title: Agents: An Open-source Framework for Autonomous Language Agents
summary: Recent advances on large language models (LLMs) enable researchers and
  developers to build autonomous language agents that can automatically solve
  various tasks and interact with environments, humans, and other agents using
  natural language interfaces. We consider language agents as a promising
  direction towards artificial general intelligence and release Agents, an
  open-source library with the goal of opening up these advances to a wider
  non-specialist audience. Agents is carefully engineered to support important
  features including planning, memory, tool usage, multi-agent communication, and
  fine-grained symbolic control. Agents is user-friendly as it enables
  non-specialists to build, customize, test, tune, and deploy state-of-the-art
  autonomous language agents without much coding. The library is also
  research-friendly as its modularized design makes it easily extensible for
  researchers. Agents is available at https://github.com/aiwaves-cn/agents.
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paper_authors.name: Shi Qiu
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paper_authors.name: Jintian Zhang
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paper_authors.fullname: Jintian Zhang
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paper_authors.user: MikeDean
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paper_authors.name: Jing Chen
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paper_authors.name: Ruipu Wu
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paper_authors.name: Shuai Wang
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paper_authors.name: Wentao Zhang
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paper_authors.name: Peng Cui
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paper_authors.name: Mrinmaya Sachan
paper_title: Agents: An Open-source Framework for Autonomous Language Agents
paper_summary: Recent advances on large language models (LLMs) enable researchers and
  developers to build autonomous language agents that can automatically solve
  various tasks and interact with environments, humans, and other agents using
  natural language interfaces. We consider language agents as a promising
  direction towards artificial general intelligence and release Agents, an
  open-source library with the goal of opening up these advances to a wider
  non-specialist audience. Agents is carefully engineered to support important
  features including planning, memory, tool usage, multi-agent communication, and
  fine-grained symbolic control. Agents is user-friendly as it enables
  non-specialists to build, customize, test, tune, and deploy state-of-the-art
  autonomous language agents without much coding. The library is also
  research-friendly as its modularized design makes it easily extensible for
  researchers. Agents is available at https://github.com/aiwaves-cn/agents.
paper_ai_keywords: large language models (LLMs) language agents autonomous language agents artificial general intelligence planning memory tool usage multi-agent communication symbolic control