Kye Gomez
4 weeks ago
parent
c43180de14
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48e7fd8a79
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# AI generate initial response
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# AI decides how many "thinking rounds" it needs
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# For each round:
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# Generates 3 alternative responses
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# Evaluates all responses
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# Picks the best one
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# Final response is the survivor of this AI battle royale
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from swarms import Agent
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# OpenAI function schema for determining thinking rounds
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thinking_rounds_schema = {
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"name": "determine_thinking_rounds",
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"description": "Determines the optimal number of thinking rounds needed for a task",
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"parameters": {
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"type": "object",
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"properties": {
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"num_rounds": {
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"type": "integer",
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"description": "The number of thinking rounds needed (1-5)",
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"minimum": 1,
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"maximum": 5,
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}
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},
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"required": ["num_rounds"],
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},
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}
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# System prompt for determining thinking rounds
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THINKING_ROUNDS_PROMPT = """You are an expert at determining the optimal number of thinking rounds needed for complex tasks. Your role is to analyze the task and determine how many rounds of thinking and evaluation would be most beneficial.
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Consider the following factors when determining the number of rounds:
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1. Task Complexity: More complex tasks may require more rounds
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2. Potential for Multiple Valid Approaches: Tasks with multiple valid solutions need more rounds
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3. Risk of Error: Higher-stakes tasks may benefit from more rounds
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4. Time Sensitivity: Balance thoroughness with efficiency
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Guidelines for number of rounds:
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- 1 round: Simple, straightforward tasks with clear solutions
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- 2-3 rounds: Moderately complex tasks with some ambiguity
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- 4-5 rounds: Highly complex tasks with multiple valid approaches or high-stakes decisions
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Your response should be a single number between 1 and 5, representing the optimal number of thinking rounds needed."""
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# Schema for generating alternative responses
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alternative_responses_schema = {
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"name": "generate_alternatives",
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"description": "Generates multiple alternative responses to a task",
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"parameters": {
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"type": "object",
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"properties": {
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"alternatives": {
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"type": "array",
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"description": "List of alternative responses",
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"items": {
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"type": "object",
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"properties": {
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"response": {
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"type": "string",
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"description": "The alternative response",
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},
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"reasoning": {
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"type": "string",
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"description": "Explanation of why this approach was chosen",
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},
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},
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"required": ["response", "reasoning"],
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},
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"minItems": 3,
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"maxItems": 3,
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}
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},
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"required": ["alternatives"],
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},
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}
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# Schema for evaluating responses
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evaluation_schema = {
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"name": "evaluate_responses",
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"description": "Evaluates and ranks alternative responses",
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"parameters": {
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"type": "object",
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"properties": {
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"evaluation": {
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"type": "object",
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"properties": {
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"best_response": {
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"type": "string",
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"description": "The selected best response",
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},
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"ranking": {
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"type": "array",
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"description": "Ranked list of responses from best to worst",
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"items": {
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"type": "object",
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"properties": {
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"response": {
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"type": "string",
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"description": "The response",
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},
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"score": {
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"type": "number",
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"description": "Score from 0-100",
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},
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"reasoning": {
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"type": "string",
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"description": "Explanation of the score",
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},
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},
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"required": [
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"response",
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"score",
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"reasoning",
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],
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},
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},
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},
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"required": ["best_response", "ranking"],
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}
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},
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"required": ["evaluation"],
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},
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}
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# System prompt for generating alternatives
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ALTERNATIVES_PROMPT = """You are an expert at generating diverse and creative alternative responses to tasks. Your role is to generate 3 distinct approaches to solving the given task.
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For each alternative:
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1. Consider a different perspective or approach
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2. Provide clear reasoning for why this approach might be effective
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3. Ensure alternatives are meaningfully different from each other
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4. Maintain high quality and relevance to the task
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Your response should include 3 alternatives, each with its own reasoning."""
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# System prompt for evaluation
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EVALUATION_PROMPT = """You are an expert at evaluating and comparing different responses to tasks. Your role is to critically analyze each response and determine which is the most effective.
