Kye Gomez
1 week ago
parent
4650c5d70e
commit
af9bc7e30b
@ -1,200 +0,0 @@
|
|||||||
# Agent Builder
|
|
||||||
|
|
||||||
The Agent Builder is a powerful class that automatically builds and manages swarms of AI agents. It provides a flexible and extensible framework for creating, coordinating, and executing multiple AI agents working together to accomplish complex tasks.
|
|
||||||
|
|
||||||
## Overview
|
|
||||||
|
|
||||||
The Agent Builder uses a boss agent to delegate work and create new specialized agents as needed. It's designed to be production-ready with robust error handling, logging, and configuration options.
|
|
||||||
|
|
||||||
## Architecture
|
|
||||||
|
|
||||||
```mermaid
|
|
||||||
graph TD
|
|
||||||
A[Agent Builder] --> B[Configuration]
|
|
||||||
A --> C[Agent Creation]
|
|
||||||
A --> D[Task Execution]
|
|
||||||
|
|
||||||
B --> B1[Name]
|
|
||||||
B --> B2[Description]
|
|
||||||
B --> B3[Model Settings]
|
|
||||||
|
|
||||||
C --> C1[Agent Pool]
|
|
||||||
C --> C2[Agent Registry]
|
|
||||||
C --> C3[Agent Configuration]
|
|
||||||
|
|
||||||
D --> D1[Task Distribution]
|
|
||||||
D --> D2[Result Collection]
|
|
||||||
D --> D3[Error Handling]
|
|
||||||
|
|
||||||
C1 --> E[Specialized Agents]
|
|
||||||
C2 --> E
|
|
||||||
C3 --> E
|
|
||||||
|
|
||||||
E --> F[Task Execution]
|
|
||||||
F --> G[Results]
|
|
||||||
```
|
|
||||||
|
|
||||||
## Class Structure
|
|
||||||
|
|
||||||
### AgentsBuilder Class
|
|
||||||
|
|
||||||
| Parameter | Type | Default | Description |
|
|
||||||
|-----------|------|---------|-------------|
|
|
||||||
| name | str | "swarm-creator-01" | The name of the swarm |
|
|
||||||
| description | str | "This is a swarm that creates swarms" | A description of the swarm's purpose |
|
|
||||||
| verbose | bool | True | Whether to output detailed logs |
|
|
||||||
| max_loops | int | 1 | Maximum number of execution loops |
|
|
||||||
| model_name | str | "gpt-4o" | The model to use for agent creation |
|
|
||||||
| return_dictionary | bool | True | Whether to return results as a dictionary |
|
|
||||||
| system_prompt | str | BOSS_SYSTEM_PROMPT | The system prompt for the boss agent |
|
|
||||||
|
|
||||||
### Methods
|
|
||||||
|
|
||||||
| Method | Description | Parameters | Returns |
|
|
||||||
|--------|-------------|------------|---------|
|
|
||||||
| run | Run the swarm on a given task | task: str, image_url: str = None, *args, **kwargs | Tuple[List[Agent], int] |
|
|
||||||
| _create_agents | Create necessary agents for a task | task: str, *args, **kwargs | List[Agent] |
|
|
||||||
| build_agent | Build a single agent with specifications | agent_name: str, agent_description: str, agent_system_prompt: str, max_loops: int = 1, model_name: str = "gpt-4o", dynamic_temperature_enabled: bool = True, auto_generate_prompt: bool = False, role: str = "worker", max_tokens: int = 8192, temperature: float = 0.5 | Agent |
|
|
||||||
|
|
||||||
## Enterprise Use Cases
|
|
||||||
|
|
||||||
### 1. Customer Service Automation
|
|
||||||
- Create specialized agents for different aspects of customer service
|
|
||||||
|
|
||||||
- Handle ticket routing, response generation, and escalation
|
|
||||||
|
|
||||||
- Maintain consistent service quality across channels
|
|
||||||
|
|
||||||
### 2. Data Analysis Pipeline
|
|
||||||
- Build agents for data collection, cleaning, analysis, and visualization
|
|
||||||
|
|
||||||
- Automate complex data processing workflows
|
|
||||||
|
|
||||||
- Generate insights and reports automatically
|
|
||||||
|
|
||||||
### 3. Content Creation and Management
|
|
||||||
- Deploy agents for content research, writing, editing, and publishing
|
|
||||||
|
|
||||||
- Maintain brand consistency across content
|
|
||||||
|
|
||||||
- Automate content scheduling and distribution
|
|
||||||
|
|
||||||
### 4. Process Automation
|
|
||||||
- Create agents for workflow automation
|
|
||||||
|
|
||||||
- Handle document processing and routing
|
|
||||||
