text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsAmadeus ToolkitAzure Cognitive Services ToolkitCSV AgentDocument ComparisonGitHubGmail ToolkitJiraJSON AgentMultion ToolkitOffice365 ToolkitOpenAPI agentsNatural Language APIsPandas Dataframe AgentPlayWright Browser ToolkitPowerBI Dataset AgentPython AgentSpark Dataframe AgentSpark SQL AgentSQL Database AgentVectorstore AgentXorbits AgentToolsVector storesGrouped by providerIntegrationsAgent toolkitsAgent toolkitsğŸ“„ï¸� Amadeus ToolkitThis notebook walks you through connecting LangChain to the Amadeus travel information APIğŸ“„ï¸� Azure Cognitive Services ToolkitThis toolkit is used to interact with the Azure Cognitive Services API to achieve some multimodal capabilities.ğŸ“„ï¸� CSV AgentThis notebook shows how to use agents to interact with a csv. It is mostly optimized for question answering.ğŸ“„ï¸� Document ComparisonThis notebook shows how to use an agent to compare two documents.ğŸ“„ï¸� GitHubThis notebook goes over how to use the GitHub tool.ğŸ“„ï¸� Gmail ToolkitThis notebook walks through connecting a LangChain email to the Gmail API.ğŸ“„ï¸� JiraThis notebook goes over how to use the Jira tool.ğŸ“„ï¸� JSON AgentThis 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