Blog#

ReasoningAgent - Tree of Thoughts with Beam Search in AG2

TL;DR: * We introduce ReasoningAgent, an AG2 agent that implements tree-of-thought reasoning with beam search to solve complex problems. * ReasoningAgent explores multiple reasoning paths in parallel and uses a grader agent to evaluate and select the most promising paths. * The exploration trajectory and thought tree can be saved locally for further analysis. These logs can even be saved as SFT dataset and preference dataset for DPO and PPO training.

Introduction

Large language models (LLMs) have shown impressive capabilities in various tasks, but they can still struggle with complex reasoning problems that require exploring multiple solution paths. To address this limitation, we introduce ReasoningAgent, an AG2 agent that implements tree-of-thought reasoning with beam search.

The key idea behind ReasoningAgent is to: 1. Generate multiple possible reasoning steps at each point 2. Evaluate these steps using a grader agent 3. Keep track of the most promising paths using beam search 4. Continue exploring those paths while pruning less promising ones

This approach allows the agent to systematically explore different reasoning strategies while managing computational resources efficiently.

Agentic testing for prompt leakage security

Introduction

As Large Language Models (LLMs) become increasingly integrated into production applications, ensuring their security has never been more crucial. One of the most pressing security concerns for these models is prompt injection, specifically prompt leakage.

LLMs often rely on system prompts (also known as system messages), which are internal instructions or guidelines that help shape their behavior and responses. These prompts can sometimes contain sensitive information, such as confidential details or internal logic, that should never be exposed to external users. However, with careful probing and targeted attacks, there is a risk that this sensitive information can be unintentionally revealed.

To address this issue, we have developed the Prompt Leakage Probing Framework, a tool designed to probe LLM agents for potential prompt leakage vulnerabilities. This framework serves as a proof of concept (PoC) for creating and testing various scenarios to evaluate how easily system prompts can be exposed. By automating the detection of such vulnerabilities, we aim to provide a powerful tool for testing the security of LLMs in real-world applications.

Building Swarm-based agents with AG2

AG2 now provides an implementation of the swarm orchestration from OpenAI's Swarm framework, with some additional features!

Introducing CaptainAgent for Adaptive Team Building

TL;DR - We introduce CaptainAgent, an agent equipped with the capability to adaptively assemble a team of agents through retrieval-selection-generation process to handle complex tasks via the nested chat conversation pattern in AG2. - CaptainAgent supports all types of ConversableAgents implemented in AG2.

AgentOps, the Best Tool for AutoGen Agent Observability

TL;DR

• AutoGen® offers detailed multi-agent observability with AgentOps.
• AgentOps offers the best experience for developers building with AutoGen in just two lines of code.
• Enterprises can now trust AutoGen in production with detailed monitoring and logging from AgentOps.

AutoGen is excited to announce an integration with AgentOps, the industry leader in agent observability and compliance. Back in February, Bloomberg declared 2024 the year of AI Agents. And it's true! We've seen AI transform from simplistic chatbots to autonomously making decisions and completing tasks on a user's behalf.

However, as with most new technologies, companies and engineering teams can be slow to develop processes and best practices. One part of the agent workflow we're betting on is the importance of observability. Letting your agents run wild might work for a hobby project, but if you're building enterprise-grade agents for production, it's crucial to understand where your agents are succeeding and failing. Observability isn't just an option; it's a requirement.

As agents evolve into even more powerful and complex tools, you should view them increasingly as tools designed to augment your team's capabilities. Agents will take on more prominent roles and responsibilities, take action, and provide immense value. However, this means you must monitor your agents the same way a good manager maintains visibility over their personnel. AgentOps offers developers observability for debugging and detecting failures. It provides the tools to monitor all the key metrics your agents use in one easy-to-read dashboard. Monitoring is more than just a “nice to have”; it's a critical component for any team looking to build and scale AI agents.

Enhanced Support for Non-OpenAI Models

TL;DR

• AutoGen has expanded integrations with a variety of cloud-based model providers beyond OpenAI.
• Leverage models and platforms from Gemini, Anthropic, Mistral AI, Together.AI, and Groq for your AutoGen agents.
• Utilise models specifically for chat, language, image, and coding.
• LLM provider diversification can provide cost and resilience benefits.

