smolagents is a simple library that enables agentic capabilities for language models, allowing them to interact with external tools and perform tasks based on real-world data.
Hugging Face's SmolAgents simplifies the creation of intelligent agents by allowing developers to build them with just a few lines of code using powerful pretrained models.
smolagents is a library that enables you to run powerful agents with minimal complexity, supporting any LLM and offering first-class support for code agents.
Example: `webagent --prompt "go to xyz.com/women, get to sale section, click the first clothing item you see. Get the product details, and the price, return them. note that I'm shopping from France"`
Example: `webagent --prompt "go to xyz.com/women, get to sale section, click the first clothing item you see. Get the product details, and the price, return them. note that I'm shopping from France"`
A workshop hosted by Princeton Language and Intelligence featuring experts on developing, evaluating, and ensuring the reliability of AI agents, with sessions on developer tools, real-world evaluations, and ensuring reliability.
This paper explores the cultural evolution of cooperation among LLM agents through a variant of the Donor Game, finding significant differences in cooperative behavior across various base models and initial strategies.
A script utilizing OpenAI's Llama models to interact within a terminal environment, allowing the models to execute Python code and communicate based on predefined prompts.
ServiceNow releases AgentLab, an open-source Python package for developing and evaluating web agents, addressing challenges in agent development such as poor scalability and difficulty in conducting reproducible experiments.
The article discusses the use of AI agents for automating and optimizing tasks in the networking industry, including network deployment, configuration, and monitoring. It outlines a workflow with four agents that collectively achieve the setup and verification of network connectivity within a Linux and SR Linux container environment.
The author demonstrates a workflow involving four AI agents designed to deploy, configure, and monitor a network:
Document Specialist Agent: This agent extracts installation, topology deployment, and node connection instructions from a specified website.
- Linux Configuration Agent: Executes the installation and configuration commands on a Debian 12 UTM VM, checks the health of the VM, and verifies the successful deployment of network containers.
- Network Configuration Specialist Agent: Configures network IP allocation, interfaces, and routing based on the network topology, including detailed BGP configurations for different network nodes.
- Senior Network Administrator Agent: Applies the generated configurations to the network nodes, checks BGP peering, and verifies end-to-end connectivity through ping tests.
The author demonstrates a workflow involving four AI agents designed to deploy, configure, and monitor a network:
Document Specialist Agent: This agent extracts installation, topology deployment, and node connection instructions from a specified website.
- Linux Configuration Agent: Executes the installation and configuration commands on a Debian 12 UTM VM, checks the health of the VM, and verifies the successful deployment of network containers.
- Network Configuration Specialist Agent: Configures network IP allocation, interfaces, and routing based on the network topology, including detailed BGP configurations for different network nodes.
- Senior Network Administrator Agent: Applies the generated configurations to the network nodes, checks BGP peering, and verifies end-to-end connectivity through ping tests.
The article discusses the emerging role of AI agents as distinct users, requiring designers to adapt their practices to account for the needs and capabilities of these intelligent systems.
- Agents are becoming active users in systems, requiring designers to extend UX principles to include both humans and A and agents.
- The future of UX lies in understanding and designing for Agent-Computer Interaction.
- Agents are becoming active users in systems, requiring designers to extend UX principles to include both humans and A and agents.
- The future of UX lies in understanding and designing for Agent-Computer Interaction.
The article discusses the role of AI agents in generative AI, focusing on tool calling and reasoning abilities, and how they can be evaluated using benchmarks like BFCL and Nexus Function Calling Benchmark.
