open-swarm 0.1.1748636259

Open-Swarm Update - 20250328

This project is now repurposed due to OpenAI officially supporting the Swarm framework under the new name "openai-agents(-python)".

Open-swarm now utilizes the openai-agents framework for enhanced capabilities, and the MCP logic has been offloaded to the openai-agents framework.

Key focus areas of this open-swarm framework include:

• Blueprints: A blueprint can be converted into an OpenAI-compatible REST endpoint (analogous to /v1/chat/completions, but with agents) and/or into CLI utilities on the shell.
• Config Loader: Blueprints and configuration management form a core aspect of the project.

Installation:

Open-swarm is available via PyPI. To install, run:

pip install open-swarm

Usage:

In development, after cloning the repository (github.com/matthewhand/open-swarm), you can run a blueprint directly with:

uv run blueprints/mcp_demo/blueprint_mcp_demo.py

To run the blueprint with a specific instruction (for example, to list its tools), execute:

uv run blueprints/mcp_demo/blueprint_mcp_demo.py --instruction "list your tools"

Alternatively, you can run the blueprint as an API endpoint using the swarm-api utility:

swarm-api --blueprint mcp_demo

In production, you can use the swarm-cli utility to manage and run blueprints. For example, to add an example blueprint:

swarm-cli add github:matthewhand/open-swarm/blueprints/mcp_demo

This command saves the blueprint to:

~/.swarm/blueprints/mcp_demo/

After adding the blueprint, you can convert it into a standalone CLI utility with:

swarm-cli install mcp_demo

Building a Basic Blueprint & Config File:

You can create your own blueprint to extend open-swarm's capabilities. Here is a walkthrough:

1. Create a Blueprint File:

   • In the blueprints/ directory, create a new Python file, for example blueprints/my_blueprint.py.
   • Define a new class that inherits from BlueprintBase and implement the required abstract methods, such as metadata and create_agents(). For instance:

     from swarm.extensions.blueprint.blueprint_base import BlueprintBase
     
     class MyBlueprint(BlueprintBase):
         @property
         def metadata(self):
             return {
                 "title": "MyBlueprint",
                 "env_vars": [],
                 "required_mcp_servers": [],
                 "max_context_tokens": 8000,
                 "max_context_messages": 50
             }
     
         def create_agents(self):
             # Create and return agents as a dictionary.
             return {"MyAgent": ...}  # Implement your agent creation logic here.
     
     if __name__ == "__main__":
         MyBlueprint.main()
     

2. Create a Configuration File:

   • Create a configuration file (e.g., swarm_config.json) at the root of the project. This file can include settings for LLM models and MCP servers. For example:

     {
       "llm": {
         "default": {
           "provider": "openai",
           "model": "gpt-4",
           "api_key": "your-openai-api-key",
           "base_url": null
         }
       },
       "mcpServers": {
         "mcp_llms_txt_server": {
           "command": "echo",
           "args": [],
           "env": {}
         },
         "everything_server": {
           "command": "echo",
           "args": [],
           "env": {}
         }
       }
     }
     

3. Running Your Blueprint:

   • To run your blueprint in development mode, use:

     uv run blueprints/my_blueprint.py
     

   • Ensure your configuration file is properly loaded by your blueprint (this might require modifications in your blueprint's initialization logic or passing a --config parameter).

Installation & Deployment via swarm-cli:

After creating your blueprint and config file, you can manage it with the swarm-cli utility. For example:

• Adding your blueprint:

  swarm-cli add github:matthewhand/open-swarm/blueprints/my_blueprint
  

• Installing as a standalone CLI utility:

  swarm-cli install my_blueprint
  

Examples:

Blueprint "mcp_demo":

The blueprint located in blueprints/mcp_demo demonstrates a key design principle:

• It creates a primary agent named Sage that leverages the MCP framework to incorporate external capabilities.
• Sage uses another agent, Explorer, as a tool to extend its functionality.

This hierarchical agent design illustrates how blueprints can compose agents that call on subagents as tools. This model serves as a prototype for creating powerful agent-driven workflows and can be deployed both as a REST endpoint and as a CLI tool.

Production Environment:

After installing the package via pip, you can manage blueprints with swarm-cli and launch them as standalone utilities or REST services.

For help with swarm-cli:

swarm-cli --help

For help with swarm-api:

swarm-api --help