diff --git a/agents-and-function-calling/open-source-agents/README.md b/agents-and-function-calling/open-source-agents/README.md
index a4ef2fa20..eb398ba75 100644
--- a/agents-and-function-calling/open-source-agents/README.md
+++ b/agents-and-function-calling/open-source-agents/README.md
@@ -2,6 +2,25 @@
 
 
 
+## Contents
+
+### [AG2](ag2/)
+Multi-agent framework with native Amazon Bedrock support.
+Examples: single agent, tool use, multi-agent GroupChat — all using
+`LLMConfig(api_type="bedrock")` with no wrapper libraries.
+
+### [CrewAI](crew.ai/)
+CrewAI agent examples with Amazon Bedrock.
+
+### [LangChain](LangChain/)
+LangChain agent examples with Amazon Bedrock.
+
+### [LangGraph](langgraph/)
+LangGraph agent examples with Amazon Bedrock.
+
+### [LlamaIndex](llamaindex/)
+LlamaIndex agent examples with Amazon Bedrock.
+
 ## Contributing
 
 We welcome community contributions! Please ensure your sample aligns with AWS [best practices](https://aws.amazon.com/architecture/well-architected/), and please update the **Contents** section of this README file with a link to your sample, along with a description.
diff --git a/agents-and-function-calling/open-source-agents/ag2/README.md b/agents-and-function-calling/open-source-agents/ag2/README.md
new file mode 100644
index 000000000..8752faeb1
--- /dev/null
+++ b/agents-and-function-calling/open-source-agents/ag2/README.md
@@ -0,0 +1,43 @@
+# AG2 with Amazon Bedrock
+
+[AG2](https://ag2.ai/) (formerly AutoGen) is an open-source multi-agent framework with
+**native Amazon Bedrock support**. Unlike other frameworks that require wrapper libraries,
+AG2 connects to Bedrock directly via `LLMConfig(api_type="bedrock")`.
+
+## Examples
+
+| Notebook | Description |
+|----------|-------------|
+| [ag2-single-agent-bedrock.ipynb](ag2-single-agent-bedrock.ipynb) | Basic single agent with Bedrock |
+| [ag2-tool-use-bedrock.ipynb](ag2-tool-use-bedrock.ipynb) | Function calling with `register_for_llm` |
+| [ag2-multi-agent-bedrock.ipynb](ag2-multi-agent-bedrock.ipynb) | Multi-agent GroupChat orchestration |
+
+## Why AG2 + Bedrock?
+
+- **Native support**: `LLMConfig(api_type="bedrock")` — no LangChain or wrapper needed
+- **AWS credential chain**: IAM roles, environment variables, or `~/.aws/credentials`
+- **All Bedrock models**: Claude, Llama, Mistral, Titan, Command R+
+- **Multi-agent**: GroupChat with automatic speaker selection
+- **500K+ monthly PyPI downloads**: Active community with frequent releases
+
+## Quick Start
+
+```bash
+pip install ag2[openai]
+```
+
+```python
+from autogen import AssistantAgent, UserProxyAgent, LLMConfig
+
+llm_config = LLMConfig(
+    api_type="bedrock",
+    model="anthropic.claude-3-sonnet-20240229-v1:0",
+    aws_region="us-east-1",
+)
+```
+
+## Resources
+
+- [AG2 Documentation](https://docs.ag2.ai/)
+- [AG2 GitHub](https://github.com/ag2ai/ag2)
+- [AG2 Bedrock Guide](https://docs.ag2.ai/docs/user-guide/models/amazon-bedrock)
diff --git a/agents-and-function-calling/open-source-agents/ag2/ag2-multi-agent-bedrock.ipynb b/agents-and-function-calling/open-source-agents/ag2/ag2-multi-agent-bedrock.ipynb
new file mode 100644
index 000000000..e8c616e86
--- /dev/null
+++ b/agents-and-function-calling/open-source-agents/ag2/ag2-multi-agent-bedrock.ipynb
@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AG2 Multi-Agent GroupChat with Amazon Bedrock\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "[AG2](https://ag2.ai/) (formerly AutoGen) provides a powerful **GroupChat** feature for multi-agent orchestration. The `GroupChatManager` uses the LLM to automatically select the next speaker based on conversation context — no hardcoded routing graphs or handoff logic required.\n",
+    "\n",
+    "In this notebook, we'll create a multi-agent research team using AG2's GroupChat with Amazon Bedrock as the LLM backend.\n",
+    "\n",
+    "## Context\n",
+    "\n",
+    "AG2's GroupChat is its flagship multi-agent feature. Unlike frameworks that require explicit handoff definitions or routing graphs, AG2's `GroupChatManager` uses the LLM itself to determine which agent should speak next based on:\n",
+    "- Each agent's name and system message\n",
+    "- The current conversation history\n",
+    "- The task at hand\n",
+    "\n",
+    "This makes it easy to add or remove agents without rewriting orchestration code. Combined with native Bedrock support, you get enterprise-grade multi-agent systems with AWS IAM authentication, VPC endpoints, and CloudTrail logging — all without wrapper libraries.\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- An AWS account with Amazon Bedrock model access enabled\n",
+    "- Python 3.10+\n",
+    "- AWS credentials configured (IAM role, environment variables, or `~/.aws/credentials`)\n",
+    "- Model access granted for `anthropic.claude-3-sonnet-20240229-v1:0` in your AWS region"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Install the AG2 package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q ag2[openai]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Code\n",
+    "\n",
+    "### Configure Bedrock\n",
+    "\n",
+    "Set up the native Bedrock connection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager, LLMConfig\n",
+    "\n",
+    "# Native Bedrock support — no OpenAI key needed\n",
+    "# ---- ⚠️ Update region for your AWS setup ⚠️ ----\n",
+    "llm_config = LLMConfig(\n",
+    "    api_type=\"bedrock\",\n",
