How-To Guides
=============

There are three types of examples in this section:

1. Agent Overview: how-to-guides for generic agent functionality
2. Agent Toolkits: how-to-guides for specific agent toolkits (agents optimized for interacting with a certain resource)
3. Agent Types: how-to-guides for working with the different agent types

Agent Overview
---------------

The first category of how-to guides here cover specific parts of working with agents.

`Load From Hub <./examples/load_from_hub.html>`_: This notebook covers how to load agents from `LangChainHub <https://github.com/hwchase17/langchain-hub>`_.

`Custom Tools <./examples/custom_tools.html>`_: How to create custom tools that an agent can use.

`Agents With Vectorstores <./examples/agent_vectorstore.html>`_: How to use vectorstores with agents.

`Intermediate Steps <./examples/intermediate_steps.html>`_: How to access and use intermediate steps to get more visibility into the internals of an agent.

`Custom Agent <./examples/custom_agent.html>`_: How to create a custom agent (specifically, a custom LLM + prompt to drive that agent).

`Multi Input Tools <./examples/multi_input_tool.html>`_: How to use a tool that requires multiple inputs with an agent.

`Search Tools <./examples/search_tools.html>`_: How to use the different type of search tools that LangChain supports.

`Max Iterations <./examples/max_iterations.html>`_: How to restrict an agent to a certain number of iterations.

`Asynchronous <./examples/async_agent.html>`_: Covering asynchronous functionality.


.. toctree::
   :maxdepth: 1
   :glob:
   :hidden:

   ./examples/*


Agent Toolkits
---------------

The next set of examples covers agents with toolkits.
As opposed to the examples above, these examples are not intended to show off an agent `type`,
but rather to show off an agent applied to particular use case.

`SQLDatabase Agent <./agent_toolkits/sql_database.html>`_: This notebook covers how to interact with an arbitrary SQL database using an agent.

`JSON Agent <./agent_toolkits/json.html>`_: This notebook covers how to interact with a JSON dictionary using an agent.

`OpenAPI Agent <./agent_toolkits/openapi.html>`_: This notebook covers how to interact with an arbitrary OpenAPI endpoint using an agent.

`VectorStore Agent <./agent_toolkits/vectorstore.html>`_: This notebook covers how to interact with VectorStores using an agent.

`Python Agent <./agent_toolkits/python.html>`_: This notebook covers how to produce and execute python code using an agent.

`Pandas DataFrame Agent <./agent_toolkits/pandas.html>`_: This notebook covers how to do question answering over a pandas dataframe using an agent. Under the hood this calls the Python agent..

`CSV Agent <./agent_toolkits/csv.html>`_: This notebook covers how to do question answering over a csv file. Under the hood this calls the Pandas DataFrame agent.

.. toctree::
   :maxdepth: 1
   :glob:
   :hidden:

   ./agent_toolkits/*


Agent Types
---------------

The final set of examples are all end-to-end example of different agent types.
In all examples there is an Agent with a particular set of tools.

- Tools: A tool can be anything that takes in a string and returns a string. This means that you can use both the primitives AND the chains found in `this <../chains.html>`_ documentation. LangChain also provides a list of easily loadable tools. For detailed information on those, please see `this documentation <./tools.html>`_
- Agents: An agent uses an LLMChain to determine which tools to use. For a list of all available agent types, see `here <./agents.html>`_.

**MRKL**

- **Tools used**: Search, SQLDatabaseChain, LLMMathChain
- **Agent used**: `zero-shot-react-description`
- `Paper <https://arxiv.org/pdf/2205.00445.pdf>`_
- **Note**: This is the most general purpose example, so if you are looking to use an agent with arbitrary tools, please start here.
- `Example Notebook <./implementations/mrkl.html>`_

**Self-Ask-With-Search**

- **Tools used**: Search
- **Agent used**: `self-ask-with-search`
- `Paper <https://ofir.io/self-ask.pdf>`_
- `Example Notebook <./implementations/self_ask_with_search.html>`_

**ReAct**

- **Tools used**: Wikipedia Docstore
- **Agent used**: `react-docstore`
- `Paper <https://arxiv.org/pdf/2210.03629.pdf>`_
- `Example Notebook <./implementations/react.html>`_





.. toctree::
   :maxdepth: 1
   :glob:
   :hidden:

   ./implementations/*