diff --git a/README.md b/README.md
index 3c22f69..9aa3fa8 100644
--- a/README.md
+++ b/README.md
@@ -16,6 +16,7 @@ Here are the currently supported quickstarts:
| Quickstart Name | Location | Description | Usage Options |
|----------------|----------|-------------|---------------|
| [Deep Research](deep_research/README.md) | `deep_research/` | A research agent that conducts multi-step web research using Tavily for URL discovery, fetches full webpage content, and coordinates work through parallel sub-agents and strategic reflection | **Jupyter Notebook** or **LangGraph Server** |
+| [Developer](developer/README.md) | `developer/` | A software developer agent that will break down a coding task into a series of steps and execute on them, using web search via `Tavily` to help find the best tooling and practices. | **Jupyter Notebook** or **LangGraph Server** |
## Built-In Deepagent Components
diff --git a/developer/.env.example b/developer/.env.example
new file mode 100644
index 0000000..7ec9a5a
--- /dev/null
+++ b/developer/.env.example
@@ -0,0 +1,12 @@
+# API Keys for Developer Agent Example
+# Copy this file to .env and fill in your actual API keys
+
+# Anthropic API Key (for Claude Sonnet 4.5)
+ANTHROPIC_API_KEY=your_anthropic_api_key_here
+
+# Tavily API Key (for web search)
+TAVILY_API_KEY=your_tavily_api_key_here
+
+# LangSmith API Key (required for LangGraph local server)
+# Get your key at: https://smith.langchain.com/settings
+LANGSMITH_API_KEY=your_langsmith_api_key_here
diff --git a/developer/.gitignore b/developer/.gitignore
new file mode 100644
index 0000000..3ccfc92
--- /dev/null
+++ b/developer/.gitignore
@@ -0,0 +1 @@
+agent_workspace
diff --git a/developer/README.md b/developer/README.md
new file mode 100644
index 0000000..1a87bfc
--- /dev/null
+++ b/developer/README.md
@@ -0,0 +1,100 @@
+# 🚀 Developer Agent
+
+## 🚀 Quickstart
+
+**Prerequisites**: Install [uv](https://docs.astral.sh/uv/) package manager:
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
+
+Ensure you are in the `developer` directory:
+```bash
+cd developer
+```
+
+Install packages:
+```bash
+uv sync
+```
+
+Set your API keys in your environment:
+
+```bash
+export ANTHROPIC_API_KEY=your_anthropic_api_key_here # Required for Claude model - or you could use any LLM model you want
+export TAVILY_API_KEY=your_tavily_api_key_here # Required for web search ([get one here](https://www.tavily.com/)) with a generous free tier
+export LANGSMITH_API_KEY=your_langsmith_api_key_here # [LangSmith API key](https://smith.langchain.com/settings) (free to sign up)
+```
+
+## Usage Options
+
+You can run this quickstart in two ways:
+
+### Option 1: Jupyter Notebook
+
+Run the interactive notebook to step through building the agent:
+
+```bash
+uv run jupyter notebook develoer_agent.ipynb
+```
+
+### Option 2: LangGraph Server
+
+Run a local [LangGraph server](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/) with a web interface:
+
+```bash
+langgraph dev
+```
+
+LangGraph server will open a new browser window with the Studio interface, which you can submit your requirements for an application to:
+
+
+
+You can also connect the LangGraph server to a [UI specifically designed for deepagents](https://github.com/langchain-ai/deep-agents-ui):
+
+```bash
+$ git clone https://github.com/langchain-ai/deepagents-ui.git
+$ cd deepagents-ui
+$ yarn install
+$ yarn dev
+```
+
+Then follow the instructions in the [deepagents-ui README](https://github.com/langchain-ai/deepagents-ui?tab=readme-ov-file#connecting-to-a-langgraph-server) to connect the UI to the running LangGraph server.
+
+This provides a user-friendly chat interface and visualization of files in state.
+
+
+
+
+### Custom Model
+
+By default, `deepagents` uses `"claude-sonnet-4-5-20250929"`. You can customize this by passing any [LangChain model object](https://python.langchain.com/docs/integrations/chat/). See the Deepagents package [README](https://github.com/langchain-ai/deepagents?tab=readme-ov-file#model) for more details.
+
+```python
+from langchain.chat_models import init_chat_model
+from deepagents import create_deep_agent
+
+# Using Claude
+model = init_chat_model(model="anthropic:claude-sonnet-4-5-20250929", temperature=0.0)
+
+# Using Gemini
+from langchain_google_genai import ChatGoogleGenerativeAI
+model = ChatGoogleGenerativeAI(model="gemini-3-pro-preview")
+
+agent = create_deep_agent(
+ model=model,
+)
+```
+
+### Custom Instructions
+
+The agent uses custom instructions defined in `developer/developer_agent/prompts.py` that complement (rather than duplicate) the default middleware instructions. You can modify these in any way you want.
+
+
+### Custom Tools
+
+The deep research agent adds the following custom tools beyond the built-in deepagent tools. You can also use your own tools, including via MCP servers. See the Deepagents package [README](https://github.com/langchain-ai/deepagents?tab=readme-ov-file#mcp) for more details.
