diff --git a/frameworks/MLflow/3.10.1/Dockerfile b/frameworks/MLflow/3.10.1/Dockerfile
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--- /dev/null
+++ b/frameworks/MLflow/3.10.1/Dockerfile
@@ -0,0 +1,25 @@
+FROM opencloudos/opencloudos9-minimal:latest
+
+LABEL maintainer="stronking 363133710@qq.com"
+LABEL org.opencontainers.image.source="https://gitee.com/OpenCloudOS/ai-agent-container"
+LABEL org.opencontainers.image.description="MLflow 3.10.1 container image based on OpenCloudOS 9"
+
+ARG MLFLOW_VERSION=3.10.1
+
+ENV LANG=en_US.UTF-8
+ENV LC_ALL=en_US.UTF-8
+ENV PYTHONUNBUFFERED=1
+
+# Install MLflow
+RUN python3 -m ensurepip
+RUN --mount=type=cache,id=pip-cache-opencloudos9-cu128,target=/root/.cache/pip   pip3 install  mlflow==$MLFLOW_VERSION
+
+# Default MLflow configuration
+ENV MLFLOW_HOST=0.0.0.0
+ENV MLFLOW_PORT=5000
+
+EXPOSE 5000
+
+WORKDIR /workspace
+
+CMD ["sh", "-c", "mlflow server --host ${MLFLOW_HOST} --port ${MLFLOW_PORT}"]
diff --git a/frameworks/MLflow/3.10.1/README.md b/frameworks/MLflow/3.10.1/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f0711c8bc61b35de50c8d618c0bc72ebc84c8dfb
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+++ b/frameworks/MLflow/3.10.1/README.md
@@ -0,0 +1,189 @@
+
+# MLflow on OpenCloudOS 9
+
+## 基本信息
+
+- **框架版本**：v3.10.1
+- **基础镜像**：opencloudos9-minimal
+- **Python 版本**：3.11
+- **CUDA 版本**：N/A
+
+---
+
+## 项目简介
+
+[MLflow](https://github.com/mlflow/mlflow) 是一个开源的机器学习生命周期管理平台，主要提供以下能力：
+
+- 实验追踪（Experiment Tracking）
+- 参数与指标记录
+- Artifact 管理
+- 模型注册（Model Registry）
+- 模型部署（Serving）
+
+本镜像基于 OpenCloudOS 9 构建，提供轻量级 MLflow Tracking Server 运行环境。
+
+---
+
+## 构建
+
+```bash
+docker build -t oc9-mlflow:3.10.1 .
+````
+
+---
+
+## 使用示例
+
+### 查看 MLflow 版本
+
+```bash
+docker run --rm oc9-mlflow:3.10.1 \
+  python3 -c "import mlflow; print(mlflow.__version__)"
+```
+
+---
+
+## 启动 MLflow Tracking Server
+
+```bash
+docker run -d \
+  --name mlflow \
+  -p 5000:5000 \
+  oc9-mlflow:3.10.1
+```
+
+访问：
+
+```text
+http://localhost:5000
+```
+
+---
+
+## 持久化实验数据
+
+默认情况下，容器中的实验数据会随着容器删除而丢失。
+
+推荐挂载数据目录：
+
+```bash
+docker run -d \
+  --name mlflow \
+  -p 5000:5000 \
+  -v $(pwd)/mlruns:/mlruns \
+  oc9-mlflow:3.10.1 \
+  sh -c "mlflow server \
+    --backend-store-uri sqlite:///mlruns/mlflow.db \
+    --default-artifact-root /mlruns \
+    --host 0.0.0.0 \
+    --port 5000"
+```
+
+目录说明：
+
+```text
+mlruns/
+├── mlflow.db
+└── artifacts
+```
+
+---
+
+## 实验追踪示例
+
+创建示例脚本：
+
+```python
+import mlflow
+import random
+
+mlflow.set_tracking_uri("http://127.0.0.1:5000")
+mlflow.set_experiment("demo-experiment")
+
+with mlflow.start_run():
+
+    mlflow.log_param("learning_rate", 0.01)
+    mlflow.log_param("epochs", 10)
+
+    for step in range(10):
+        loss = 1.0 / (step + 1)
+        accuracy = 0.8 + random.random() * 0.1
+
+        mlflow.log_metric("loss", loss, step=step)
+        mlflow.log_metric("accuracy", accuracy, step=step)
+
+    with open("result.txt", "w") as f:
+        f.write("training completed")
+
+    mlflow.log_artifact("result.txt")
+```
+
+运行：
+
+```bash
+python3 train.py
+```
+
+然后访问：
+
+```text
+http://localhost:5000
+```
+
+即可查看实验参数、指标和 Artifact。
+
+---
+
+## 容器网络使用示例
+
+如果训练任务运行在其他容器中，建议使用 Docker Network。
+
+创建网络：
+
+```bash
+docker network create mlflow-net
+```
+
+启动 MLflow：
+
+```bash
+docker run -d \
