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50 lines
2.7 KiB
Markdown
50 lines
2.7 KiB
Markdown
# Agents
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> [Conceptual Guide](https://docs.langchain.com/docs/use-cases/personal-assistants)
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Agents can be used for a variety of tasks.
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Agents combine the decision making ability of a language model with tools in order to create a system
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that can execute and implement solutions on your behalf. Before reading any more, it is highly
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recommended that you read the documentation in the `agent` module to understand the concepts associated with agents more.
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Specifically, you should be familiar with what the `agent`, `tool`, and `agent executor` abstractions are before reading more.
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- [Agent Documentation](../modules/agents.rst) (for interacting with the outside world)
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## Create Your Own Agent
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Once you have read that documentation, you should be prepared to create your own agent.
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What exactly does that involve?
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Here's how we recommend getting started with creating your own agent:
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### Step 1: Create Tools
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Agents are largely defined by the tools they can use.
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If you have a specific task you want the agent to accomplish, you have to give it access to the right tools.
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We have many tools natively in LangChain, so you should first look to see if any of them meet your needs.
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But we also make it easy to define a custom tool, so if you need custom tools you should absolutely do that.
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### (Optional) Step 2: Modify Agent
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The built-in LangChain agent types are designed to work well in generic situations,
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but you may be able to improve performance by modifying the agent implementation.
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There are several ways you could do this:
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1. Modify the base prompt. This can be used to give the agent more context on how it should behave, etc.
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2. Modify the output parser. This is necessary if the agent is having trouble parsing the language model output.
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### (Optional) Step 3: Modify Agent Executor
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This step is usually not necessary, as this is pretty general logic.
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Possible reasons you would want to modify this include adding different stopping conditions, or handling errors
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## Examples
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Specific examples of agents include:
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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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- [Plug-and-PlAI (Plugins Database)](agents/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb): an implementation of an agent that is designed to be able to use all AI Plugins retrieved from PlugNPlAI.
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- [Wikibase Agent](agents/wikibase_agent.ipynb): an implementation of an agent that is designed to interact with Wikibase.
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- [Sales GPT](agents/sales_agent_with_context.ipynb): This notebook demonstrates an implementation of a Context-Aware AI Sales agent.
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- [Multi-Modal Output Agent](agents/multi_modal_output_agent.ipynb): an implementation of a multi-modal output agent that can generate text and images.
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