+
+| Tool Name | Description |
+|-----------|-------------|
+| `tavily_search` | Web search tool that uses Tavily purely as a URL discovery engine. Performs searches using Tavily API to find relevant URLs, fetches full webpage content via HTTP with proper User-Agent headers (avoiding 403 errors), and returns the complete content without summarization to preserve all information for the agent's analysis.
+| `fetch_webpage_content` | Fetches the content at a webpage and converts it to a human and LLM-friendy markdown format.
diff --git a/developer/agent.py b/developer/agent.py
new file mode 100644
index 0000000..4ad6970
--- /dev/null
+++ b/developer/agent.py
@@ -0,0 +1,35 @@
+import os
+import asyncio
+
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend
+from langchain.chat_models import init_chat_model
+
+from developer_agent import DEVELOPER_SYSTEM_PROMPT, tavily_search, fetch_webpage_content
+
+WORKSPACE_DIR = "agent_workspace"
+
+
+# Model Claude 4.5 - can replace with whichever you like
+model = init_chat_model(model="anthropic:claude-sonnet-4-5-20250929", temperature=0.0)
+
+
+def initialize_backend():
+ # Check if agent_workspace folder exists, create it if it doesn't
+ if not os.path.exists(WORKSPACE_DIR):
+ os.makedirs(WORKSPACE_DIR)
+ # Create a FileSystem backend for our agent to do its work in
+ return FilesystemBackend(
+ root_dir=WORKSPACE_DIR,
+ virtual_mode=True)
+
+
+async def agent():
+ backend = await asyncio.to_thread(initialize_backend)
+ agent = create_deep_agent(
+ model=model,
+ backend=backend,
+ system_prompt=DEVELOPER_SYSTEM_PROMPT,
+ tools=[tavily_search, fetch_webpage_content],
+ )
+ return agent
diff --git a/developer/developer_agent.ipynb b/developer/developer_agent.ipynb
new file mode 100644
index 0000000..069e0ee
--- /dev/null
+++ b/developer/developer_agent.ipynb
@@ -0,0 +1,698 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "235036ae",
+ "metadata": {
+ "vscode": {
+ "languageId": "plaintext"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "from dotenv import load_dotenv\n",
+ "load_dotenv(\".env\", override=True)\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "34061f7f-61bb-4ee8-99d1-555ca1d97574",
+ "metadata": {},
+ "source": [
+ "# Deepagent for Software Development\n",
+ "\n",
+ "We will use the `deepagents` package to create a software developer agent. When using the `deepagents` package, it's important to: \n",
+ "\n",
+ "1. Understand the native tools available\n",
+ "2. Supply task-specific tools\n",
+ "3. Supply task-specific instructions\n",
+ "4. Supply task-specific sub-agents, if necessary\n",
+ "\n",
+ "You can see an overview of the native tools in the [deepagents package README](https://github.com/langchain-ai/deepagents?tab=readme-ov-file#model) as well as the [quickstarts README](https://github.com/langchain-ai/deepagents-quickstarts). We'll extend this with two task-specific tools. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2f9e0208-03a5-47ca-87af-836d8e91097c",
+ "metadata": {},
+ "source": [
+ "## Task-Specific Tools \n",
+ "\n",
+ "Between the tools built in to the `deepagents` package and the LLM's internal knowledge of coding, we would have a very useful agent without any additional tools. However, we want our code to reflect the latest and greatest libraries, developer tools, and best practices, so let's give our agent access to the internet so it can do some research as it does its work. \n",
+ "\n",
+ "\n",
+ "\n",
+ "### Search Tool \n",
+ "\n",
+ "There are many different search tools that we can use. For this example, we'll use [Tavily](https://www.tavily.com/) to search the web for helpful content. \n",
+ "\n",
+ "### Fetch Webpage Content Tool\n",
+ "\n",
+ "We'll add a tool to fetch content from any webpage, including one we found via the search tool, and return it to our agent in a well-formatted markdown block. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "b285f6bc-eb44-4509-b564-ec2eb7ea57a4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from developer_agent.tools import tavily_search, fetch_webpage_content\n",
+ "\n",
+ "tools = [tavily_search, fetch_webpage_content]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9ac6508e-d928-4b23-bd73-c475522b53eb",
+ "metadata": {},
+ "source": [
+ "## Filesystem Access\n",
+ "\n",
+ "By the default, the agent will use LangGraph's built in short-term memory as a temporary file system. Let's instead give our agent access to a real directory on our computer, so it can write its output code to place where we can find it. \n",
+ "\n",
+ "We will give the agent a file system in \"virtual mode\", so from our agent's perspective, the entire file system is inside that directory - we're creating a very basic sandbox on our machine!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "1f70bf7b-d044-43c8-89c8-296907f53b7d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os \n",
+ "\n",
+ "WORKSPACE_DIR = \"agent_workspace\"\n",
+ "\n",
+ "# Create a workspace directory\n",
+ "if not os.path.exists(WORKSPACE_DIR):\n",
+ " os.makedirs(WORKSPACE_DIR)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "2e6a0de0-89ee-4401-8ab1-aafbe7dc3963",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from deepagents.backends import FilesystemBackend\n",
+ "\n",
+ "back_end = FilesystemBackend(\n",
+ " root_dir=WORKSPACE_DIR,\n",
+ " virtual_mode=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0023827f-78c7-4f34-ac7b-52495a2eb785",
+ "metadata": {},
+ "source": [
+ "## Task-Specfic Instructions\n",
+ "\n",
+ "Let's define a system prompt to tell our deep agent how to use apply itself to the task at hand:\n",
+ "\n",
+ "1. Begin by assigning the agent a role\n",
