sudo cp bridge-registration.yaml /etc/matrix-synapse/appservices/After placing the registration file in the correct directory, restart Synapse to apply the changes - this could be via sudo systemctl restart matrix-synapse or another method like Docker, depending on how you've set up your homeserver.
Check the Synapse logs to ensure that your AppService has been successfully registered. If there are no errors related to the AppService in the logs, your bridge should now be recognised by Synapse and ready to receive events.
With the AppService registered, you can proceed to write the bridge code that will handle the incoming events and establish communication between the Matrix homeserver and your external chat service.
Writing the bridge is a pivotal step in your project. It involves creating the Python script that will act as the intermediary, processing events from the Matrix homeserver and translating them into actions on the external chat service, and vice versa. Let's delve into the details of how to set up your bridge script.
Begin by creating a new Python file named bridge.py. This file will house the core logic of your bridge. Start with the necessary imports and initialise the AsyncClient from matrix-nio, which will handle the communication with the Matrix homeserver:
# bridge.py
import asyncio
from nio import AsyncClient, RoomMessageText, MatrixRoom, LoginResponse
# Configuration details - replace these with your actual details
HOMESERVER_URL = "https://your-homeserver.com"
BRIDGE_USER_ID = "@example-bridge-bot:your-homeserver.com"
BRIDGE_AS_TOKEN = "YOUR_AS_TOKEN"
# Initialise the AsyncClient for the bridge
client = AsyncClient(HOMESERVER_URL, BRIDGE_USER_ID)Now, let's encapsulate the bridge's functionality within a class called MatrixBridge:
class MatrixBridge:
def __init__(self, client):
self.client = client
async def start(self):
# Use the AS token to log in to the homeserver
response = await self.client.login(token=BRIDGE_AS_TOKEN)
if isinstance(response, LoginResponse):
print("Successfully logged into the homeserver!")
else:
print(f"Login failed: {response}")
return
# Register a callback for RoomMessageText events
self.client.add_event_callback(self.on_message, RoomMessageText)
# Keep the client syncing with the homeserver
await self.client.sync_forever(timeout=30000)We need to define how the bridge will handle incoming messages from Matrix rooms. This is where you'll eventually add the logic to relay messages to the external chat service:
async def on_message(self, room: MatrixRoom, event: RoomMessageText):
# Prevent the bridge from responding to its own messages
if event.sender == BRIDGE_USER_ID:
return
print(f"Received message from {event.sender} in {room.room_id}: {event.body}")
# Placeholder for relaying messages to the external service
# For now, we'll send a confirmation message back to the Matrix room
await self.send_message_to_matrix(room.room_id, "Message received and acknowledged!")We also need a method to send messages to Matrix rooms, which will be used to relay messages from the external chat service back to Matrix:
async def send_message_to_matrix(self, room_id: str, message: str):
# Send a text message to the specified Matrix room
await self.client.room_send(
room_id=room_id,
message_type="m.room.message",
content={
"msgtype": "m.text",
"body": message,
}
)To run the bridge, we'll define an asynchronous main function that creates an instance of MatrixBridge and starts the syncing process:
async def main():
bridge = MatrixBridge(client)
await bridge.start()
if __name__ == "__main__":
asyncio.run(main())This setup will log the bridge into the Matrix server and listen for messages in the rooms it's participating in. When a message is received, it prints the message to the console and sends a confirmation message back to the room.
Run your bridge with the following command:
python bridge.pyJoin a Matrix room with your bridge bot and send a message. The bridge should print the message to the console and send a confirmation message back to the room.
To achieve two-way communication between the Matrix ecosystem and an external chat service, you'll need to integrate both systems' APIs. This integration will allow messages to flow from Matrix to the external service and back again. Below, we'll walk through the process of setting up this communication using a hypothetical external chat service API.
