Agents

Description

Agents is a systematic framework for training language agents, inspired by neural network learning. It implements loss function, back-propagation, and weight optimization for agent training, supporting both single and multi-agent systems.

Key Features

• Analogous Structure to Neural Networks
• Agent Pipeline: Corresponds to the computational graph of a neural network
• Nodes: Equivalent to layers in a neural network
• Prompts and Tools: Act as the weights of a layer
• Core Components
• Loss Function: Implemented using prompt-based evaluation
• Back-Propagation: Generates textual analyses and reflections for each node
• Weight Optimizer: Updates symbolic components based on language gradients
• Training Process
• Forward Pass: Execute agent actions and store inputs, outputs, prompts, and tool usage
• Loss Evaluation: Use prompt-based loss function to assess outcomes
• Back-Propagation: Generate language gradients through textual analysis
• Update: Modify symbolic components and computational graph structure

Use Cases

• NLP Classroom: Interactive communication between professor and students
• Prisoner's Dilemma: Classic game theory scenario with rational agents
• Software Design: Collaborative code generation with writer, tester, and reviewer
• Database Administrator (DBA): System anomaly detection and diagnosis
• Text Evaluation (ChatEval): Multi-agent referee team for assessing text quality
• Pokemon: Interactive game world with multiple characters (available in release-0.1)

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