LLM Streaming: Resumption Tokens and IDs for a Seamless Experience

Lucas Moraes (CEO Toolzz AI)
15 de março de 2026

In interactions with large language models (LLMs), connection interruption is a reality. Without adequate mechanisms, users are forced to repeat prompts, generating unnecessary costs and frustration. Resumption tokens and last event IDs are the solution to ensure a continuous and efficient experience, allowing the stream to resume exactly where it left off. This article explores how these mechanisms work and what you need to consider to implement them.

How Resumption Tokens Work

The concept behind resumption tokens is relatively simple. Each message or token sent in the stream receives a unique sequential identifier. The client stores the ID of the last successfully received message. In case of interruption, the client informs the server of the last known ID, which responds by sending all subsequent messages, allowing stream resumption without repetition.

This approach involves four main components:

• Message Identifiers: Each token or message receives a sequential and increasing ID.
• Client State: The client tracks the ID of the last received message, storing it locally.
• Reconnection Protocol: When reconnecting, the client sends the last known ID to the server.
• Recovery Delivery: The server sends the missed messages in order, before resuming the live stream.

The stream becomes the source of truth, ensuring the client receives all information, even after interruptions.

SSE and the Last-Event-ID Header

Server-Sent Events (SSE) already natively implements this mechanism through the Last-Event-ID header. When an SSE connection is interrupted, the browser automatically includes this header in the reconnection request, allowing the server to resume the stream from the correct point.

javascript // Example of SSE event event: token id: 150 data: {"content": "production"}

event: token id: 151 data: {"content": " systems"}

// On reconnection, the browser automatically sends: GET /stream HTTP/1.1 Last-Event-ID: 150

// The server resumes from event 151

While SSE simplifies implementation, it has limitations. Its unidirectional nature and HTTP restriction make it difficult to implement features like dynamic flow routing and state management in distributed infrastructures.

Implementing Resumption with WebSockets

WebSockets, on the other hand, do not offer native support for stream resumption. Implementation requires building all the necessary logic, including:

• Generating session IDs at the start of the stream.
• Storing session state on the server.
• Message recovery and replay logic.
• Buffer management for undelivered messages.

While more complex, WebSockets offers greater flexibility and control, being ideal for applications requiring bidirectional communication.

The Token Storage Challenge

One of the biggest challenges in implementing resumable streams is token storage management. Storing each token individually can quickly overload the system, especially in long conversations or with many simultaneous users. A more efficient approach is to treat each LLM response as a single logical message, attaching tokens to it. This drastically reduces the amount of stored data and improves recovery performance.

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Handling Duplicates and Gaps

Duplicates and gaps are inevitable in unstable networks. Detecting and correcting these inconsistencies is crucial to ensure stream reliability. Using message IDs as deduplication keys on the client side is an effective solution. Gap detection requires logic to request missing messages and handle situations where recovery is not possible.

Implications in Distributed Environments

In distributed environments, connection routing becomes a challenge. Reconnecting a client to a different server instance requires access to shared session state storage. Solutions like Redis can be used to ensure state consistency across server instances.

The Importance of Multi-Device Persistence

True cross-device continuity requires separation of connection state. Conversation state must be stored in a persistent channel or storage, allowing any device to connect and resume the conversation where it left off. This differs from the connection resumption model, which focuses on restoring the stream after an interruption in the same connection.

When Streaming Resumption is Essential

Streaming resumption is particularly important in scenarios such as:

• Mobile Clients: Unstable networks and frequent connection changes.
• Long Responses: Higher probability of interruptions during generation.
• Multi-Device Usage: Need for seamless synchronization across devices.

Avoiding the need to restart a conversation significantly improves user experience and reduces costs.

Building or Using an Existing Infrastructure?

Building a robust streaming resumption system is a complex undertaking that requires careful planning and expertise in various areas. An alternative is to use a data transport infrastructure that already implements these features. Tools like Toolzz Chat offer a ready-to-use solution, simplifying development and ensuring the reliability of your AI stream. With Toolzz Chat, you focus on your application logic, while the platform handles the complexity of data transport, ensuring a continuous and efficient experience for your users. Toolzz AI integrates seamlessly with Toolzz Chat, allowing you to create intelligent and automated agents with built-in resilience and connection resumption.