+ "2. Deepagents comes with built-in subagent creation and management, but we'll add some details about how to effectively use subagents for coding tasks.\n",
+ "3. Likewise, we'll add some details about how to use the built-in tools along with the ones we've added. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "15884bb0-f729-4e03-b9fc-35de7659664a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "╭──────────────────────────────────────────────────── Prompt ─────────────────────────────────────────────────────╮\n",
+ "│ │\n",
+ "│ │\n",
+ "│ You are an AI assistant that helps users by building applications for them. │\n",
+ "│ │\n",
+ "│ │\n",
+ "│ ## Working with Subagents (task tool) │\n",
+ "│ When delegating to subagents: │\n",
+ "│ - **Use filesystem for large I/O**: If input instructions are large (>500 words) OR expected output is large, │\n",
+ "│ communicate via files │\n",
+ "│ - Write input context/instructions to a file, tell subagent to read it │\n",
+ "│ - Ask subagent to write their output to a file, then read it after they return │\n",
+ "│ - This prevents token bloat and keeps context manageable in both directions │\n",
+ "│ - **Parallelize independent work**: When tasks are independent, spawn parallel subagents to work │\n",
+ "│ simultaneously │\n",
+ "│ - **Clear specifications**: Tell subagent exactly what format/structure you need in their response or output │\n",
+ "│ file │\n",
+ "│ - **Main agent synthesizes**: Subagents gather/execute, main agent integrates results into final deliverable │\n",
+ "│ │\n",
+ "│ ## Tools │\n",
+ "│ │\n",
+ "│ ### File Tools │\n",
+ "│ - read_file: Read file contents (use absolute paths) │\n",
+ "│ - edit_file: Replace exact strings in files (must read first, provide unique old_string) │\n",
+ "│ - write_file: Create or overwrite files │\n",
+ "│ - ls: List directory contents │\n",
+ "│ - glob: Find files by pattern (e.g., \"**/*.py\") │\n",
+ "│ - grep: Search file contents │\n",
+ "│ │\n",
+ "│ Always use absolute paths starting with /. │\n",
+ "│ │\n",
+ "│ ### web_search │\n",
+ "│ Search for documentation, error solutions, and code examples. │\n",
+ "│ │\n",
+ "│ ### fetch_webpage_content │\n",
+ "│ Download the contents of a webpage and return the markdown-formatted content. │\n",
+ "│ │\n",
+ "│ ## Code References │\n",
+ "│ When referencing code, use format: `file_path:line_number` │\n",
+ "│ │\n",
+ "│ ## Documentation │\n",
+ "│ - Do NOT create excessive markdown summary/documentation files after completing work │\n",
+ "│ - Focus on the work itself, not documenting what you did │\n",
+ "│ - Only create documentation when explicitly requested │\n",
+ "│ │\n",
+ "│ │\n",
+ "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[34m╭─\u001b[0m\u001b[34m───────────────────────────────────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;32mPrompt\u001b[0m\u001b[34m \u001b[0m\u001b[34m────────────────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m You are an AI assistant that helps users by building applications for them. \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[1;35m## Working with Subagents (task tool)\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m When delegating to subagents: \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - **Use filesystem for large I/O**: If input instructions are large (>500 words) OR expected output is large, \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m communicate via files \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - Write input context/instructions to a file, tell subagent to read it \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - Ask subagent to write their output to a file, then read it after they return \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - This prevents token bloat and keeps context manageable in both directions \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - **Parallelize independent work**: When tasks are independent, spawn parallel subagents to work \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m simultaneously \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - **Clear specifications**: Tell subagent exactly what format/structure you need in their response or output \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m file \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - **Main agent synthesizes**: Subagents gather/execute, main agent integrates results into final deliverable \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[1;35m## Tools\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[1;36m#\u001b[0m\u001b[1;36m## File Tools\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - read_file: Read file contents (use absolute paths) \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - edit_file: Replace exact strings in files (must read first, provide unique old_string) \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - write_file: Create or overwrite files \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - ls: List directory contents \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - glob: Find files by pattern (e.g., \"**/*.py\") \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - grep: Search file contents \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m Always use absolute paths starting with /. \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[1;36m#\u001b[0m\u001b[1;36m## web_search\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m Search for documentation, error solutions, and code examples. \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[1;36m#\u001b[0m\u001b[1;36m## fetch_webpage_content\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m