> For the complete documentation index, see [llms.txt](https://answeringservicecare.com/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://answeringservicecare.com/docs/api-reference/user-portal-apis/messages.md). # Messages The **Messages** API is how external systems send, receive, and manage communications that flow through Answering Service Care, chats, AI-generated responses, call-origin notes, and operator messages all live here as unified message objects. Messages are created by agents and stored on your account. Each message consists of standard data fields and can also contain custom fields, which appear alongside standard fields with the prefix `content_with_additional_details_table__{labelName}`. > **Retention:** Messages are retained for **180+ days** in line with our privacy and data > retention policy. Older messages may not be retrievable. #### What you can do * **List** every message visible to the authenticated user, with optional filters for origin (`chat`, `to-do`, `call`, `sms`, `ai`), delivery status, date range (`from_date` / `to_date` in `MM-DD-YYYY`), and page size. * **Retrieve** a single message by ID, including its linked call recording if one exists. * **Send** a new message on behalf of an account, AI agent, or escalation thread. * **Update** a message's state, acknowledge it, or flip its read/unread status, useful for clearing inboxes programmatically or syncing read state with an external CRM. #### Common use cases * Pulling a day's worth of messages into a reporting warehouse. * Sending AI-generated summaries back into Answering Service Care from an external workflow. * Marking messages handled once an external ticket is closed. Every message carries an `account_id`, `agent_id`, `contact_id`, and many optional fields (address, phone, email, escalation metadata, form answers) so you can cleanly attribute it to a customer account and upstream context. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://answeringservicecare.com/docs/api-reference/user-portal-apis/messages.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.