|
|
||||||
- Manage approval chains and notifications
|
|
||||||
|
|
||||||
### 5. Research and Development
|
|
||||||
- Build agents for literature review, experiment design, and data collection
|
|
||||||
|
|
||||||
- Automate research documentation and reporting
|
|
||||||
|
|
||||||
- Facilitate collaboration between research teams
|
|
||||||
|
|
||||||
## Example Usage
|
|
||||||
|
|
||||||
```python
|
|
||||||
from swarms import AgentsBuilder
|
|
||||||
|
|
||||||
# Initialize the agent builder
|
|
||||||
agents_builder = AgentsBuilder(
|
|
||||||
name="enterprise-automation",
|
|
||||||
description="Enterprise workflow automation swarm",
|
|
||||||
verbose=True
|
|
||||||
)
|
|
||||||
|
|
||||||
# Define a use-case for building agents
|
|
||||||
task = "Develop a swarm of agents to automate the generation of personalized marketing strategies based on customer data and market trends"
|
|
||||||
|
|
||||||
# Run the swarm
|
|
||||||
agents = agents_builder.run(task)
|
|
||||||
|
|
||||||
# Access results
|
|
||||||
print(agents)
|
|
||||||
```
|
|
||||||
|
|
||||||
## Best Practices
|
|
||||||
|
|
||||||
1. **Error Handling**
|
|
||||||
- Always implement proper error handling for agent failures
|
|
||||||
- Use retry mechanisms for transient failures
|
|
||||||
- Log all errors for debugging and monitoring
|
|
||||||
|
|
||||||
2. **Resource Management**
|
|
||||||
- Monitor agent resource usage
|
|
||||||
- Implement rate limiting for API calls
|
|
||||||
- Use connection pooling for database operations
|
|
||||||
|
|
||||||
3. **Security**
|
|
||||||
- Implement proper authentication and authorization
|
|
||||||
- Secure sensitive data and API keys
|
|
||||||
- Follow least privilege principle for agent permissions
|
|
||||||
|
|
||||||
4. **Monitoring and Logging**
|
|
||||||
- Implement comprehensive logging
|
|
||||||
- Monitor agent performance metrics
|
|
||||||
- Set up alerts for critical failures
|
|
||||||
|
|
||||||
5. **Scalability**
|
|
||||||
- Design for horizontal scaling
|
|
||||||
- Implement load balancing
|
|
||||||
- Use distributed systems when needed
|
|
||||||
|
|
||||||
## Integration Patterns
|
|
||||||
|
|
||||||
```mermaid
|
|
||||||
graph LR
|
|
||||||
A[External System] --> B[API Gateway]
|
|
||||||
B --> C[Agent Builder]
|
|
||||||
C --> D[Agent Pool]
|
|
||||||
D --> E[Specialized Agents]
|
|
||||||
E --> F[External Services]
|
|
||||||
|
|
||||||
subgraph "Monitoring"
|
|
||||||
G[Logs]
|
|
||||||
H[Metrics]
|
|
||||||
I[Alerts]
|
|
||||||
end
|
|
||||||
|
|
||||||
C --> G
|
|
||||||
C --> H
|
|
||||||
C --> I
|
|
||||||
```
|
|
||||||
|
|
||||||
## Performance Considerations
|
|
||||||
|
|
||||||
1. **Agent Pool Management**
|
|
||||||
- Implement connection pooling
|
|
||||||
- Use caching for frequently accessed data
|
|
||||||
- Optimize agent creation and destruction
|
|
||||||
|
|
||||||
2. **Task Distribution**
|
|
||||||
- Implement load balancing
|
|
||||||
- Use priority queues for task scheduling
|
|
||||||
- Handle task timeouts and retries
|
|
||||||
|
|
||||||
3. **Resource Optimization**
|
|
||||||
- Monitor memory usage
|
|
||||||
- Implement garbage collection
|
|
||||||
- Use efficient data structures
|
|
||||||
|
|
||||||
## Troubleshooting
|
|
||||||
|
|
||||||
Common issues and solutions:
|
|
||||||
|
|
||||||
1. **Agent Creation Failures**
|
|
||||||
- Check API credentials
|
|
||||||
- Verify model availability
|
|
||||||
- Review system prompts
|
|
||||||
|
|
||||||
2. **Performance Issues**
|
|
||||||
- Monitor resource usage
|
|
||||||
- Check network latency
|
|
||||||
- Review agent configurations
|
|
||||||
|
|
||||||
3. **Integration Problems**
|
|
||||||
- Verify API endpoints
|
|
||||||
- Check authentication
|
|
||||||
- Review data formats
|
|
||||||
@ -1,9 +0,0 @@
|
|||||||
from swarms.structs.agent_builder import AgentsBuilder
|
|
||||||
|
|
||||||
example_task = "Write a blog post about the benefits of using Swarms for AI agents."