First, let's assume that the external chat service provides a Python SDK that we'll refer to as external_chat_sdk. You'll need to install this SDK in your virtual environment:
# Install the external chat service SDK
pip install external_chat_sdkNow, we'll extend our MatrixBridge class to include the setup and event handling for the external chat service:
# bridge.py
# Import the external chat service SDK
from external_chat_sdk import ExternalChatClient, ExternalMessageEvent
class MatrixBridge:
def __init__(self, client, external_api_key):
self.client = client
self.external_client = ExternalChatClient(api_key=external_api_key)
# Register event callback for incoming messages from the external service
self.external_client.on_message(self.on_external_message)
async def on_external_message(self, event: ExternalMessageEvent):
# Convert the external message to a Matrix message format
matrix_room_id = self.map_external_room_to_matrix(event.room_id)
await self.send_message_to_matrix(matrix_room_id, event.sender, event.text)
def map_external_room_to_matrix(self, external_room_id):
# Implement your logic to map the external room ID to a Matrix room ID
# This could involve a lookup in a database or a predefined mapping
return f"!mapped_room_id:your-homeserver.com"
# ... (rest of the existing MatrixBridge methods)In the __init__ method, we initialise the external chat client and set up an event callback for incoming messages. The on_external_message method is responsible for handling these messages and sending them to the appropriate Matrix room.
Next, we'll handle messages coming from Matrix and relay them to the external chat service. We'll modify the on_message method within the MatrixBridge class:
class MatrixBridge:
# ... (existing methods)
async def on_message(self, room: MatrixRoom, event: RoomMessageText):
# Prevent the bridge from responding to its own messages
if event.sender == self.client.user_id:
return
# Map the Matrix room ID to the external room ID
external_room_id = self.map_matrix_room_to_external(room.room_id)
# Send the message to the external chat service
await self.external_client.send_message(external_room_id, event.body)
def map_matrix_room_to_external(self, matrix_room_id):
# Implement your logic to map the Matrix room ID to an external room ID
# This could involve a lookup in a database or a predefined mapping
return "mapped_external_room_id"
# ... (rest of the existing MatrixBridge methods)In the modified on_message method, we map the Matrix room ID to the corresponding external room ID and send the message through the external chat client.
Finally, we'll need to ensure that both the Matrix client and the external chat client are running. We'll modify the main function to start both services:
async def main():
bridge = MatrixBridge(client, "your_external_api_key")
# Start the Matrix client
matrix_client_task = asyncio.create_task(bridge.start())
# Start the external chat client
external_client_task = asyncio.create_task(bridge.external_client.connect())
# Run both clients concurrently
await asyncio.gather(matrix_client_task, external_client_task)
if __name__ == "__main__":
asyncio.run(main())With these changes, your bridge will now be able to handle two-way communication between Matrix and the external chat service. Messages sent in a Matrix room will be relayed to the corresponding room in the external service, and vice versa.
Replicating users over the bridge is a crucial step in ensuring that conversations flow naturally between the Matrix ecosystem and the external chat service. This involves creating "virtual" Matrix users that represent users from the external service within Matrix. Here's how you can implement user replication in your bridge.
First, we need to define a method to register virtual users with the Matrix homeserver. These virtual users will correspond to the users on the external chat service.
# bridge.py
# ... (previous code)
class MatrixBridge:
# ...
async def register_virtual_user(self, external_username):
# Generate a Matrix user ID for the external user
matrix_user_id = f"@{external_username}:your-homeserver.com"
# Check if the virtual user already exists on the homeserver
response = await self.client.register(
username=external_username,
password=None, # No password needed as these are virtual users
admin=False, # Virtual users should not have admin rights
kind="user" # This is a regular user account
)
if isinstance(response, LoginResponse):
print(f"Virtual user {matrix_user_id} registered successfully.")
else:
print(f"Failed to register virtual user {matrix_user_id}: {response}")
return matrix_user_id
# ...
# ... (rest of the code)In the register_virtual_user method, we're using the register coroutine of the AsyncClient to create a new user on the Matrix homeserver. We're assuming that the external service provides a unique username for each user, which we use to create the Matrix user ID.
