This article introduces ROSA, a Robot Operating System (ROS) framework designed to seamlessly integrate Large Language Models (LLMs) into embodied AI systems. ROSA addresses the challenges of connecting LLMs to robotic hardware by providing a standardized interface for perception, planning, and action.
The framework utilizes a prompt-based approach, converting robot tasks into natural language prompts for the LLM. This allows for flexible task specification and reasoning.
ROSA also includes tools for managing LLM outputs, ensuring safe and reliable robot behavior. The authors demonstrate ROSA’s capabilities through various experiments, showcasing its potential for creating more intelligent and adaptable robots.
The framework utilizes a prompt-based approach, converting robot tasks into natural language prompts for the LLM. This allows for flexible task specification and reasoning.
ROSA also includes tools for managing LLM outputs, ensuring safe and reliable robot behavior. The authors demonstrate ROSA’s capabilities through various experiments, showcasing its potential for creating more intelligent and adaptable robots.
agentic_TRACE is a framework designed to build LLM-powered data analysis agents that prioritize data integrity and auditability. It addresses the risks associated with directly feeding data to LLMs, such as fabrication, inaccurate calculations, and context window limitations. The core principle is to separate the LLM's orchestration role from the actual data processing, which is handled by deterministic tools.
This approach ensures prompts remain concise, minimizes hallucination risks, and provides a complete audit trail of data transformations. The framework is domain-agnostic, allowing users to extend it with custom tools and data sources for specific applications. A working example, focusing on stock market analysis, demonstrates its capabilities.
This approach ensures prompts remain concise, minimizes hallucination risks, and provides a complete audit trail of data transformations. The framework is domain-agnostic, allowing users to extend it with custom tools and data sources for specific applications. A working example, focusing on stock market analysis, demonstrates its capabilities.
Learn how to equip your Microsoft Agent Framework agents with portable, reusable skill packages that provide domain expertise on demand using Agent Skills. This article covers what Agent Skills are, progressive disclosure, creating skills, connecting skills to an agent (with .NET and Python examples), use cases, and security considerations.
An easy starting point for larger and more complicated Dash dashboards. This article presents a sensible, and fully functional, multi-file project structure, containing all the essentials to get started.
The article discusses the OVON agentic framework for mitigating hallucinations in Large Language Models (LLMs). It explains the structured, collaborative pipeline involving front-end and reviewer agents, the use of 'Conversation Envelopes' and 'Whispers' for efficient data exchange, and novel KPIs for measuring success. The article also addresses future directions and the importance of trust in AI systems.
Minimalist LLM Framework in 100 Lines. Enable LLMs to Program Themselves.
Encore is an open-source backend framework and cloud platform for building distributed systems. It automates infrastructure, offers end-to-end type-safety, and provides built-in observability.
- Automated Infrastructure:
Local & Cloud (AWS/GCP) provisioning
Infrastructure semantics within application code
No separate config tools needed
- Type-Safe Microservices:
Define & call APIs like normal functions
Full type-safety & IDE auto-complete
Automatic protocol communication boilerplate
- Faster Development:
Hot reload & automatic local infrastructure setup
Simplified, speedier development process
Observability:
API explorer, distributed tracing, architecture diagrams
Service catalog with automatic API documentation
- Cloud Platform:
Seamless workflow with CI/CD, testing, & infrastructure provisioning
Preview environments for every PR
- Security & Scalability:
Battle-tested AWS/GCP services
Best practices for security & scalability
Metrics & logging for critical aspects
- Automated Infrastructure:
Local & Cloud (AWS/GCP) provisioning
Infrastructure semantics within application code
No separate config tools needed
- Type-Safe Microservices:
Define & call APIs like normal functions
Full type-safety & IDE auto-complete
Automatic protocol communication boilerplate
- Faster Development:
Hot reload & automatic local infrastructure setup
Simplified, speedier development process
Observability:
API explorer, distributed tracing, architecture diagrams
Service catalog with automatic API documentation
- Cloud Platform:
Seamless workflow with CI/CD, testing, & infrastructure provisioning
Preview environments for every PR
- Security & Scalability:
Battle-tested AWS/GCP services
Best practices for security & scalability
Metrics & logging for critical aspects
The article discusses the limitations of Large Language Models (LLMs) in planning and self-verification tasks, and proposes an LLM-Modulo framework to leverage their strengths in a more effective manner. The framework combines LLMs with external model-based verifiers to generate, evaluate, and improve plans, ensuring their correctness and efficiency.
"Simply put, we take the stance that LLMs are amazing giant external non-veridical memories that can serve as powerful cognitive orthotics for human or machine agents, if rightly used."
"Simply put, we take the stance that LLMs are amazing giant external non-veridical memories that can serve as powerful cognitive orthotics for human or machine agents, if rightly used."
Mesop is a Python-based UI framework that allows you to rapidly build web apps like demos and internal apps. Easy to get started, fast iteration, and flexible & composable.
open-source LLM tools offer transparency, flexibility, cost-effectiveness, and heightened data security.
