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Description
CAMEL-AI represents the inaugural framework for multi-agent systems based on large language models and fosters an open-source community focused on investigating the scaling dynamics of agents. This innovative platform allows users to design customizable agents through modular components that are specifically suited for particular tasks, thereby promoting the creation of multi-agent systems that tackle issues related to autonomous collaboration. Serving as a versatile foundation for a wide range of applications, the framework is ideal for tasks like automation, data generation, and simulations of various environments. By conducting extensive studies on agents, CAMEL-AI.org seeks to uncover critical insights into their behaviors, capabilities, and the potential risks they may pose. The community prioritizes thorough research and seeks to strike a balance between the urgency of findings and the patience required for in-depth exploration, while also welcoming contributions that enhance its infrastructure, refine documentation, and bring innovative research ideas to life. The platform is equipped with a suite of components, including models, tools, memory systems, and prompts, designed to empower agents, and it also facilitates integration with a wide array of external tools and services, thereby expanding its utility and effectiveness in real-world applications. As the community grows, it aims to inspire further advancements in the field of artificial intelligence and collaborative systems.
Description
The Agentic Era represents a significant shift from the conventional application-focused computing landscape to a new domain characterized by agentic AI, which comprises autonomous, context-sensitive systems adept at acting, learning, and collaborating within intricate, ever-changing environments. These advanced intelligent agents are not limited to merely executing commands; rather, they are equipped to handle entire tasks, retain context and memory through large language models that are specifically designed for various fields, and have the capability to scale across multiple industries, potentially affecting millions. This progression necessitates an innovative operational mindset known as AgenticOps, alongside a revamped management framework based on three core principles: ensuring that humans remain engaged to contribute creativity and discernment, allowing agents to function effectively across disconnected systems with comprehensive cross-domain insights, and utilizing specialized models meticulously adjusted for their unique functions. Cisco brings this vision to fruition with AI Canvas, the first generative workspace in the industry that utilizes a multi-data and multi-agent architecture, paving the way for enhanced collaboration and efficiency. Furthermore, this pioneering approach signifies a major advancement in how organizations can leverage AI to enhance productivity and foster innovation.
API Access
Has API
API Access
Has API
Integrations
Cisco AI Assistant
Cisco AgenticOps
OWL
OpenAgents
TraceRoot.AI
Integrations
Cisco AI Assistant
Cisco AgenticOps
OWL
OpenAgents
TraceRoot.AI
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
CAMEL-AI
Founded
2023
Country
United States
Website
www.camel-ai.org
Vendor Details
Company Name
Cisco
Founded
1984
Country
United States
Website
blogs.cisco.com/news/welcome-to-the-agentic-era-humans-agents-achieving-more-together