Next, we need to handle incoming messages from the external chat service and send them as the virtual Matrix users we've registered.
# bridge.py
# ... (previous code)
class MatrixBridge:
# ...
async def on_external_message(self, event):
# Map the external username to a Matrix user ID
matrix_user_id = await self.register_virtual_user(event.sender_username)
# Find or create a Matrix room to send the message to
room_id = await self.get_or_create_matrix_room(event.chat_room_id)
# Send the message as the virtual Matrix user
await self.send_message_as_user(room_id, matrix_user_id, event.text)
async def send_message_as_user(self, room_id, user_id, text):
# Impersonate the virtual user
await self.client.impersonate_user(user_id)
# Send the message
await self.client.room_send(
room_id=room_id,
message_type="m.room.message",
content={
"msgtype": "m.text",
"body": text,
}
)
# Stop impersonating the user
await self.client.impersonate_user(None)
# ...
# ... (rest of the code)In the on_external_message method, we're calling register_virtual_user to ensure that a corresponding Matrix user exists for the external user sending the message. We then call send_message_as_user to send the message to the appropriate Matrix room as the virtual user.
Optionally, you might want to replicate user profile information, such as display names and avatars, from the external service to Matrix. Here's an example of how you might set a virtual user's display name:
# bridge.py
# ... (previous code)
class MatrixBridge:
# ...
async def set_virtual_user_profile(self, user_id, display_name, avatar_url=None):
# Impersonate the virtual user
await self.client.impersonate_user(user_id)
# Set the user's display name
await self.client.set_displayname(display_name)
# Optionally, set the user's avatar if an URL is provided
if avatar_url:
await self.client.set_avatar_url(avatar_url)
# Stop impersonating the user
await self.client.impersonate_user(None)
# ...
# ... (rest of the code)In the set_virtual_user_profile method, we're using the set_displayname and set_avatar_url coroutines of the AsyncClient to set the virtual user's profile information.
When building a Matrix bridge, managing the state and metadata is vital for ensuring a smooth and consistent experience across both the Matrix ecosystem and the external chat service. This includes tracking user presence, room membership, and any custom data that needs to be synchronised between the two platforms.
To keep the bridge aware of the room state, you need to handle state events such as room membership changes. Here's an example of how you might track room members:
# bridge.py
# ... (previous code)
class MatrixBridge:
# ...
async def on_room_member(self, room: MatrixRoom, event):
# This event is triggered whenever a room member's state changes
if event.membership == "join":
print(f"{event.state_key} joined {room.room_id}")
# Add user to the room's state
self.add_user_to_room_state(room.room_id, event.state_key)
elif event.membership == "leave":
print(f"{event.state_key} left {room.room_id}")
# Remove user from the room's state
self.remove_user_from_room_state(room.room_id, event.state_key)
def add_user_to_room_state(self, room_id, user_id):
# Implement logic to add a user to the room's state
# This could involve updating an in-memory structure or a database entry
pass
def remove_user_from_room_state(self, room_id, user_id):
# Implement logic to remove a user from the room's state
# This could involve updating an in-memory structure or a database entry
pass
# ...
# ... (rest of the code)In this example, we handle on_room_member events to track when users join or leave rooms. The methods add_user_to_room_state and remove_user_from_room_state would contain the logic to update the state accordingly.
