A quickstart guide to installing, configuring, and using the Goose AI agent for software development tasks.
This blueprint demonstrates how to build an AI agent that automates blog post creation using LlamaIndex and NVIDIA's language and retrieval models, ensuring high-quality, well-researched content.
An article detailing the capabilities and application of PydanticAI in building production-grade AI applications, particularly focusing on multi-agent systems.
A tutorial on using Qwen2.5–7B-Instruct for creating a local, open-source, multi-agentic RAG system.
The implementation described in the article focuses on creating a multi-agentic Retrieval-Augmented Generation (RAG) system using code agents and the Qwen2.5–7B-Instruct model. The system consists of three agents working together in a hierarchical structure:
1. **Manager Agent**: This top-level agent breaks down user questions into sub-tasks, utilizes the Wikipedia search agent to find information, and combines the results to provide a final answer. Its system prompt is tailored to guide it through the process of decomposing tasks and coordinating with other agents.
2. **Wikipedia Search Agent**: This agent interacts with the Wikipedia search tool to identify relevant pages and their summaries. It further delegates to the page search agent for detailed information retrieval from specific pages if needed. Its prompt is designed to help it navigate Wikipedia effectively and extract necessary information.
3. **Page Search Agent**: This agent specializes in extracting precise information from a given Wikipedia page. It uses a semantic search tool to locate specific passages related to the query.
To implement the multi-agent system efficiently, the article mentions several key decisions and modifications to the default Hugging Face implementation:
- **Prompting**: Customized prompts for each agent, including specific examples that mirror the model’s chat template, to improve task-specific performance.
- **History Summarization**: Limiting the history passed to each step to avoid excessive context length and improve execution speed.
- **Tool Wrapping**: Wrapping managed agents as tools to allow better control over the prompts and streamline the architecture.
- **Error Handling**: Implementing mechanisms to handle tool execution errors effectively.
- **Execution Limiting**: Setting a maximum number of attempts for the page search agent to prevent infinite loops when searching for information that might not be present on the page.
- **Tool Response Modification**: Adapting the tool response format to fit the Qwen2.5–7B-Instruct model’s chat template, which supports only system, user, and assistant roles.
By structuring the implementation with these considerations, the system achieves the capability to perform complex, multi-hop question-answering tasks efficiently, despite being powered by a relatively small model running on consumer-grade hardware
The implementation described in the article focuses on creating a multi-agentic Retrieval-Augmented Generation (RAG) system using code agents and the Qwen2.5–7B-Instruct model. The system consists of three agents working together in a hierarchical structure:
1. **Manager Agent**: This top-level agent breaks down user questions into sub-tasks, utilizes the Wikipedia search agent to find information, and combines the results to provide a final answer. Its system prompt is tailored to guide it through the process of decomposing tasks and coordinating with other agents.
2. **Wikipedia Search Agent**: This agent interacts with the Wikipedia search tool to identify relevant pages and their summaries. It further delegates to the page search agent for detailed information retrieval from specific pages if needed. Its prompt is designed to help it navigate Wikipedia effectively and extract necessary information.
3. **Page Search Agent**: This agent specializes in extracting precise information from a given Wikipedia page. It uses a semantic search tool to locate specific passages related to the query.
To implement the multi-agent system efficiently, the article mentions several key decisions and modifications to the default Hugging Face implementation:
- **Prompting**: Customized prompts for each agent, including specific examples that mirror the model’s chat template, to improve task-specific performance.
- **History Summarization**: Limiting the history passed to each step to avoid excessive context length and improve execution speed.
- **Tool Wrapping**: Wrapping managed agents as tools to allow better control over the prompts and streamline the architecture.
- **Error Handling**: Implementing mechanisms to handle tool execution errors effectively.
- **Execution Limiting**: Setting a maximum number of attempts for the page search agent to prevent infinite loops when searching for information that might not be present on the page.
- **Tool Response Modification**: Adapting the tool response format to fit the Qwen2.5–7B-Instruct model’s chat template, which supports only system, user, and assistant roles.
By structuring the implementation with these considerations, the system achieves the capability to perform complex, multi-hop question-answering tasks efficiently, despite being powered by a relatively small model running on consumer-grade hardware
A proof concept for an AI-powered hedge fund that explores the use of AI to make trading decisions for educational purposes.
An article on building an AI agent to interact with Apache Airflow using PydanticAI and Gemini 2.0, providing a structured and reliable method for managing DAGs through natural language queries.
- Agent interacts with Apache Airflow via the Airflow REST API.
- Agent can understand natural language queries about workflows, fetch real-time status updates, and return structured data.
- Sample DAGs are implemented for demonstration purposes.
- Agent interacts with Apache Airflow via the Airflow REST API.
- Agent can understand natural language queries about workflows, fetch real-time status updates, and return structured data.
- Sample DAGs are implemented for demonstration purposes.
This article discusses methods to measure and improve the accuracy of Large Language Model (LLM) applications, focusing on building an SQL Agent where precision is crucial. It covers setting up the environment, creating a prototype, evaluating accuracy, and using techniques like self-reflection and retrieval-augmented generation (RAG) to enhance performance.
A collection of lightweight AI-powered tools built with LLaMA.cpp and small language models.
All Hands AI has released OpenHands CodeAct 2.1, an open-source software development agent that can solve over 50% of real GitHub issues in SWE-Bench. The agent uses Anthropic’s Claude-3.5 model, function calling, and improved directory traversal to achieve this milestone.
This paper describes a computational cognitive model of instrument operations at the Linac Coherent Light Source (LCLS), a leading scientific user facility.
- The model simulates aspects of human cognition at multiple scales, ranging from seconds to hours, and among agents playing multiple roles.
- The model can predict impacts stemming from proposed changes to operational interfaces and workflows, and its code is open source.
- Example results demonstrate the model's potential in guiding modifications to improve operational efficiency and scientific output.
The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
Conclusions:
1. The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
2. The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
3. The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
4. Future work includes extending the model to capture more detailed measurements of individual and team behavior, inter- and intra-team communications, and learning at multiple scales.
- The model simulates aspects of human cognition at multiple scales, ranging from seconds to hours, and among agents playing multiple roles.
- The model can predict impacts stemming from proposed changes to operational interfaces and workflows, and its code is open source.
- Example results demonstrate the model's potential in guiding modifications to improve operational efficiency and scientific output.
The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
Conclusions:
1. The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
2. The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
3. The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
4. Future work includes extending the model to capture more detailed measurements of individual and team behavior, inter- and intra-team communications, and learning at multiple scales.