In addition to the recently released AutoGen Google Gemini client, new client classes for Mistral AI, Anthropic, Together.AI, and Groq enable you to utilize over 75 different large language models in your AutoGen agent workflow.

These new client classes tailor AutoGen's underlying messages to each provider's unique requirements and remove that complexity from the developer, who can then focus on building their AutoGen workflow.

Using them is as simple as installing the client-specific library and updating your LLM config with the relevant api_type and model. We'll demonstrate how to use them below.

The community is continuing to enhance and build new client classes as cloud-based inference providers arrive. So, watch this space, and feel free to discuss or develop another one.

AgentEval: A Developer Tool to Assess Utility of LLM-powered Applications

Fig.1 illustrates the general flow of AgentEval with verification step

TL;DR: * As a developer, how can you assess the utility and effectiveness of an LLM-powered application in helping end users with their tasks? * To shed light on the question above, we previously introduced AgentEval — a framework to assess the multi-dimensional utility of any LLM-powered application crafted to assist users in specific tasks. We have now embedded it as part of the AutoGen library to ease developer adoption. * Here, we introduce an updated version of AgentEval that includes a verification process to estimate the robustness of the QuantifierAgent. More details can be found in this paper.

Introduction

Previously introduced AgentEval is a comprehensive framework designed to bridge the gap in assessing the utility of LLM-powered applications. It leverages recent advancements in LLMs to offer a scalable and cost-effective alternative to traditional human evaluations. The framework comprises three main agents: CriticAgent, QuantifierAgent, and VerifierAgent, each playing a crucial role in assessing the task utility of an application.

Agents in AutoGen

TL;DR

• AutoGen agents unify different agent definitions.
• When talking about multi vs. single agents, it is beneficial to clarify whether we refer to the interface or the architecture.

I often get asked two common questions: 1. What's an agent? 1. What are the pros and cons of multi vs. single agent?

This blog collects my thoughts from several interviews and recent learnings.

What's an agent?

There are many different types of definitions of agents. When building AutoGen, I was looking for the most generic notion that can incorporate all these different types of definitions. And to do that we really need to think about the minimal set of concepts that are needed.

In AutoGen, we think about the agent as an entity that can act on behalf of human intent. They can send messages, receive messages, respond to other agents after taking actions and interact with other agents. We think it's a minimal set of capabilities that an agent needs to have underneath. They can have different types of backends to support them to perform actions and generate replies. Some of the agents can use AI models to generate replies. Some other agents can use functions underneath to generate tool-based replies and other agents can use human input as a way to reply to other agents. And you can also have agents that mix these different types of backends or have more complex agents that have internal conversations among multiple agents. But on the surface, other agents still perceive it as a single entity to communicate to.

With this definition, we can incorporate both very simple agents that can solve simple tasks with a single backend, but also we can have agents that are composed of multiple simpler agents. One can recursively build up more powerful agents. The agent concept in AutoGen can cover all these different complexities.

AutoDefense - Defend against jailbreak attacks with AutoGen

TL;DR

• We propose AutoDefense, a multi-agent defense framework using AutoGen to protect LLMs from jailbreak attacks.
• AutoDefense employs a response-filtering mechanism with specialized LLM agents collaborating to analyze potentially harmful responses.
• Experiments show our three-agents (consisting of an intention analyzer, a prompt analyzer, and a judge) defense agency with LLaMA-2-13B effectively reduces jailbreak attack success rate while maintaining low false positives on normal user requests.

What's New in AutoGen?

TL;DR

• AutoGen has received tremendous interest and recognition.
• AutoGen has many exciting new features and ongoing research.

Five months have passed since the initial spinoff of AutoGen from FLAML. What have we learned since then? What are the milestones achieved? What's next?

Background

AutoGen was motivated by two big questions:

• What are future AI applications like?
• How do we empower every developer to build them?

Last year, I worked with my colleagues and collaborators from Penn State University and University of Washington, on a new multi-agent framework, to enable the next generation of applications powered by large language models. We have been building AutoGen, as a programming framework for agentic AI, just like PyTorch for deep learning. We developed AutoGen in an open source project FLAML: a fast library for AutoML and tuning. After a few studies like EcoOptiGen and MathChat, in August, we published a technical report about the multi-agent framework. In October, we moved AutoGen from FLAML to a standalone repo on GitHub, and published an updated technical report.