Llama-agents: an async-first framework for building production ready agents
The GitHub repository `llama-agents` provides an async-first framework for multi-agent systems. It includes features like communication, tool execution, and human-in-the-loop functions. Detailed installation, workflows, examples, and API guidance are available.
Read original article
The GitHub repository for `llama-agents` offers an async-first framework for creating, refining, and deploying multi-agent systems. It encompasses functionalities like multi-agent communication, distributed tool execution, human-in-the-loop functions, and additional features. The repository contains comprehensive guidance on installation procedures, initial steps, local/notebook workflow, server workflow, illustrative examples, components of a `llama-agents` system, and the low-level API within `llama-agents`. For more specific information or assistance, users are encouraged to inquire directly within the repository.
Related
Run the strongest open-source LLM model: Llama3 70B with just a single 4GB GPU
The article discusses the release of open-source Llama3 70B model, highlighting its performance compared to GPT-4 and Claude3 Opus. It emphasizes training enhancements, data quality, and the competition between open and closed-source models.
GitHub – Karpathy/LLM101n: LLM101n: Let's Build a Storyteller
The GitHub repository "LLM101n: Let's build a Storyteller" offers a course on creating a Storyteller AI Large Language Model using Python, C, and CUDA. It caters to beginners, covering language modeling, deployment, programming, data types, deep learning, and neural nets. Additional chapters and appendices are available for further exploration.
How to run an LLM on your PC, not in the cloud, in less than 10 minutes
You can easily set up and run large language models (LLMs) on your PC using tools like Ollama, LM Suite, and Llama.cpp. Ollama supports AMD GPUs and AVX2-compatible CPUs, with straightforward installation across different systems. It offers commands for managing models and now supports select AMD Radeon cards.
AWS Lambda Web Adapter
The GitHub repository provides details on the AWS Lambda Web Adapter, allowing developers to build web apps on AWS Lambda with features like endpoint support, response encoding, and local debugging.
LLMs on the Command Line
Simon Willison presented a Python command-line utility for accessing Large Language Models (LLMs) efficiently, supporting OpenAI models and plugins for various providers. The tool enables running prompts, managing conversations, accessing specific models like Claude 3, and logging interactions to a SQLite database. Willison highlighted using LLM for tasks like summarizing discussions and emphasized the importance of embeddings for semantic search, showcasing LLM's support for content similarity queries and extensibility through plugins and OpenAI API compatibility.
9 commentsWhich is essentially a next.js app where SSR is used to communicate with the LLMs/agents. Personally I used to hate next.js, but its application architecture is uniquely suited to UX with LLMs.
Clearly the asynchronous tasks taken by agents shouldnt run on next.js server side, but the integration between the user and agent will need to be so tight, that it's hard to imagine the value in some purely asynchronous system. A huge portion of the system/state will need to be synchronously available to the user.
LLMs are not good enough to run purely on their own, and probably wont be for atleast another year.
If I was to guess, Agent systems like this will run on serverless AWS/cloud architectures.
We have a solid set of things to improve, and now is the best time to contribute and shape the project.
Feel free to ask me anything!
Related
Run the strongest open-source LLM model: Llama3 70B with just a single 4GB GPU
The article discusses the release of open-source Llama3 70B model, highlighting its performance compared to GPT-4 and Claude3 Opus. It emphasizes training enhancements, data quality, and the competition between open and closed-source models.
GitHub – Karpathy/LLM101n: LLM101n: Let's Build a Storyteller
The GitHub repository "LLM101n: Let's build a Storyteller" offers a course on creating a Storyteller AI Large Language Model using Python, C, and CUDA. It caters to beginners, covering language modeling, deployment, programming, data types, deep learning, and neural nets. Additional chapters and appendices are available for further exploration.
How to run an LLM on your PC, not in the cloud, in less than 10 minutes
You can easily set up and run large language models (LLMs) on your PC using tools like Ollama, LM Suite, and Llama.cpp. Ollama supports AMD GPUs and AVX2-compatible CPUs, with straightforward installation across different systems. It offers commands for managing models and now supports select AMD Radeon cards.
AWS Lambda Web Adapter
The GitHub repository provides details on the AWS Lambda Web Adapter, allowing developers to build web apps on AWS Lambda with features like endpoint support, response encoding, and local debugging.
LLMs on the Command Line
Simon Willison presented a Python command-line utility for accessing Large Language Models (LLMs) efficiently, supporting OpenAI models and plugins for various providers. The tool enables running prompts, managing conversations, accessing specific models like Claude 3, and logging interactions to a SQLite database. Willison highlighted using LLM for tasks like summarizing discussions and emphasized the importance of embeddings for semantic search, showcasing LLM's support for content similarity queries and extensibility through plugins and OpenAI API compatibility.