text: But we also make it easy to define a custom tool, so if you need custom tools you should absolutely do that.(Optional) Step 2: Modify Agentâ€‹The built-in LangChain agent types are designed to work well in generic situations,
  but you may be able to improve performance by modifying the agent implementation.
  There are several ways you could do this:Modify the base prompt. This can be used to give the agent more context on how it should behave, etc.Modify the output parser. This is necessary if the agent is having trouble parsing the language model output.(Optional) Step 3: Modify Agent Executorâ€‹This step is usually not necessary, as this is pretty general logic.
  Possible reasons you would want to modify this include adding different stopping conditions, or handling errorsExamplesâ€‹Specific examples of agents include:AI Plugins: an implementation of an agent that is designed to be able to use all AI Plugins.Plug-and-PlAI (Plugins Database): an implementation of an agent that is designed to be able to use all AI Plugins retrieved from PlugNPlAI.Wikibase Agent: an implementation of an agent that is designed to interact with Wikibase.Sales GPT: This notebook demonstrates an implementation of a Context-Aware AI Sales agent.Multi-Modal Output Agent: an implementation of a multi-modal output agent that can generate text and images.PreviousTwo-Player Dungeons & DragonsNextBabyAGI User GuideCreate Your Own AgentStep 1: Create Tools(Optional) Step 2: Modify Agent(Optional) Step 3: Modify Agent ExecutorExamplesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.