+    "    model=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
+    "    aws_region=\"us-east-1\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Specialist Agents\n",
+    "\n",
+    "We'll create a team of three specialist agents, each with a distinct role:\n",
+    "- **Researcher**: Gathers information and provides factual analysis\n",
+    "- **Writer**: Creates clear, well-structured content from research findings\n",
+    "- **Critic**: Reviews content for accuracy and completeness, and terminates when satisfied"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with llm_config:\n",
+    "    researcher = AssistantAgent(\n",
+    "        name=\"Researcher\",\n",
+    "        system_message=(\n",
+    "            \"You are a research analyst. Search for information and provide \"\n",
+    "            \"factual analysis. Focus on key data points and trends. \"\n",
+    "            \"Cite sources when possible.\"\n",
+    "        ),\n",
+    "    )\n",
+    "    writer = AssistantAgent(\n",
+    "        name=\"Writer\",\n",
+    "        system_message=(\n",
+    "            \"You are a technical writer. Take research findings and create \"\n",
+    "            \"clear, well-structured summaries for business stakeholders. \"\n",
+    "            \"Use bullet points and concise language.\"\n",
+    "        ),\n",
+    "    )\n",
+    "    critic = AssistantAgent(\n",
+    "        name=\"Critic\",\n",
+    "        system_message=(\n",
+    "            \"You review content for accuracy, completeness, and clarity. \"\n",
+    "            \"Provide constructive feedback. When the output meets quality \"\n",
+    "            \"standards, say TERMINATE to end the conversation.\"\n",
+    "        ),\n",
+    "    )\n",
+    "    user_proxy = UserProxyAgent(\n",
+    "        name=\"user\",\n",
+    "        human_input_mode=\"NEVER\",\n",
+    "        max_consecutive_auto_reply=0,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set Up GroupChat\n",
+    "\n",
+    "The `GroupChat` collects agents into a group, and the `GroupChatManager` orchestrates the conversation. The `speaker_selection_method=\"auto\"` setting lets the LLM decide which agent speaks next based on context.\n",
+    "\n",
+    "The `max_round` parameter limits the total number of turns to prevent runaway conversations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "groupchat = GroupChat(\n",
+    "    agents=[user_proxy, researcher, writer, critic],\n",
+    "    messages=[],\n",
+    "    max_round=8,\n",
+    "    speaker_selection_method=\"auto\",\n",
+    ")\n",
+    "manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the Multi-Agent Conversation\n",
+    "\n",
+    "The user proxy sends the initial request to the GroupChatManager, which then automatically routes messages to the appropriate agents. Watch the conversation to see how:\n",
+    "1. The **Researcher** gathers information\n",
+    "2. The **Writer** creates a structured summary\n",
+    "3. The **Critic** reviews and provides feedback or approves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = user_proxy.initiate_chat(\n",
+    "    manager,\n",
+    "    message=(\n",
+    "        \"Research the current state of generative AI adoption in enterprise. \"\n",
+    "        \"Write a brief executive summary with key trends and challenges.\"\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Understanding the Output\n",
+    "\n",
+    "The GroupChatManager automatically routed the conversation through the agents:\n",
+    "- The **Researcher** provided data points and analysis\n",
+    "- The **Writer** structured the findings into an executive summary\n",
+    "- The **Critic** reviewed the output and said TERMINATE when satisfied\n",
+    "\n",
+    "The key advantage of AG2's GroupChat: the `GroupChatManager` uses the LLM to automatically select the next speaker based on conversation context. You don't need to define explicit routing logic or handoff patterns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best Practices\n",
+    "\n",
+    "- **Speaker selection**: Use `\"auto\"` for LLM-based routing, `\"round_robin\"` for predictable sequential flow\n",
+    "- **Max rounds**: Set `max_round` to prevent runaway conversations — 6-10 is a good starting range\n",
+    "- **Termination**: Include `TERMINATE` in one agent's system message to end gracefully\n",
+    "- **Agent count**: 3-5 agents works well; more agents increases speaker selection complexity\n",
+    "- **Distinct roles**: Give each agent a clear, non-overlapping system message for better routing\n",
+    "- **Native Bedrock advantages**: No OpenAI key, AWS IAM auth, supports Claude/Llama/Mistral/Titan, enterprise-grade security with VPC endpoints and CloudTrail logging\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Single agent**: See [ag2-single-agent-bedrock.ipynb](ag2-single-agent-bedrock.ipynb) for the basic setup\n",
+    "- **Tool use**: See [ag2-tool-use-bedrock.ipynb](ag2-tool-use-bedrock.ipynb) for function calling with Bedrock\n",
+    "- **AG2 GroupChat Guide**: [docs.ag2.ai/docs/user-guide/basic-concepts/orchestration/group-chat](https://docs.ag2.ai/docs/user-guide/basic-concepts/orchestration/group-chat)\n",
+    "- **AG2 Documentation**: [docs.ag2.ai](https://docs.ag2.ai/)\n",
+    "- **AG2 Bedrock Guide**: [docs.ag2.ai/docs/user-guide/models/amazon-bedrock](https://docs.ag2.ai/docs/user-guide/models/amazon-bedrock)\n",