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Consider the following criteria when evaluating:
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1. Relevance to the task
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2. Completeness of the solution
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3. Creativity and innovation
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4. Practicality and feasibility
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5. Clarity and coherence
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Your response should include:
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1. The best response selected
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2. A ranked list of all responses with scores and reasoning"""
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class CortAgent:
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def __init__(
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self,
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alternative_responses: int = 3,
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):
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self.thinking_rounds = Agent(
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agent_name="CortAgent",
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agent_description="CortAgent is a multi-step agent that uses a battle royale approach to determine the best response to a task.",
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model_name="gpt-4o-mini",
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max_loops=1,
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dynamic_temperature_enabled=True,
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tools_list_dictionary=thinking_rounds_schema,
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system_prompt=THINKING_ROUNDS_PROMPT,
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)
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self.alternatives_agent = Agent(
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agent_name="CortAgentAlternatives",
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agent_description="Generates multiple alternative responses to a task",
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model_name="gpt-4o-mini",
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max_loops=1,
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dynamic_temperature_enabled=True,
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tools_list_dictionary=alternative_responses_schema,
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system_prompt=ALTERNATIVES_PROMPT,
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)
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self.evaluation_agent = Agent(
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agent_name="CortAgentEvaluation",
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agent_description="Evaluates and ranks alternative responses",
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model_name="gpt-4o-mini",
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max_loops=1,
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dynamic_temperature_enabled=True,
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tools_list_dictionary=evaluation_schema,
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system_prompt=EVALUATION_PROMPT,
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)
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def run(self, task: str):
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# First determine number of thinking rounds
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num_rounds = self.thinking_rounds.run(task)
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# Initialize with the task
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current_task = task
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best_response = None
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# Run the battle royale for the determined number of rounds
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for round_num in range(num_rounds):
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# Generate alternatives
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alternatives = self.alternatives_agent.run(current_task)
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# Evaluate alternatives
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evaluation = self.evaluation_agent.run(alternatives)
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# Update best response and current task for next round
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best_response = evaluation["evaluation"]["best_response"]
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current_task = f"Previous best response: {best_response}\nOriginal task: {task}"
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return best_response
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@ -0,0 +1,196 @@
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# Model Integration in Agents
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!!! info "About Model Integration"
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Agents supports multiple model providers through LiteLLM integration, allowing you to easily switch between different language models. This document outlines the available providers and how to use them with agents.
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## Important Note on Model Names
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!!! warning "Required Format"
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When specifying a model in Swarms, you must use the format `provider/model_name`. For example:
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```python
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"openai/gpt-4"
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"anthropic/claude-3-opus-latest"
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"cohere/command-r-plus"
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```
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This format ensures Swarms knows which provider to use for the specified model.
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## Available Model Providers
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### OpenAI
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??? info "OpenAI Models"
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- **Provider name**: `openai`
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- **Available Models**:
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- `gpt-4`
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- `gpt-3.5-turbo`
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- `gpt-4-turbo-preview`
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### Anthropic
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??? info "Anthropic Models"
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- **Provider name**: `anthropic`
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- **Available Models**:
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- **Claude 3 Opus**:
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- `claude-3-opus-latest`
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- `claude-3-opus-20240229`
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- **Claude 3 Sonnet**:
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- `claude-3-sonnet-20240229`
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- `claude-3-5-sonnet-latest`
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- `claude-3-5-sonnet-20240620`
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- `claude-3-7-sonnet-latest`
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- `claude-3-7-sonnet-20250219`
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- `claude-3-5-sonnet-20241022`
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- **Claude 3 Haiku**:
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- `claude-3-haiku-20240307`
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- `claude-3-5-haiku-20241022`
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- `claude-3-5-haiku-latest`
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- **Legacy Models**:
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- `claude-2`
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- `claude-2.1`
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- `claude-instant-1`
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- `claude-instant-1.2`
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### Cohere
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??? info "Cohere Models"
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- **Provider name**: `cohere`
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- **Available Models**:
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- **Command**:
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- `command`
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- `command-r`
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- `command-r-08-2024`
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- `command-r7b-12-2024`
