diff --git a/daprdocs/content/en/developing-ai/agent-integrations/langgraph/_index.md b/daprdocs/content/en/developing-ai/agent-integrations/langgraph/_index.md
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+---
+type: docs
+title: "LangGraph"
+linkTitle: "LangGraph"
+weight: 25
+description: "Dapr first-class integrations with LangGraph Agents"
+---
+
+###  What is the Dapr LangGraph integration?
+
+Dapr provides LangGraph agents a first class integration to agent session management (checkpointers).
diff --git a/daprdocs/content/en/developing-ai/agent-integrations/langgraph/langgraph-agents-sessions.md b/daprdocs/content/en/developing-ai/agent-integrations/langgraph/langgraph-agents-sessions.md
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+---
+type: docs
+title: "Agent Sessions"
+linkTitle: "Agent Sessions"
+weight: 30
+description: "How to use Dapr reliably and securely manage LangGraph Agent Checkpointers"
+---
+
+## Overview
+
+The Dapr Python SDK provides integration with LangGraph Checkpointer using the `dapr-ext-langgraph` extension.
+
+## Getting Started
+
+Initialize Dapr locally to set up a self-hosted environment for development. This process fetches and installs the Dapr sidecar binaries, runs essential services as Docker containers, and prepares a default components folder for your application. For detailed steps, see the official [guide on initializing Dapr locally]({{% ref install-dapr-cli.md %}}).
+
+To initialize the Dapr control plane containers and create a default configuration file, run:
+
+```bash
+dapr init
+```
+
+Verify you have container instances with `daprio/dapr`, `openzipkin/zipkin`, and `redis` images running:
+
+```bash
+docker ps
+```
+
+### Install Python
+
+{{% alert title="Note" color="info" %}}
+Make sure you have Python already installed. `Python >=3.10`. For installation instructions, visit the official [Python installation guide](https://www.python.org/downloads/).
+{{% /alert %}}
+
+### Download Dependencies
+
+Download and install the Dapr LangGraph extension with:
+
+{{< tabpane text=true >}}
+
+{{% tab header="Stable" %}}
+
+```bash
+pip install dapr-ext-langgraph langchain_openai langchain_core langgraph langgraph-prebuilt
+```
+
+{{% /tab %}}
+
+{{% tab header="Development" %}}
+{{% alert title="Note" color="warning" %}}
+The development package will contain features and behavior that will be compatible with the pre-release version of the Dapr runtime. Make sure to uninstall any stable versions of the Python SDK extension before installing the `dapr-dev` package.
+{{% /alert %}}
+
+```bash
+pip install dapr-ext-langgraph-dev langchain_openai langchain_core langgraph langgraph-prebuilt
+```
+
+{{% /tab %}}
+
+{{< /tabpane >}}
+
+### Create a LangGraph Agent
+
+To let Dapr handle the agent memory, utilize the `DaprCheckpointer` as the checkpointer object when compiling the graph. Pass the checkpointer just like any other checkpointer provider:
+
+```python
+from dapr.ext.langgraph import DaprCheckpointer
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage, SystemMessage
+from langgraph.graph import START, MessagesState, StateGraph
+from langgraph.prebuilt import ToolNode, tools_condition
+
+
+def add(a: int, b: int) -> int:
+    """Adds a and b.
+
+    Args:
+        a: first int
+        b: second int
+    """
+    return a + b
+
+tools = [add]
+llm = ChatOpenAI(model="gpt-4o")
+llm_with_tools = llm.bind_tools(tools)
+
+sys_msg = SystemMessage(
+    content='You are a helpful assistant tasked with performing arithmetic on a set of inputs.'
+)
+
+def assistant(state: MessagesState):
+    return {'messages': [llm_with_tools.invoke([sys_msg] + state['messages'])]}
+
+builder = StateGraph(MessagesState)
+builder.add_node('assistant', assistant)
+builder.add_node('tools', ToolNode(tools))
+builder.add_edge(START, 'assistant')
+builder.add_conditional_edges(
+    'assistant',
+    tools_condition,
+)
+builder.add_edge('tools', 'assistant')
+
+memory = DaprCheckpointer(store_name='statestore', key_prefix='dapr')
+react_graph_memory = builder.compile(checkpointer=memory)
+
+config = {'configurable': {'thread_id': '1'}}
+
+messages = [HumanMessage(content='Add 3 and 4.')]
+messages = react_graph_memory.invoke({'messages': messages}, config)
+for m in messages['messages']:
+    m.pretty_print()
+```
+
+### Set an OpenAI API key
+
+```bash
+export OPENAI_API_KEY=sk-...
+```
+
+### Create a Python venv
+
+```bash
+python -m venv .venv                                                                                                                                                                      
+source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+```
+
+## Create the database component
+
+The component file is how Dapr connects to your databae. The full list of supported databases can be found [here]({{% ref supported-state-stores %}}). Create a `components` directory and this file in it:
+
+```yaml
+apiVersion: dapr.io/v1alpha1
+kind: Component
+metadata:
+  name: statestore
+spec:
+  type: state.redis
+  version: v1
+  metadata:
+  - name: redisHost
+    value: localhost:6379
+  - name: redisPassword
+    value: ""
+```
+
+## Next Steps
+
+Now that you have a LangGraph agent using Dapr to manage the agent sessions, explore more you can do with the [State API]({{% ref "state-management-overview" %}}) and how to enable [resiliency policies]({{% ref resiliency-overview %}}) for enhanced reliability.
diff --git a/daprdocs/content/en/developing-ai/agent-integrations/openai-agents/_index.md b/daprdocs/content/en/developing-ai/agent-integrations/openai-agents/_index.md
index 83d119d11d3..85fd5e20c5e 100644
--- a/daprdocs/content/en/developing-ai/agent-integrations/openai-agents/_index.md
+++ b/daprdocs/content/en/developing-ai/agent-integrations/openai-agents/_index.md
@@ -1,7 +1,7 @@
 ---
 type: docs
-title: "OpenAI Agents"
-linkTitle: "OpenAI Agents"
+title: "OpenAI"
+linkTitle: "OpenAI"
 weight: 25
 description: "Dapr first-class integrations for OpenAI Agents"
 ---