Download the contents of a webpage and return the markdown-formatted content. \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[1;35m## Code References\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m When referencing code, use format: `file_path:line_number` \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[1;35m## Documentation\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - Do NOT create excessive markdown summary/documentation files after completing work \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - Focus on the work itself, not documenting what you did \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m - Only create documentation when explicitly requested \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from utils import show_prompt, format_messages\n",
+ "from developer_agent.prompts import DEVELOPER_SYSTEM_PROMPT\n",
+ "\n",
+ "show_prompt(DEVELOPER_SYSTEM_PROMPT)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1a61fc9f-b3fd-427d-b1e9-b0cc7eb25ccd",
+ "metadata": {},
+ "source": [
+ "## Putting it all together\n",
+ "\n",
+ "Let's create our agent and try it out on a basic use case."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "22135d8f-d0a2-4a1c-b4a8-625d6ebf494d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from IPython.display import Image, display\n",
+ "from langchain.chat_models import init_chat_model\n",
+ "from deepagents import create_deep_agent\n",
+ "\n",
+ "# Model Claude 4.5 - or you can use any you like\n",
+ "model = init_chat_model(model=\"anthropic:claude-sonnet-4-5-20250929\", temperature=0.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "95a60f5d-0cd9-47e9-983a-d9e8ee433c9e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Create the agent\n",
+ "agent = create_deep_agent(\n",
+ " model=model,\n",
+ " tools=tools, \n",
+ " system_prompt=DEVELOPER_SYSTEM_PROMPT\n",
+ " )\n",
+ " \n",
+ "# Show the agent\n",
+ "display(Image(agent.get_graph().draw_mermaid_png()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5c135dc4-a11b-4146-84ad-5bb289fc42fe",
+ "metadata": {},
+ "source": [
+ "### Basic Test\n",
+ "\n",
+ "Let's see how our agent does when we ask it to write a simple script."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "fc1c30de-b39e-4d0b-a1d8-85674eb5603a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "╭─────────────────────────────────────────────────── 🧑 Human ────────────────────────────────────────────────────╮\n",
+ "│ Write a fizzbuzz script to a file called `fizzbuz.py` │\n",
+ "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[34m╭─\u001b[0m\u001b[34m──────────────────────────────────────────────────\u001b[0m\u001b[34m 🧑 Human \u001b[0m\u001b[34m───────────────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n",
+ "\u001b[34m│\u001b[0m Write a fizzbuzz script to a file called `fizzbuz.py` \u001b[34m│\u001b[0m\n",
+ "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮\n",
+ "│ I'll write a fizzbuzz script to a file called `fizzbuz.py`. │\n",
+ "│ │\n",
+ "│ 🔧 Tool Call: write_file │\n",
+ "│ Args: { │\n",
+ "│ \"file_path\": \"/fizzbuz.py\", │\n",
+ "│ \"content\": \"#!/usr/bin/env python3\\n\\\"\\\"\\\"\\nFizzBuzz implementation\\nPrints numbers from 1 to 100, but:\\n- │\n",
+ "│ For multiples of 3, print \\\"Fizz\\\"\\n- For multiples of 5, print \\\"Buzz\\\"\\n- For multiples of both 3 and 5, │\n",
+ "│ print \\\"FizzBuzz\\\"\\n\\\"\\\"\\\"\\n\\ndef fizzbuzz(n=100):\\n \\\"\\\"\\\"\\n Print FizzBuzz sequence from 1 to n\\n \\n │\n",
+ "│ Args:\\n n: The upper limit (inclusive) for the sequence\\n \\\"\\\"\\\"\\n for i in range(1, n + 1):\\n │\n",
+ "│ if i % 3 == 0 and i % 5 == 0:\\n print(\\\"FizzBuzz\\\")\\n elif i % 3 == 0:\\n │\n",
+ "│ print(\\\"Fizz\\\")\\n elif i % 5 == 0:\\n print(\\\"Buzz\\\")\\n else:\\n │\n",
+ "│ print(i)\\n\\nif __name__ == \\\"__main__\\\":\\n fizzbuzz()\\n\" │\n",
+ "│ } │\n",
+ "│ ID: toolu_01KDed9S9LQqFiyVM347Vyem │\n",
+ "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[37m╭─\u001b[0m\u001b[37m────────────────────────────────────────────────────\u001b[0m\u001b[37m 📝 AI \u001b[0m\u001b[37m────────────────────────────────────────────────────\u001b[0m\u001b[37m─╮\u001b[0m\n",
+ "\u001b[37m│\u001b[0m I'll write a fizzbuzz script to a file called `fizzbuz.py`. \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m 🔧 Tool Call: write_file \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m Args: { \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \"file_path\": \"/fizzbuz.py\", \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \"content\": \"#!/usr/bin/env python3\\n\\\"\\\"\\\"\\nFizzBuzz implementation\\nPrints numbers from 1 to 100, but:\\n- \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m For multiples of 3, print \\\"Fizz\\\"\\n- For multiples of 5, print \\\"Buzz\\\"\\n- For multiples of both 3 and 5, \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m print \\\"FizzBuzz\\\"\\n\\\"\\\"\\\"\\n\\ndef fizzbuzz(n=100):\\n \\\"\\\"\\\"\\n Print FizzBuzz sequence from 1 to n\\n \\n \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m Args:\\n n: The upper limit (inclusive) for the sequence\\n \\\"\\\"\\\"\\n for i in range(1, n + 1):\\n \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m if i % 3 == 0 and i % 5 == 0:\\n print(\\\"FizzBuzz\\\")\\n elif i % 3 == 0:\\n \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m print(\\\"Fizz\\\")\\n elif i % 5 == 0:\\n print(\\\"Buzz\\\")\\n else:\\n \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m print(i)\\n\\nif __name__ == \\\"__main__\\\":\\n fizzbuzz()\\n\" \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m } \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m ID: toolu_01KDed9S9LQqFiyVM347Vyem \u001b[37m│\u001b[0m\n",
