diff --git a/README.md b/README.md
index 0c7250a..9cabdc7 100644
--- a/README.md
+++ b/README.md
@@ -244,6 +244,41 @@ response = pipe(prompt)
 print(response)
 ```
 
+### Using internLM with other AI-Agent application development frameworks
+
+Expand the entries below for detailed usage 
+
+1.  <details><summary> <b><a href=https://github.com/langchain-ai/langchain>LangChain</a></b>: LangChain is a framework for developing applications powered by large language models (LLMs).
+    </details>
+
+1.  <details><summary> <b><a href=https://github.com/run-llama/llama_index>LlamaIndex</a></b>: LlamaIndex is a data framework for your LLM applications.</summary>
+    </details>
+
+1.  <details><summary> <b><a href=https://github.com/LazyAGI/LazyLLM>LazyLLM</a></b>: Easyest and lazyest way for building multi-agent LLMs applications.</summary>
+
+    Once you have installed `lazyllm`, and then you can use the following code to build your own chatbot:
+
+    ```python
+    from lazyllm import TrainableModule, WebModule
+    # Model will be download automatically if you have an internet connection
+    m = TrainableModule('internlm2-chat-7b')
+    # will launch a chatbot server
+    WebModule(m).start().wait()
+    ```
+
+    You can use the following code to finetune your model if needed.
+
+    ```python
+    from lazyllm import TrainableModule, WebModule
+    m = TrainableModule('internlm2-chat-7b').trainset('/patt/to/your_data.json')
+    # TrainableModule m will be finetuned and deployed when web module is update once dataset is set
+    WebModule(m).update().wait()
+    ```
+
+    LazyLLM Documents: https://lazyllm.readthedocs.io/
+
+    </details>
+
 ## Agent
 
 InternLM2.5-Chat models have excellent tool utilization capabilities and can work with function calls in a zero-shot manner. It also supports to conduct analysis by collecting information from more than 100 web pages. See more examples in [agent section](./agent/).