It is a lightweight framework designed for building LLM-based (Large Language Model-based) agents, enabling users to create multi-agent applications with a focus on simplicity and flexibility.
It is a lightweight framework designed for building LLM-based (Large Language Model-based) agents, enabling users to create multi-agent applications with a focus on simplicity and flexibility. The framework is inspired by the design philosophy of PyTorch, using an analogy of neural network layers to make the workflow intuitive. Users can focus on creating layers and defining message passing between them in a Pythonic way. Agents communicate using AgentMessage, and both input and output messages are added to the agent’s memory during each forward pass, which is handled in the `__call__` method rather than `forward`.
The framework provides tools for inspecting and clearing memory, with a default aggregator that converts AgentMessage to OpenAI message format. Users can implement custom aggregators and use tools like ToolParser to parse model outputs. ActionExecutor, which shares the same communication structure as Agent, requires input AgentMessage content to be a dictionary. Custom hooks for message conversion can also be registered. Lagent includes InternLMActionProcessor, which is adapted for messages formatted by ToolParser.
Lagent adopts a dual interface design, offering both synchronous and asynchronous variants for components like LLMs, actions, and action executors. Synchronous agents are recommended for debugging, while asynchronous agents are ideal for large-scale inference to optimize CPU and GPU resource utilization. Consistency is emphasized, ensuring asynchronous agents are paired with asynchronous LLMs and action executors.
The framework supports various agent types, such as math agents for problem-solving through programming and asynchronous blogging agents for improving writing quality via self-refinement. It also enables multi-agent workflows for tasks like information retrieval, data collection, and chart plotting. Lagent is released under the Apache 2.0 license and encourages users to cite the project if used in research. Installation and usage details, along with examples, are provided in the documentation. Users can join the community on platforms like š¯•¸ (Twitter), Discord, and WeChat for further engagement.
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