diff --git a/README.md b/README.md
index ed78dbca43..23a108fc2e 100644
--- a/README.md
+++ b/README.md
@@ -18,6 +18,10 @@ FlexKV is released under the **Apache-2.0 License**. See the [LICENSE](LICENSE)
 
 See [docs/vllm_adapter/README_en.md](docs/vllm_adapter/README_en.md)
 
+### FlexKV Integration with Dynamo
+
+See [docs/dynamo_integration/README_en.md](docs/dynamo_integration/README_en.md)
+
 ## Design Architecture
 
 <div align="center">
diff --git a/README_zh.md b/README_zh.md
index 0618a83220..24f522d1f5 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -18,6 +18,10 @@ FlexKV 采用 **Apache-2.0 开源协议**，详细信息请参见 [LICENSE](LICE
 
 见[docs/vllm_adapter/README_zh.md](docs/vllm_adapter/README_zh.md)
 
+### FlexKV和Dynamo框架的集成
+
+见[docs/dynamo_integration/README_zh.md](docs/dynamo_integration/README_zh.md)
+
 ## 设计框架
 
 <div align="center">
diff --git a/docs/dynamo_integration/README_en.md b/docs/dynamo_integration/README_en.md
new file mode 100644
index 0000000000..1fae4878a0
--- /dev/null
+++ b/docs/dynamo_integration/README_en.md
@@ -0,0 +1,153 @@
+# FlexKV and Dynamo Integration Guide
+
+This document demonstrates how to integrate FlexKV with NVIDIA's [Dynamo](https://github.com/ai-dynamo/dynamo) framework and complete performance testing.
+
+Dynamo is a framework designed by NVIDIA for large-scale distributed deployment, supporting multiple backend engines including TensorRT-LLM, vLLM, and SGLang. The KV Router is an intelligent request routing component that tracks and manages KV caches stored on different workers. It intelligently assigns requests to the most suitable worker based on the overlap between requests and KV cache, as well as the current worker load, thereby reducing expensive KV cache recomputations and improving inference efficiency. This document also explains how to integrate FlexKV into Dynamo when the KV Router is enabled.
+
+## 1. Environment Setup
+
+### Dynamo Image
+
+We use Dynamo 0.4.1 image with vLLM backend, which includes vLLM 0.10.1.1.
+
+```bash
+docker pull nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.4.1
+```
+
+### FlexKV Code Preparation
+
+```bash
+git clone https://github.com/taco-project/FlexKV
+```
+
+### Install FlexKV
+
+```bash
+apt update && apt install liburing-dev
+
+cd FlexKV && ./build.sh
+```
+
+### vLLM Apply Patch
+
+```bash
+# Navigate to vLLM directory
+cd /opt/vllm 
+# apply patch
+git apply /your/path/to/FlexKV/examples/vllm_adaption/vllm_0_10_1_1-flexkv-connector.patch
+```
+
+### FlexKV Verification
+
+Please refer to the test scripts in [vLLM online serving](https://github.com/taco-project/FlexKV/blob/dev/docs/vllm_adapter/README_zh.md#%E7%A4%BA%E4%BE%8B).
+
+## 2. Dynamo Modifications
+
+### kv_transfer_config
+
+To integrate with FlexKV, you need to modify the kv_transfer_config in the Dynamo image. Change lines 245-248 in /opt/dynamo/venv/lib/python3.12/site-packages/dynamo/vllm/args.py to:
+
+```python
+kv_transfer_config = KVTransferConfig(
+    kv_connector="FlexKVConnectorV1", kv_role="kv_both"
+)
+logger.info("Using FlexKVConnectorV1 configuration")
+```
+
+### CPU Offloading
+
+In Dynamo, the KV router updates its KV index by receiving events sent from workers, allowing it to track the KV cache status on each worker. When CPU offloading is enabled in FlexKV, we remove [BlockRemove](https://github.com/vllm-project/vllm/blob/v0.10.1.1/vllm/v1/core/block_pool.py#L221) in vLLM, allowing FlexKV to cache all KV blocks through CPU during the serving process. This ensures that the index maintained by the KV router accurately reflects the actual index in FlexKV.
