diff --git a/docs/hub/_toctree.yml b/docs/hub/_toctree.yml
index feaceef4f..e86b9a053 100644
--- a/docs/hub/_toctree.yml
+++ b/docs/hub/_toctree.yml
@@ -398,6 +398,8 @@
       title: "Protect AI"
     - local: security-jfrog
       title: "JFrog"
+  - local: agents
+    title: Agents on Hub
   - local: moderation
     title: Moderation
   - local: paper-pages
diff --git a/docs/hub/agents.md b/docs/hub/agents.md
new file mode 100644
index 000000000..cb4bc87e3
--- /dev/null
+++ b/docs/hub/agents.md
@@ -0,0 +1,113 @@
+# Agents on the Hub
+
+This page compiles all the libraries and tools Hugging Face offers for agentic workflows: huggingface.js mcp-client, Gradio MCP Server and smolagents.
+
+## smolagents
+
+[smolagents](https://github.com/huggingface/smolagents) is a lightweight library to cover all agentic use cases, from code-writing agents to computer use, in few lines of code. It is model agnostic, supporting local models served with Hugging Face Transformers, as well as models offered with [Inference Providers](../inference-providers/index.md), and proprietary model providers. 
+
+It offers a unique kind of agent :`CodeAgent`, an agent that writes its actions in Python code.
+It also supports the standard agent that writes actions in JSON blobs as most other agentic frameworks do, called `ToolCallingAgent`.
+To learn more about write actions in code vs JSON, check out our [new short course on DeepLearning.AI](https://www.deeplearning.ai/short-courses/building-code-agents-with-hugging-face-smolagents/).
+
+If you want to avoid defining agents yourself, the easiest way to start an agent is through the CLI, using the `smolagent` command.
+
+```bash
+smolagent "Plan a trip to Tokyo, Kyoto and Osaka between Mar 28 and Apr 7." \
+--model-type "InferenceClientModel" \
+--model-id "Qwen/Qwen2.5-Coder-32B-Instruct" \
+--imports "pandas numpy" \
+--tools "web_search"
+```
+
+Agents can be pushed to Hugging Face Hub as Spaces. Check out all the cool agents people have built [here](https://huggingface.co/spaces?filter=smolagents&sort=likes).
+
+smolagents also supports MCP servers as tools, as follows:
+
+```python
+# pip install --upgrade smolagents mcp
+from smolagents import MCPClient, CodeAgent
+from mcp import StdioServerParameters
+import os
+
+server_parameters = StdioServerParameters(
+    command="uvx",  # Using uvx ensures dependencies are available
+    args=["--quiet", "pubmedmcp@0.1.3"],
+    env={"UV_PYTHON": "3.12", **os.environ},
+)
+
+with MCPClient(server_parameters) as tools:
+    agent = CodeAgent(tools=tools, model=model, add_base_tools=True)
+    agent.run("Please find the latest research on COVID-19 treatment.")
+```
+
+Learn more [in the documentation](https://huggingface.co/docs/smolagents/tutorials/tools#use-mcp-tools-with-mcpclient-directly).
+
+## huggingface.js mcp-client
+
+Huggingface.js offers an MCP client served with [Inference Providers](https://huggingface.co/docs/inference-providers/en/index) or local LLMs. Getting started with them is as simple as running `pnpm agent`. You can plug and play different models and providers by setting `PROVIDER` and `MODEL_ID` environment variables. 
+
+```bash
+export HF_TOKEN="hf_..."
+export MODEL_ID="Qwen/Qwen2.5-72B-Instruct"
+export PROVIDER="nebius"
+npx @huggingface/mcp-client
+```
+
+or, you can use any Local LLM (for example via lmstudio):
+
+```bash
+ENDPOINT_URL=http://localhost:1234/v1 \
+MODEL_ID=lmstudio-community/Qwen3-14B-GGUF \
+npx @huggingface/mcp-client
+```
+
+You can get more information about mcp-client [here](https://huggingface.co/docs/huggingface.js/en/mcp-client/README).
+
+
+## Gradio MCP Server / Tools
+
+You can build an MCP server in just a few lines of Python with Gradio. If you have an existing Gradio app or Space you'd like to use as an MCP server / tool, it's just a single-line change.
+
+To make a Gradio application an MCP server, simply pass in `mcp_server=True` when launching your demo like follows.
+
+```python
+# pip install gradio
+
+import gradio as gr
+
+def generate_image(prompt: str):
+   """
+   Generate an image based on a text prompt
+   
+   Args:
+       prompt: a text string describing the image to generate
+   """
+   pass
+
+demo = gr.Interface(
+    fn=generate_image,
+    inputs="text",
+    outputs="image",
+    title="Image Generator"
+)
+
+demo.launch(mcp_server=True)
+```
+
+The MCP server will be available at `http://your-space-id.hf.space/gradio_api/mcp/sse` where your application is served. It will have a tool corresponding to each function in your Gradio app, with the tool description automatically generated from the docstrings of your functions.
+
+Lastly, add this to the settings of the MCP Client of your choice (e.g. Cursor).
+
+```json
+{
+  "mcpServers": {
+    "gradio": {
+      "url": "http://your-server:port/gradio_api/mcp/sse"
+    }
+  }
+}
+```
+
+This is very powerful because it lets the LLM use any Gradio application as a tool. You can find thousands of them on [Spaces](https://huggingface.co/spaces). Learn more [here](https://www.gradio.app/guides/building-mcp-server-with-gradio).
+