+ "\u001b[37m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮\n",
+ "│ Updated file /fizzbuz.py │\n",
+ "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[33m╭─\u001b[0m\u001b[33m───────────────────────────────────────────────\u001b[0m\u001b[33m 🔧 Tool Output \u001b[0m\u001b[33m────────────────────────────────────────────────\u001b[0m\u001b[33m─╮\u001b[0m\n",
+ "\u001b[33m│\u001b[0m Updated file /fizzbuz.py \u001b[33m│\u001b[0m\n",
+ "\u001b[33m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮\n",
+ "│ Done! I've created the `fizzbuz.py` file with a complete FizzBuzz implementation. The script: │\n",
+ "│ │\n",
+ "│ - Prints numbers from 1 to 100 │\n",
+ "│ - Prints \"Fizz\" for multiples of 3 │\n",
+ "│ - Prints \"Buzz\" for multiples of 5 │\n",
+ "│ - Prints \"FizzBuzz\" for multiples of both 3 and 5 │\n",
+ "│ - Includes a reusable `fizzbuzz()` function that accepts a parameter for the upper limit │\n",
+ "│ │\n",
+ "│ You can run it with: `python /fizzbuz.py` │\n",
+ "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[37m╭─\u001b[0m\u001b[37m────────────────────────────────────────────────────\u001b[0m\u001b[37m 📝 AI \u001b[0m\u001b[37m────────────────────────────────────────────────────\u001b[0m\u001b[37m─╮\u001b[0m\n",
+ "\u001b[37m│\u001b[0m Done! I've created the `fizzbuz.py` file with a complete FizzBuzz implementation. The script: \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m - Prints numbers from 1 to 100 \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m - Prints \"Fizz\" for multiples of 3 \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m - Prints \"Buzz\" for multiples of 5 \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m - Prints \"FizzBuzz\" for multiples of both 3 and 5 \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m - Includes a reusable `fizzbuzz()` function that accepts a parameter for the upper limit \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m You can run it with: `python /fizzbuz.py` \u001b[37m│\u001b[0m\n",
+ "\u001b[37m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "result = agent.invoke(\n",
+ " {\n",
+ " \"messages\": [\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"Write a fizzbuzz script to a file called `fizzbuz.py`\",\n",
+ " }\n",
+ " ],\n",
+ " }, \n",
+ ")\n",
+ "format_messages(result[\"messages\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ba85a110-ecaf-4aa0-9127-a72c803e6ac5",
+ "metadata": {},
+ "source": [
+ "Great - remember that we gave our agent a single directory on our computer as its workspace, so let's take a look there to check out the code!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "c9ed33cb-79b0-44d5-ab61-a7106747b459",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['frontend', 'fizzbuzz.py', 'QUICKSTART.md', 'backend', '__pycache__', 'README.md']\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(os.listdir('agent_workspace'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "ef456766-8231-49e8-9f49-a83589028dc1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "#!/usr/bin/env python3\n",
+ "\"\"\"\n",
+ "FizzBuzz: Print numbers from 1 to 100, but:\n",
+ "- Print \"Fizz\" for multiples of 3\n",
+ "- Print \"Buzz\" for multiples of 5\n",
+ "- Print \"FizzBuzz\" for multiples of both 3 and 5\n",
+ "\"\"\"\n",
+ "\n",
+ "def fizzbuzz(n=100):\n",
+ " \"\"\"\n",
+ " Print FizzBuzz sequence from 1 to n.\n",
+ " \n",
+ " Args:\n",
+ " n: The upper limit (default: 100)\n",
+ " \"\"\"\n",
+ " for i in range(1, n + 1):\n",
+ " if i % 3 == 0 and i % 5 == 0:\n",
+ " print(\"FizzBuzz\")\n",
+ " elif i % 3 == 0:\n",
+ " print(\"Fizz\")\n",
+ " elif i % 5 == 0:\n",
+ " print(\"Buzz\")\n",
+ " else:\n",
+ " print(i)\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " fizzbuzz()\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "with open('agent_workspace/fizzbuzz.py', 'r') as f:\n",
+ " contents = f.read()\n",
+ "\n",
+ "print(contents)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fc59bce1-1a79-4112-8a81-40dd4c155aeb",
+ "metadata": {},
+ "source": [
+ "## Advanced Use Case\n",
+ "\n",
+ "Now, let's see what happens if we ask the the agent to write an entire application with a front end and back end. We're not going to wait for the agent to actually write the code - since we know that the deepagents package is designed to first create a to-do list before starting complex projects, we can interrupt the graph after the first tool call completes to inspect the agent's plan before it executes on it. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "9d3ea518-d83d-4607-84c9-fa26c42a50fa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "╭─────────────────────────────────────────────────── 🧑 Human ────────────────────────────────────────────────────╮\n",
+ "│ │\n",
+ "│ Write an app with a FastAPI back end that checks the weather, and a React front end to │\n",
+ "│ allow │\n",
+ "│ the user to look up the weather for a given location. │\n",
+ "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[34m╭─\u001b[0m\u001b[34m──────────────────────────────────────────────────\u001b[0m\u001b[34m 🧑 Human \u001b[0m\u001b[34m───────────────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n",
+ "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m Write an app with a FastAPI back end that checks the weather, and a React front end to \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m allow \u001b[34m│\u001b[0m\n",
+ "\u001b[34m│\u001b[0m the user to look up the weather for a given location. \u001b[34m│\u001b[0m\n",