Metadata can include any additional information that needs to be tracked, such as room aliases, user profiles, or custom data related to the external chat service. Here's an example of managing room aliases:
# bridge.py
# ... (previous code)
class MatrixBridge:
# ...
async def on_room_alias(self, room: MatrixRoom, event):
# This event is triggered when a room alias is added or removed
if event.alias:
print(f"Alias {event.alias} added to {room.room_id}")
# Store the new alias
self.add_room_alias(room.room_id, event.alias)
else:
print(f"Alias for {room.room_id} removed")
# Remove the alias
self.remove_room_alias(room.room_id)
def add_room_alias(self, room_id, alias):
# Implement logic to store a new room alias
# This could involve a database entry or an in-memory data structure
pass
def remove_room_alias(self, room_id):
# Implement logic to remove a room alias
# This could involve a database entry or an in-memory data structure
pass
# ...
# ... (rest of the code)Here, the on_room_alias event handler is used to manage room aliases. When an alias is added or removed, we update our metadata storage using add_room_alias and remove_room_alias.
If your bridge requires custom data to be synchronised between Matrix and the external chat service, you should implement methods to handle this. For example, you might need to keep track of message edits or reactions:
# bridge.py
# ... (previous code)
class MatrixBridge:
# ...
async def on_message_edit(self, room: MatrixRoom, event):
# This event is triggered when a message is edited
original_event_id = event.replaces_event
new_message_body = event.new_content['body']
print(f"Message {original_event_id} in {room.room_id} edited to: {new_message_body}")
# Update the message in your storage
self.update_message(original_event_id, new_message_body)
def update_message(self, original_event_id, new_message_body):
# Implement logic to update an edited message
# This could involve a database update or an in-memory data structure
pass
# ...
# ... (rest of the code)In this example, on_message_edit is triggered when a message edit event is received. The update_message method would contain the logic to update the stored message content.
Deploying your Matrix bridge is a critical step to ensure it runs reliably and efficiently. Below, we'll cover the necessary steps to deploy your bridge, including containerisation with Docker and setting up a systemd service.
Using Docker to containerise your bridge can simplify deployment and provide an isolated environment for your application. Here's how to create a Docker container for your bridge:
-
Create a Dockerfile
Start by creating a
Dockerfilein the root of your project directory. This file will define the environment in which your bridge will run.# Use an official Python runtime as a base image FROM python:3.12-slim # Set the working directory in the container WORKDIR /usr/src/app # Copy the local code to the container COPY . . # Install any dependencies RUN pip install --no-cache-dir -r requirements.txt # Make port 5000 available to the world outside this container EXPOSE 5000 # Run bridge.py when the container launches CMD ["python", "./bridge.py"]
-
Build the Docker Image
With the
Dockerfilein place, build your Docker image using the following command:docker build -t my-matrix-bridge . -
Run the Docker Container
Once the image is built, run your bridge inside a Docker container:
docker run -d --name my-matrix-bridge-instance -p 5000:5000 my-matrix-bridge
This command runs the bridge in detached mode, maps port 5000 of the container to port 5000 on the host, and names the container instance
my-matrix-bridge-instance.
If you prefer not to use Docker and you're deploying on a Linux server, you can set up a systemd service to manage your bridge.
-
Create a Service File
Create a service file for systemd. Save this file as
/etc/systemd/system/matrix-bridge.service:[Unit] Description=Matrix Bridge Service After=network.target [Service] Type=simple User=<your-user> WorkingDirectory=/path/to/your/bridge ExecStart=/path/to/your/bridge/venv/bin/python /path/to/your/bridge/bridge.py Restart=on-failure [Install] WantedBy=multi-user.target
Replace
<your-user>with the user you want the service to run under, and/path/to/your/bridgewith the actual path to your bridge application. -
Enable and Start the Service
Enable the service to start on boot and then start the service immediately:
sudo systemctl enable matrix-bridge.service sudo systemctl start matrix-bridge.service -
Check the Service Status
To ensure that the service is running correctly, check its status:
sudo systemctl status matrix-bridge.service
After deploying your Matrix bridge, it's crucial to ensure it operates reliably and efficiently. This section covers monitoring your bridge's performance, setting up logging, handling errors, and performing regular maintenance tasks.
