# Pypestream Documentation > Pypestream's APIs enable you to build, test, deploy, monitor, and optimize best-in-class conversational AI solutions at scale. Append .md to any documentation page URL to get its markdown version. ## Guides - [Connect AWS Connect](https://developers.pypestream.com/docs/connect-aws-connect.md): Enable AWS Connect for Pypestream Live Agent (Customer Side) ## API Reference - [Single Agent](https://developers.pypestream.com/reference/get_single-agent-agent-id.md): Retrieves specific agent information identified by the agent ID. - [Multiple Agents](https://developers.pypestream.com/reference/get_multi-agent.md): Retrieves agents' information between a specific 24-hour time frame. - [Single Session](https://developers.pypestream.com/reference/get_single-chat-chat-id.md): Retrieves a specific session's metadata identified by the Chat/Session ID. - [Multiple Sessions](https://developers.pypestream.com/reference/get_multi-chat.md): Retrieves metadata for multiple conversations between a specific time frame, no more than one hour apart. - [Single Transcript](https://developers.pypestream.com/reference/get_single-chat-transcript-chat-id.md): Retrieves a specific transcript identified by the Chat/Session ID. - [Multiple Transcripts](https://developers.pypestream.com/reference/get_multi-chat-transcript.md): Retrieves transcripts for multiple conversations between a specific time frame, no more than one hour apart. - [List the integrations](https://developers.pypestream.com/reference/listintegrations-1.md) - [Create a new integration](https://developers.pypestream.com/reference/createintegration-1.md) - [Delete the integration](https://developers.pypestream.com/reference/deleteintegration-1.md) - [Get the integration](https://developers.pypestream.com/reference/getintegration-1.md) - [Patch an integration](https://developers.pypestream.com/reference/patchintegration-1.md) - [Update an integration](https://developers.pypestream.com/reference/updateintegration-1.md) - [End Chat](https://developers.pypestream.com/reference/end-chat.md): Use this request to end a chat. - [Retrieve Conversation History](https://developers.pypestream.com/reference/retrieve-conversation-history.md): This method returns the conversation history between the automation and the end-user. - [Send Message](https://developers.pypestream.com/reference/send-message.md): Use this method to send a message to an agent or an end-user. - [Retrieve Metadata](https://developers.pypestream.com/reference/getmetadata.md): This method returns users metadata. - [Update Metadata](https://developers.pypestream.com/reference/updatemetadata.md): This method enables to update the users metadata, on the agent side. - [Send Typing Status](https://developers.pypestream.com/reference/send-typing.md): This method sets the user's typing status. - [Overview](https://developers.pypestream.com/reference/documentation.md) - [/api/datasets/](https://developers.pypestream.com/reference/list.md): Dataset: A distinct collection of analytics data, typically corresponding to an environment (e.g., "Production", "Testing", "Development"). Get a list of datasets that you have access to. - [/api/datasets/{id}/](https://developers.pypestream.com/reference/retrieve.md): Dataset: A distinct collection of analytics data, typically corresponding to an environment (e.g., "Production", "Testing", "Development"). Get a specific dataset by ID. - [Overview](https://developers.pypestream.com/reference/overview-2.md): The Pypestream Analytics API provides developers with complete control over their product analytics pipeline. It supports the full data lifecycle: from tracking events and analyzing user behavior within Pypestream, to exporting raw data in batches for deep analysis in your own data warehouse, BI tools, and custom applications. - [/api/datasets/{dataset_id}/annotations/](https://developers.pypestream.com/reference/annotations_list.md): Annotations: Dated notes or comments attached to timelines or Insight charts to provide shared context about specific events (e.g., feature releases, marketing campaigns) that might influence metrics. Managed under Data Management and viewable on charts. Get a list of annotations for a specific dataset. - [/api/datasets/{dataset_id}/annotations/{id}/](https://developers.pypestream.com/reference/annotations_retrieve.md): Annotations: Dated notes or comments attached to timelines or Insight charts