ComponentsAgents and toolkitsAgents and toolkits📄️ AINetworkAI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).📄️ Airbyte Question AnsweringThis notebook shows how to do question answering over structured data, in this case using the AirbyteStripeLoader.📄️ AmadeusThis notebook walks you through connecting LangChain to the Amadeus travel information API📄️ Azure Cognitive ServicesThis toolkit is used to interact with the Azure Cognitive Services API to achieve some multimodal capabilities.📄️ ClickUp Langchiain ToolkitGet Authenticated📄️ CSVThis notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering.📄️ Document ComparisonThis notebook shows how to use an agent to compare two documents.📄️ GithubThe Github toolkit contains tools that enable an LLM agent to interact with a github repository.📄️ GitlabThe Gitlab toolkit contains tools that enable an LLM agent to interact with a gitlab repository.📄️ GmailThis notebook walks through connecting a LangChain email to the Gmail API.📄️ Google Drive toolThis notebook walks through connecting a LangChain to the Google Drive API.📄️ JiraThis notebook goes over how to use the Jira toolkit.📄️ JSONThis notebook showcases an agent interacting with large JSON/dict objects.📄️ MultiOnThis notebook walks you through connecting LangChain to the MultiOn Client in your browser📄️ Office365This notebook walks through connecting LangChain to Office365 email and calendar.📄️ OpenAPIWe can construct agents to consume arbitrary APIs, here APIs conformant to the OpenAPI/Swagger specification.📄️ Natural Language APIsNatural Language API Toolkits (NLAToolkits) permit LangChain Agents to efficiently plan and combine calls across endpoints.📄️ Pandas DataframeThis notebook shows how to use agents to interact with a Pandas DataFrame. It is mostly optimized for question answering.📄️ PlayWright BrowserThis toolkit is used to interact with the browser. While other tools (like the Requests tools) are fine for static sites, PlayWright Browser toolkits let your agent navigate the web and interact with dynamically rendered sites.📄️ PowerBI DatasetThis notebook showcases an agent interacting with a Power BI Dataset. The agent is answering more general questions about a dataset, as well as recover from errors.📄️ PythonThis notebook showcases an agent designed to write and execute Python code to answer a question.📄️ Spark DataframeThis notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.📄️ Spark SQLThis notebook shows how to use agents to interact with Spark SQL. Similar to SQL Database Agent, it is designed to address general inquiries about Spark SQL and facilitate error recovery.📄️ SQL DatabaseThis notebook showcases an agent designed to interact with a SQL databases.📄️ VectorstoreThis notebook showcases an agent designed to retrieve information from one or more vectorstores, either with or without sources.📄️ XorbitsThis notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.PreviousZapier Natural Language ActionsNextAINetwork