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AI agents often draw information from external sources such as documents, web pages, or databases. Citations to those sources enable users to verify information, explore sources in detail, and understand where responses came from. Ably’s message annotations provide a model-agnostic, structured way to attach source citations to AI responses without modifying the response content. It enables clients to append information to existing messages on a channel. This pattern works when publishing complete responses as messages on a channel or when streaming responses using the message-per-response pattern.

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Why citations matter

Including citations on AI responses provides:
  • Transparency: Users can verify claims and understand the basis for AI responses. This builds trust and allows users to fact-check information independently.
  • Source exploration: Citations enable users to dive deeper into topics by accessing original sources. This is particularly valuable for research, learning, and decision-making workflows.
  • Attribution: Proper attribution respects content creators and helps users understand which sources informed the AI’s response.
  • Audit trails: For enterprise applications, citations provide explicit traceability between LLM responses and the information sources that were consulted when generating them.

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How it works

Use message annotations to attach source metadata to AI response messages without modifying the response content:
  1. The agent publishes an AI response as a single message, or builds it incrementally using message appends.
  2. The agent publishes one or more annotations to attach citations to the response message, each referencing the response message serial.
  3. Ably automatically aggregates annotations and generates summaries showing total counts and groupings (for example, by source domain name).
  4. Clients receive citation summaries automatically and can optionally subscribe to individual annotation events for detailed citation data as part of the realtime stream. Alternatively, clients can obtain annotations for a given message via the REST API.

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Enable message annotations

Message append functionality requires “Message annotations, updates, deletes and appends” to be enabled in a channel rule associated with the channel. To enable the channel rule:
  1. Go to the Ably dashboard and select your app.
  2. Navigate to the “Configuration” > “Rules” section from the left-hand navigation bar.
  3. Choose “Add new rule”.
  4. Enter a channel name or namespace pattern (for example, ai for all channels starting with ai:).
  5. Select the “Message annotations, updates, deletes and appends” option from the list.
  6. Click “Create channel rule”.
The examples in this guide use the ai: namespace prefix, which assumes you have configured the rule for ai.

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Citation data model

Citations are implemented using message annotations. Each citation includes an annotation type that determines how citations are aggregated into summaries, and a data payload containing the citation details.

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Annotation type

Annotation types determine how annotations are processed and aggregated into summaries. The type is a string of the format namespace:summarization_method:
  • namespace is a string that logically groups related annotations. For example, use citations for AI response citations.
  • summarization_method specifies how annotations are aggregated to produce summaries.
Use the multiple.v1 summarization method for AI response citations. This is well suited for citations because:
  • AI responses often reference the same source multiple times, and multiple.v1 counts each citation separately.
  • Citations can be grouped by source using the name field (for example, by domain name), so clients can display “3 citations from wikipedia.org, 2 from nasa.gov”.
The examples below use the annotation type citations:multiple.v1.

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Annotation data

The annotation data field can contain any structured data relevant to your citation use case. For example, a citation for a web search result might include: In this example:
  • url is the source URL.
  • title is the title of the web page.
  • startOffset is the character position in the response where this citation begins.
  • endOffset is the character position in the response where the citation ends.
  • snippet is a short excerpt from the source content for preview displays.
Including character offsets in annotation data allow UIs to attach inline citation markers to specific portions of the response text.

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Publish citations

Agents create citations by publishing message annotations that reference the serial of the response message:

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Subscribe to summaries

Clients can display a summary of the citations attached to a response by using annotation summaries. Clients receive realtime updates to annotation summaries automatically when subscribing to a channel, which are delivered as messages with an action of message.summary. When using multiple.v1 summarization, counts are grouped by the annotation name. In the example below, the name is set to the domain name of the citation source, so summaries show counts per domain: The multiple.v1 summary groups counts by the annotation name, with totals and per-client breakdowns for each group: When agents publish citations with a clientId, summaries include a per-client count showing how many citations each agent contributed. Citations published by unidentified clients are counted in the totalUnidentified field.

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Subscribe to individual citations

To access the full citation data, subscribe to individual annotation events: Each annotation event includes the messageSerial of the response message it is attached to, the name used for grouping in summaries, and the full citation data payload. This data can be used to render clickable source links or attach inline citation markers to specific portions of the response text:

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Retrieve citations on demand

Annotations can also be retrieved via the REST API without maintaining a realtime subscription.

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