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# `langchain.agents.agent_types`.AgentType

class langchain.agents.agent_types.AgentType(value)

  [Deprecated] An enum for agent types.

  See documentation: https://python.langchain.com/docs/modules/agents/agent_types/

  Notes

  Deprecated since version 0.1.0: Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. instead.

  ZERO_SHOT_REACT_DESCRIPTION = 'zero-shot-react-description'

    A zero shot agent that does a reasoning step before acting.

  REACT_DOCSTORE = 'react-docstore'

    A zero shot agent that does a reasoning step before acting.

    This agent has access to a document store that allows it to look up relevant information to answering the question.

  SELF_ASK_WITH_SEARCH = 'self-ask-with-search'

    An agent that breaks down a complex question into a series of simpler questions.

    This agent uses a search tool to look up answers to the simpler questions in order to answer the original complex question.

  CONVERSATIONAL_REACT_DESCRIPTION = 'conversational-react-description'
  CHAT_ZERO_SHOT_REACT_DESCRIPTION = 'chat-zero-shot-react-description'

    A zero shot agent that does a reasoning step before acting.

    This agent is designed to be used in conjunction

  CHAT_CONVERSATIONAL_REACT_DESCRIPTION = 'chat-conversational-react-description'
  STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION = 'structured-chat-zero-shot-react-description'

    An zero-shot react agent optimized for chat models.

    This agent is capable of invoking tools that have multiple inputs.

  OPENAI_FUNCTIONS = 'openai-functions'

    An agent optimized for using open AI functions.

  OPENAI_MULTI_FUNCTIONS = 'openai-multi-functions'

# Examples using AgentType

- ANTHROPIC_API_KEY=<YOUR ANTHROPIC API KEY>

- Answer with ‘Zhu’

- Artifacts are charts created by matplotlib when `plt.show()` is called

- Connect to Comet if no API Key is set

- Copilot Sandbox

- Create a dataframe

- Debugging

- Extract pdf content

- First, define custom callback handler implementations

- Flyte

- For Windows/Linux

- How YahooFinanceNewsTool works? {#how-yahoofinancenewstool-works}

- IMPORTANT: If you plan to use this account in the future, make sure to save the

- If this is your first time using playwright, you’ll have to install a browser executable.

- Initialize the language model

- LLMonitor

- SCENARIO 1 - LLM

- SearchApi

- Select the LLM to use. Here, we use gpt-3.5-turbo-instruct

- Serper - Google Search API

- Set environmental variables here

- Set this to your Zep server URL

- Set your environment variables using os.environ

- Setup and use the ClearML Callback

- Specify your Connery Runner credentials.

- Streamlit

- These are sample parameters for Falcon 40B Instruct Deployed from Amazon SageMaker JumpStart

- Used by the agent in this tutorial

- Using https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2

- airbyte_structured_qa.md

- argilla.md

- awslambda.md

- bash.md

- chatgpt_plugins.md

- document_comparison_toolkit.md

- edenai_tools.md

- eleven_labs_tts.md

- folder_id=’1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5’

- get from https://platform.openai.com/

- google_finance.md

- google_serper.md

- graphql.md

- jira.md

- memorize.md

- nasa.md

- need to use GPT-4 here as GPT-3.5 does not understand, however hard you insist, that

- openweathermap.md

- os.environ[“OPENAI_API_KEY”] = “”

- pandas.md

- scenario 1 - LLM

- search_tools.md

- searchapi.md

- steam.md

- token_usage_tracking.md

- use it with langchain {#use-it-with-langchain}

- wandb documentation to configure wandb using env variables

- xata_chat_message_history.md