|
|
||||||
|
|
||||||
agents_builder = AgentsBuilder()
|
|
||||||
|
|
||||||
agents = agents_builder.run(example_task)
|
|
||||||
|
|
||||||
print(agents)
|
|
||||||
@ -0,0 +1,76 @@
|
|||||||
|
|
||||||
|
BOSS_SYSTEM_PROMPT = """
|
||||||
|
# Swarm Intelligence Orchestrator
|
||||||
|
|
||||||
|
You are the Chief Orchestrator of a sophisticated agent swarm. Your primary responsibility is to analyze tasks and create the optimal team of specialized agents to accomplish complex objectives efficiently.
|
||||||
|
|
||||||
|
## Agent Creation Protocol
|
||||||
|
|
||||||
|
1. **Task Analysis**:
|
||||||
|
- Thoroughly analyze the user's task to identify all required skills, knowledge domains, and subtasks
|
||||||
|
- Break down complex problems into discrete components that can be assigned to specialized agents
|
||||||
|
- Identify potential challenges and edge cases that might require specialized handling
|
||||||
|
|
||||||
|
2. **Agent Design Principles**:
|
||||||
|
- Create highly specialized agents with clearly defined roles and responsibilities
|
||||||
|
- Design each agent with deep expertise in their specific domain
|
||||||
|
- Provide agents with comprehensive and extremely extensive system prompts that include:
|
||||||
|
* Precise definition of their role and scope of responsibility
|
||||||
|
* Detailed methodology for approaching problems in their domain
|
||||||
|
* Specific techniques, frameworks, and mental models to apply
|
||||||
|
* Guidelines for output format and quality standards
|
||||||
|
* Instructions for collaboration with other agents
|
||||||
|
* In-depth examples and scenarios to illustrate expected behavior and decision-making processes
|
||||||
|
* Extensive background information relevant to the tasks they will undertake
|
||||||
|
|
||||||
|
3. **Cognitive Enhancement**:
|
||||||
|
- Equip agents with advanced reasoning frameworks:
|
||||||
|
* First principles thinking to break down complex problems
|
||||||
|
* Systems thinking to understand interconnections
|
||||||
|
* Lateral thinking for creative solutions
|
||||||
|
* Critical thinking to evaluate information quality
|
||||||
|
- Implement specialized thought patterns:
|
||||||
|
* Step-by-step reasoning for complex problems
|
||||||
|
* Hypothesis generation and testing
|
||||||
|
* Counterfactual reasoning to explore alternatives
|
||||||
|
* Analogical reasoning to apply solutions from similar domains
|
||||||
|
|
||||||
|
4. **Swarm Architecture**:
|
||||||
|
- Design optimal agent interaction patterns based on task requirements
|
||||||
|
- Consider hierarchical, networked, or hybrid structures
|
||||||
|
- Establish clear communication protocols between agents
|
||||||
|
- Define escalation paths for handling edge cases
|
||||||
|
|
||||||
|
5. **Agent Specialization Examples**:
|
||||||
|
- Research Agents: Literature review, data gathering, information synthesis
|
||||||
|
- Analysis Agents: Data processing, pattern recognition, insight generation
|
||||||
|
- Creative Agents: Idea generation, content creation, design thinking
|
||||||
|
- Planning Agents: Strategy development, resource allocation, timeline creation
|
||||||
|
- Implementation Agents: Code writing, document drafting, execution planning
|
||||||
|
- Quality Assurance Agents: Testing, validation, error detection
|
||||||
|
- Integration Agents: Combining outputs, ensuring consistency, resolving conflicts
|
||||||
|
|
||||||
|
## Output Format
|
||||||
|
|
||||||
|
For each agent, provide:
|
||||||
|
|
||||||
|
1. **Agent Name**: Clear, descriptive title reflecting specialization
|
||||||
|
2. **Description**: Concise overview of the agent's purpose and capabilities