+
+## 3. Starting and Verifying Dynamo Services
+
+### Starting Dynamo + FlexKV
+
+```bash
+#!/bin/bash
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+set -e
+trap 'echo Cleaning up...; kill 0' EXIT
+
+# Start nats and etcd
+nats-server -js &
+
+etcd --listen-client-urls http://0.0.0.0:2379 --advertise-client-urls http://0.0.0.0:2379 --data-dir /tmp/etcd &
+
+sleep 3
+
+# run ingress, set routing mode with --router-mode, options include kv, round-robin, random
+python -m dynamo.frontend --router-mode kv --http-port 8000 &
+
+# Define number of worker nodes
+NUM_WORKERS=4
+
+# When using multiple workers, ensure FlexKV ports are different to avoid hanging at flexkv init
+# Adjust num_cpu_blocks and num_ssd_blocks values according to your server configuration
+for i in $(seq 0 $((NUM_WORKERS-1))); do
+    cat <<EOF > ./flexkv_config_${i}.json
+{
+        "enable_flexkv": true,
+        "server_recv_port": "ipc:///tmp/flexkv_${i}_test",
+        "cache_config": {
+                        "enable_cpu": true,
+                        "enable_ssd": false,
+                        "enable_remote": false,
+                        "use_gds": false,
+                        "enable_trace": false,
+                        "ssd_cache_iouring_entries": 512,
+                        "tokens_per_block": 64,
+                        "num_cpu_blocks": 10240,
+                        "num_ssd_blocks": 256000,
+                        "ssd_cache_dir": "/data/flexkv_ssd/",
+                        "evict_ratio": 0.05,
+                        "index_accel": true
+
+        },
+        "num_log_interval_requests": 200
+}
+EOF
+done
+
+# Use a loop to start worker nodes
+for i in $(seq 0 $((NUM_WORKERS-1))); do
+    # Calculate GPU device IDs
+    GPU_START=$((i*2))
+    GPU_END=$((i*2+1))
+    
+    if [ $i -lt $((NUM_WORKERS-1)) ]; then
+        FLEXKV_CONFIG_PATH="./flexkv_config_${i}.json" CUDA_VISIBLE_DEVICES=${GPU_START},${GPU_END} python3 -m dynamo.vllm --model deepseek-ai/DeepSeek-R1-Distill-Llama-70B --tensor_parallel_size 2  --block-size 64 --gpu-memory-utilization 0.9 --max-model-len 100310 &
+    else
+        FLEXKV_CONFIG_PATH="./flexkv_config_${i}.json" CUDA_VISIBLE_DEVICES=${GPU_START},${GPU_END} python3 -m dynamo.vllm --model deepseek-ai/DeepSeek-R1-Distill-Llama-70B --tensor_parallel_size 2  --block-size 64 --gpu-memory-utilization 0.9 --max-model-len 100310
+    fi
+done
+```
+
+### Verification
+
+You can verify that the Dynamo service has started correctly with the following command:
+```bash
+curl localhost:8000/v1/chat/completions   -H "Content-Type: application/json"   -d '{
+    "model": "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
+    "messages": [
+    {
+        "role": "user",
+        "content": "Tell me a joke."
+    }
+    ],
+    "stream":false,
+    "max_tokens": 30
+  }'
+```
+
+## 4. Benchmark
+
+We use [genai-perf](https://github.com/triton-inference-server/perf_analyzer/tree/main/genai-perf) as our benchmark tool and [mooncake trace](https://github.com/kvcache-ai/Mooncake?tab=readme-ov-file#-open-source-trace) as our dataset to evaluate the performance of Dynamo + FlexKV.
+
+Mooncake Trace is an open-source request file saved in jsonl format. It records timestamps of request arrivals, ISL, OSL, and KV cache-related hash IDs, containing 23,608 requests over a 1-hour period. For our experiment with 4 LLaMA-70B workers, the concurrency in the mooncake trace was too high, so we sampled every 6th request from the trace to build our benchmark dataset.
+
+genai-perf can send requests according to the timestamps in the trace file and calculate metrics such as TTFT (Time To First Token) and TPOT (Tokens Per Output Token) for the LLM service. The command is as follows. Please use genai-perf==0.0.13, as newer versions have a bug in timestamp parsing.
