GitHub Repository
FreeAn open-source framework for data-centric, self-evolving autonomous language agents
FreeFree tier
About GitHub Repository
Agents is an open-source framework for data-centric, self-evolving autonomous language agents. It introduces agent symbolic learning, a systematic framework for training language agents inspired by connectionist learning. The framework implements forward pass (agent execution), language loss computation, back-propagation of language gradients, and weight update of prompts and tools. It supports optimizing multi-agent systems by treating nodes as different agents. The project is developed by aiwaves-cn and is hosted on GitHub with over 6,000 stars.
Key Features
Agent symbolic learning framework
Data-centric self-evolving agents
Language-based loss and gradients for agent training
Forward pass (execution) and back-propagation of language loss
Supports optimizing multi-agent systems
Open-source with extensive documentation and examples
Pros & Cons
Pros
- Open-source and freely available
- Innovative symbolic learning approach for agents
- Supports multi-agent systems
- Data-centric and self-evolving capabilities
- Well-documented with research paper
Cons
- Requires understanding of symbolic learning concepts
- May need significant computational resources for training (typical for AI frameworks)
- Relatively new framework, community and maturity may be limited
Best For
Training autonomous language agentsResearch in agent symbolic learning and self-evolving AIBuilding and optimizing multi-agent systemsAcademic research and development of AI agents
FAQ
What is agent symbolic learning?
Agent symbolic learning is a systematic framework for training language agents, analogous to connectionist learning in neural networks. It uses language-based loss, gradients, and weight updates to train agent pipelines.
Is Agents open-source?
Yes, Agents is an open-source framework hosted on GitHub under the aiwaves-cn organization with a permissive license.
Does Agents support multi-agent systems?
Yes, the framework naturally supports optimizing multi-agent systems by considering nodes as different agents or allowing multiple agents to take actions in one node.