Agents Framework by aiwaves-cn
The Agents Framework by aiwaves-cn is an innovative open-source platform designed for creating and training data-centric, self-evolving autonomous language agents. Inspired by connectionist learning in neural networks, this framework introduces symbolic learning for language agents, mapping agent pipelines to computational graphs and enabling advanced training techniques like back-propagation and gradient-based updates using language-based loss and prompts.
Key Features
- Symbolic Learning: Mimics neural network training with language-based loss functions, back-propagation, and weight updates through prompts, allowing agents to self-evolve.
- Agent Pipeline as Computational Graph: Treats each node in the agent pipeline as a layer in a neural net, with prompts and tools acting as adjustable weights.
- Multi-Agent System Support: Facilitates optimization of multi-agent setups by treating nodes as different agents or enabling collaborative actions within a node.
- Comprehensive Training Workflow: Implements a 'forward pass' for agent execution, stores trajectories, evaluates outcomes with language loss, and updates components based on language gradients.
- Version 2.0 Update: Adds robust support for agent learning and evaluation, enhancing the original framework's capabilities.
Use Cases
- Task Automation: Ideal for automating complex workflows through self-improving language agents.
- Research and Development: Useful for researchers exploring autonomous AI systems and symbolic learning methodologies.
- Custom Agent Development: Enables developers to build tailored language agents for specific domains or tasks.
Target Users
This framework targets AI researchers, developers, and organizations looking to leverage autonomous language agents for innovative applications. Its unique selling point lies in its novel approach to agent training, bridging neural network concepts with language processing, and offering a scalable solution for multi-agent systems.
For more details, visit the project page, read the research paper, or explore the documentation.
