Agent toolkits

📄️ Amadeus Toolkit

This notebook walks you through connecting LangChain to the Amadeus travel information API

📄️ Azure Cognitive Services Toolkit

This toolkit is used to interact with the Azure Cognitive Services API to achieve some multimodal capabilities.

📄️ CSV Agent

This notebook shows how to use agents to interact with a csv. It is mostly optimized for question answering.

📄️ Document Comparison

This notebook shows how to use an agent to compare two documents.

📄️ GitHub

This notebook goes over how to use the GitHub tool.

📄️ Gmail Toolkit

This notebook walks through connecting a LangChain email to the Gmail API.

📄️ Jira

This notebook goes over how to use the Jira tool.

📄️ JSON Agent

This notebook showcases an agent designed to interact with large JSON/dict objects. This is useful when you want to answer questions about a JSON blob that's too large to fit in the context window of an LLM. The agent is able to iteratively explore the blob to find what it needs to answer the user's question.

📄️ Multion Toolkit

This notebook walks you through connecting LangChain to the MultiOn Client in your browser

📄️ Office365 Toolkit

This notebook walks through connecting LangChain to Office365 email and calendar.

📄️ OpenAPI agents

We can construct agents to consume arbitrary APIs, here APIs conformant to the OpenAPI/Swagger specification.

📄️ Natural Language APIs

Natural Language API Toolkits (NLAToolkits) permit LangChain Agents to efficiently plan and combine calls across endpoints. This notebook demonstrates a sample composition of the Speak, Klarna, and Spoonacluar APIs.

📄️ Pandas Dataframe Agent

This notebook shows how to use agents to interact with a pandas dataframe. It is mostly optimized for question answering.

📄️ PlayWright Browser Toolkit

This toolkit is used to interact with the browser. While other tools (like the Requests tools) are fine for static sites, Browser toolkits let your agent navigate the web and interact with dynamically rendered sites. Some tools bundled within the Browser toolkit include:

📄️ PowerBI Dataset Agent

This notebook showcases an agent designed to interact with a Power BI Dataset. The agent is designed to answer more general questions about a dataset, as well as recover from errors.

📄️ Python Agent

This notebook showcases an agent designed to write and execute python code to answer a question.

📄️ Spark Dataframe Agent

This notebook shows how to use agents to interact with a Spark dataframe and Spark Connect. It is mostly optimized for question answering.

📄️ Spark SQL Agent

This notebook shows how to use agents to interact with a Spark SQL. Similar to SQL Database Agent, it is designed to address general inquiries about Spark SQL and facilitate error recovery.

📄️ SQL Database Agent

This notebook showcases an agent designed to interact with a sql databases. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors.

📄️ Vectorstore Agent

This notebook showcases an agent designed to retrieve information from one or more vectorstores, either with or without sources.

📄️ Xorbits Agent

This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.