aiwaves-cn/agents
Agents
This is an LLM agent framework and symbolic learning system designed for building self-evolving autonomous agents. It functions as a computational graph orchestrator that organizes agent interactions and tool sequences as a trainable graph of nodes.
The framework focuses on data-centric agent optimization, allowing agent pipelines and prompts to be upgraded through data-driven training rather than manual engineering. It utilizes a symbolic learning process that applies language-based loss and textual reflections to refine the operational logic and symbolic components of an agent.
The system covers multi-agent coordination and the orchestration of complex AI workflows. It enables the creation of agents that continuously refine their own behavior through iterative evaluation and the application of language-based gradients to prompts and tools.
Features
- Autonomous Agent Orchestration - Provides a computational graph orchestrator to build and manage complex AI workflows using sequences of prompts and tools.
- Symbolic Learning Systems - Optimizes agent behavior by applying language-based loss and textual reflections to update operational logic.
- Agent Training Tools - Implements a symbolic learning process that applies language-based loss and gradients to refine agent prompts and tools.
- Agentic LLM Frameworks - Provides a comprehensive framework for building self-evolving autonomous agents powered by LLMs with symbolic learning.
- Self-Evolving Agent Frameworks - Provides a framework for agents to autonomously evolve their behavior through continuous training and evaluation processes.
- Data-Driven Prompt Optimizers - Automates the improvement of prompts and agent pipelines through data-driven training instead of manual engineering.
- Data-Centric Agent Optimization - Uses training data and symbolic learning to automatically improve agent prompts and pipelines without manual engineering.
- Data-Centric Agent Optimizers - Automatically upgrades agent pipelines through data-driven training instead of manual prompt engineering.
- Symbolic Gradient Optimizers - Applies language-based loss and gradients to optimize the symbolic components of an autonomous AI agent.
- Symbolic Learning Optimizers - Optimizes agent prompts and tools by calculating language-based loss and using textual reflections to update symbolic components.
- Agentic Task Orchestration - Enables the construction of complex task pipelines using prompts and tools organized as computational graphs.
- Graph Orchestration - Functions as an execution engine that organizes agent interactions and tool sequences as a trainable graph of nodes.
- Multi-Agent Coordination Systems - Manages the interactions and collective behaviors of multiple AI agents through a trainable graph of nodes.
- Multi-Agent Training - Optimizes multi-agent coordination by treating individual actions and collective behaviors as nodes within a trainable graph.
- Agent Frameworks - Framework for self-evolving autonomous language agents.
- Multi-Agent Frameworks - Library for building multi-agent systems with memory and tool usage.
- Multi-Agent Systems - Symbolic learning for self-evolving agents.
- Reasoning And Planning - General-purpose framework for building autonomous language agents.
- Task Automation Agents - Open-source framework for building autonomous language agents.
