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new 2e96ca89 [docs] Refresh get-started overview for 0.3 features (#849)
2e96ca89 is described below
commit 2e96ca898743d86e946773fa8e44e0d2fb3c0bb7
Author: Wenjin Xie <[email protected]>
AuthorDate: Mon Jun 15 15:00:57 2026 +0800
[docs] Refresh get-started overview for 0.3 features (#849)
Co-authored-by: Claude Opus 4.8 (1M context) <[email protected]>
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1 file changed, 5 insertions(+), 5 deletions(-)
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@@ -24,18 +24,18 @@ under the License.
## What is Flink Agents?
-**Apache Flink Agents** is a brand-new sub-project from the Apache Flink
community, providing an **open-source framework for building event-driven
streaming agents**.
+**Apache Flink Agents** is a **streaming Agent OS** for enterprise,
production-grade scenarios, built as a sub-project of the Apache Flink
community. It brings AI agents into the Flink streaming pipeline - an agent
becomes a first-class operator in your real-time datastream, making AI
decisions in the flow of live events rather than in response to human prompts.
-Building on Flink's battle-tested streaming engine, Apache Flink Agents
inherits **distributed, at-scale, fault-tolerant structured data processing and
mature state management**, and adds first-class abstractions for Agentic AI
building blocks and functionalities - **large language models (LLMs)**,
**prompts**, **tools**, **memory**, **dynamic orchestration**,
**observability**, and more.
+Like any Agent OS, it manages the core building blocks of an agent -
orchestration, context, memory, skills, and tool/MCP invocation. Built on
Flink's battle-tested streaming engine, it additionally inherits **distributed,
at-scale, fault-tolerant** processing and mature state management, so agents
run **event-driven, distributed, and reliable** at production scale.
## Features
The key features of Apache Flink Agents include:
- **Massive Scale and Millisecond Latency**: Processes massive-scale event
streams in real time, leveraging Flink's distributed processing engine.
- **Seamless Data and AI Integration**: Agents interact directly with Flink's
DataStream and Table APIs for input and output, enabling a smooth integration
of structured data processing and semantic AI capabilities within Flink.
-- **Exactly-Once Action Consistency**: Ensures exactly-once consistency for
agent actions and their side effects by integrating Flink's checkpointing with
an external write-ahead log.
-- **Familiar Agent Abstractions**: Leverages well-known AI agent concepts,
making it easy for developers experienced with agent-based systems to quickly
adopt and build on Apache Flink Agents without a steep learning curve.
-- **Multi-Language Supports**: Provides native APIs in both Python and Java,
enabling seamless integration into diverse development environments and
allowing teams to use their preferred programming language. For guidance on
choosing Java or Python, see [Should I choose Java or Python?]({{< ref
"docs/faq/faq#q3-should-i-choose-java-or-python" >}}).
+- **Reliable and Exactly-Once Execution**: Ensures exactly-once consistency
for agent actions and their side effects by integrating Flink's checkpointing
with an external write-ahead log, and supports durable execution - optionally
with reconcilers to reconcile in-flight side effects upon failure recovery - so
agents run reliably without human supervision.
+- **Familiar Agent Abstractions**: Leverages well-known AI agent concepts -
skills, short/long-term memory, prompts, tools, and dynamic orchestration -
making it easy for developers experienced with agent-based systems to quickly
adopt and build on Apache Flink Agents without a steep learning curve.
+- **Multi-Language Supports**: Provides native APIs in Python, Java, and a
declarative YAML API, enabling seamless integration into diverse development
environments and allowing teams to use their preferred programming style. You
can even mix languages, authoring actions, tools, and events in one and running
them in an agent built in the other. For guidance on choosing Java or Python,
see [Should I choose Java or Python?]({{< ref
"docs/faq/faq#q3-should-i-choose-java-or-python" >}}).
- **Rich Ecosystem**: Natively integrates mainstream LLMs, vector stores from
diverse providers, and tools or prompts hosted on MCP servers into your agents,
while enabling customizable extensions.
- **Observability**: Adopts an event-centric orchestration approach, where all
agent actions are connected and controlled by events, enabling observation and
understanding of agent behavior through the event log.