to provide shared context about specific events (e.g., feature releases, marketing campaigns) that might influence metrics. Managed under Data Management and viewable on charts. Get a specific annotation from a specific dataset. - [/api/datasets/{dataset_id}/actions/](https://developers.pypestream.com/reference/actions_list.md): Actions: A collection of one or more events grouped together in Data Management to represent a specific user behavior or process without requiring code changes. Can be used like standard events in analysis. Get a list of actions for a specific dataset. - [/api/datasets/{dataset_id}/actions/{id}/](https://developers.pypestream.com/reference/actions_retrieve.md): Actions: A collection of one or more events grouped together in Data Management to represent a specific user behavior or process without requiring code changes. Can be used like standard events in analysis. Get a single action from a specific dataset. - [/api/datasets/{dataset_id}/cohorts/](https://developers.pypestream.com/reference/cohorts_list.md): Cohorts: Groups of users defined in Data Management based on shared characteristics (properties) or behaviors (events performed or not performed) within a specific timeframe. Used for targeted analysis and filtering. Get a list of cohorts for a specific dataset. - [/api/datasets/{dataset_id}/cohorts/{id}/](https://developers.pypestream.com/reference/cohorts_retrieve.md): Cohorts: Groups of users defined in Data Management based on shared characteristics (properties) or behaviors (events performed or not performed) within a specific timeframe. Used for targeted analysis and filtering. Get a specific cohort from a specific dataset. - [/api/datasets/{dataset_id}/cohorts/{id}/persons/](https://developers.pypestream.com/reference/cohorts_persons_retrieve.md): Cohorts: Groups of users defined in Data Management based on shared characteristics (properties) or behaviors (events performed or not performed) within a specific timeframe. Used for targeted analysis and filtering. Person / Persons: A feature within Data Management representing individual users tracked by Analytics. The "Persons" list allows accessing detailed profiles for each user, showing their associated properties, event history, cohort memberships, and related replays. Get all persons in a specific cohort from a specific dataset. - [/api/datasets/{dataset_id}/dashboards/](https://developers.pypestream.com/reference/dashboards_list.md): Dashboard: A customizable page within Analytics used for organizing and displaying multiple key metrics, charts, and Insights in one place for monitoring and reporting. Get a list of dashboards for a specific dataset. - [/api/datasets/{dataset_id}/dashboards/{id}/](https://developers.pypestream.com/reference/dashboards_retrieve.md): Dashboard: A customizable page within Analytics used for organizing and displaying multiple key metrics, charts, and Insights in one place for monitoring and reporting. Get a specific dashboard from a specific dataset. - [/api/datasets/{dataset_id}/event_definitions/](https://developers.pypestream.com/reference/event_definitions_list.md): Definitions of events used in analytics. Get a list of event definitions for a specific dataset. - [/api/datasets/{dataset_id}/event_definitions/{id}/](https://developers.pypestream.com/reference/event_definitions_retrieve.md): Definitions of events used in analytics. Get a single event definition from a specific dataset. - [/api/datasets/{dataset_id}/events/](https://developers.pypestream.com/reference/events_list.md): Events: Specific user actions (e.g., clicks, page views, form submissions, Pype escalations) or system occurrences (e.g., Pype session started) captured by Analytics. They form the basis of most analyses. Note: this endpoint is for overview/exploration purposes. For data export use batch_exports endpoints. Get a list of events for a specific dataset. - [/api/datasets/{dataset_id}/events/{id}/](https://developers.pypestream.com/reference/events_retrieve.md): Events: Specific user actions (e.g., clicks, page views, form submissions, Pype escalations) or system occurrences (e.g., Pype session started) captured by Analytics. They form the basis of most analyses. Get a specific event from a specific dataset. - [/api/datasets/{dataset_id}/insights/](https://developers.pypestream.com/reference/insights_list.md): Insights: The core analytical components within Analytics where users create