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

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 Autogen
#CODE Semantic-kernel
#CODE Letta (MemGPT)
#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

Resources

Courses

Code

References