Streaming
Akka Streaming is an actor-based stream processing engine for real-time AI. It lets your services and agents act on data the moment it arrives: telemetry from sensors, audio and video frames, chat messages, API feeds, and other multimodal inputs. No batch jobs, no polling loops.
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Akka Streaming is the SDK capability described here: real-time events processed through consumers and producers over durable event streams. It is built on the lower-level Akka Streams library, a Source/Flow/Sink/back-pressure framework. For the SDK capability, see Streaming; for the library, see Akka Streams. |
Streaming is one of the building blocks of the platform, alongside agents, orchestration, and memory. Where memory gives an agent durable context and orchestration coordinates work across agents, streaming is how live data flows into that system and how intermediate results flow back out to users in real time.
Why stream processing for AI
Agentic systems are only as current as the data feeding them. A model that reasons over a stale snapshot produces stale answers. Stream processing keeps agents grounded in what is happening now:
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Real-time grounding — continuously ingest event streams from sensors, messaging systems, and external services so an agent reasons over live state rather than a periodic export.
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Responsive experiences — expose an agent’s reasoning, intermediate steps, and tool responses as they happen, streaming partial results to the user instead of blocking until a full answer is ready.
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Continuous adaptation — route streamed data into memory for storage and transformation, or forward it to other agents, keeping data, context, and behavior aligned as conditions change.
Core concepts
Producers and consumers
A producer emits an event stream: a state change, a domain event, a chunk of model output, or an ingested external message. A consumer subscribes to that stream and reacts to each element in order. Consumers are the primary way to build stream processing in Akka: they attach to a source, receive elements as they are published, and drive downstream effects such as updating state, calling a model, or emitting a new stream.
Because consumers process a durable, ordered log of events, a consumer that falls behind or restarts resumes from where it left off. This gives you at-least-once delivery: every event is delivered, and consumers are written to be idempotent so that a redelivered event produces the same result as the first delivery.
Event streams
Data moves through the system as event streams rather than one-off request and response calls. A stream is an ordered, potentially unbounded sequence of elements. Modeling data this way means the same feed can drive many independent consumers: one updating a projection, another triggering an agent, a third fanning results out to connected clients. The producer never needs to know who is listening.
Brokerless, decentralized pub/sub
Streams can flow directly between services and agents without routing every message through centralized broker infrastructure. Producers publish and consumers subscribe over a brokerless pub/sub fabric, which removes a network hop and a single point of contention from the hot path, reducing latency and operational overhead.
When you do need to interoperate with existing infrastructure, agents can also subscribe to streams from external brokers or direct endpoints, so brokerless internal messaging and broker-backed integration coexist in the same system.
Back-pressure and flow control
High-frequency event sources arrive in bursts. Left unchecked, a fast producer will overwhelm a slower consumer: the model that consumer calls, the database it writes to, or the downstream agent it feeds. Akka Streaming manages flow in both directions automatically:
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When a consumer cannot keep up, the stream slows the producer rather than buffering without bound.
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When input rates spike, consumers are protected from being flooded.
This back-pressure mechanism keeps agents responsive during traffic bursts and guarantees bounded resource usage at any scale. The system degrades gracefully under load instead of exhausting memory.
What you can build
Stream processing underpins a broad set of real-time AI patterns:
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Live ingestion pipelines — turn continuous sensor, audio, or video feeds into grounded actions in a live environment.
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Streaming agent responses — surface tokens, tool calls, and reasoning steps to a user interface as the agent works.
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Event-driven agents — trigger agent behavior directly from domain events as they occur, rather than on a schedule.
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Fan-out and transformation — split, filter, enrich, and route a single source stream to many consumers, services, or memory stores.
How it fits together
A typical streaming flow in an Akka application looks like this:
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A producer publishes an event stream — from an Akka service, an external broker, or a direct endpoint.
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One or more consumers subscribe to that stream and process each element in order, with at-least-once delivery.
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A consumer reasons over the data, invokes a model or tool, and updates memory or emits a new stream.
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Results stream back to the caller, token by token or event by event, while back-pressure keeps every stage within its capacity.