+    "\n",
+    "## Cleanup\n",
+    "\n",
+    "No resources to clean up — this notebook uses only local compute and Bedrock API calls. To stop incurring Bedrock charges, simply stop running the notebook."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/agents-and-function-calling/open-source-agents/ag2/ag2-single-agent-bedrock.ipynb b/agents-and-function-calling/open-source-agents/ag2/ag2-single-agent-bedrock.ipynb
new file mode 100644
index 000000000..eb5f44261
--- /dev/null
+++ b/agents-and-function-calling/open-source-agents/ag2/ag2-single-agent-bedrock.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AG2 Single Agent with Amazon Bedrock\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "[AG2](https://ag2.ai/) (formerly AutoGen) is an open-source multi-agent framework with **native Amazon Bedrock support** — no wrapper libraries or OpenAI API keys required.\n",
+    "\n",
+    "In this notebook, we'll create a simple conversational agent using AG2 with Amazon Bedrock as the LLM backend.\n",
+    "\n",
+    "## Context\n",
+    "\n",
+    "AG2 is a community-driven fork of AutoGen with 400K+ monthly PyPI downloads. Its key differentiator for Bedrock users is `LLMConfig(api_type=\"bedrock\")` — native integration without wrapper libraries like LangChain's ChatBedrock.\n",
+    "\n",
+    "This means you can use any Bedrock-supported model (Claude, Llama, Mistral, Titan, Command R+) with the standard AWS credential chain — IAM roles, environment variables, or `~/.aws/credentials`.\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- An AWS account with Amazon Bedrock model access enabled\n",
+    "- Python 3.10+\n",
+    "- AWS credentials configured (IAM role, environment variables, or `~/.aws/credentials`)\n",
+    "- Model access granted for `anthropic.claude-3-sonnet-20240229-v1:0` in your AWS region"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Install the AG2 package. The `[openai]` extra includes the required dependencies for LLM integration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q ag2[openai]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Code\n",
+    "\n",
+    "### Configure AG2 with Amazon Bedrock\n",
+    "\n",
+    "AG2 supports Bedrock natively via `LLMConfig(api_type=\"bedrock\")`. This uses the default AWS credential chain — IAM roles, environment variables, or `~/.aws/credentials` — so no API keys need to be hardcoded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from autogen import AssistantAgent, UserProxyAgent, LLMConfig\n",
+    "\n",
+    "# Native Bedrock support — no OpenAI key needed\n",
+    "# ---- ⚠️ Update region for your AWS setup ⚠️ ----\n",
+    "llm_config = LLMConfig(\n",
+    "    api_type=\"bedrock\",\n",
+    "    model=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
+    "    aws_region=\"us-east-1\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Agents\n",
+    "\n",
+    "AG2 uses a two-agent pattern: an `AssistantAgent` (LLM-powered reasoning) and a `UserProxyAgent` (executes tools, provides human input).\n",
+    "\n",
+    "The `with llm_config:` context manager applies the Bedrock configuration to all agents created within it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with llm_config:\n",
+    "    assistant = AssistantAgent(\n",
+    "        name=\"assistant\",\n",
+    "        system_message=\"You are a helpful AI assistant. Answer questions concisely.\",\n",
+    "    )\n",
+    "    user_proxy = UserProxyAgent(\n",
+    "        name=\"user\",\n",
+    "        human_input_mode=\"NEVER\",\n",
+    "        max_consecutive_auto_reply=1,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the Conversation\n",
+    "\n",
+    "The `initiate_chat` method sends a message from the `UserProxyAgent` to the `AssistantAgent` and starts the conversation loop. The assistant will use Amazon Bedrock (Claude) to generate its response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = user_proxy.initiate_chat(\n",
+    "    assistant,\n",
+    "    message=\"What are the main benefits of using Amazon Bedrock for enterprise AI?\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best Practices\n",
+    "\n",
+    "- **Use IAM roles** over hardcoded credentials for production deployments\n",
+    "- **Set `max_consecutive_auto_reply`** to prevent infinite conversation loops\n",
+    "- **Use `human_input_mode=\"NEVER\"`** for automated pipelines, `\"TERMINATE\"` for interactive use\n",
+    "- **Region selection**: Choose the AWS region closest to your workload for lower latency\n",
+    "- **Model selection**: AG2 supports all Bedrock models — use Claude for complex reasoning, Llama for open-source flexibility, Mistral for multilingual tasks\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Tool use**: See [ag2-tool-use-bedrock.ipynb](ag2-tool-use-bedrock.ipynb) for function calling with Bedrock\n",
+    "- **Multi-agent**: See [ag2-multi-agent-bedrock.ipynb](ag2-multi-agent-bedrock.ipynb) for GroupChat orchestration\n",
+    "- **AG2 Documentation**: [docs.ag2.ai](https://docs.ag2.ai/)\n",
+    "- **AG2 GitHub**: [github.com/ag2ai/ag2](https://github.com/ag2ai/ag2)\n",
+    "- **AG2 Bedrock Guide**: [docs.ag2.ai/docs/user-guide/models/amazon-bedrock](https://docs.ag2.ai/docs/user-guide/models/amazon-bedrock)\n",
+    "\n",
+    "## Cleanup\n",
+    "\n",
