How Flink Enables Real‑Time AI Inference and Agent Construction

1. Introduction to Apache Flink

Apache Flink is a distributed stream‑processing engine that treats data generation as a continuous flow, enabling real‑time consumption, processing, and analysis. Unlike batch processing, which groups data into fixed windows and incurs latency, Flink supports both streaming and batch workloads on a single platform, offers exactly‑once semantics, event‑time handling, and a layered API stack (SQL, Table API, DataStream API, and low‑level Process Functions). Its deployment provides low latency, high throughput, elastic scaling in cloud environments, and cross‑zone high availability.

2. Apache Flink Agents

Flink Agents is an open‑source framework that simplifies the creation of event‑driven intelligent agents on top of Flink’s streaming pipelines. The framework abstracts common agent boilerplate and integrates with Flink’s Table API or DataStream API. Development steps are:

Install the Python SDK: pip install flink-agents Implement business logic using Flink’s Table API or DataStream API.

Submit the job to a Flink cluster with flink run.

Agents consist of two core modules: Event (automatically generated from input streams) and Action (user‑defined processing that can invoke tools or LLMs). The framework supports both Workflow and ReAct execution modes and provides built‑in memory management (short‑term, long‑term, and sensory memory) backed by Flink’s state backend. Language bindings are available for Python and Java.

2.1 Architecture

The agent operator sits in the Flink pipeline as a specialized operator. Its input and output are Table API/DataStream API records. Internally it contains modules for LLM chat, tool invocation, prompt management, and memory handling. The operator can be chained with other Flink operators such as joins, aggregations, or custom sinks.

2.2 Prompts, Tools, and LLM Connections

Prompts can be defined as plain text or chat messages. Multiple LLM providers (OpenAI‑compatible, Anthropic, Alibaba Tongyi, local Ollama models) are supported via a configurable connection that specifies provider, endpoint, API key, model name, and inference parameters (temperature, top‑k, etc.). Tools are registered Python or Java functions (e.g., sending email) and can also be external services via MCP.

3. Alibaba Cloud Real‑Time Computing Flink AI Function

Flink AI Function extends Flink SQL and Table API with native LLM capabilities. Users create a model with CREATE MODEL, specifying input/output columns, provider, endpoint, and system prompt. After registration, inference can be performed with ML_PREDICT in SQL or Table API, without repeating prompt definitions.

Supported model types include chat/completion, embedding, and specialized vertical functions (sentiment analysis, classification, translation). Vector‑based retrieval is enabled via AI_EMBED (real‑time embedding) and VICTOR_SEARCH (vector search). The enterprise edition adds real‑time RAG capabilities, allowing pipelines such as:

Ingest raw text from Kafka or MySQL.

Apply AI_EMBED to generate embeddings and store them in a vector table.

Perform similarity search with VICTOR_SEARCH and combine results with downstream business logic.

Non‑structured data (images, audio, video) is also being integrated: newly uploaded objects trigger events that feed into Flink, which then calls AI Functions for classification or information extraction in real time.

4. Demonstrations and Typical Scenarios

Demo 1 – VOC (Voice of Customer) processing: New product reviews are streamed via Kafka, joined with product dimension tables, and processed by an agent that computes a satisfaction score (0‑5). Low‑score reviews trigger automated notifications (e.g., email to logistics manager). The demo runs locally in a Python environment and can be deployed to a Flink cluster using flink run.

Demo 2 – Real‑time live‑stream analytics: User comments (弹幕) are ingested, enriched with user profiles, and analyzed for sentiment and topic classification every minute. Results are fed back to the streamer to adjust content in real time, achieving a fine‑grained “stream‑of‑water” traffic pattern instead of bursty spikes.

Demo 3 – Intelligent operations: An agent monitors job exceptions, calls health‑check APIs, and generates actionable suggestions. After operator approval, the agent can automatically execute remediation actions, reducing manual intervention.

Comparison with other agent frameworks (e.g., LangChain, Pathway) highlights Flink Agents’ reliance on Flink’s exactly‑once guarantees, low‑latency processing, and seamless integration with existing Flink pipelines.

5. Performance Optimizations – Flink + Fluss

To further reduce end‑to‑end latency, the authors propose replacing Kafka with Fluss, a stream‑native storage system that supports real‑time reads/writes, partial column updates, and push‑down queries. Benchmarks show latency improvements from minutes to seconds for typical ETL and CEP workloads.

6. Roadmap

The open‑source project was released in October 2025 and is fully available on GitHub. Version 0.2 (expected early 2026) will add more user stories, expanded tool integrations, and additional vertical AI Functions.

7. Conclusion

The session reviewed Flink’s core capabilities, introduced the Flink Agents framework for building event‑driven AI agents, and demonstrated how Flink AI Function brings real‑time LLM inference, embedding, and vector search into streaming pipelines. Readers are encouraged to try the Serverless Flink offering on Alibaba Cloud.