From da2319d69383b56efe511bfa26acfe2f98479184 Mon Sep 17 00:00:00 2001
From: Mendon Kissling <59585235+mendonk@users.noreply.github.com>
Date: Wed, 24 Sep 2025 17:46:27 -0400
Subject: [PATCH] init

---
 docs/docs/core-components/agents.mdx | 30 ++++++++++++++++++++++++++++
 docs/sidebars.js                     | 11 ++++++++++
 2 files changed, 41 insertions(+)
 create mode 100644 docs/docs/core-components/agents.mdx

diff --git a/docs/docs/core-components/agents.mdx b/docs/docs/core-components/agents.mdx
new file mode 100644
index 00000000..6eda9420
--- /dev/null
+++ b/docs/docs/core-components/agents.mdx
@@ -0,0 +1,30 @@
+---
+title: Agents powered by Langflow
+slug: /agents
+---
+
+OpenRAG leverages Langflow's Agent component to power the OpenRAG Open Search Agent flow.
+
+This flow intelligently chats with your knowledge by embedding your query, comparing it the vector database embeddings, and generating a response with the LLM.
+
+The Agent component shines here in its ability to make decisions on not only what query should be sent, but when a query is necessary to solve the problem at hand. 
+
+<details closed>
+<summary>How do agents work?</summary>
+
+Agents extend Large Language Models (LLMs) by integrating tools, which are functions that provide additional context and enable autonomous task execution. These integrations make agents more specialized and powerful than standalone LLMs.
+
+Whereas an LLM might generate acceptable, inert responses to general queries and tasks, an agent can leverage the integrated context and tools to provide more relevant responses and even take action. For example, you might create an agent that can access your company's documentation, repositories, and other resources to help your team with tasks that require knowledge of your specific products, customers, and code.
+
+Agents use LLMs as a reasoning engine to process input, determine which actions to take to address the query, and then generate a response. The response could be a typical text-based LLM response, or it could involve an action, like editing a file, running a script, or calling an external API.
+
+In an agentic context, tools are functions that the agent can run to perform tasks or access external resources. A function is wrapped as a Tool object with a common interface that the agent understands. Agents become aware of tools through tool registration, which is when the agent is provided a list of available tools typically at agent initialization. The Tool object's description tells the agent what the tool can do so that it can decide whether the tool is appropriate for a given request.
+
+</details>
+
+## Use the OpenRAG Open Search Agent flow
+
+## Modify the OpenRAG Open Search Agent flow
+
+All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic.
+If you want to try a different LLM, or modify the agent's internal prompt, here's how.
\ No newline at end of file
diff --git a/docs/sidebars.js b/docs/sidebars.js
index 6d6db6b3..a0e7103a 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -43,6 +43,17 @@ const sidebars = {
         },
       ],
     },
+    {
+      type: "category",
+      label: "Core components",
+      items: [
+        {
+          type: "doc",
+          id: "core-components/agents",
+          label: "Langflow Agent"
+        },
+      ],
+    },
     {
       type: "category",
       label: "Configuration",