+    "No resources to clean up — this notebook uses only local compute and Bedrock API calls. To stop incurring Bedrock charges, simply stop running the notebook."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/agents-and-function-calling/open-source-agents/ag2/ag2-tool-use-bedrock.ipynb b/agents-and-function-calling/open-source-agents/ag2/ag2-tool-use-bedrock.ipynb
new file mode 100644
index 000000000..679a199f9
--- /dev/null
+++ b/agents-and-function-calling/open-source-agents/ag2/ag2-tool-use-bedrock.ipynb
@@ -0,0 +1,221 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AG2 Tool Use with Amazon Bedrock\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "[AG2](https://ag2.ai/) (formerly AutoGen) supports tool/function calling with Amazon Bedrock models natively. This notebook demonstrates AG2's dual registration pattern — `register_for_llm` (the LLM decides **when** to call a tool) and `register_for_execution` (the UserProxy **executes** it).\n",
+    "\n",
+    "## Context\n",
+    "\n",
+    "AG2's tool calling approach separates **tool selection** from **tool execution**:\n",
+    "- The `AssistantAgent` (backed by a Bedrock model) decides which tool to call and with what arguments\n",
+    "- The `UserProxyAgent` actually executes the function and returns the result\n",
+    "\n",
+    "This separation gives full control over tool execution — you can add sandboxing, logging, approval flows, or rate limiting at the execution layer without modifying the LLM configuration.\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- An AWS account with Amazon Bedrock model access enabled\n",
+    "- Python 3.10+\n",
+    "- AWS credentials configured (IAM role, environment variables, or `~/.aws/credentials`)\n",
+    "- Model access granted for `anthropic.claude-3-sonnet-20240229-v1:0` in your AWS region"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Install the AG2 package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q ag2[openai]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Code\n",
+    "\n",
+    "### Configure Bedrock\n",
+    "\n",
+    "Set up the native Bedrock connection using AG2's `LLMConfig`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Annotated\n",
+    "from autogen import AssistantAgent, UserProxyAgent, LLMConfig\n",
+    "\n",
+    "# Native Bedrock support — no OpenAI key needed\n",
+    "# ---- ⚠️ Update region for your AWS setup ⚠️ ----\n",
+    "llm_config = LLMConfig(\n",
+    "    api_type=\"bedrock\",\n",
+    "    model=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
+    "    aws_region=\"us-east-1\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define Tools\n",
+    "\n",
+    "Define Python functions that will be available to the agent. Use `Annotated` type hints to provide parameter descriptions — Bedrock models use these to understand the tool schema."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_stock_price(\n",
+    "    symbol: Annotated[str, \"Stock ticker symbol (e.g., AMZN, GOOGL)\"],\n",
+    ") -> dict:\n",
+    "    \"\"\"Look up the current stock price for a given ticker symbol.\"\"\"\n",
+    "    # Simulated data for demo purposes\n",
+    "    prices = {\n",
+    "        \"AMZN\": {\"price\": 186.45, \"change\": \"+2.3%\"},\n",
+    "        \"GOOGL\": {\"price\": 175.20, \"change\": \"-0.8%\"},\n",
+    "        \"MSFT\": {\"price\": 420.15, \"change\": \"+1.1%\"},\n",
+    "    }\n",
+    "    return prices.get(symbol.upper(), {\"error\": f\"Unknown symbol: {symbol}\"})\n",
+    "\n",
+    "\n",
+    "def get_company_info(\n",
+    "    company: Annotated[str, \"Company name to look up\"],\n",
+    ") -> str:\n",
+    "    \"\"\"Get brief information about a company.\"\"\"\n",
+    "    info = {\n",
+    "        \"amazon\": \"Amazon.com, Inc. — e-commerce, cloud computing (AWS), AI, streaming.\",\n",
+    "        \"google\": \"Alphabet Inc. — search, advertising, cloud, AI research.\",\n",
+    "        \"microsoft\": \"Microsoft Corp. — software, cloud (Azure), gaming, AI.\",\n",
+    "    }\n",
+    "    return info.get(company.lower(), f\"No info available for {company}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Agents and Register Tools\n",
+    "\n",
+    "AG2 uses a **dual registration** pattern:\n",
+    "1. `register_for_llm` — tells the AssistantAgent (LLM) that this tool exists and how to call it\n",
+    "2. `register_for_execution` — tells the UserProxyAgent to execute the function when the LLM requests it\n",
+    "\n",
+    "This separation means the LLM decides **when** to call a tool, but the UserProxy controls **how** it's executed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with llm_config:\n",
+    "    assistant = AssistantAgent(\n",
+    "        name=\"FinanceAssistant\",\n",
+    "        system_message=\"You are a financial assistant. Use the available tools to look up stock data and company information. Provide clear analysis based on the data.\",\n",
+    "    )\n",
+    "    user_proxy = UserProxyAgent(\n",
+    "        name=\"user\",\n",
+    "        human_input_mode=\"NEVER\",\n",
+    "        max_consecutive_auto_reply=5,\n",
+    "    )\n",
+    "\n",
+    "# Register tools for the LLM (tool schema) and for execution (function call)\n",
+    "assistant.register_for_llm(description=\"Look up current stock price\")(get_stock_price)\n",
+    "assistant.register_for_llm(description=\"Get company information\")(get_company_info)\n",
+    "user_proxy.register_for_execution()(get_stock_price)\n",