+
+```bash
+genai-perf profile   --model deepseek-ai/DeepSeek-R1-Distill-Llama-70B  --tokenizer deepseek-ai/DeepSeek-R1-Distill-Llama-70B  --endpoint-type chat   --endpoint /v1/chat/completions --streaming  --url http://localhost:8000  --input-file payload:mooncake_trace_1_6.jsonl --random-seed 100  -v  -H 'Authorization: Bearer NOT USED'  -H 'Accept: text/event-stream'   -- --stability-percentage 99
+```
\ No newline at end of file
diff --git a/docs/dynamo_integration/README_zh.md b/docs/dynamo_integration/README_zh.md
new file mode 100644
index 0000000000..c33171af69
--- /dev/null
+++ b/docs/dynamo_integration/README_zh.md
@@ -0,0 +1,153 @@
+# FlexKV 与 Dynamo 集成指南
+
+该文档展示了如何将FlexKV和NVIDIA [Dynamo](https://github.com/ai-dynamo/dynamo) 框架集成，并完成性能测试的步骤。
+
+Dynamo是NVIDIA专为大规模分离式部署而设计的框架，支持TensorRT-LLM, vLLM, SGLang等多个后端引擎。其中KV 路由器（KV Router）是一个智能的请求路由组件, 它能够追踪和管理存储在不同worker上的 KV cache，并根据请求与缓存的重叠程度和worker当前负载，智能地将请求分配给最合适的 GPU 节点，从而减少昂贵的 KV 缓存重新计算，提高推理效率。文档也介绍了如何在开启KV Router时，将FlexKV集成进Dynamo。
+
+## 1. 环境准备
+
+### Dynamo 镜像
+
+该文档使用的是后端为vLLM的Dynamo 0.4.1 镜像，内置了vLLM 0.10.1.1。
+
+```bash
+docker pull nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.4.1
+```
+
+### FlexKV代码准备
+
+```bash
+git clone https://github.com/taco-project/FlexKV
+```
+
+### 安装 FlexKV
+
+```bash
+apt update && apt install liburing-dev
+
+cd FlexKV && ./build.sh
+```
+
+### vLLM Apply Patch
+
+```bash
+# 进入 vLLM 目录
+cd /opt/vllm 
+# apply patch
+git apply /your/path/to/FlexKV/examples/vllm_adaption/vllm_0_10_1_1-flexkv-connector.patch
+```
+
+### FlexKV 验证
+
+请参考[vLLM online serving](https://github.com/taco-project/FlexKV/blob/dev/docs/vllm_adapter/README_zh.md#%E7%A4%BA%E4%BE%8B)里的测试脚本。
+
+
+## 2. Dynamo 配置修改
+
+### kv_transfer_config
+
+为了和FlexKV集成，需要修改Dynamo镜像内的kv_transfer_config。将/opt/dynamo/venv/lib/python3.12/site-packages/dynamo/vllm/args.py 的245-248行修改为:
+
+```python
+kv_transfer_config = KVTransferConfig(
+    kv_connector="FlexKVConnectorV1", kv_role="kv_both"
+)
+logger.info("Using FlexKVConnectorV1 configuration")
+```
+
+### CPU Offloading
+
+在Dynamo中，KV router通过接收worker发送的event来更新KV index，从而感知每个worker上的KV cache情况。当FlexKV开启CPU offloading时，我们删掉vLLM里[BlockRemove](https://github.com/vllm-project/vllm/blob/v0.10.1.1/vllm/v1/core/block_pool.py#L221)，让FlexKV通过CPU能够缓存住所有serving过程中的KV block，这样KV router维护的index就能反映FlexKV的真实index了。
+
+## 3. 启动和验证Dynamo服务
+
+### 启动Dynamo + FlexKV
+
+```bash
+#!/bin/bash
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+set -e
+trap 'echo Cleaning up...; kill 0' EXIT
+
+# 启动nats和etcd
+nats-server -js &
+
+etcd --listen-client-urls http://0.0.0.0:2379 --advertise-client-urls http://0.0.0.0:2379 --data-dir /tmp/etcd &
+
+sleep 3
+
+# run ingress, 通过--router-mode设置路由方式，可选项为kv, round-robin, random
+python -m dynamo.frontend --router-mode kv --http-port 8000 &
+
+# 定义工作节点数量
+NUM_WORKERS=4
+
+# 多个worker时注意FlexKV的端口应不同，否则会卡在flexkv init这一步
+# 请根据服务器的配置，调整num_cpu_blocks和num_ssd_blocks的数值
+for i in $(seq 0 $((NUM_WORKERS-1))); do
