Agents

Multi AgentGeneral purposeAgent Building Frameworks and Platforms

What is Agents?

Agents is an open-source framework for developing self-evolving AI agents using symbolic learning techniques. It enables systematic agent training by implementing innovative methods like language-based loss functions and computational graph optimization. The project allows for dynamic learning and adaptation of autonomous language agents.

Features

Long-short Term Memory with VectorDB and Semantic Search

Tool Usage via function-calling

Multi-agent Communication with dynamic controller

Human-Agent interaction for seamless collaboration

Links

Website Documentation GitHub

Pros and Cons of Agents

Pros

Enables gradient-based optimization of agent pipelines

Supports backpropagation of "language loss" for improved performance

Allows for systematic tuning of agent prompts and tool usage

Provides a framework for analyzing and improving agent behavior

Facilitates the development of more robust and efficient agents

Cons

Relies heavily on the quality of the "language loss" function

Requires careful design of prompt pipelines and loss functions

May be computationally expensive for complex agent architectures

Potential for bias in the learned agent behavior

Agents Use Cases

Building complex AI-powered workflows

Developing conversational AI applications

Automating business processes with AI

Researching and experimenting with multi-agent systems

Creating custom AI assistants for specific needs

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