+    "user_proxy.register_for_execution()(get_company_info)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the Conversation\n",
+    "\n",
+    "The assistant will use Bedrock (Claude) to decide which tools to call, and the user proxy will execute them. Watch the conversation to see the tool calls happening automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = user_proxy.initiate_chat(\n",
+    "    assistant,\n",
+    "    message=\"Compare Amazon and Google — show me their stock prices and company info.\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best Practices\n",
+    "\n",
+    "- **Use `Annotated` type hints** for parameter descriptions — Bedrock models use these for tool schemas\n",
+    "- **Keep tools simple and focused** — one function per responsibility\n",
+    "- **Use `max_consecutive_auto_reply`** to limit tool call loops and prevent runaway execution\n",
+    "- **Return structured data** (dict/JSON) from tools for consistent parsing\n",
+    "- **Add docstrings** to tool functions — the LLM uses these to understand when to call each tool\n",
+    "- **Error handling**: Return error messages as data (not exceptions) so the LLM can reason about failures\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Multi-agent**: See [ag2-multi-agent-bedrock.ipynb](ag2-multi-agent-bedrock.ipynb) for GroupChat orchestration with tools\n",
+    "- **Single agent**: See [ag2-single-agent-bedrock.ipynb](ag2-single-agent-bedrock.ipynb) for the basic setup\n",
+    "- **AG2 Tool Use Guide**: [docs.ag2.ai/docs/user-guide/basic-concepts/tools](https://docs.ag2.ai/docs/user-guide/basic-concepts/tools)\n",
+    "- **AG2 Documentation**: [docs.ag2.ai](https://docs.ag2.ai/)\n",
+    "\n",
+    "## Cleanup\n",
+    "\n",
+    "No resources to clean up — this notebook uses only local compute and Bedrock API calls. To stop incurring Bedrock charges, simply stop running the notebook."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/docs/agents-and-function-calling/open-source-agents/ag2/ag2-multi-agent-bedrock.md b/docs/agents-and-function-calling/open-source-agents/ag2/ag2-multi-agent-bedrock.md
new file mode 100644
index 000000000..0eb584538
--- /dev/null
+++ b/docs/agents-and-function-calling/open-source-agents/ag2/ag2-multi-agent-bedrock.md
@@ -0,0 +1,155 @@
+---
+tags:
+    - Agents/ Function Calling
+    - Open Source/ AG2
+---
+
+!!! tip inline end "[Open in github](https://github.com/aws-samples/amazon-bedrock-samples/blob/main/agents-and-function-calling/open-source-agents/ag2/ag2-multi-agent-bedrock.ipynb){:target="_blank"}"
+
+<h2>AG2 Multi-Agent GroupChat with Amazon Bedrock</h2>
+
+<h3>Overview</h3>
+
+[AG2](https://ag2.ai/) (formerly AutoGen) provides a powerful **GroupChat** feature for multi-agent orchestration. The `GroupChatManager` uses the LLM to automatically select the next speaker based on conversation context — no hardcoded routing graphs or handoff logic required.
+
+In this notebook, we'll create a multi-agent research team using AG2's GroupChat with Amazon Bedrock as the LLM backend.
+
+<h3>Context</h3>
+
+AG2's GroupChat is its flagship multi-agent feature. Unlike frameworks that require explicit handoff definitions or routing graphs, AG2's `GroupChatManager` uses the LLM itself to determine which agent should speak next based on:
+- Each agent's name and system message
+- The current conversation history
+- The task at hand
+
+This makes it easy to add or remove agents without rewriting orchestration code. Combined with native Bedrock support, you get enterprise-grade multi-agent systems with AWS IAM authentication, VPC endpoints, and CloudTrail logging — all without wrapper libraries.
+
+<h3>Prerequisites</h3>
+
+- An AWS account with Amazon Bedrock model access enabled
+- Python 3.10+
+- AWS credentials configured (IAM role, environment variables, or `~/.aws/credentials`)
+- Model access granted for `anthropic.claude-3-sonnet-20240229-v1:0` in your AWS region
+
+<h2>Setup</h2>
+
+Install the AG2 package.
+
+```python
+%pip install -q ag2[openai]
+```
+
+<h2>Code</h2>
+
+<h3>Configure Bedrock</h3>
+
+Set up the native Bedrock connection.
+
+```python
+from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager, LLMConfig
+
+# Native Bedrock support — no OpenAI key needed
+# ---- ⚠️ Update region for your AWS setup ⚠️ ----
+llm_config = LLMConfig(
+    api_type="bedrock",
+    model="anthropic.claude-3-sonnet-20240229-v1:0",
+    aws_region="us-east-1",
+)
+```
+
+<h3>Create Specialist Agents</h3>
+
+We'll create a team of three specialist agents, each with a distinct role:
+- **Researcher**: Gathers information and provides factual analysis
+- **Writer**: Creates clear, well-structured content from research findings
+- **Critic**: Reviews content for accuracy and completeness, and terminates when satisfied
+
+```python
+with llm_config:
+    researcher = AssistantAgent(
+        name="Researcher",
+        system_message=(
+            "You are a research analyst. Search for information and provide "
+            "factual analysis. Focus on key data points and trends. "
+            "Cite sources when possible."
+        ),
+    )
+    writer = AssistantAgent(
+        name="Writer",
+        system_message=(
+            "You are a technical writer. Take research findings and create "
+            "clear, well-structured summaries for business stakeholders. "
+            "Use bullet points and concise language."
