Plugins | Solace Agent Mesh
Description: Plugins provide a mechanism to extend the functionality of Agent Mesh in a modular, shareable, and reusable way. The current plugin ecosystem includes agents, gateways, and specialized integrations.

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# Plugins

Plugins provide a mechanism to extend the functionality of Agent Mesh in a modular, shareable, and reusable way. The current plugin ecosystem includes agents, gateways, and specialized integrations.

In one sentence

Plugins are modular Python packages that extend Solace the capabilities of Agent Mesh through agents, gateways, and specialized integrations.

Plugins are packaged as Python modules that can be installed using various package managers (`pip`, `uv`, `poetry`, `conda`). They integrate seamlessly with the A2A protocol and can provide:

- Agent Plugins: Specialized agents with domain-specific capabilities
- Gateway Plugins: New interface types for external system integration
- Custom Plugins: Custom integrations such as HR providers.

All plugin interactions (create, build, add) are managed through the Agent Mesh CLI.

info

Run `sam plugin --help` to see the list of available commands for plugins.

### Official Core Plugins​

Agent Mesh comes with a set of official core plugins that can be used to extend the functionality of the system. You can find the repository of the official core plugins here 🔗.

For more information about how to use the official core plugins, see Use Plugins.

## Create a Plugin​

To get started, install the Agent Mesh CLI and run the following command:

    solace-agent-mesh plugin create <plugin-name>

Follow the prompts to create a new plugin. A plugin can be one of the following types:

- Agent Plugin: Contains custom agents that can be used in a Agent Mesh project.
- Gateway Plugin: Contains custom gateways that can be used in a Agent Mesh project.
- Custom Plugin: Contains custom integrations such as HR providers or other specialized functionality.

The Agent Mesh CLI creates a directory with the provided name and the following structure:

    plugin-name/
    ├─ config.yaml
    ├─ src/
    │  ├─ __init__.py
    │  ├─ [...Other type specific python files]
    ├─ .gitignore
    ├─ pyproject.toml
    ├─ README.md

- The `src` directory contains the python source code.
- The `config.yaml` file holds the configuration for the plugin, and how to be used in a Agent Mesh application.

Once the plugin is created, you can start customizing the config.yaml or the python files.

### Build the Plugin​

Building the plugin creates a Python wheel package that can be installed using `pip` or other package managers.

Python `build` package must be installed already since `sam plugin build` command uses `build` package, if not, run `pip install build`.

To build the plugin, run the following Agent Mesh CLI command:

    solace-agent-mesh plugin build

The plugin uses the standard `pyproject.toml` file to build the package.

### Share the Plugin​

To share the plugin, you can upload the wheel package to a package repository or share the wheel package directly, or any other valid way to share a `pyproject` project.

## Use a Plugin​

To use a plugin in your project, use the `plugin add` command, which performs two steps under-the-hood:

- Locates the plugin or installs the plugin package using a Python package manager (like `pip` or `uv`)
- Creates a component instance based on the plugin

    solace-agent-mesh plugin add <COMPONENT_NAME> --plugin <PLUGIN_NAME>

where:

`<COMPONENT_NAME>` is the name you choose for the component instance in your project.

`<PLUGIN_NAME>`, you can use:

- Name of the plugin as published to a package manager like `pypi`, for example `my-plugin`
- Name of the plugin that has been already installed into your Python environment.
- A local path to the plugin directory, for example `./my-plugin`
- A path to a wheel package, for example `./my-plugin/dist/my_plugin-0.1.0-py3-none-any.whl`
- A URL to a git repository, for example `git+https://github.com/<USERNAME>/<REPOSITORY>`
  - If the plugin is in a subdirectory of the repository, you can specify the subdirectory using the `git+https://github.com/<USERNAME>/<REPOSITORY>#subdirectory=<PLUGIN_NAME>` syntax.

The CLI handles both steps automatically, or you can manage the plugin installation yourself using your preferred Python package manager.

tip

You can also customize the python package manager command used to install the plugin by setting the `SAM_PLUGIN_INSTALL_COMMAND` environment variable or passing the `--install-command` option to the `plugin add` command. For example, to use `uv` as the package manager, you can run:

    export SAM_PLUGIN_INSTALL_COMMAND="uv pip install {package}"

or

    solace-agent-mesh plugin add <COMPONENT_NAME> --plugin <PLUGIN_NAME> --install-command "uv pip install {package}"

This command adds the plugin instance configuration to your `configs` directory.

Depending on the plugin, you may need to update the newly added plugin configuration file. Follow the instructions provided by the plugin author for any specific configurations.

## Plugin Catalog Dashboard​

You can manage available plugins with the `plugin catalog` command, which launches a user-friendly interface.

    solace-agent-mesh plugin catalog

## Agent or Plugin: Which To Use?​

In simple terms, plugins of type agent are just packaged agents. However, there are distinct advantages to each approach, and choosing the right one depends on your use case.

Here’s a detailed comparison to help you decide.

Feature | Standalone Agent (`sam add agent`) | Agent Plugin (`sam plugin create`)
--- | --- | ---
Creation | A single command creates a configuration file in your project. | Creates a complete, standard Python project structure.
Structure | Consists of a YAML configuration file and associated Python tool files within a Agent Mesh project. | A self-contained Python package with `pyproject.toml`, a `src` directory, and configuration templates.
Packaging | Not packaged. It exists as a component within a larger Agent Mesh project. | Packaged into a standard Python wheel (`.whl`) file using `sam plugin build`.
Distribution | Shared by copying files or sharing the entire project. | Easily distributed as a wheel file, via a Git repository, or published to a package index like PyPI.
Reusability | Primarily for use within the project where it was created. | Designed for high reusability across different projects, teams, and communities.
Installation | No installation needed. The agent is configured and run as part of the main project. | Installed into the Python environment using `sam plugin add`, which handles the package installation.
Versioning | Versioned along with the main project. | Can be versioned independently according to Python packaging standards (e.g., `v0.1.0`, `v0.2.0`).
Development | Simple and direct. Edit files and run. Ideal for rapid prototyping. | Involves a build/install cycle. Better for structured, long-term development.

### When To Use a Standalone Agent​

Create a standalone agent when:

- You need to quickly test an idea or build a proof-of-concept.
- The agent is tightly coupled to a single project and is not intended for reuse.
- You want the most straightforward path to adding a simple agent without the overhead of a full package structure.

### When To Use an Agent Plugin​

Create an agent as a plugin when:

- You plan to use the same agent in multiple projects.
- You want to share your agent with other developers, teams, or the open-source community.
- You are building a robust, production-ready agent that benefits from a formal package structure, dependency management, and versioning.
- You are building a collection of standardized agents for your organization.

### Recommendation​

The choice of how to build your agent depends on your goals and the requirements of your project:

- Standalone Agents should be viewed as tactical tools for rapid, isolated prototyping. They serve immediate, project-specific needs but do not contribute to a scalable, long-term asset library.

- Agent Plugins are the foundation for building a robust, governable, and reusable AI ecosystem. This model treats AI capabilities as enterprise assets, promoting standardization, reducing redundant development costs, and accelerating innovation across the organization. For any capability intended for broader use or long-term value, the plugin framework is the mandated path to maximize return on investment and ensure architectural integrity.