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- **Command Light**:
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- `command-light`
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- **Command R Plus**:
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- `command-r-plus`
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- `command-r-plus-08-2024`
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### Google
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??? info "Google Models"
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- **Provider name**: `google`
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- **Available Models**:
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- `gemini-pro`
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- `gemini-pro-vision`
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### Mistral
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??? info "Mistral Models"
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- **Provider name**: `mistral`
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- **Available Models**:
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- `mistral-tiny`
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- `mistral-small`
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- `mistral-medium`
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## Using Different Models with Swarms
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To use a different model with your Swarms agent, specify the model name in the `model_name` parameter when initializing the Agent, using the provider/model_name format:
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```python
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from swarms.structs.agent import Agent
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# Using OpenAI's GPT-4
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agent = Agent(
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agent_name="Research-Agent",
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model_name="openai/gpt-4o", # Note the provider/model_name format
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# ... other parameters
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)
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# Using Anthropic's Claude
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agent = Agent(
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agent_name="Analysis-Agent",
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model_name="anthropic/claude-3-sonnet-20240229", # Note the provider/model_name format
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# ... other parameters
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)
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# Using Cohere's Command
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agent = Agent(
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agent_name="Text-Agent",
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model_name="cohere/command-r-plus", # Note the provider/model_name format
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# ... other parameters
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)
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```
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## Model Configuration
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When using different models, you can configure various parameters:
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```python
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agent = Agent(
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agent_name="Custom-Agent",
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model_name="openai/gpt-4",
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temperature=0.7, # Controls randomness (0.0 to 1.0)
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max_tokens=2000, # Maximum tokens in response
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top_p=0.9, # Nucleus sampling parameter
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frequency_penalty=0.0, # Reduces repetition
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presence_penalty=0.0, # Encourages new topics
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# ... other parameters
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)
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```
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## Best Practices
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### Model Selection
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!!! tip "Choosing the Right Model"
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- Choose models based on your specific use case
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- Consider cost, performance, and feature requirements
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- Test different models for your specific task
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### Error Handling
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!!! warning "Error Management"
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- Implement proper error handling for model-specific errors
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- Handle rate limits and API quotas appropriately
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### Cost Management
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!!! note "Cost Considerations"
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- Monitor token usage and costs
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- Use appropriate model sizes for your needs
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## Example Use Cases
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### 1. Complex Analysis (GPT-4)
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```python
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agent = Agent(
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agent_name="Analysis-Agent",
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model_name="openai/gpt-4", # Note the provider/model_name format
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temperature=0.3, # Lower temperature for more focused responses
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max_tokens=4000
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)
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```
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### 2. Creative Tasks (Claude)
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```python
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agent = Agent(
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agent_name="Creative-Agent",
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model_name="anthropic/claude-3-sonnet-20240229", # Note the provider/model_name format
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temperature=0.8, # Higher temperature for more creative responses
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max_tokens=2000
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)
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```
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### 3. Vision Tasks (Gemini)
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```python
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agent = Agent(
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agent_name="Vision-Agent",
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model_name="google/gemini-pro-vision", # Note the provider/model_name format
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temperature=0.4,
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max_tokens=1000
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)
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```
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## Troubleshooting
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!!! warning "Common Issues"
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If you encounter issues with specific models:
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1. Verify your API keys are correctly set
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2. Check model availability in your region
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3. Ensure you have sufficient quota/credits
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4. Verify the model name is correct and supported
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## Additional Resources
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- [LiteLLM Documentation](https://docs.litellm.ai/){target=_blank}
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- [OpenAI API Documentation](https://platform.openai.com/docs/api-reference){target=_blank}
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- [Anthropic API Documentation](https://docs.anthropic.com/claude/reference/getting-started-with-the-api){target=_blank}
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- [Google AI Documentation](https://ai.google.dev/docs){target=_blank}
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Reference in new issue