+        ),
+    )
+    critic = AssistantAgent(
+        name="Critic",
+        system_message=(
+            "You review content for accuracy, completeness, and clarity. "
+            "Provide constructive feedback. When the output meets quality "
+            "standards, say TERMINATE to end the conversation."
+        ),
+    )
+    user_proxy = UserProxyAgent(
+        name="user",
+        human_input_mode="NEVER",
+        max_consecutive_auto_reply=0,
+    )
+```
+
+<h3>Set Up GroupChat</h3>
+
+The `GroupChat` collects agents into a group, and the `GroupChatManager` orchestrates the conversation. The `speaker_selection_method="auto"` setting lets the LLM decide which agent speaks next based on context.
+
+```python
+groupchat = GroupChat(
+    agents=[user_proxy, researcher, writer, critic],
+    messages=[],
+    max_round=8,
+    speaker_selection_method="auto",
+)
+manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)
+```
+
+<h3>Run the Multi-Agent Conversation</h3>
+
+The user proxy sends the initial request to the GroupChatManager, which then automatically routes messages to the appropriate agents.
+
+```python
+result = user_proxy.initiate_chat(
+    manager,
+    message=(
+        "Research the current state of generative AI adoption in enterprise. "
+        "Write a brief executive summary with key trends and challenges."
+    ),
+)
+```
+
+<h3>Understanding the Output</h3>
+
+The GroupChatManager automatically routed the conversation through the agents:
+- The **Researcher** provided data points and analysis
+- The **Writer** structured the findings into an executive summary
+- The **Critic** reviewed the output and said TERMINATE when satisfied
+
+The key advantage of AG2's GroupChat: the `GroupChatManager` uses the LLM to automatically select the next speaker based on conversation context. You don't need to define explicit routing logic or handoff patterns.
+
+<h2>Best Practices</h2>
+
+- **Speaker selection**: Use `"auto"` for LLM-based routing, `"round_robin"` for predictable sequential flow
+- **Max rounds**: Set `max_round` to prevent runaway conversations — 6-10 is a good starting range
+- **Termination**: Include `TERMINATE` in one agent's system message to end gracefully
+- **Agent count**: 3-5 agents works well; more agents increases speaker selection complexity
+- **Distinct roles**: Give each agent a clear, non-overlapping system message for better routing
+- **Native Bedrock advantages**: No OpenAI key, AWS IAM auth, supports Claude/Llama/Mistral/Titan, enterprise-grade security with VPC endpoints and CloudTrail logging
+
+<h2>Next Steps</h2>
+
+- **Single agent**: See [ag2-single-agent-bedrock.ipynb](https://github.com/aws-samples/amazon-bedrock-samples/blob/main/agents-and-function-calling/open-source-agents/ag2/ag2-single-agent-bedrock.ipynb) for the basic setup
+- **Tool use**: See [ag2-tool-use-bedrock.ipynb](https://github.com/aws-samples/amazon-bedrock-samples/blob/main/agents-and-function-calling/open-source-agents/ag2/ag2-tool-use-bedrock.ipynb) for function calling with Bedrock
+- **AG2 GroupChat Guide**: [docs.ag2.ai/docs/user-guide/basic-concepts/orchestration/group-chat](https://docs.ag2.ai/docs/user-guide/basic-concepts/orchestration/group-chat)
+- **AG2 Documentation**: [docs.ag2.ai](https://docs.ag2.ai/)
+- **AG2 Bedrock Guide**: [docs.ag2.ai/docs/user-guide/models/amazon-bedrock](https://docs.ag2.ai/docs/user-guide/models/amazon-bedrock)
+
+<h2>Cleanup</h2>
+
+No resources to clean up — this notebook uses only local compute and Bedrock API calls. To stop incurring Bedrock charges, simply stop running the notebook.
diff --git a/docs/agents-and-function-calling/open-source-agents/ag2/ag2-single-agent-bedrock.md b/docs/agents-and-function-calling/open-source-agents/ag2/ag2-single-agent-bedrock.md
new file mode 100644
index 000000000..3ab34b883
--- /dev/null
+++ b/docs/agents-and-function-calling/open-source-agents/ag2/ag2-single-agent-bedrock.md
@@ -0,0 +1,104 @@
+---
+tags:
+    - Agents/ Function Calling
+    - Open Source/ AG2
+---
+
+!!! tip inline end "[Open in github](https://github.com/aws-samples/amazon-bedrock-samples/blob/main/agents-and-function-calling/open-source-agents/ag2/ag2-single-agent-bedrock.ipynb){:target="_blank"}"
+
+<h2>AG2 Single Agent with Amazon Bedrock</h2>
+
+<h3>Overview</h3>
+
+[AG2](https://ag2.ai/) (formerly AutoGen) is an open-source multi-agent framework with **native Amazon Bedrock support** — no wrapper libraries or OpenAI API keys required.
+
+In this notebook, we'll create a simple conversational agent using AG2 with Amazon Bedrock as the LLM backend.
+
+<h3>Context</h3>
+
+AG2 is a community-driven fork of AutoGen with 400K+ monthly PyPI downloads. Its key differentiator for Bedrock users is `LLMConfig(api_type="bedrock")` — native integration without wrapper libraries like LangChain's ChatBedrock.
+
+This means you can use any Bedrock-supported model (Claude, Llama, Mistral, Titan, Command R+) with the standard AWS credential chain — IAM roles, environment variables, or `~/.aws/credentials`.