+  --name mlflow \
+  --network mlflow-net \
+  -p 5000:5000 \
+  oc9-mlflow:3.10.1
+```
+
+训练容器中配置：
+
+```bash
+export MLFLOW_TRACKING_URI=http://mlflow:5000
+```
+
+---
+
+## 默认配置
+
+| 配置项               | 默认值        |
+| ----------------- | ---------- |
+| Host              | 0.0.0.0    |
+| Port              | 5000       |
+| Working Directory | /workspace |
+
+---
+
+## 已知问题
+
+* 当前镜像为轻量版，不包含 PyTorch、TensorFlow 等深度学习框架
+* 不包含 CUDA 与 GPU 运行环境
+* Model Serving 场景下，部分依赖需用户自行安装
+
+---
+
+## 上游项目
+
+* MLflow: https://github.com/mlflow/mlflow
+* OpenCloudOS: https://gitee.com/OpenCloudOS
+
+```
+```
diff --git a/frameworks/MLflow/3.10.1/build.conf b/frameworks/MLflow/3.10.1/build.conf
new file mode 100644
index 0000000000000000000000000000000000000000..baaea75b532f11438fad7a26353f8747c7547a94
--- /dev/null
+++ b/frameworks/MLflow/3.10.1/build.conf
@@ -0,0 +1,4 @@
+# MLflow 3.10.1 on OpenCloudOS 9
+IMAGE_NAME=oc9-mlflow
+IMAGE_TAG=3.10.1
+GPU_TEST=false
\ No newline at end of file
diff --git a/frameworks/MLflow/3.10.1/test.sh b/frameworks/MLflow/3.10.1/test.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1cb9a5c4f2d347be491716445d15ce75375f157d
--- /dev/null
+++ b/frameworks/MLflow/3.10.1/test.sh
@@ -0,0 +1,77 @@
+#!/bin/bash
+set -e
+
+IMAGE="${1:?ERROR: 缺少镜像参数。用法: bash test.sh <image:tag>}"
+
+DOCKER_CMD="docker run --rm -e GIT_PYTHON_REFRESH=quiet"
+
+echo "=== MLflow 基础功能测试 ==="
+
+echo -n "检查 MLflow import 和版本... "
+$DOCKER_CMD "$IMAGE" python3 -c "import mlflow; print(mlflow.__version__)" >/tmp/mlflow_import.log 2>&1 \
+  && echo "✓ 通过" || { echo "✗ 失败"; cat /tmp/mlflow_import.log; exit 1; }
+
+echo -n "检查 MLflow CLI... "
+$DOCKER_CMD "$IMAGE" mlflow --version >/tmp/mlflow_cli.log 2>&1 \
+  && echo "✓ 通过" || { echo "✗ 失败"; cat /tmp/mlflow_cli.log; exit 1; }
+
+echo -n "检查实验追踪核心功能... "
+$DOCKER_CMD "$IMAGE" python3 -c "
+import os
+import tempfile
+import mlflow
+
+tmpdir = tempfile.mkdtemp()
+db_path = os.path.join(tmpdir, 'mlflow.db')
+
+mlflow.set_tracking_uri('sqlite:///' + db_path)
+mlflow.set_experiment('ci-test-experiment')
+
+with mlflow.start_run():
+    mlflow.log_param('learning_rate', 0.01)
+    mlflow.log_metric('accuracy', 0.95)
+
+    run = mlflow.active_run()
+    assert run is not None
+    assert run.info.run_id is not None
+
+print('MLflow experiment tracking works')
+" >/tmp/mlflow_tracking.log 2>&1 \
+  && echo "✓ 通过" || { echo "✗ 失败"; cat /tmp/mlflow_tracking.log; exit 1; }
+
+echo -n "检查 MLflow Tracking Server 启动... "
+$DOCKER_CMD "$IMAGE" bash -c "
+set -e
+
+TMPDIR=\$(mktemp -d)
+
+mlflow server \
+  --backend-store-uri sqlite:///\$TMPDIR/mlflow.db \
+  --default-artifact-root \$TMPDIR/artifacts \
+  --host 127.0.0.1 \
+  --port 5000 >/tmp/mlflow_server.log 2>&1 &
+
+PID=\$!
+
+for i in \$(seq 1 60); do
+  if curl -fs http://127.0.0.1:5000/health >/dev/null 2>&1; then
+    kill \$PID
+    wait \$PID 2>/dev/null || true
+    exit 0
+  fi
+
+  if ! kill -0 \$PID 2>/dev/null; then
+    cat /tmp/mlflow_server.log
+    exit 1
+  fi
+
+  sleep 1
+done
+
+cat /tmp/mlflow_server.log
+kill \$PID 2>/dev/null || true
+exit 1
+" >/tmp/mlflow_server_check.log 2>&1 \
+  && echo "✓ 通过" || { echo "✗ 失败"; cat /tmp/mlflow_server_check.log; exit 1; }
+
+echo "=== 所有测试通过 ==="
\ No newline at end of file
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