specific analyses and visualizations from their data. Examples include Trends, Funnels, Retention, User Paths, Stickiness, Lifecycle, and SQL Insights. Get a list of insights for a specific dataset. - [/api/datasets/{dataset_id}/insights/{id}/](https://developers.pypestream.com/reference/insights_retrieve.md): Insights: The core analytical components within Analytics where users create specific analyses and visualizations from their data. Examples include Trends, Funnels, Retention, User Paths, Stickiness, Lifecycle, and SQL Insights. Get a specific insight from a specific dataset. - [/api/datasets/{dataset_id}/insights/funnel/](https://developers.pypestream.com/reference/insights_funnel_retrieve.md): Insights: The core analytical components within Analytics where users create specific analyses and visualizations from their data. Examples include Trends, Funnels, Retention, User Paths, Stickiness, Lifecycle, and SQL Insights. Get a funnel insight for a specific dataset. - [/api/datasets/{dataset_id}/insights/path/](https://developers.pypestream.com/reference/insights_path_retrieve.md): Insights: The core analytical components within Analytics where users create specific analyses and visualizations from their data. Examples include Trends, Funnels, Retention, User Paths, Stickiness, Lifecycle, and SQL Insights. Get a path insight for a specific dataset. - [/api/datasets/{dataset_id}/insights/retention/](https://developers.pypestream.com/reference/insights_retention_retrieve.md): Insights: The core analytical components within Analytics where users create specific analyses and visualizations from their data. Examples include Trends, Funnels, Retention, User Paths, Stickiness, Lifecycle, and SQL Insights. Get a retention insight for a specific dataset. - [/api/datasets/{dataset_id}/insights/trend/](https://developers.pypestream.com/reference/insights_trend_retrieve.md): Insights: The core analytical components within Analytics where users create specific analyses and visualizations from their data. Examples include Trends, Funnels, Retention, User Paths, Stickiness, Lifecycle, and SQL Insights. Get a trend insight for a specific dataset. - [/api/datasets/{dataset_id}/kpis/](https://developers.pypestream.com/reference/kpis_list.md): Get a list of KPIs for a specific dataset. - [/api/datasets/{dataset_id}/kpis/{id}/](https://developers.pypestream.com/reference/kpis_retrieve.md): Get a specific KPI from a specific dataset. - [/api/datasets/{dataset_id}/persons/](https://developers.pypestream.com/reference/persons_list.md): Person / Persons: A feature within Data Management representing individual users tracked by Analytics. The "Persons" list allows accessing detailed profiles for each user, showing their associated properties, event history, cohort memberships, and related replays. Get a list of persons for a specific dataset. - [/api/datasets/{dataset_id}/persons/{id}/](https://developers.pypestream.com/reference/persons_retrieve.md): Person / Persons: A feature within Data Management representing individual users tracked by Analytics. The "Persons" list allows accessing detailed profiles for each user, showing their associated properties, event history, cohort memberships, and related replays. Get a specific person from a specific dataset. - [/api/datasets/{dataset_id}/property_definitions/](https://developers.pypestream.com/reference/property_definitions_retrieve.md): Property Definitions: Metadata about the properties associated with events, persons, or groups in Analytics. Property definitions provide context and details about the attributes tracked, such as data type, usage frequency, and whether they are numerical. Get a list of property definitions for a specific dataset. - [/api/datasets/{dataset_id}/property_definitions/{id}/](https://developers.pypestream.com/reference/property_definitions_retrieve_2.md): Property Definitions: Metadata about the properties associated with events, persons, or groups in Analytics. Property definitions provide context and details about the attributes tracked, such as data type, usage frequency, and whether they are numerical. Get a specific property definition from a specific dataset. - [/api/organization/](https://developers.pypestream.com/reference/organization_list.md): Organization: The top-level entity within the Pypestream platform structure. An organization can contain multiple Projects. Access might be switched via an Organization