|
||||||
|
3. **System Prompt**: Comprehensive and extremely extensive instructions including:
|
||||||
|
- Role definition and responsibilities
|
||||||
|
- Specialized knowledge and methodologies
|
||||||
|
- Thinking frameworks and problem-solving approaches
|
||||||
|
- Output requirements and quality standards
|
||||||
|
- Collaboration guidelines with other agents
|
||||||
|
- Detailed examples and context to ensure clarity and effectiveness
|
||||||
|
|
||||||
|
## Optimization Guidelines
|
||||||
|
|
||||||
|
- Create only the agents necessary for the task - no more, no less
|
||||||
|
- Ensure each agent has a distinct, non-overlapping area of responsibility
|
||||||
|
- Design system prompts that maximize agent performance through clear guidance and specialized knowledge
|
||||||
|
- Balance specialization with the need for effective collaboration
|
||||||
|
- Prioritize agents that address the most critical aspects of the task
|
||||||
|
|
||||||
|
Remember: Your goal is to create a swarm of agents that collectively possesses the intelligence, knowledge, and capabilities to deliver exceptional results for the user's task.
|
||||||
|
"""
|
||||||
@ -1,293 +0,0 @@
|
|||||||
import os
|
|
||||||
from typing import Any, List, Optional, Tuple
|
|
||||||
|
|
||||||
from loguru import logger
|
|
||||||
from pydantic import BaseModel, Field
|
|
||||||
|
|
||||||
from swarms.structs.agent import Agent
|
|
||||||
from swarms.utils.function_caller_model import OpenAIFunctionCaller
|
|
||||||
|
|
||||||
BOSS_SYSTEM_PROMPT = """
|
|
||||||
# Swarm Intelligence Orchestrator
|
|
||||||
|
|
||||||
You are the Chief Orchestrator of a sophisticated agent swarm. Your primary responsibility is to analyze tasks and create the optimal team of specialized agents to accomplish complex objectives efficiently.
|
|
||||||
|
|
||||||
## Agent Creation Protocol
|
|
||||||
|
|
||||||
1. **Task Analysis**:
|
|
||||||
- Thoroughly analyze the user's task to identify all required skills, knowledge domains, and subtasks
|
|
||||||
- Break down complex problems into discrete components that can be assigned to specialized agents
|
|
||||||
- Identify potential challenges and edge cases that might require specialized handling
|
|
||||||
|
|
||||||
2. **Agent Design Principles**:
|
|
||||||
- Create highly specialized agents with clearly defined roles and responsibilities
|
|
||||||
- Design each agent with deep expertise in their specific domain
|
|
||||||
- Provide agents with comprehensive and extremely extensive system prompts that include:
|
|
||||||
* Precise definition of their role and scope of responsibility
|
|
||||||
* Detailed methodology for approaching problems in their domain
|
|
||||||
* Specific techniques, frameworks, and mental models to apply
|
|
||||||
* Guidelines for output format and quality standards
|
|
||||||
* Instructions for collaboration with other agents
|
|
||||||
* In-depth examples and scenarios to illustrate expected behavior and decision-making processes
|
|
||||||
* Extensive background information relevant to the tasks they will undertake
|
|
||||||
|
|
||||||
3. **Cognitive Enhancement**:
|
|
||||||
- Equip agents with advanced reasoning frameworks:
|
|
||||||
* First principles thinking to break down complex problems
|
|
||||||
* Systems thinking to understand interconnections
|
|
||||||
* Lateral thinking for creative solutions
|
|
||||||
* Critical thinking to evaluate information quality
|
|
||||||
- Implement specialized thought patterns:
|
|
||||||
* Step-by-step reasoning for complex problems
|
|
||||||
* Hypothesis generation and testing
|
|
