Learn about CAMEL's agent types, with a focus on ChatAgent and advanced agent architectures for AI-powered automation.
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Agents in CAMEL are autonomous entities capable of performing specific tasks through interaction with language models and other components. Each agent is designed with a particular role and capability, allowing them to work independently or collaboratively to achieve complex goals.
All CAMEL agents inherit from the
BaseAgent| Method | Purpose | Description |
|---|---|---|
| State Management | Resets the agent to its initial state |
| Task Execution | Performs a single step of the agent's operation |
The
ChatAgentCriticAgent
Specialized agent for evaluating and critiquing responses or solutions. Used in scenarios requiring quality assessment or validation.
DeductiveReasonerAgent
Focused on logical reasoning and deduction. Breaks down complex problems into smaller, manageable steps.
EmbodiedAgent
Designed for embodied AI scenarios, capable of understanding and responding to physical world contexts.
KnowledgeGraphAgent
Specialized in building and utilizing knowledge graphs for enhanced reasoning and information management.
MultiHopGeneratorAgent
Handles multi-hop reasoning tasks, generating intermediate steps to reach conclusions.
SearchAgent
Focused on information retrieval and search tasks across various data sources.
TaskAgent
Handles task decomposition and management, breaking down complex tasks into manageable subtasks.
from camel.agents import ChatAgent # Create a chat agent with a system message agent = ChatAgent(system_message="You are a helpful assistant.") # Step through a conversation response = agent.step("Hello, can you help me?")
The
ChatAgentfrom camel.agents import ChatAgent from camel.models import ModelFactory from camel.types import ModelPlatformType, ModelType # Method 1: Using just a string for the model name (default model platform is used) agent_1 = ChatAgent("You are a helpful assistant.", model="gpt-4o-mini") # Method 2: Using a ModelType enum (default model platform is used) agent_2 = ChatAgent("You are a helpful assistant.", model=ModelType.GPT_4O_MINI) # Method 3: Using a tuple of strings (platform, model) agent_3 = ChatAgent("You are a helpful assistant.", model=("openai", "gpt-4o-mini")) # Method 4: Using a tuple of enums agent_4 = ChatAgent( "You are a helpful assistant.", model=(ModelPlatformType.ANTHROPIC, ModelType.CLAUDE_3_5_SONNET), ) # Method 5: Using default model platform and default model type when none is specified agent_5 = ChatAgent("You are a helpful assistant.") # Method 6: Using a pre-created model with ModelFactory (original approach) model = ModelFactory.create( model_platform=ModelPlatformType.OPENAI, # Using enum model_type=ModelType.GPT_4O_MINI, # Using enum ) agent_6 = ChatAgent("You are a helpful assistant.", model=model) # Method 7: Using ModelFactory with string parameters model = ModelFactory.create( model_platform="openai", # Using string model_type="gpt-4o-mini", # Using string ) agent_7 = ChatAgent("You are a helpful assistant.", model=model)
from camel.agents import ChatAgent from camel.toolkits import FunctionTool # Define a tool def calculator(a: int, b: int) -> int: return a + b # Create agent with tool agent = ChatAgent(tools=[calculator]) # The agent can now use the calculator tool in conversations response = agent.step("What is 5 + 3?")
CAMEL's
ChatAgentHere's how you can get a structured response from a
ChatAgentBaseModelfrom pydantic import BaseModel, Field from typing import List class JokeResponse(BaseModel): joke: str = Field(description="A joke") funny_level: int = Field(description="Funny level, from 1 to 10") # Create agent with structured output agent = ChatAgent(model="gpt-4o-mini") response = agent.step("Tell me a joke.", response_format=JokeResponse) # The response content is a JSON string print(response.msgs[0].content) # '{"joke": "Why don't scientists trust atoms? Because they make up everything!", "funny_level": 8}' # Access the parsed Pydantic object parsed_response = response.msgs[0].parsed print(parsed_response.joke) # "Why don't scientists trust atoms? Because they make up everything!" print(parsed_response.funny_level) # 8
You can also use nested Pydantic models and lists to define more complex structures. In this example, we define a
StudentListStudentfrom pydantic import BaseModel from typing import List class Student(BaseModel): name: str age: str email: str class StudentList(BaseModel): students: List[Student] # Create agent with structured output agent = ChatAgent(model="gpt-4o-mini") response = agent.step( "Create a list of two students with their names, ages, and email addresses.", response_format=StudentList, ) # Access the parsed Pydantic object parsed_response = response.msgs[0].parsed for student in parsed_response.students: print(f"Name: {student.name}, Age: {student.age}, Email: {student.email}") # Name: Alex, Age: 22, Email: [email protected] # Name: Beth, Age: 25, Email: [email protected]
agent.add_model_scheduling_strategy("custom", custom_strategy) ``` </CodeGroup>