Selector. Get a list of organizations. - [/api/organization/{id}/](https://developers.pypestream.com/reference/organization_retrieve.md): Organization: The top-level entity within the Pypestream platform structure. An organization can contain multiple Projects. Access might be switched via an Organization Selector. Get a specific organization by ID. - [/api/projects/](https://developers.pypestream.com/reference/list_2.md): Project: A container within a Pypestream Organization used to group related solutions, analytics data, and settings. Users select the Project they want to work within via the Project Selector. Get a list of projects. - [/api/projects/{id}/](https://developers.pypestream.com/reference/retrieve_2.md): Project: A container within a Pypestream Organization used to group related solutions, analytics data, and settings. Users select the Project they want to work within via the Project Selector. Get a specific project by ID. - [/api/datasets/{dataset_id}/batch_exports/](https://developers.pypestream.com/reference/batch_exports_list.md): Retrieves a paginated list of all batch export configurations for the specified dataset. Use this to view all active and inactive exports, their destinations, and schedules. - [/api/datasets/{dataset_id}/batch_exports/](https://developers.pypestream.com/reference/batch_exports_create.md): Creates a new batch export configuration. This endpoint requires a destination type (e.g., S3, Snowflake), an interval (e.g., daily, hourly), and the necessary credentials to configure the export pipeline. **Recommended Snowflake DDL:** ```sql CREATE TABLE your_table_name ( uuid VARCHAR(36), timestamp TIMESTAMP_NTZ, created_at TIMESTAMP_NTZ, event VARCHAR, properties VARIANT, distinct_id VARCHAR(36), person_id VARCHAR(36), person_properties VARIANT, elements_chain VARCHAR ); ``` **Beta notice:** Snowflake support is currently in beta. - [/api/datasets/{dataset_id}/batch_exports/{parent_lookup_batch_export_id}/runs/](https://developers.pypestream.com/reference/batch_exports_runs_list.md): Retrieves the execution history for a specific batch export. This returns a list of past runs, allowing you to monitor the success or failure status of the export schedule. - [/api/datasets/{dataset_id}/batch_exports/{parent_lookup_batch_export_id}/runs/{id}/](https://developers.pypestream.com/reference/batch_exports_runs_retrieve.md): Retrieves the details of a single execution run. This is useful for debugging specific failures or verifying the timestamps and data volume of a particular export cycle. - [/api/datasets/{dataset_id}/batch_exports/{id}/](https://developers.pypestream.com/reference/batch_exports_retrieve.md): Retrieves the full configuration details of a specific batch export identified by its ID. - [/api/datasets/{dataset_id}/batch_exports/{id}/](https://developers.pypestream.com/reference/batch_exports_update.md): Updates an existing batch export configuration. This is a full update that replaces the current configuration with the data provided in the request body. - [/api/datasets/{dataset_id}/batch_exports/{id}/](https://developers.pypestream.com/reference/batch_exports_partial_update.md): Partially updates an existing batch export configuration. Use this to modify specific fields (such as the interval or destination settings) without replacing the entire object. - [/api/datasets/{dataset_id}/batch_exports/{id}/](https://developers.pypestream.com/reference/batch_exports_destroy.md): Permanently deletes a batch export configuration. This action stops all future runs and removes the export setup from the project. - [/api/datasets/{dataset_id}/batch_exports/{id}/backfill/](https://developers.pypestream.com/reference/batch_exports_backfill_create.md): Manually triggers a backfill job for a specific historical date range. This allows you to export data that was collected before the batch export was originally created or during a period where the export was paused. - [/api/datasets/{dataset_id}/batch_exports/{id}/pause/](https://developers.pypestream.com/reference/batch_exports_pause_create.md): Pauses the batch export schedule. New runs will not be triggered until the export is unpaused, but the configuration remains saved. - [/api/datasets/{dataset_id}/batch_exports/{id}/unpause/](https://developers.pypestream.com/reference/batch_exports_unpause_create.md): Resumes a paused batch export. The schedule will restart based on the configured interval.