| Name | multi-swarm JSON |
| Version |
1.0.1
JSON |
| download |
| home_page | None |
| Summary | A framework for creating collaborative AI agent swarms |
| upload_time | 2025-01-31 20:35:41 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2024 Bart Van Spitaels
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
ai
agents
llm
claude
gemini
multi-agent
rag
docker
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
anthropic
google-generativeai
python-dotenv
pydantic
docker
sentence-transformers
faiss-cpu
transformers
torch
numpy
pytest
pytest-asyncio
pytest-cov
black
isort
mypy
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Multi-Swarm Framework
[](https://badge.fury.io/py/multi-swarm)
[](https://github.com/bartvanspitaels99/multi-swarm/actions/workflows/ci.yml)
[](https://codecov.io/gh/bartvanspitaels99/multi-swarm)
[](https://pypi.org/project/multi-swarm/)
[](https://opensource.org/licenses/MIT)
A powerful framework for creating collaborative AI agent swarms, enabling complex task completion through coordinated agent interactions.
## Features
- Create specialized AI agents with distinct roles and capabilities
- Configure communication flows between agents
- Manage shared resources and knowledge
- Support for multiple LLM providers (Claude and Gemini)
- Built-in security and resource management
## Installation
Basic installation:
```bash
pip install multi-swarm
```
For development installation with testing tools:
```bash
pip install multi-swarm[dev]
```
## Environment Setup
1. Set up your environment variables:
```bash
# .env
ANTHROPIC_API_KEY=your_claude_api_key
GOOGLE_API_KEY=your_gemini_api_key
```
2. If using Cursor AI (recommended):
- Copy the `.cursorrules` file to your project's root directory
- This file contains essential instructions for Cursor's Claude agent to better assist with Multi-Swarm development
- The `.cursorrules` file helps maintain consistent agent behavior and framework best practices
## Quick Start
1. Create a custom agent:
```python
from multi_swarm import Agent
class MyAgent(Agent):
def __init__(self):
super().__init__(
name="MyAgent",
description="A custom agent for specific tasks",
instructions="path/to/instructions.md",
tools_folder="path/to/tools",
llm_provider="claude", # or "gemini" - framework automatically selects best model
provider_config={
"model": "claude-3-5-sonnet-latest", # Latest Claude model
"max_tokens": 4096,
"api_version": "2024-03"
},
temperature=0.7
)
```
2. Create and run your agency:
```python
from multi_swarm import Agency
# Initialize agents
agent1 = MyAgent()
agent2 = MyAgent()
# Create agency with communication flows
agency = Agency(
agents=[
agent1, # Entry point for user communication
[agent1, agent2], # agent1 can communicate with agent2
],
shared_instructions="agency_manifesto.md"
)
# Run the agency
agency.run_demo()
```
## LLM Provider Configuration
The framework automatically selects the most appropriate LLM model based on the agent's role:
### Claude Models (Anthropic)
- Default model: `claude-3-5-sonnet-latest`
- API version: `2024-03`
- Used for: Complex reasoning, code generation, and detailed analysis
- Best for agents handling: Research, documentation, code review, planning
### Gemini Models (Google)
- Default model: `gemini-2.0-flash-exp`
- API version: `2024-01`
- Used for: Quick responses, data processing, and technical tasks
- Best for agents handling: Data analysis, API integration, system operations
The framework intelligently switches between providers based on:
- Task complexity
- Required capabilities
- Response time needs
- Cost considerations
## Examples
Check out the `examples` directory for complete implementations:
- Research Assistant Agency
- Development Agency
- Trends Analysis Agency
## Documentation
Full documentation is available at [docs/](docs/).