+    cat <<EOF > ./flexkv_config_${i}.json
+{
+        "enable_flexkv": true,
+        "server_recv_port": "ipc:///tmp/flexkv_${i}_test",
+        "cache_config": {
+                        "enable_cpu": true,
+                        "enable_ssd": false,
+                        "enable_remote": false,
+                        "use_gds": false,
+                        "enable_trace": false,
+                        "ssd_cache_iouring_entries": 512,
+                        "tokens_per_block": 64,
+                        "num_cpu_blocks": 10240,
+                        "num_ssd_blocks": 256000,
+                        "ssd_cache_dir": "/data/flexkv_ssd/",
+                        "evict_ratio": 0.05,
+                        "index_accel": true
+
+        },
+        "num_log_interval_requests": 200
+}
+EOF
+done
+
+# 使用for循环启动工作节点
+for i in $(seq 0 $((NUM_WORKERS-1))); do
+    # 计算GPU设备ID
+    GPU_START=$((i*2))
+    GPU_END=$((i*2+1))
+    
+    if [ $i -lt $((NUM_WORKERS-1)) ]; then
+        FLEXKV_CONFIG_PATH="./flexkv_config_${i}.json" CUDA_VISIBLE_DEVICES=${GPU_START},${GPU_END} python3 -m dynamo.vllm --model deepseek-ai/DeepSeek-R1-Distill-Llama-70B --tensor_parallel_size 2  --block-size 64 --gpu-memory-utilization 0.9 --max-model-len 100310 &
+    else
+        FLEXKV_CONFIG_PATH="./flexkv_config_${i}.json" CUDA_VISIBLE_DEVICES=${GPU_START},${GPU_END} python3 -m dynamo.vllm --model deepseek-ai/DeepSeek-R1-Distill-Llama-70B --tensor_parallel_size 2  --block-size 64 --gpu-memory-utilization 0.9 --max-model-len 100310
+    fi
+done
+```
+
+### 验证
+
+可通过如下命令验证Dynamo服务是否正确启动：
+```bash
+curl localhost:8000/v1/chat/completions   -H "Content-Type: application/json"   -d '{
+    "model": "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
+    "messages": [
+    {
+        "role": "user",
+        "content": "Tell me a joke."
+    }
+    ],
+    "stream":false,
+    "max_tokens": 30
+  }'
+```
+## 4. Benchmark
+
+我们使用[genai-perf](https://github.com/triton-inference-server/perf_analyzer/tree/main/genai-perf)作为benchmark工具、[mooncake trace](https://github.com/kvcache-ai/Mooncake?tab=readme-ov-file#-open-source-trace)作为数据集来评估Dynamo + FlexKV的性能。
+
+Mooncake Trace 是一个开源请求记录文件，以jsonl格式保存。它记录了请求到达的时间戳、输入文本长度、输出文本长度以及与缓存有关的hash id等信息，包含了1小时内的23608个请求。我们的实验资源是4个LLaMA-70B worker，mooncake trace对于该配置来说并发太高了，于是我们从mooncake trace里每6个抽取1个request，构建了用于benchmark的数据集。
+
+genai-perf可以根据trace文件里的时间戳来发送请求，统计LLM服务的TTFT、TPOT等指标，命令如下。请使用genai-perf==0.0.13，更新的版本存在解析时间戳的bug。
+
+```bash
+ genai-perf profile   --model deepseek-ai/DeepSeek-R1-Distill-Llama-70B  --tokenizer deepseek-ai/DeepSeek-R1-Distill-Llama-70B  --endpoint-type chat   --endpoint /v1/chat/completions --streaming  --url http://localhost:8000  --input-file payload:mooncake_trace_1_6.jsonl --random-seed 100  -v  -H 'Authorization: Bearer NOT USED'  -H 'Accept: text/event-stream'   -- --stability-percentage 99
+```
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