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Anthropic's most intelligent model with toggleable extended thinking

Airbnb coding styles for React/JSX

Create a new Next.js page based on the following description.

A model context supports storage and retrieval of Chat Completion messages. It is always used together with a model client to generate LLM-based responses. For example, BufferedChatCompletionContext is a most-recent-used (MRU) context that stores the most recent buffer_size number of messages. This is useful to avoid context overflow in many LLMs. Let’s see an example that uses BufferedChatCompletionContext.

AutoGen provides a suite of built-in model clients for using ChatCompletion API. All model clients implement the ChatCompletionClient protocol class. Currently we support the following built-in model clients:

An agent in AutoGen core can react to, send, and publish messages, and messages are the only means through which agents can communicate with each other.

An agent is a software entity that communicates via messages, maintains its own state, and performs actions in response to received messages or changes in its state. These actions may modify the agent’s state and produce external effects, such as updating message logs, sending new messages, executing code, or making API calls.

AutoGen core offers an easy way to quickly build event-driven, distributed, scalable, resilient AI agent systems. Agents are developed by using the Actor model. You can build and run your agent system locally and easily move to a distributed system in the cloud when you are ready. Key features of AutoGen core include:

Tools are code that can be executed by an agent to perform actions. A tool can be a simple function such as a calculator, or an API call to a third-party service such as stock price lookup or weather forecast. In the context of AI agents, tools are designed to be executed by agents in response to model-generated function calls. AutoGen provides the autogen_core.tools module with a suite of built-in tools and utilities for creating and running custom tools.

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A Workbench provides a collection of tools that share state and resources. Different from Tool, which provides an interface to a single tool, a workbench provides an interface to call different tools and receive results as the same types.

The Exa MCP server enables AI assistants to perform web searches using Exa's powerful search API, providing structured search results including titles, URLs, and content snippets

AutoGen is a framework for creating multi-agent AI applications that can act autonomously or work alongside humans.

Rules to help creation of autogen components: agents, tools, workflows, etc.

Anthropic's most intelligent model with toggleable extended thinking