Blog - AG2
AG2 Now Integrated with CopilotKit
TL;DR
- Instant Multi-Agent UI with CopilotKit: Connect AG2's powerful multi-agent systems to polished React UIs without building custom components.
- Standardized Communication: Uses the AG UI protocol to create a consistent bridge between frontend and backend, eliminating custom API development.
- Rapid Development: Build production-ready AG2 powered AI applications in hours through pre-built and custom UI components using CopilotKit.
- Ready to Go: Starter repo containing sample code for a travel planning assistant to get you started immediately.
We’re excited to announce that CopilotKit now integrates with AG2, bringing together AG2’s multi-agent orchestration capabilities with CopilotKit’s React UI components. This integration creates a more seamless development experience for building AI-powered applications.
AG2 v0.9 Release: Introducing the New Group Chat
TL;DR
- AG2
v0.9unifies the previous Group Chat and Swarm into a single, powerful, new Group Chat - This unification provides improved flexibility, control, and better foundations for scalability
- Features include pre-built orchestration patterns, enhanced workflow control, and robust context variables
- Includes all capabilities of the previous Group Chat and Swarm
- Swarm is now deprecated but still available. We recommend migrating to the new Group Chat pattern
The Myth of Reasoning
TL;DR
- Human reasoning is often mischaracterized as purely logical; it's iterative, intuitive, and driven by communication needs.
- AI can be a valuable partner in augmenting human reasoning.
- Viewing AI as a system of components, rather than a monolithic model, may better capture the iterative nature of reasoning.
One major criticism of AI today, including the state-of-the-art LLMs, is that they fall short in reasoning capability compared to humans. This criticism often stems from a fundamental misunderstanding of how human reasoning actually works. We tend to hold up an idealized image of human thought – rational, logical, step-by-step – and judge AI against this standard. But is this image accurate?
DeepResearchAgent - Your Shortcut for Faster Research
Get Communicating with Discord, Slack, and Telegram
Welcome DiscordAgent, SlackAgent, and TelegramAgent
We want to help you focus on building workflows and enhancing agents, so we're building reference agents to get you going quicker.
Say hello to three new AG2 communication agents - DiscordAgent, SlackAgent, and TelegramAgent, here so that you can use an agentic application to send and retrieve messages from messaging platforms.
Riding the Web with WebSurferAgent
Introduction
In our Adding Browsing Capabilities to AG2 guide, we explored how to build agents with basic web surfing capabilities. Now, let's take it to the next level with WebSurferAgent—a powerful agent that comes with built-in web browsing tools right out of the box!
With WebSurferAgent, your agents can seamlessly browse the web, retrieve real-time information, and interact with web pages—all with minimal setup.
Adding Browsing Capabilities to AG2
Introduction
Previously, in our Cross-Framework LLM Tool Integration guide, we combined tools from frameworks like LangChain, CrewAI, and PydanticAI to enhance AG2.
Now, we’re taking AG2 even further by integrating Browser Use and Crawl4AI, enabling agents to navigate websites, extract dynamic content, and interact with web pages. This unlocks new possibilities for automated data collection, web automation, and more.
RealtimeAgent with Gemini API
TL;DR:
- RealtimeAgent now supports Gemini Multimodal Live API
Why is this important?
We previously supported a Realtime Agent powered by OpenAI. In December 2024, Google rolled out Gemini 2.0, which includes the multi-modal live APIs. These APIs enable advanced capabilities such as real-time processing of audio inputs in live conversational settings. To ensure developers can fully leverage the capabilities of the latest LLMs, we now also support a RealtimeAgent powered by Gemini.
Tools with ChatContext Dependency Injection
Introduction
In this post, we’ll build upon the concepts introduced in our previous blog on Tools with Dependency Injection. We’ll take a deeper look at how ChatContext can be used to manage the flow of conversations in a more structured and secure way.
By using ChatContext, we can track and control the sequence of function calls during a conversation. This is particularly useful in situations where one task must be completed before another — for example, ensuring that a user logs in before they can check their account balance. This approach helps to prevent errors and enhances the security of the system.
Benefits of Using ChatContext:
- Flow Control: Ensures tasks are performed in the correct order, reducing the chance of mistakes.
- Enhanced Security: Prevents unauthorized actions, such as accessing sensitive data before authentication.
- Simplified Debugging: Logs the conversation history, making it easier to trace and resolve issues.
Note
This blog builds on the concepts shared in the notebook.
Streaming input and output using WebSockets
TL;DR
- Learn how to build an agent chat application using WebSockets and
IOStream - Explore a hands-on example of connecting a web application to a responsive chat with agents over WebSockets.
- Streamlined Real-Time Interactions: WebSockets offer a low-latency, persistent connection for sending and receiving data in real time.