Effective logging is essential for monitoring the bridge's activity and diagnosing issues. Python's built-in logging module provides a flexible framework for emitting log messages from your application.
Here's a basic setup for logging within your bridge:
# bridge.py
import logging
# ... (other imports)
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
class MatrixBridge:
# ... (existing methods)
async def on_message(self, room: MatrixRoom, event: RoomMessageText):
# ... (existing code)
logger.info(f"Received message from {event.sender} in {room.room_id}: {event.body}")
# ... (rest of the method)
# ... (rest of the MatrixBridge class)
# ... (rest of the script)This configuration sets the logging level to INFO and defines a format that includes the timestamp, logger name, log level, and message. You can adjust the logging level and format to suit your needs.
Robust error handling is necessary to keep your bridge running smoothly. You should catch and log exceptions where appropriate, and ensure that the bridge can recover from common errors without manual intervention.
Here's an example of adding error handling to the on_message method:
class MatrixBridge:
# ... (existing methods)
async def on_message(self, room: MatrixRoom, event: RoomMessageText):
try:
# ... (existing code)
except Exception as e:
logger.error(f"Error handling message from {event.sender}: {e}")
# Handle the error or retry as appropriate
# ... (rest of the MatrixBridge class)Implementing health checks can help you monitor the bridge's status and quickly detect when something goes wrong. You might set up an HTTP endpoint that returns the bridge's current status or use a third-party service to ping your bridge at regular intervals.
Here's a simple health check endpoint using the aiohttp library:
# bridge.py
from aiohttp import web
# ... (other imports)
async def health_check(request):
# Perform necessary health checks here
return web.Response(text="OK")
app = web.Application()
app.router.add_get('/health', health_check)
# ... (rest of the script)
if __name__ == "__main__":
# ... (existing code to start the bridge)
# Start the web server for health checks
web.run_app(app, port=8080)Schedule regular maintenance for your bridge to keep it up-to-date with the latest security patches and feature updates. This includes updating dependencies, the Python runtime, and any other components your bridge relies on.
Here's an example of how you might update your dependencies:
# Activate your virtual environment
source venv/bin/activate
# Update all installed packages
pip install --upgrade -r requirements.txtConsider integrating monitoring tools like Prometheus for metrics collection, Grafana for dashboards, or Sentry for real-time error tracking. These tools can provide insights into your bridge's performance and alert you to issues as they arise.
For example, to integrate Prometheus metrics, you could use the prometheus_client library:
# bridge.py
from prometheus_client import start_http_server, Summary
# ... (other imports)
# Create a metric to track time spent processing messages and the number of messages processed
PROCESSING_TIME = Summary('bridge_processing_seconds', 'Time spent processing messages')
class MatrixBridge:
# ... (existing methods)
@PROCESSING_TIME.time()
async def on_message(self, room: MatrixRoom, event: RoomMessageText):
# ... (existing code for processing messages)
# ... (rest of the script)
if __name__ == "__main__":
# Start up the server to expose the metrics.
start_http_server(8000)
# ... (existing code to start the bridge)Congratulations on reaching the end of this guide on developing a Matrix bridge in Python! By now, you should have a functional bridge that facilitates communication between the Matrix ecosystem and an external chat service. This bridge represents a significant step towards creating a more interconnected and open messaging environment.
While this guide gives you a solid foundation, there's always room for improvement and expansion. Here are some suggestions for next steps:
- Enhance Error Handling: Improve your bridge's robustness by implementing more comprehensive error handling and recovery mechanisms.
- Optimise Performance: Profile and optimise your bridge's performance to handle larger volumes of traffic and more complex operations.
- Expand Features: Consider adding additional features such as message edits, reactions, and more sophisticated user profile synchronisation.
- Improve Security: Ensure that your bridge has strong security measures in place, such as encrypted communication and secure token management.
- Community Feedback: Engage with the community of users and developers to gather feedback, address issues, and guide the future development of your bridge.