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Deep Web Research with AG2 and GPT Researcher

GPT Researcher x AG2

Ask an LLM to research a topic and write a report. It'll produce something -- sometimes quite good. But probe the sources, check the claims, or ask for substantial revisions and the limitations show up fast. The agent that searched the web is also the one drawing conclusions from it. There's no review step between research and writing. Errors and gaps pass straight through to the final document.

The fix isn't a better prompt. It's division of labor.

GPT Researcher, created by Assaf Elovic, is built on this idea: specialized agents, each with a distinct job, working through a staged pipeline. A researcher gathers, an editor outlines, a reviewer challenges the findings, a revisor incorporates the feedback, and a writer only touches finalized content. Every handoff is a quality check.

This post walks through the build-with-ag2 example, which runs the full pipeline with AG2 as the orchestration layer. The example ships with two run modes -- a terminal script and a web UI built on AG-UI that shows pipeline progress in real time and includes a human-in-the-loop review step before writing begins.

Give Your AG2 Agent its own UI with AG-UI

AG2 x AG-UI

You've built an agent with AG2. It reasons, calls tools, and streams responses. But right now it lives in a terminal or a script. The moment you want to put it in front of a user (like for a blog, perhaps) and stream text token by token, show tool activity as it happens, and handle errors gracefully you're building custom plumbing: WebSocket handlers, bespoke JSON formats, state synchronization logic.

AG-UI (Agent-User Interaction Protocol) is an open, lightweight, event-based protocol that standardizes this agent-to-UI layer. AG2 now supports AG-UI natively, meaning you can connect any ConversableAgent to an AG-UI-compatible frontend with just a couple of lines of code.

In this post, we'll look under the hood at how the protocol works by building a working agent chat application from scratch using a ConversableAgent with a weather tool, sending AG-UI events to a browser frontend that renders streaming text and an interactive tool card. Simple HTML, no frontend framework required.

AG2 Agent Chat powered by AG-UI

GPT-5.1 Apply Patch Tool: Automated Code Editing in AG2

AG2 now supports the apply_patch tool (from GPT-5.1 onward) through OpenAI's Responses API, enabling agents to create, update, and delete files using structured diffs. This integration brings precise, controlled file operations directly into your agent workflows.

This article explores how to use apply_patch in AG2, with practical examples for automated code editing, project scaffolding, and multi-file refactoring.

What is Apply Patch?

The apply_patch tool is a built-in capability in GPT-5.1 and above models that enables agents to perform structured file operations using unified diff format. Unlike traditional code generation approaches where agents output raw code blocks that you must manually integrate, apply_patch provides a standardized interface for file modifications that can be directly applied to your codebase.

The tool handles three core operations: - create_file: Generate new files with specified content - update_file: Modify existing files using unified diff format - delete_file: Remove files from the workspace

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.

WebSurferAgent Example

  • in Tools
  • 15 min read

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. Browser Use Example

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.

  • in Tools
  • 6 min read

Tools Dependency Injection

Dependency Injection is a secure way to connect external functions to agents without exposing sensitive data such as passwords, tokens, or personal information. This approach ensures that sensitive information remains protected while still allowing agents to perform their tasks effectively, even when working with large language models (LLMs).

In this guide, we’ll explore how to build secure workflows that handle sensitive data safely.

As an example, we’ll create an agent that retrieves user's account balance. The best part is that sensitive data like username and password are never shared with the LLM. Instead, it’s securely injected directly into the function at runtime, keeping it safe while maintaining seamless functionality.

Why Dependency Injection Is Essential

Here’s why dependency injection is a game-changer for secure LLM workflows:

  • Enhanced Security: Your sensitive data is never directly exposed to the LLM.
  • Simplified Development: Secure data can be seamlessly accessed by functions without requiring complex configurations.
  • Unmatched Flexibility: It supports safe integration of diverse workflows, allowing you to scale and adapt with ease.

In this guide, we’ll explore how to set up dependency injection and build secure workflows. Let’s dive in!

Note: This blog builds upon the concepts covered in the following notebook.