Agents

An AI agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools.
See:

AI/Deep Learning/Multimodal learning

LLMs

Resources

LangChain State of AI Agents Report
- There is an admiration for these capabilities of AI agents:
  - Managing multistep tasks
  - Automating repetitive tasks
  - Task routing & collaboration
  - Human-like reasoning
ChatGPT’s next level is Agent AI: Auto-GPT, BabyAGI, AgentGPT, Microsoft Jarvis & friends
Agents @ Work: Dust.tt - Latent Space
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous
- https://significant-gravitas.github.io/Auto-GPT/
- https://autogpt.net/
AgentGPT
JARVIS / HuggingGPT
Building a Local Voice AI Assistant with Llama 3.2 & OpenAI Whisper Turbo 3 - YouTube
Agents in AutoGen | AutoGen (microsoft.github.io)
- Agent - an entity that can act on behalf of human intent
- we can incorporate both very simple agents that can solve simple tasks with a single backend, but also we can have agents that are composed of multiple simpler agents
- When we use the word 'multi-agent' and 'single-agent', I think there are at least two different dimensions:
  - Interface
    - From the user's point of view, do they interact with the system in a single interaction point or do they see explicitly multiple agents working and need to interact with multiple of them?
    - A single interaction point can make many applications' user experience more straightforward
  - Architecture
    - Are there multiple agents underneath running at the backend?
    - The multi-agent design of the architecture is easier to maintain, understand and extend than a single agent system
    - It's very important to recognize that the multi-agent architecture is a good way to build a single agent
- A particular system can have a single-agent interface and a multi-agent architecture
- Ways of combining models and agents:
  - Different conversation patterns or chats: sequential, group, constrained group, customized group, nested
  - Different prompting and reasoning techniques such as reflection, ReAct
  - Tool use and code execution
  - Planning and task decomposition
  - Retrieve augmentation
  - Integrating multiple models, APIs, modalities and memories
Stateflow - enhancing LLM task-solving through state-driven workflows
EcoAssistant - Using LLM Assistants More Accurately and Affordably | AutoGen (microsoft.github.io)
Agent for text-to-SQL with automatic error correction - Hugging Face Open-Source AI Cookbook
- Agent for text-to-SQL with automatic error correction
- Uses ReactCodeAgent in transformers.agents
Natural Language Queries Across Databases with LLMs, AutoGen, and Langchain (PostgreSQL) | by Manojpn | Medium
- Autogen for SQL queries
Step by Step guide to develop AI Multi-Agent system using Microsoft Semantic Kernel and GPT-4o | by Akshay Kokane | Jun, 2024 | Medium
- Multi-agent system with semantic kernel and openai assistant. Using .NET
https://numbersstation.ai/text-to-sql-that-isnt/
- https://www.youtube.com/watch?v=xmzda44hUgk
Agent for text-to-SQL with automatic error correction - Hugging Face Open-Source AI Cookbook
Natural Language Queries Across Databases with LLMs, AutoGen, and Langchain (PostgreSQL) | by Manojpn | Medium

Courses

Code

#CODE AG2 - AG2 (formerly AutoGen): The Open-Source AgentOS
- AgentOS
- AutoGen | AG2
#CODE Autogen - A programming framework for agentic AI 🤖
#CODE Semantic-kernel
#CODE Letta - Letta (formerly MemGPT) is a framework for creating LLM services with memory
- MemGPT
- MemGPT + AutoGen
#CODE Mem0
- Agent with memory using Mem0 | AutoGen
- Mem0: Long-Term Memory and Personalization for Agents | AutoGen
#CODE CrewAI - Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks
- CrewAI
#CODE LangGraph
#CODE Llama-agents
#CODE Transformers-agents: License to Call: Introducing Transformers Agents 2.0 (huggingface.co)
#CODE Agentscope
- Start building LLM-empowered multi-agent applications in an easier way
- AgentScope Documentation — AgentScope documentation
#CODE Atomic-agents
- The Atomic Agents framework is designed to be extremely lightweight, modular, extensible, and easy to use. Its main goal is to eliminate redundant complexity, unnecessary abstractions, and hidden assumptions while still providing a flexible and powerful platform for building AI applications through atomicity

Resources

Courses

Code

References