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Choosing the right agent framework

Choosing the right agent framework

Pros and cons of some top agent frameworks, testing o1 against GPT-4o and Sonnet 3.5, and more

Michael Cunningham
September 26, 2024

Welcome back to Building AI Agents, your biweekly guide to everything new in the AI agent field! Enjoy this first of many Thursday issues

We’re excited about the wide range of applications for LLM agents, but we hope that most will be a little less dystopian than this

In today’s issue…

OpenAI hires for a multi-agent research team
NVIDIA pursues “foundation agents”
Choosing the right agent framework
How o1 compares to other LLMs for agentic workflows

…and more

📰 NEWS

Source: OpenAI

OpenAI hiring for multi-agent research team

OpenAI is reportedly recruiting machine learning engineers for a new research team devoted to multi-agent systems, which the company sees as a path to the next level of AI reasoning capabilities.

Gumloop launches Chrome extension for building browser agents

Low-code agent framework provider Gumloop released a Google Chrome extension, allowing users to easily build AI-powered browser agents.

NVIDIA is working on “foundation agents”

NVIDIA researcher Jim Fan elaborated on his vision of generalist “foundation agents” analogous to foundation models, which will be capable of performing both virtual and embodied tasks.

Amazon launches update to Q Developer Agent

Amazon released a new version of its coding assistant agent, allowing users to interact with it directly in their IDE via natural language.

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🛠️ USEFUL STUFF

Source: FlickrCC

How to choose the right agent framework

A guide to choosing between LangChain and LlamaIndex, breaking down both frameworks’ advantages and disadvantages.

A repository of agent frameworks, tutorials, and knowledge

A GitHub repo containing an extensive collection of agent implementations and tutorials for a variety of applications.

o1 vs Sonnet 3.5 and GPT-4o

The authors of this report benchmarked some of the top closed- and open-source LLMs on a set of agentic tasks, finding that OpenAI’s o1 performed the best on average, but with highly variable performance.

A deep dive on LangGraph

A detailed, 45-minute video tutorial on building an AI research agent with LangGraph.

💡 ANALYSIS

Source: Wikimedia Commons

Should your firm build its own AI agents?

This article weighs the pros and cons for companies of building their own AI agents in-house versus outsourcing the task to specialized consultants.

🧪 RESEARCH

Source: arXiv

Iteration of Thought: leveraging internal dialogue to enhance LLM reasoning

The authors of this paper build on the Tree of Thoughts paradigm to introduce Iteration of Thoughts, in which an LLM iteratively refines its own outputs as it reasons its way towards a correct answer—an approach strikingly similar to OpenAI’s o1 models.

Improving LLM reasoning with a tree-of-thought validator agent

This paper takes an alternative approach to improving Tree of Thoughts, using Reasoner agents to generate the thoughts, and a Validator agent to discard unsound reasoning traces.

Thanks for reading! Until next time, keep learning and building!

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