+ "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮\n",
+ "│ I'll help you build a weather app with a FastAPI backend and React frontend. Let me break this down into │\n",
+ "│ manageable steps. │\n",
+ "│ │\n",
+ "│ 🔧 Tool Call: write_todos │\n",
+ "│ Args: { │\n",
+ "│ \"todos\": [ │\n",
+ "│ { │\n",
+ "│ \"content\": \"Research weather API options and set up project structure\", │\n",
+ "│ \"status\": \"in_progress\" │\n",
+ "│ }, │\n",
+ "│ { │\n",
+ "│ \"content\": \"Create FastAPI backend with weather endpoint\", │\n",
+ "│ \"status\": \"pending\" │\n",
+ "│ }, │\n",
+ "│ { │\n",
+ "│ \"content\": \"Create React frontend with weather lookup interface\", │\n",
+ "│ \"status\": \"pending\" │\n",
+ "│ }, │\n",
+ "│ { │\n",
+ "│ \"content\": \"Create configuration files (requirements.txt, package.json, README)\", │\n",
+ "│ \"status\": \"pending\" │\n",
+ "│ } │\n",
+ "│ ] │\n",
+ "│ } │\n",
+ "│ ID: toolu_013ZnN9puayv7Mvaxz28NLKc │\n",
+ "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[37m╭─\u001b[0m\u001b[37m────────────────────────────────────────────────────\u001b[0m\u001b[37m 📝 AI \u001b[0m\u001b[37m────────────────────────────────────────────────────\u001b[0m\u001b[37m─╮\u001b[0m\n",
+ "\u001b[37m│\u001b[0m I'll help you build a weather app with a FastAPI backend and React frontend. Let me break this down into \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m manageable steps. \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m 🔧 Tool Call: write_todos \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m Args: { \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \"todos\": [ \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m { \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \"content\": \"Research weather API options and set up project structure\", \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \"status\": \"in_progress\" \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m }, \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m { \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \"content\": \"Create FastAPI backend with weather endpoint\", \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \"status\": \"pending\" \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m }, \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m { \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \"content\": \"Create React frontend with weather lookup interface\", \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \"status\": \"pending\" \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m }, \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m { \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \"content\": \"Create configuration files (requirements.txt, package.json, README)\", \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m \"status\": \"pending\" \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m } \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m ] \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m } \u001b[37m│\u001b[0m\n",
+ "\u001b[37m│\u001b[0m ID: toolu_013ZnN9puayv7Mvaxz28NLKc \u001b[37m│\u001b[0m\n",
+ "\u001b[37m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮\n",
+ "│ Updated todo list to [{'content': 'Research weather API options and set up project structure', 'status': │\n",
+ "│ 'in_progress'}, {'content': 'Create FastAPI backend with weather endpoint', 'status': 'pending'}, {'content': │\n",
+ "│ 'Create React frontend with weather lookup interface', 'status': 'pending'}, {'content': 'Create configuration │\n",
+ "│ files (requirements.txt, package.json, README)', 'status': 'pending'}] │\n",
+ "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[33m╭─\u001b[0m\u001b[33m───────────────────────────────────────────────\u001b[0m\u001b[33m 🔧 Tool Output \u001b[0m\u001b[33m────────────────────────────────────────────────\u001b[0m\u001b[33m─╮\u001b[0m\n",
+ "\u001b[33m│\u001b[0m Updated todo list to [{'content': 'Research weather API options and set up project structure', 'status': \u001b[33m│\u001b[0m\n",
+ "\u001b[33m│\u001b[0m 'in_progress'}, {'content': 'Create FastAPI backend with weather endpoint', 'status': 'pending'}, {'content': \u001b[33m│\u001b[0m\n",
+ "\u001b[33m│\u001b[0m 'Create React frontend with weather lookup interface', 'status': 'pending'}, {'content': 'Create configuration \u001b[33m│\u001b[0m\n",
+ "\u001b[33m│\u001b[0m files (requirements.txt, package.json, README)', 'status': 'pending'}] \u001b[33m│\u001b[0m\n",
+ "\u001b[33m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "result = agent.invoke(\n",
+ " {\n",
+ " \"messages\": [\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"\"\"\n",
+ " Write an app with a FastAPI back end that checks the weather, and a React front end to allow \n",
+ " the user to look up the weather for a given location.\"\"\",\n",
+ " }\n",
+ " ],\n",
+ " }\n",
+ " # Remove this line if you want the agent to actually write the application\n",
+ " interrupt_after=[\"tools\"]\n",
+ ")\n",
+ "format_messages(result[\"messages\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dce66138-2a48-4522-aa4c-d76677b4e1a4",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.13.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/developer/developer_agent/__init__.py b/developer/developer_agent/__init__.py
new file mode 100644
index 0000000..486ece2
--- /dev/null
+++ b/developer/developer_agent/__init__.py
@@ -0,0 +1,18 @@
+"""Developer Agent Example
+
+This module demonstrates building a developer agent using the deepagents package
+with custom tools for web search and downloads.