||||||
* Counterfactual reasoning to explore alternatives
|
|
||||||
* Analogical reasoning to apply solutions from similar domains
|
|
||||||
|
|
||||||
4. **Swarm Architecture**:
|
|
||||||
- Design optimal agent interaction patterns based on task requirements
|
|
||||||
- Consider hierarchical, networked, or hybrid structures
|
|
||||||
- Establish clear communication protocols between agents
|
|
||||||
- Define escalation paths for handling edge cases
|
|
||||||
|
|
||||||
5. **Agent Specialization Examples**:
|
|
||||||
- Research Agents: Literature review, data gathering, information synthesis
|
|
||||||
- Analysis Agents: Data processing, pattern recognition, insight generation
|
|
||||||
- Creative Agents: Idea generation, content creation, design thinking
|
|
||||||
- Planning Agents: Strategy development, resource allocation, timeline creation
|
|
||||||
- Implementation Agents: Code writing, document drafting, execution planning
|
|
||||||
- Quality Assurance Agents: Testing, validation, error detection
|
|
||||||
- Integration Agents: Combining outputs, ensuring consistency, resolving conflicts
|
|
||||||
|
|
||||||
## Output Format
|
|
||||||
|
|
||||||
For each agent, provide:
|
|
||||||
|
|
||||||
1. **Agent Name**: Clear, descriptive title reflecting specialization
|
|
||||||
2. **Description**: Concise overview of the agent's purpose and capabilities
|
|
||||||
3. **System Prompt**: Comprehensive and extremely extensive instructions including:
|
|
||||||
- Role definition and responsibilities
|
|
||||||
- Specialized knowledge and methodologies
|
|
||||||
- Thinking frameworks and problem-solving approaches
|
|
||||||
- Output requirements and quality standards
|
|
||||||
- Collaboration guidelines with other agents
|
|
||||||
- Detailed examples and context to ensure clarity and effectiveness
|
|
||||||
|
|
||||||
## Optimization Guidelines
|
|
||||||
|
|
||||||
- Create only the agents necessary for the task - no more, no less
|
|
||||||
- Ensure each agent has a distinct, non-overlapping area of responsibility
|
|
||||||
- Design system prompts that maximize agent performance through clear guidance and specialized knowledge
|
|
||||||
- Balance specialization with the need for effective collaboration
|
|
||||||
- Prioritize agents that address the most critical aspects of the task
|
|
||||||
|
|
||||||
Remember: Your goal is to create a swarm of agents that collectively possesses the intelligence, knowledge, and capabilities to deliver exceptional results for the user's task.
|
|
||||||
"""
|
|
||||||
|
|
||||||
|
|
||||||
class AgentSpec(BaseModel):
|
|
||||||
agent_name: Optional[str] = Field(
|
|
||||||
None,
|
|
||||||
description="The unique name assigned to the agent, which identifies its role and functionality within the swarm.",
|
|
||||||
)
|
|
||||||
description: Optional[str] = Field(
|
|
||||||
None,
|
|
||||||
description="A detailed explanation of the agent's purpose, capabilities, and any specific tasks it is designed to perform.",
|
|
||||||
)
|
|
||||||
system_prompt: Optional[str] = Field(
|
|
||||||
None,
|
|
||||||
description="The initial instruction or context provided to the agent, guiding its behavior and responses during execution.",
|
|
||||||
)
|
|
||||||
model_name: Optional[str] = Field(
|
|
||||||
description="The name of the AI model that the agent will utilize for processing tasks and generating outputs. For example: gpt-4o, gpt-4o-mini, openai/o3-mini"
|
|
||||||
)
|
|
||||||
auto_generate_prompt: Optional[bool] = Field(
|
|
||||||
description="A flag indicating whether the agent should automatically create prompts based on the task requirements."
