Agent Frameworks
Agents are semi-autonomous programs that can adjust their behavior based on what they encounter.
Zhou et al. (2023) says this is hard to get right, and that current systems perform poorly.
We experiment with several baseline agents, integrating recent techniques such as reasoning before acting. The results demonstrate that solving complex tasks is challenging: our best GPT-4-based agent only achieves an end-to-end task success rate of 14.41%, significantly lower than the human performance of 78.24%.
General Discussion
Zvi (Don’t Worry about the Vase) offers a summary on AutoGPT:
Lilian Weng has an in-depth description at LLM Powered Autonomous Agents
Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.
Microsoft AutoGen
AutoGen is a GitHub-based framework for development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.
AutoGPT
AutoGPT is a framework for creating a GPT-powered agent. You define a series of scripts that interact with the OpenAI API to generate text, save or process the results as necessary, and then send followup queries.
AutoGPT.net is a whole web site devoted to using AutoGPT
Installation and how to get started
Twitter thread by @hasantoxr
AutoChain
AutoChain takes inspiration from LangChain and AutoGPT and aims to solve both problems by providing a lightweight and extensible framework for developers to build their own agents using LLMs with custom tools and automatically evaluating different user scenarios with simulated conversations. Experienced user of LangChain would find AutoChain is easy to navigate since they share similar but simpler concepts
MetaGPT
MetaGPT: The Multi-Agent Framework > MetaGPT: Simulates a whole software company 🤔 > > It assigns roles like product managers, architects, project managers, and engineers to GPTs. > > With just one line of code, MetaGPT generates user stories, competitive analyses, requirements, data structures, APIs, documents, and more.
via TuringPost
GPT Researcher
The agent can produce detailed, factual and unbiased research reports, with customization options for focusing on relevant resources, outlines, and lessons. Inspired by AutoGPT and the recent Plan-and-Solve paper, GPT Researcher addresses issues of speed and determinism, offering a more stable performance and increased speed through parallelized agent work, as opposed to synchronous operations.
MiniChain
This chaining tool might be the first actually truly useful one for both prototyping and production. It uses function decorators and YAML templates in a clever and powerful way to enable chaining. Examples show that you can write a chat bot, vector database, and more in just 20 lines.
Langchain
Langchain, developed by Harrison Chase, is a Python and JavaScript library for interfacing with OpenAI’s GPT APIs (later expanding to more models) for AI text generation. More specifically, it’s an implementation of the paper ReAct: Synergizing Reasoning and Acting in Language Models published October 2022, colloquially known as the ReAct paper, which demonstrates a prompting technique to allow the model to “reason” (with a chain-of-thoughts) and “act” (by being able to use a tool from a predefined set of tools, such as being able to search the internet). This combination is shown to drastically improve output text quality and give large language models the ability to correctly solve problems.
and Langchain in Realworld, a detailed list of use cases and how to build them.
and LangGraph : a library for building stateful, multi-actor applications with LLMs (Highly recommended by AI Tinkerer Jay Bartot - “Otto - A Virtual Travel Agency”. https://linkedin.com/in/jay-bartot)
Max Woolf’s explanation of why LangChain isn’t necessary: “LangChain’s vaunted prompt engineering is just f-strings, a feature present in every modern Python installation, but with extra steps.”
(and see HN)
Brighten.ai