CAMEL — Communicative Agents for Multi-Agent LLMs

CAMEL

CAMEL (Communicative Agents for Mind Exploration of Large Language Model Society) is one of the earliest and most comprehensive multi-agent frameworks. It enables role-playing conversations between agents, automated task solving, and research into emergent agent behaviours.

Key Features

• Role-playing: user/assistant, debate, reflection, society patterns
• 20+ built-in agent types (ChatAgent, TaskSpecifyAgent, CriticAgent, …)
• Rich tool ecosystem: web search, code execution, Wikipedia, arXiv, OpenAPI
• Multi-modal support: vision, audio, document understanding
• RAG integration with 15+ vector stores
• Benchmark datasets and evaluation harness
• Synthetic data generation pipelines

Quick Start

from camel.agents import ChatAgent
from camel.messages import BaseMessage
from camel.models import ModelFactory
from camel.types import ModelType, ModelPlatformType

model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O,
)
agent = ChatAgent(system_message="You are a helpful assistant.", model=model)
resp = agent.step(BaseMessage.make_user_message("Hello, CAMEL!", role_name="User"))
print(resp.msg.content)

Install via ai-supply

npx ai-supply add camel-ai-multi-agent-framework

Curated mirror of the open-source CAMEL (Apache-2.0). Get it from the source.