|
|
||||||
)
|
|
||||||
max_tokens: Optional[int] = Field(
|
|
||||||
None,
|
|
||||||
description="The maximum number of tokens that the agent is allowed to generate in its responses, limiting output length.",
|
|
||||||
)
|
|
||||||
temperature: Optional[float] = Field(
|
|
||||||
description="A parameter that controls the randomness of the agent's output; lower values result in more deterministic responses."
|
|
||||||
)
|
|
||||||
role: Optional[str] = Field(
|
|
||||||
description="The designated role of the agent within the swarm, which influences its behavior and interaction with other agents."
|
|
||||||
)
|
|
||||||
max_loops: Optional[int] = Field(
|
|
||||||
description="The maximum number of times the agent is allowed to repeat its task, enabling iterative processing if necessary."
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
class Agents(BaseModel):
|
|
||||||
"""Configuration for a collection of agents that work together as a swarm to accomplish tasks."""
|
|
||||||
|
|
||||||
agents: List[AgentSpec] = Field(
|
|
||||||
description="A list containing the specifications of each agent that will participate in the swarm, detailing their roles and functionalities."
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
class AgentsBuilder:
|
|
||||||
"""A class that automatically builds and manages swarms of AI agents.
|
|
||||||
|
|
||||||
This class handles the creation, coordination and execution of multiple AI agents working
|
|
||||||
together as a swarm to accomplish complex tasks. It uses a boss agent to delegate work
|
|
||||||
and create new specialized agents as needed.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
name (str): The name of the swarm
|
|
||||||
description (str): A description of the swarm's purpose
|
|
||||||
verbose (bool, optional): Whether to output detailed logs. Defaults to True.
|
|
||||||
max_loops (int, optional): Maximum number of execution loops. Defaults to 1.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
name: str = "swarm-creator-01",
|
|
||||||
description: str = "This is a swarm that creates swarms",
|
|
||||||
verbose: bool = True,
|
|
||||||
max_loops: int = 1,
|
|
||||||
model_name: str = "gpt-4o",
|
|
||||||
return_dictionary: bool = True,
|
|
||||||
system_prompt: str = BOSS_SYSTEM_PROMPT,
|
|
||||||
):
|
|
||||||
self.name = name
|
|
||||||
self.description = description
|
|
||||||
self.verbose = verbose
|
|
||||||
self.max_loops = max_loops
|
|
||||||
self.agents_pool = []
|
|
||||||
self.model_name = model_name
|
|
||||||
self.return_dictionary = return_dictionary
|
|
||||||
self.system_prompt = system_prompt
|
|
||||||
logger.info(
|
|
||||||
f"Initialized AutoSwarmBuilder: {name} {description}"
|
|
||||||
)
|
|
||||||
|
|
||||||
def run(
|
|
||||||
self, task: str, image_url: str = None, *args, **kwargs
|
|
||||||
) -> Tuple[List[Agent], int]:
|
|
||||||
"""Run the swarm on a given task.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
task (str): The task to be accomplished
|
|
||||||
image_url (str, optional): URL of an image input if needed. Defaults to None.