+
+<h3>Prerequisites</h3>
+
+- An AWS account with Amazon Bedrock model access enabled
+- Python 3.10+
+- AWS credentials configured (IAM role, environment variables, or `~/.aws/credentials`)
+- Model access granted for `anthropic.claude-3-sonnet-20240229-v1:0` in your AWS region
+
+<h2>Setup</h2>
+
+Install the AG2 package. The `[openai]` extra includes the required dependencies for LLM integration.
+
+```python
+%pip install -q ag2[openai]
+```
+
+<h2>Code</h2>
+
+<h3>Configure AG2 with Amazon Bedrock</h3>
+
+AG2 supports Bedrock natively via `LLMConfig(api_type="bedrock")`. This uses the default AWS credential chain — IAM roles, environment variables, or `~/.aws/credentials` — so no API keys need to be hardcoded.
+
+```python
+from autogen import AssistantAgent, UserProxyAgent, LLMConfig
+
+# Native Bedrock support — no OpenAI key needed
+# ---- ⚠️ Update region for your AWS setup ⚠️ ----
+llm_config = LLMConfig(
+    api_type="bedrock",
+    model="anthropic.claude-3-sonnet-20240229-v1:0",
+    aws_region="us-east-1",
+)
+```
+
+<h3>Create Agents</h3>
+
+AG2 uses a two-agent pattern: an `AssistantAgent` (LLM-powered reasoning) and a `UserProxyAgent` (executes tools, provides human input).
+
+The `with llm_config:` context manager applies the Bedrock configuration to all agents created within it.
+
+```python
+with llm_config:
+    assistant = AssistantAgent(
+        name="assistant",
+        system_message="You are a helpful AI assistant. Answer questions concisely.",
+    )
+    user_proxy = UserProxyAgent(
+        name="user",
+        human_input_mode="NEVER",
+        max_consecutive_auto_reply=1,
+    )
+```
+
+<h3>Run the Conversation</h3>
+
+The `initiate_chat` method sends a message from the `UserProxyAgent` to the `AssistantAgent` and starts the conversation loop. The assistant will use Amazon Bedrock (Claude) to generate its response.
+
+```python
+result = user_proxy.initiate_chat(
+    assistant,
+    message="What are the main benefits of using Amazon Bedrock for enterprise AI?",
+)
+```
+
+<h2>Best Practices</h2>
+
+- **Use IAM roles** over hardcoded credentials for production deployments
+- **Set `max_consecutive_auto_reply`** to prevent infinite conversation loops
+- **Use `human_input_mode="NEVER"`** for automated pipelines, `"TERMINATE"` for interactive use
+- **Region selection**: Choose the AWS region closest to your workload for lower latency
+- **Model selection**: AG2 supports all Bedrock models — use Claude for complex reasoning, Llama for open-source flexibility, Mistral for multilingual tasks
+
+<h2>Next Steps</h2>
+
+- **Tool use**: See [ag2-tool-use-bedrock.ipynb](https://github.com/aws-samples/amazon-bedrock-samples/blob/main/agents-and-function-calling/open-source-agents/ag2/ag2-tool-use-bedrock.ipynb) for function calling with Bedrock
+- **Multi-agent**: See [ag2-multi-agent-bedrock.ipynb](https://github.com/aws-samples/amazon-bedrock-samples/blob/main/agents-and-function-calling/open-source-agents/ag2/ag2-multi-agent-bedrock.ipynb) for GroupChat orchestration
+- **AG2 Documentation**: [docs.ag2.ai](https://docs.ag2.ai/)
+- **AG2 GitHub**: [github.com/ag2ai/ag2](https://github.com/ag2ai/ag2)
+- **AG2 Bedrock Guide**: [docs.ag2.ai/docs/user-guide/models/amazon-bedrock](https://docs.ag2.ai/docs/user-guide/models/amazon-bedrock)
+
+<h2>Cleanup</h2>
+
+No resources to clean up — this notebook uses only local compute and Bedrock API calls. To stop incurring Bedrock charges, simply stop running the notebook.
diff --git a/docs/agents-and-function-calling/open-source-agents/ag2/ag2-tool-use-bedrock.md b/docs/agents-and-function-calling/open-source-agents/ag2/ag2-tool-use-bedrock.md
new file mode 100644
index 000000000..5b7d4b247
--- /dev/null
+++ b/docs/agents-and-function-calling/open-source-agents/ag2/ag2-tool-use-bedrock.md
@@ -0,0 +1,143 @@
+---
+tags:
+    - Agents/ Function Calling
+    - Open Source/ AG2
+---
+
+!!! tip inline end "[Open in github](https://github.com/aws-samples/amazon-bedrock-samples/blob/main/agents-and-function-calling/open-source-agents/ag2/ag2-tool-use-bedrock.ipynb){:target="_blank"}"
+
+<h2>AG2 Tool Use with Amazon Bedrock</h2>
+
+<h3>Overview</h3>
+
+[AG2](https://ag2.ai/) (formerly AutoGen) supports tool/function calling with Amazon Bedrock models natively. This notebook demonstrates AG2's dual registration pattern — `register_for_llm` (the LLM decides **when** to call a tool) and `register_for_execution` (the UserProxy **executes** it).
+
+<h3>Context</h3>
+
+AG2's tool calling approach separates **tool selection** from **tool execution**:
+- The `AssistantAgent` (backed by a Bedrock model) decides which tool to call and with what arguments
+- The `UserProxyAgent` actually executes the function and returns the result
+
+This separation gives full control over tool execution — you can add sandboxing, logging, approval flows, or rate limiting at the execution layer without modifying the LLM configuration.
