AI agents

Trace & Evaluate your Agent with Arize Phoenix

Building an autonomous financial analyst with o1 and o3-mini

Endex builds the future of financial analysis, powered by OpenAI’s reasoning models.

Open-source DeepResearch – Freeing our search agents

DABStep: Data Agent Benchmark for Multi-step Reasoning

Introducing deep research

An agent that uses reasoning to synthesize large amounts of online information and complete multi-step research tasks for you. Available to Pro users today, Plus and Team next.

Computer-Using Agent

AI Agents Are Here. What Now?

Introducing smolagents: simple agents that write actions in code.

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering.

Creating agent and human collaboration with GPT 4o

Altera uses GPT-4o to build a new area of human collaboration

Tool Use, Unified

Our Transformers Code Agent beats the GAIA benchmark 🏅

Achieving 10x growth with agentic sales prospecting

Automating customer support agents

MavenAGI is a new software company for the AI era. They recently launched an AI customer service agent, built on the flexibility of GPT-4, which a number of companies like Tripadvisor, Clickup and Rho are already using to save time and better serve their customers.

License to Call: Introducing Transformers Agents 2.0

Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent

Klarna's AI assistant does the work of 700 full-time agents

Klarna is using AI to revolutionize personal shopping, customer service, and employee productivity.

MELON: Reconstructing 3D objects from images with unknown poses

Posted by Mark Matthews, Senior Software Engineer, and Dmitry Lagun, Research Scientist, Google Research A person's prior experience and understanding of the world generally enables them to easily infer what an object looks like in whole, even if only looking at a few 2D pictures of it. Yet the capacity for a computer to reconstruct the shape of an object in 3D given only a few images has remained a difficult algorithmic problem for years.

Social learning: Collaborative learning with large language models

Posted by Amirkeivan Mohtashami, Research Intern, and Florian Hartmann, Software Engineer, Google Research Large language models (LLMs) have significantly improved the state of the art for solving tasks specified using natural language, often reaching performance close to that of people. As these models increasingly enable assistive agents, it could be beneficial for them to learn effectively from each other, much like people do in social settings, which would allow LLM-based agents to improve each other’s performance.

Graph neural networks in TensorFlow

Posted by Dustin Zelle, Software Engineer, Google Research, and Arno Eigenwillig, Software Engineer, CoreML Objects and their relationships are ubiquitous in the world around us, and relationships can be as important to understanding an object as its own attributes viewed in isolation — take for example transportation networks, production networks, knowledge graphs, or social networks. Discrete mathematics and computer science have a long history of formalizing such networks as graphs , consisting of nodes connected by edges in various irregular ways.