diff --git a/daprdocs/content/en/developing-applications/dapr-agents/dapr-agents-quickstarts.md b/daprdocs/content/en/developing-applications/dapr-agents/dapr-agents-quickstarts.md
index f2a519f9a8b..cbf9a652261 100644
--- a/daprdocs/content/en/developing-applications/dapr-agents/dapr-agents-quickstarts.md
+++ b/daprdocs/content/en/developing-applications/dapr-agents/dapr-agents-quickstarts.md
@@ -18,8 +18,8 @@ description: "Get started with Dapr Agents through practical step-by-step exampl
| Scenario | What You'll Learn |
|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------|
| [Hello World](https://github.com/dapr/dapr-agents/tree/main/quickstarts/01-hello-world)
A rapid introduction that demonstrates core Dapr Agents concepts through simple, practical examples. | - **Basic LLM Usage**: Simple text generation with OpenAI models
- **Creating Agents**: Building agents with custom tools in under 20 lines of code
- **Simple Workflows**: Setting up multi-step LLM processes
- **DurableAgent Hosting**: Learn `AgentRunner.run`, `AgentRunner.subscribe`, and `AgentRunner.serve` using the `03_durable_agent_*.py` samples |
-| [LLM Call with Dapr Chat Client](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_dapr)
Explore interaction with Language Models through Dapr Agents' `DaprChatClient`, featuring basic text generation with plain text prompts and templates. | - **Text Completion**: Generating responses to prompts
- **Swapping LLM providers**: Switching LLM backends without application code change
- **Resilience**: Setting timeout, retry and circuit-breaking
- **PII Obfuscation**: Automatically detect and mask sensitive user information |
-| [LLM Call with OpenAI Client](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_open_ai)
Leverage native LLM client libraries with Dapr Agents using the OpenAI Client for chat completion, audio processing, and embeddings. | - **Text Completion**: Generating responses to prompts
- **Structured Outputs**: Converting LLM responses to Pydantic objects
*Note: Other quickstarts for specific clients are available for [Elevenlabs](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_elevenlabs), [Hugging Face](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_hugging_face), and [Nvidia](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_nvidia).* |
+| [LLM Call with Dapr Chat Client](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02-llm-call-dapr)
Explore interaction with Language Models through Dapr Agents' `DaprChatClient`, featuring basic text generation with plain text prompts and templates. | - **Text Completion**: Generating responses to prompts
- **Swapping LLM providers**: Switching LLM backends without application code change
- **Resilience**: Setting timeout, retry and circuit-breaking
- **PII Obfuscation**: Automatically detect and mask sensitive user information |
+| [LLM Call with OpenAI Client](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02-llm-call-open-ai)
Leverage native LLM client libraries with Dapr Agents using the OpenAI Client for chat completion, audio processing, and embeddings. | - **Text Completion**: Generating responses to prompts
- **Structured Outputs**: Converting LLM responses to Pydantic objects
*Note: Other quickstarts for specific clients are available for [Elevenlabs](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02-llm-call-elevenlabs), [Hugging Face](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02-llm-call-hugging-face), and [Nvidia](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02-llm-call-nvidia).* |
| Standalone & Durable Agents
[Standalone Agent Tool Call](https://github.com/dapr/dapr-agents/tree/main/quickstarts/03-standalone-agent-tool-call) ยท [Durable Agent Tool Call](https://github.com/dapr/dapr-agents/tree/main/quickstarts/03-durable-agent-tool-call) | - **Standalone Agents**: Build conversational agents with tools in under 20 lines using the `Agent` class
- **Durable Agents**: Upgrade to workflow-backed `DurableAgent` instances with `AgentRunner.run/subscribe/serve`
- **Tool Definition**: Reuse tools with the `@tool` decorator and structured args models
- **Function Calling**: Let LLMs invoke Python functions safely |
| [Agentic Workflow](https://github.com/dapr/dapr-agents/tree/main/quickstarts/04-llm-based-workflows)
Dive into stateful workflows with Dapr Agents by orchestrating sequential and parallel tasks through powerful workflow capabilities. | - **LLM-powered Tasks**: Using language models in workflows
- **Task Chaining**: Creating resilient multi-step processes executing in sequence
- **Fan-out/Fan-in**: Executing activities in parallel; then synchronizing these activities until all preceding activities have completed |
| [Multi-Agent Workflows](https://github.com/dapr/dapr-agents/tree/main/quickstarts/05-multi-agent-workflows)
Explore advanced event-driven workflows featuring a Lord of the Rings themed multi-agent system where autonomous agents collaborate to solve problems. | - **Multi-agent Systems**: Creating a network of specialized agents
- **Event-driven Architecture**: Implementing pub/sub messaging between agents
- **Workflow Orchestration**: Coordinating agents through different selection strategies|