diff --git a/docs/content/docs/get-started/quickstart/react_agent.md b/docs/content/docs/get-started/quickstart/react_agent.md
index 73bba445..74d7efe2 100644
--- a/docs/content/docs/get-started/quickstart/react_agent.md
+++ b/docs/content/docs/get-started/quickstart/react_agent.md
@@ -22,6 +22,154 @@ specific language governing permissions and limitations
 under the License.
 -->
 
-{{< hint warning >}}
-**TODO**: Follow the same structure as workflow_agent.md, and add the content.
-{{< /hint >}}
+## Overview
+
+ReAct Agent is a general paradigm that combines reasoning and action capabilities to solve complex tasks. Leveraging this paradigm, the user only needs to specify the goal with prompt and provide available tools, and the LLM will decide how to achieve the goal and take actions autonomously.
+
+This quickstart introduces a small example that demonstrate how to build a streaming ReAct Agent with Flink Agents:
+
+The **Review Analysis** agent processes a stream of product reviews and uses a single agent to extract a rating (1–5) and unsatisfied reasons from each review. And notify the shipping manager if a review includes dissatisfaction with the shipping process.
+
+## Code Walkthrough
+
+### Prepare Agents Execution Environment
+
+Create the agents execution environment, and register the available chat model connections and tools to the environment. 
+```python
+# Set up the Flink streaming environment and the Agents execution environment.
+env = StreamExecutionEnvironment.get_execution_environment()
+agents_env = AgentsExecutionEnvironment.get_execution_environment(env)
+
+# Add Ollama chat model connection and notify shipping manager tool to be used
+# by the Agent.
+agents_env.add_resource(
+    "ollama_server",
+    ResourceDescriptor(clazz=OllamaChatModelConnection, request_timeout=120),
+).add_resource(
+    "notify_shipping_manager", Tool.from_callable(notify_shipping_manager)
+)
+```
+
+### Create the ReAct Agent
+Create the ReAct Agent instance, configure the chat model, prompt and the output schema of result to be used.
+```python
+review_analysis_react_agent = ReActAgent(
+    chat_model=ResourceDescriptor(
+        clazz=OllamaChatModelSetup,
+        connection="ollama_server",
+        model="qwen3:8b",
+        tools=["notify_shipping_manager"],
+    ),
+    prompt=review_analysis_react_prompt,
+    output_schema=ProductReviewAnalysisRes,
+)
+```
+
+### Integrate the ReAct Agent with Flink
+Produce a source DataStream by reading a product review text file, and use the ReAct Agent to analyze the review and generate result DataStream. Finally print the result DataStream.
+```python
+# Read product reviews from a text file as a streaming source.
+# Each line in the file should be a JSON string representing a ProductReview.
+product_review_stream = env.from_source(
+    source=FileSource.for_record_stream_format(
+        StreamFormat.text_line_format(),
+        f"file:///{current_dir}/resources/",
+    )
+    .monitor_continuously(Duration.of_minutes(1))
+    .build(),
+    watermark_strategy=WatermarkStrategy.no_watermarks(),
+    source_name="streaming_agent_example",
+).map(
+    lambda x: ProductReview.model_validate_json(
+        x
+    )  # Deserialize JSON to ProductReview.
+)
+
+# Use the ReAct agent to analyze each product review and notify the shipping manager
+# when needed.
+review_analysis_res_stream = (
+    agents_env.from_datastream(
+        input=product_review_stream, key_selector=lambda x: x.id
+    )
+    .apply(review_analysis_react_agent)
+    .to_datastream()
+)
+
+# Print the analysis results to stdout.
+review_analysis_res_stream.print()
+```
+
+## Run the Example
+
+### Prerequisites
+
+* Unix-like environment (we use Linux, Mac OS X, Cygwin, WSL)
+* Git
+* Java 11
+* Python 3.10 or 3.11
+
+### Preparation
+
+#### Prepare Flink
+
+Download a stable release of Flink 1.20.3, then extract the archive:
+
+```bash
+curl -LO https://archive.apache.org/dist/flink/flink-1.20.3/flink-1.20.3-bin-scala_2.12.tgz
+tar -xzf flink-1.20.3-bin-scala_2.12.tgz
+
+# Copy the flink-python jar from opt to lib
+cp ./flink-1.20.3/opt/flink-python-1.20.3.jar ./flink-1.20.3/lib
+```
+You can refer to the [local installation](https://nightlies.apache.org/flink/flink-docs-release-1.20/docs/try-flink/local_installation/) instructions for more detailed step.
+
+
+#### Prepare Flink Agents
+
+We recommend creating a Python virtual environment to install the Flink Agents Python library.
+
+Follow the [installation]({{< ref "docs/get-started/installation" >}}) instructions to install the Flink Agents Python and Java libraries.
+
+#### Deploy a Standalone Flink Cluster
+
+You can deploy a standalone Flink cluster in your local environment with the following command.
+
+```bash
+export PYTHONPATH=$(python -c 'import sysconfig; print(sysconfig.get_paths()["purelib"])')
+./flink-1.20.3/bin/start-cluster.sh
+```
+You can refer to the [local cluster](https://nightlies.apache.org/flink/flink-docs-release-1.20/docs/try-flink/local_installation/#starting-and-stopping-a-local-cluster) instructions for more detailed step.
+
+
+> **NOTE:** If you can't navigate to the web UI at [localhost:8081](localhost:8081), you can find the reason in `./flink-1.20.3/log`. If the reason is port conflict, you can change the port in `./flink-1.20.3/conf/config.yaml`.
+
+#### Prepare Ollama
+
+Download and install Ollama from the official [website](https://ollama.com/download).
+
+Then run the qwen3:8b model, which is required by the quickstart examples
+
+```bash
+ollama run qwen3:8b
+```
+
+### Submit Flink Agents Job to Standalone Flink Cluster
+
+#### Clone the Flink Agents repo
+
+```bash
+git clone https://github.com/apache/flink-agents.git
+```
+
+#### Submit to Flink Cluster
+```bash
+export PYTHONPATH=$(python -c 'import sysconfig; print(sysconfig.get_paths()["purelib"])')
+
+# Run review analysis example
+./flink-1.20.3/bin/flink run -py ./flink-agents/python/flink_agents/examples/quickstart/react_agent_example.py
+```
+
+Now you should see a Flink job submitted to the Flink Cluster in Flink web UI [localhost:8081](localhost:8081)
+
+After a few minutes, you can check for the output in the TaskManager output log.
+
diff --git a/python/flink_agents/examples/quickstart/react_agent_example.py b/python/flink_agents/examples/quickstart/react_agent_example.py
index 2d546030..eb638e90 100644
--- a/python/flink_agents/examples/quickstart/react_agent_example.py
+++ b/python/flink_agents/examples/quickstart/react_agent_example.py
@@ -53,7 +53,7 @@ def main() -> None:
     env = StreamExecutionEnvironment.get_execution_environment()
     agents_env = AgentsExecutionEnvironment.get_execution_environment(env)
 
-    # Add Ollama chat model connection and record shipping question tool to be used
+    # Add Ollama chat model connection and notify shipping manager tool to be used
     # by the Agent.
     agents_env.add_resource(
         "ollama_server",
@@ -91,8 +91,8 @@ def main() -> None:
         output_schema=ProductReviewAnalysisRes,
     )
 
-    # Use the ReAct agent to analyze each product review and
-    # record shipping question.
+    # Use the ReAct agent to analyze each product review and notify the shipping manager
+    # when needed.
     review_analysis_res_stream = (
         agents_env.from_datastream(
             input=product_review_stream, key_selector=lambda x: x.id