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
Raw data
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"description": "# Multi-Swarm Framework\r\n\r\n[](https://badge.fury.io/py/multi-swarm)\r\n[](https://github.com/bartvanspitaels99/multi-swarm/actions/workflows/ci.yml)\r\n[](https://codecov.io/gh/bartvanspitaels99/multi-swarm)\r\n[](https://pypi.org/project/multi-swarm/)\r\n[](https://opensource.org/licenses/MIT)\r\n\r\nA powerful framework for creating collaborative AI agent swarms, enabling complex task completion through coordinated agent interactions.\r\n\r\n## Features\r\n\r\n- Create specialized AI agents with distinct roles and capabilities\r\n- Configure communication flows between agents\r\n- Manage shared resources and knowledge\r\n- Support for multiple LLM providers (Claude and Gemini)\r\n- Built-in security and resource management\r\n\r\n## Installation\r\n\r\nBasic installation:\r\n```bash\r\npip install multi-swarm\r\n```\r\n\r\nFor development installation with testing tools:\r\n```bash\r\npip install multi-swarm[dev]\r\n```\r\n\r\n## Environment Setup\r\n\r\n1. Set up your environment variables:\r\n```bash\r\n# .env\r\nANTHROPIC_API_KEY=your_claude_api_key\r\nGOOGLE_API_KEY=your_gemini_api_key\r\n```\r\n\r\n2. If using Cursor AI (recommended):\r\n - Copy the `.cursorrules` file to your project's root directory\r\n - This file contains essential instructions for Cursor's Claude agent to better assist with Multi-Swarm development\r\n - The `.cursorrules` file helps maintain consistent agent behavior and framework best practices\r\n\r\n## Quick Start\r\n\r\n1. Create a custom agent:\r\n```python\r\nfrom multi_swarm import Agent\r\n\r\nclass MyAgent(Agent):\r\n def __init__(self):\r\n super().__init__(\r\n name=\"MyAgent\",\r\n description=\"A custom agent for specific tasks\",\r\n instructions=\"path/to/instructions.md\",\r\n tools_folder=\"path/to/tools\",\r\n llm_provider=\"claude\", # or \"gemini\" - framework automatically selects best model\r\n provider_config={\r\n \"model\": \"claude-3-5-sonnet-latest\", # Latest Claude model\r\n \"max_tokens\": 4096,\r\n \"api_version\": \"2024-03\"\r\n },\r\n temperature=0.7\r\n )\r\n```\r\n\r\n2. Create and run your agency:\r\n```python\r\nfrom multi_swarm import Agency\r\n\r\n# Initialize agents\r\nagent1 = MyAgent()\r\nagent2 = MyAgent()\r\n\r\n# Create agency with communication flows\r\nagency = Agency(\r\n agents=[\r\n agent1, # Entry point for user communication\r\n [agent1, agent2], # agent1 can communicate with agent2\r\n ],\r\n shared_instructions=\"agency_manifesto.md\"\r\n)\r\n\r\n# Run the agency\r\nagency.run_demo()\r\n```\r\n\r\n## LLM Provider Configuration\r\n\r\nThe framework automatically selects the most appropriate LLM model based on the agent's role:\r\n\r\n### Claude Models (Anthropic)\r\n- Default model: `claude-3-5-sonnet-latest`\r\n- API version: `2024-03`\r\n- Used for: Complex reasoning, code generation, and detailed analysis\r\n- Best for agents handling: Research, documentation, code review, planning\r\n\r\n### Gemini Models (Google)\r\n- Default model: `gemini-2.0-flash-exp`\r\n- API version: `2024-01`\r\n- Used for: Quick responses, data processing, and technical tasks\r\n- Best for agents handling: Data analysis, API integration, system operations\r\n\r\nThe framework intelligently switches between providers based on:\r\n- Task complexity\r\n- Required capabilities\r\n- Response time needs\r\n- Cost considerations\r\n\r\n## Examples\r\n\r\nCheck out the `examples` directory for complete implementations:\r\n- Research Assistant Agency\r\n- Development Agency\r\n- Trends Analysis Agency\r\n\r\n## Documentation\r\n\r\nFull documentation is available at [docs/](docs/).\r\n\r\n## Contributing\r\n\r\n1. Fork the repository\r\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\r\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\r\n4. Push to the branch (`git push origin feature/amazing-feature`)\r\n5. Open a Pull Request\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. \r\n",
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