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Overview Agents is an open-source library/framework for building autonomous language agents. The library is carefully engineered to support important features including long-short term memory, tool usage, web navigation, multi-agent communication, and brand new features including human-agent interaction and symbolic control. With Agents, one can customize a language agent or a multi-agent system by simply filling in a config file in natural language and deploy the language agents in a terminal, a Gradio interface, or a backend service.
One major difference between Agents and other existing frameworks for language agents is that our framework allows users to provide fine-grained control and guidance to language agents via an SOP (Standard Operation Process). An SOP defines subgoals/subtasks for the overall task and allows users to customize a fine-grained workflow for the language agents.
Highlights Long-short Term Memory: Language agents in the library are equipped with both long-term memory implemented via VectorDB + Semantic Search and short-term memory (working memory) maintained and updated by an LLM. Tool Usage: Language agents in the library can use any external tools via function-calling and developers can add customized tools/APIs here. Web Navigation: Language agents in the library can use search engines to navigate the web and get useful information. Multi-agent Communication: In addition to single language agents, the library supports building multi-agent systems in which language agents can communicate with other language agents and the environment. Different from most existing frameworks for multi-agent systems that use pre-defined rules to control the order for agents' action, Agents includes a controller function that dynamically decides which agent will perform the next action using an LLM by considering the previous actions, the environment, and the target of the current states. This makes multi-agent communication more flexible. Human-Agent interaction: In addition to letting language agents communicate with each other in an environment, our framework seamlessly supports human users to play the role of the agent by himself/herself and input his/her own actions, and interact with other language agents in the environment. Symbolic Control: Different from existing frameworks for language agents that only use a simple task description to control the entire multi-agent system over the whole task completion process, Agents allows users to use an SOP (Standard Operation Process) that defines subgoals/subtasks for the overall task to customize fine-grained workflows for the language agents.
https://github.com/aiwaves-cn/agents