# Agents Manager
A lightweight Python package for managing multi-agent orchestration. Easily define agents with custom instructions, tools, and models, and orchestrate their interactions seamlessly. Perfect for building modular, collaborative AI systems.
## Features
- Define agents with specific roles and instructions
- Assign models to agents (e.g., OpenAI models)
- Equip agents with tools for performing tasks
- Seamlessly orchestrate interactions between multiple agents
## Supported Models
- OpenAI
- Grok
- DeepSeek
- Anthropic
- Llama
```python
from agents_manager.models import OpenAi, Grok, DeepSeek, Anthropic, Llama
```
## Installation
Install the package via pip:
```sh
pip install agents-manager
```
## Quick Start
```python
from agents_manager import Agent, AgentManager
from agents_manager.models import OpenAi, Grok, DeepSeek, Anthropic, Llama
from dotenv import load_dotenv
load_dotenv()
# Define the OpenAi model
model = OpenAi(name="gpt-4o-mini")
# Define the Grok model
# model = Grok(name="grok-2-latest")
# Define the DeepSeek model
# model = DeepSeek(name="deepseek-chat")
# Define the Anthropic model
# model = Anthropic(
# name="claude-3-5-sonnet-20241022",
# max_tokens= 1024,
# stream=True,
# )
# Define the Llama model
# model = Llama(name="llama3.1-70b")
def multiply(a: int, b: int) -> int:
"""
Multiply two numbers.
"""
return a * b
def transfer_to_agent_3_for_math_calculation() -> Agent:
"""
Transfer to agent 3 for math calculation.
"""
return agent3
def transfer_to_agent_2_for_math_calculation() -> Agent:
"""
Transfer to agent 2 for math calculation.
"""
return agent2
# Define agents
agent3 = Agent(
name="agent3",
instruction="You are a maths teacher, explain properly how you calculated the answer.",
model=model,
tools=[multiply]
)
agent2 = Agent(
name="agent2",
instruction="You are a maths calculator bro",
model=model,
tools=[transfer_to_agent_3_for_math_calculation]
)
agent1 = Agent(
name="agent1",
instruction="You are a helpful assistant",
model=model,
tools=[transfer_to_agent_2_for_math_calculation]
)
# Initialize Agent Manager and run agent
agent_manager = AgentManager()
agent_manager.add_agent(agent1)
response = agent_manager.run_agent("agent1", "What is 459 * 1?")
print(response)
```
## How It Works
1. **Define Agents**: Each agent has a name, a specific role (instruction), and a model.
2. **Assign Tools**: Agents can be assigned tools (functions) to perform tasks.
3. **Create an Agent Manager**: The `AgentManager` manages the orchestration of agents.
4. **Run an Agent**: Start an agent to process a request and interact with other agents as needed.
## Use Cases
- AI-powered automation systems
- Multi-agent chatbots
- Complex workflow orchestration
- Research on AI agent collaboration
## Contributing
Contributions are welcome! Feel free to submit issues and pull requests.
## License
MIT License
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"description": "# Agents Manager\n\nA lightweight Python package for managing multi-agent orchestration. Easily define agents with custom instructions, tools, and models, and orchestrate their interactions seamlessly. Perfect for building modular, collaborative AI systems.\n\n## Features\n\n- Define agents with specific roles and instructions\n- Assign models to agents (e.g., OpenAI models)\n- Equip agents with tools for performing tasks\n- Seamlessly orchestrate interactions between multiple agents\n\n## Supported Models\n\n- OpenAI\n- Grok\n- DeepSeek\n- Anthropic\n- Llama\n\n```python\n\nfrom agents_manager.models import OpenAi, Grok, DeepSeek, Anthropic, Llama\n\n```\n\n## Installation\n\nInstall the package via pip:\n\n```sh\npip install agents-manager\n```\n\n## Quick Start\n\n```python\nfrom agents_manager import Agent, AgentManager\nfrom agents_manager.models import OpenAi, Grok, DeepSeek, Anthropic, Llama\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n# Define the OpenAi model\nmodel = OpenAi(name=\"gpt-4o-mini\")\n\n\n# Define the Grok model\n# model = Grok(name=\"grok-2-latest\")\n\n\n# Define the DeepSeek model\n# model = DeepSeek(name=\"deepseek-chat\")\n\n\n# Define the Anthropic model\n# model = Anthropic(\n# name=\"claude-3-5-sonnet-20241022\",\n# max_tokens= 1024,\n# stream=True,\n# )\n\n# Define the Llama model\n# model = Llama(name=\"llama3.1-70b\")\n\ndef multiply(a: int, b: int) -> int:\n \"\"\"\n Multiply two numbers.\n \"\"\"\n return a * b\n\n\ndef transfer_to_agent_3_for_math_calculation() -> Agent:\n \"\"\"\n Transfer to agent 3 for math calculation.\n \"\"\"\n return agent3\n\n\ndef transfer_to_agent_2_for_math_calculation() -> Agent:\n \"\"\"\n Transfer to agent 2 for math calculation.\n \"\"\"\n return agent2\n\n# Define agents\nagent3 = Agent(\n name=\"agent3\",\n instruction=\"You are a maths teacher, explain properly how you calculated the answer.\",\n model=model,\n tools=[multiply]\n)\n\nagent2 = Agent(\n name=\"agent2\",\n instruction=\"You are a maths calculator bro\",\n model=model,\n tools=[transfer_to_agent_3_for_math_calculation]\n)\n\nagent1 = Agent(\n name=\"agent1\",\n instruction=\"You are a helpful assistant\",\n model=model,\n tools=[transfer_to_agent_2_for_math_calculation]\n)\n\n# Initialize Agent Manager and run agent\nagent_manager = AgentManager()\nagent_manager.add_agent(agent1)\n\nresponse = agent_manager.run_agent(\"agent1\", \"What is 459 * 1?\")\n\nprint(response)\n```\n\n## How It Works\n\n1. **Define Agents**: Each agent has a name, a specific role (instruction), and a model.\n2. **Assign Tools**: Agents can be assigned tools (functions) to perform tasks.\n3. **Create an Agent Manager**: The `AgentManager` manages the orchestration of agents.\n4. **Run an Agent**: Start an agent to process a request and interact with other agents as needed.\n\n## Use Cases\n\n- AI-powered automation systems\n- Multi-agent chatbots\n- Complex workflow orchestration\n- Research on AI agent collaboration\n\n## Contributing\n\nContributions are welcome! Feel free to submit issues and pull requests.\n\n## License\n\nMIT License\n\n",
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