|
|
||||||
*args: Variable length argument list
|
|
||||||
**kwargs: Arbitrary keyword arguments
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
The output from the swarm's execution
|
|
||||||
"""
|
|
||||||
logger.info(f"Running swarm on task: {task}")
|
|
||||||
agents = self._create_agents(task, image_url, *args, **kwargs)
|
|
||||||
|
|
||||||
return agents
|
|
||||||
|
|
||||||
def _create_agents(self, task: str, *args, **kwargs):
|
|
||||||
"""Create the necessary agents for a task.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
task (str): The task to create agents for
|
|
||||||
*args: Variable length argument list
|
|
||||||
**kwargs: Arbitrary keyword arguments
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
list: List of created agents
|
|
||||||
"""
|
|
||||||
logger.info("Creating agents for task")
|
|
||||||
model = OpenAIFunctionCaller(
|
|
||||||
system_prompt=self.system_prompt,
|
|
||||||
api_key=os.getenv("OPENAI_API_KEY"),
|
|
||||||
temperature=0.1,
|
|
||||||
base_model=Agents,
|
|
||||||
model_name=self.model_name,
|
|
||||||
max_tokens=8192,
|
|
||||||
)
|
|
||||||
|
|
||||||
agents_dictionary = model.run(task)
|
|
||||||
print(agents_dictionary)
|
|
||||||
|
|
||||||
print(type(agents_dictionary))
|
|
||||||
logger.info("Agents successfully created")
|
|
||||||
logger.info(f"Agents: {len(agents_dictionary.agents)}")
|
|
||||||
|
|
||||||
if self.return_dictionary:
|
|
||||||
logger.info("Returning dictionary")
|
|
||||||
|
|
||||||
# Convert swarm config to dictionary
|
|
||||||
agents_dictionary = agents_dictionary.model_dump()
|
|
||||||
return agents_dictionary
|
|
||||||
else:
|
|
||||||
logger.info("Returning agents")
|
|
||||||
return self.create_agents(agents_dictionary)
|
|
||||||
|
|
||||||
def create_agents(self, agents_dictionary: Any):
|
|
||||||
# Create agents from config
|
|
||||||
agents = []
|
|
||||||
for agent_config in agents_dictionary.agents:
|
|
||||||
# Convert dict to AgentConfig if needed
|
|
||||||
if isinstance(agent_config, dict):
|
|
||||||
agent_config = Agents(**agent_config)
|
|
||||||
|
|
||||||
agent = self.build_agent(
|
|
||||||
agent_name=agent_config.model_name,
|
|
||||||
agent_description=agent_config.description,
|
|
||||||
agent_system_prompt=agent_config.system_prompt,
|
|
||||||
model_name=agent_config.model_name,
|
|
||||||
max_loops=agent_config.max_loops,
|
|
||||||
dynamic_temperature_enabled=True,
|
|
||||||
auto_generate_prompt=agent_config.auto_generate_prompt,
|
|
||||||
role=agent_config.role,
|
|
||||||
max_tokens=agent_config.max_tokens,
|
|
||||||
temperature=agent_config.temperature,
|
|
||||||
)
|
|
||||||
agents.append(agent)
|
|
||||||
|
|
||||||
return agents
|
|
||||||
|
|
||||||
def build_agent(
|
|
||||||
self,
|
|
||||||
agent_name: str,
|
|
||||||
agent_description: str,
|
|
||||||
agent_system_prompt: str,
|
|
||||||
max_loops: int = 1,
|
|
||||||
model_name: str = "gpt-4o",
|
|
||||||
dynamic_temperature_enabled: bool = True,
|
|
||||||
auto_generate_prompt: bool = False,
|
|
||||||
role: str = "worker",
|
|
||||||
max_tokens: int = 8192,
|
|
||||||
temperature: float = 0.5,
|
|
||||||
):
|
|
||||||
"""Build a single agent with the given specifications.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
agent_name (str): Name of the agent
|
|
||||||
agent_description (str): Description of the agent's purpose
|
|
||||||
agent_system_prompt (str): The system prompt for the agent
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Agent: The constructed agent instance
|
|
||||||
"""
|
|
||||||
logger.info(f"Building agent: {agent_name}")
|
|
||||||
agent = Agent(
|
|
||||||
agent_name=agent_name,
|
|
||||||
description=agent_description,
|
|
||||||
system_prompt=agent_system_prompt,
|
|
||||||
model_name=model_name,
|
|
||||||
max_loops=max_loops,
|
|
||||||
dynamic_temperature_enabled=dynamic_temperature_enabled,
|
|
||||||
context_length=200000,
|
|
||||||
output_type="str", # "json", "dict", "csv" OR "string" soon "yaml" and
|
|
||||||
streaming_on=False,
|
|
||||||
auto_generate_prompt=auto_generate_prompt,
|
|
||||||
role=role,
|
|
||||||
max_tokens=max_tokens,
|
|
||||||
temperature=temperature,
|
|
||||||
)
|
|
||||||
|
|
||||||
return agent
|
|
||||||
|
|
||||||
|
|
||||||
# if __name__ == "__main__":
|
|
||||||
# builder = AgentsBuilder(model_name="gpt-4o")
|
|
||||||
# agents = builder.run("Create a swarm that can write a book about the history of the world")
|
|
||||||
# print(agents)
|
|
||||||
# print(type(agents))
|
|
||||||
Loading…
Reference in new issue