+"""
+
+from developer_agent.prompts import (
+ DEVELOPER_SYSTEM_PROMPT,
+)
+
+
+from developer_agent.tools import tavily_search, fetch_webpage_content
+
+__all__ = [
+ "DEVELOPER_SYSTEM_PROMPT",
+ "tavily_search",
+ "fetch_webpage_content",
+]
diff --git a/developer/developer_agent/prompts.py b/developer/developer_agent/prompts.py
new file mode 100644
index 0000000..3fe8bb2
--- /dev/null
+++ b/developer/developer_agent/prompts.py
@@ -0,0 +1,40 @@
+DEVELOPER_SYSTEM_PROMPT = """
+You are an AI assistant that helps users by building applications for them.
+
+
+## Working with Subagents (task tool)
+When delegating to subagents:
+- **Use filesystem for large I/O**: If input instructions are large (>500 words) OR expected output is large, communicate via files
+ - Write input context/instructions to a file, tell subagent to read it
+ - Ask subagent to write their output to a file, then read it after they return
+ - This prevents token bloat and keeps context manageable in both directions
+- **Parallelize independent work**: When tasks are independent, spawn parallel subagents to work simultaneously
+- **Clear specifications**: Tell subagent exactly what format/structure you need in their response or output file
+- **Main agent synthesizes**: Subagents gather/execute, main agent integrates results into final deliverable
+
+## Tools
+
+### File Tools
+- read_file: Read file contents (use absolute paths)
+- edit_file: Replace exact strings in files (must read first, provide unique old_string)
+- write_file: Create or overwrite files
+- ls: List directory contents
+- glob: Find files by pattern (e.g., "**/*.py")
+- grep: Search file contents
+
+Always use absolute paths starting with /.
+
+### web_search
+Search for documentation, error solutions, and code examples.
+
+### fetch_webpage_content
+Download the contents of a webpage and return the markdown-formatted content.
+
+## Code References
+When referencing code, use format: `file_path:line_number`
+
+## Documentation
+- Do NOT create excessive markdown summary/documentation files after completing work
+- Focus on the work itself, not documenting what you did
+- Only create documentation when explicitly requested
+"""
\ No newline at end of file
diff --git a/developer/developer_agent/tools.py b/developer/developer_agent/tools.py
new file mode 100644
index 0000000..52993a7
--- /dev/null
+++ b/developer/developer_agent/tools.py
@@ -0,0 +1,88 @@
+"""Research Tools.
+
+This module provides search and content processing utilities for the research agent,
+using Tavily for URL discovery and fetching full webpage content.
+"""
+
+import httpx
+from langchain_core.tools import InjectedToolArg, tool
+from markdownify import markdownify
+from tavily import TavilyClient
+from typing_extensions import Annotated, Literal
+
+tavily_client = TavilyClient()
+
+
+def fetch_webpage_content(url: str, timeout: float = 10.0) -> str:
+ """Fetch and convert webpage content to markdown.
+
+ Args:
+ url: URL to fetch
+ timeout: Request timeout in seconds
+
+ Returns:
+ Webpage content as markdown
+ """
+ headers = {
+ "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+ }
+
+ try:
+ response = httpx.get(url, headers=headers, timeout=timeout)
+ response.raise_for_status()
+ return markdownify(response.text)
+ except Exception as e:
+ return f"Error fetching content from {url}: {str(e)}"
+
+
+@tool(parse_docstring=True)
+def tavily_search(
+ query: str,
+ max_results: Annotated[int, InjectedToolArg] = 1,
+ topic: Annotated[
+ Literal["general", "news", "finance"], InjectedToolArg
+ ] = "general",
+) -> str:
+ """Search the web for information on a given query.