+
+<h3>Prerequisites</h3>
+
+- An AWS account with Amazon Bedrock model access enabled
+- Python 3.10+
+- AWS credentials configured (IAM role, environment variables, or `~/.aws/credentials`)
+- Model access granted for `anthropic.claude-3-sonnet-20240229-v1:0` in your AWS region
+
+<h2>Setup</h2>
+
+Install the AG2 package.
+
+```python
+%pip install -q ag2[openai]
+```
+
+<h2>Code</h2>
+
+<h3>Configure Bedrock</h3>
+
+Set up the native Bedrock connection using AG2's `LLMConfig`.
+
+```python
+from typing import Annotated
+from autogen import AssistantAgent, UserProxyAgent, LLMConfig
+
+# Native Bedrock support — no OpenAI key needed
+# ---- ⚠️ Update region for your AWS setup ⚠️ ----
+llm_config = LLMConfig(
+    api_type="bedrock",
+    model="anthropic.claude-3-sonnet-20240229-v1:0",
+    aws_region="us-east-1",
+)
+```
+
+<h3>Define Tools</h3>
+
+Define Python functions that will be available to the agent. Use `Annotated` type hints to provide parameter descriptions — Bedrock models use these to understand the tool schema.
+
+```python
+def get_stock_price(
+    symbol: Annotated[str, "Stock ticker symbol (e.g., AMZN, GOOGL)"],
+) -> dict:
+    """Look up the current stock price for a given ticker symbol."""
+    # Simulated data for demo purposes
+    prices = {
+        "AMZN": {"price": 186.45, "change": "+2.3%"},
+        "GOOGL": {"price": 175.20, "change": "-0.8%"},
+        "MSFT": {"price": 420.15, "change": "+1.1%"},
+    }
+    return prices.get(symbol.upper(), {"error": f"Unknown symbol: {symbol}"})
+
+
+def get_company_info(
+    company: Annotated[str, "Company name to look up"],
+) -> str:
+    """Get brief information about a company."""
+    info = {
+        "amazon": "Amazon.com, Inc. — e-commerce, cloud computing (AWS), AI, streaming.",
+        "google": "Alphabet Inc. — search, advertising, cloud, AI research.",
+        "microsoft": "Microsoft Corp. — software, cloud (Azure), gaming, AI.",
+    }
+    return info.get(company.lower(), f"No info available for {company}")
+```
+
+<h3>Create Agents and Register Tools</h3>
+
+AG2 uses a **dual registration** pattern:
+1. `register_for_llm` — tells the AssistantAgent (LLM) that this tool exists and how to call it
+2. `register_for_execution` — tells the UserProxyAgent to execute the function when the LLM requests it
+
+This separation means the LLM decides **when** to call a tool, but the UserProxy controls **how** it's executed.
+
+```python
+with llm_config:
+    assistant = AssistantAgent(
+        name="FinanceAssistant",
+        system_message="You are a financial assistant. Use the available tools to look up stock data and company information. Provide clear analysis based on the data.",
+    )
+    user_proxy = UserProxyAgent(
+        name="user",
+        human_input_mode="NEVER",
+        max_consecutive_auto_reply=5,
+    )
+
+# Register tools for the LLM (tool schema) and for execution (function call)
+assistant.register_for_llm(description="Look up current stock price")(get_stock_price)
+assistant.register_for_llm(description="Get company information")(get_company_info)
+user_proxy.register_for_execution()(get_stock_price)
+user_proxy.register_for_execution()(get_company_info)
+```
+
+<h3>Run the Conversation</h3>
+
+The assistant will use Bedrock (Claude) to decide which tools to call, and the user proxy will execute them.
+
+```python
+result = user_proxy.initiate_chat(
+    assistant,
+    message="Compare Amazon and Google — show me their stock prices and company info.",
+)
+```
+
+<h2>Best Practices</h2>
+
+- **Use `Annotated` type hints** for parameter descriptions — Bedrock models use these for tool schemas
+- **Keep tools simple and focused** — one function per responsibility
+- **Use `max_consecutive_auto_reply`** to limit tool call loops and prevent runaway execution
+- **Return structured data** (dict/JSON) from tools for consistent parsing
+- **Add docstrings** to tool functions — the LLM uses these to understand when to call each tool
+- **Error handling**: Return error messages as data (not exceptions) so the LLM can reason about failures
+
+<h2>Next Steps</h2>
+
+- **Multi-agent**: See [ag2-multi-agent-bedrock.ipynb](https://github.com/aws-samples/amazon-bedrock-samples/blob/main/agents-and-function-calling/open-source-agents/ag2/ag2-multi-agent-bedrock.ipynb) for GroupChat orchestration with tools
+- **Single agent**: See [ag2-single-agent-bedrock.ipynb](https://github.com/aws-samples/amazon-bedrock-samples/blob/main/agents-and-function-calling/open-source-agents/ag2/ag2-single-agent-bedrock.ipynb) for the basic setup
+- **AG2 Tool Use Guide**: [docs.ag2.ai/docs/user-guide/basic-concepts/tools](https://docs.ag2.ai/docs/user-guide/basic-concepts/tools)
+- **AG2 Documentation**: [docs.ag2.ai](https://docs.ag2.ai/)
+
+<h2>Cleanup</h2>
+
+No resources to clean up — this notebook uses only local compute and Bedrock API calls. To stop incurring Bedrock charges, simply stop running the notebook.