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26 lines
1.4 KiB
Markdown
26 lines
1.4 KiB
Markdown
# Personal Assistants (Agents)
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> [Conceptual Guide](https://docs.langchain.com/docs/use-cases/personal-assistants)
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We use "personal assistant" here in a very broad sense.
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Personal assistants have a few characteristics:
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- They can interact with the outside world
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- They have knowledge of your data
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- They remember your interactions
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Really all of the functionality in LangChain is relevant for building a personal assistant.
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Highlighting specific parts:
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- [Agent Documentation](../modules/agents.rst) (for interacting with the outside world)
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- [Index Documentation](../modules/indexes.rst) (for giving them knowledge of your data)
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- [Memory](../modules/memory.rst) (for helping them remember interactions)
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Specific examples of this include:
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- [Baby AGI](agents/baby_agi.ipynb): a notebook implementing [BabyAGI](https://github.com/yoheinakajima/babyagi) by Yohei Nakajima as LLM Chains
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- [Baby AGI with Tools](agents/baby_agi_with_agent.ipynb): building off the above notebook, this example substitutes in an agent with tools as the execution tools, allowing it to actually take actions.
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- [CAMEL](agents/camel_role_playing.ipynb): an implementation of the CAMEL (Communicative Agents for “Mind” Exploration of Large Scale Language Model Society) paper, where two agents communicate with eachother.
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- [AI Plugins](agents/custom_agent_with_plugin_retrieval.ipynb): an implementation of an agent that is designed to be able to use all AI Plugins.
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