+
+ Uses Tavily to discover relevant URLs, then fetches and returns full webpage content as markdown.
+
+ Args:
+ query: Search query to execute
+ max_results: Maximum number of results to return (default: 1)
+ topic: Topic filter - 'general', 'news', or 'finance' (default: 'general')
+
+ Returns:
+ Formatted search results with full webpage content
+ """
+ # Use Tavily to discover URLs
+ search_results = tavily_client.search(
+ query,
+ max_results=max_results,
+ topic=topic,
+ )
+
+ # Fetch full content for each URL
+ result_texts = []
+ for result in search_results.get("results", []):
+ url = result["url"]
+ title = result["title"]
+
+ # Fetch webpage content
+ content = fetch_webpage_content(url)
+
+ result_text = f"""## {title}
+**URL:** {url}
+
+{content}
+
+---
+"""
+ result_texts.append(result_text)
+
+ # Format final response
+ response = f"""🔍 Found {len(result_texts)} result(s) for '{query}':
+
+{chr(10).join(result_texts)}"""
+
+ return response
diff --git a/developer/langgraph.json b/developer/langgraph.json
new file mode 100644
index 0000000..f16b3ec
--- /dev/null
+++ b/developer/langgraph.json
@@ -0,0 +1,7 @@
+{
+ "dependencies": ["."],
+ "graphs": {
+ "developer": "./agent.py:agent"
+ },
+ "env": ".env"
+}
\ No newline at end of file
diff --git a/developer/pyproject.toml b/developer/pyproject.toml
new file mode 100644
index 0000000..b47067c
--- /dev/null
+++ b/developer/pyproject.toml
@@ -0,0 +1,13 @@
+[project]
+name = "developer"
+version = "0.1.0"
+description = "Deep-research based application developer"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+ "deepagents>=0.2.8",
+ "langchain-anthropic>=1.2.0",
+ "markdownify>=1.2.2",
+ "python-dotenv>=1.2.1",
+ "tavily>=1.1.0",
+]
diff --git a/developer/utils.py b/developer/utils.py
new file mode 100644
index 0000000..48aecfb
--- /dev/null
+++ b/developer/utils.py
@@ -0,0 +1,94 @@
+"""Utility functions for displaying messages and prompts in Jupyter notebooks."""
+
+import json
+
+from rich.console import Console
+from rich.panel import Panel
+from rich.text import Text
+
+console = Console()
+
+
+def format_message_content(message):
+ """Convert message content to displayable string."""
+ parts = []
+ tool_calls_processed = False
+
+ # Handle main content
+ if isinstance(message.content, str):
+ parts.append(message.content)
+ elif isinstance(message.content, list):
+ # Handle complex content like tool calls (Anthropic format)
+ for item in message.content:
+ if item.get("type") == "text":
+ parts.append(item["text"])
+ elif item.get("type") == "tool_use":
+ parts.append(f"\n🔧 Tool Call: {item['name']}")
+ parts.append(f" Args: {json.dumps(item['input'], indent=2)}")
+ parts.append(f" ID: {item.get('id', 'N/A')}")
+ tool_calls_processed = True
+ else:
+ parts.append(str(message.content))
+
+ # Handle tool calls attached to the message (OpenAI format) - only if not already processed
+ if (
+ not tool_calls_processed
+ and hasattr(message, "tool_calls")
+ and message.tool_calls
+ ):
+ for tool_call in message.tool_calls:
+ parts.append(f"\n🔧 Tool Call: {tool_call['name']}")
+ parts.append(f" Args: {json.dumps(tool_call['args'], indent=2)}")
+ parts.append(f" ID: {tool_call['id']}")
+
+ return "\n".join(parts)
+
+
+def format_messages(messages):
+ """Format and display a list of messages with Rich formatting."""
+ for m in messages:
+ msg_type = m.__class__.__name__.replace("Message", "")
+ content = format_message_content(m)
+
+ if msg_type == "Human":
+ console.print(Panel(content, title="🧑 Human", border_style="blue"))
+ elif msg_type == "Ai":
+ console.print(Panel(content, title="🤖 Assistant", border_style="green"))
+ elif msg_type == "Tool":
+ console.print(Panel(content, title="🔧 Tool Output", border_style="yellow"))
+ else:
+ console.print(Panel(content, title=f"📝 {msg_type}", border_style="white"))
+
+
+def format_message(messages):
+ """Alias for format_messages for backward compatibility."""
+ return format_messages(messages)
+
+
+def show_prompt(prompt_text: str, title: str = "Prompt", border_style: str = "blue"):
+ """Display a prompt with rich formatting and XML tag highlighting.
+
+ Args:
+ prompt_text: The prompt string to display
+ title: Title for the panel (default: "Prompt")
+ border_style: Border color style (default: "blue")
+ """
+ # Create a formatted display of the prompt
+ formatted_text = Text(prompt_text)
+ formatted_text.highlight_regex(r"<[^>]+>", style="bold blue") # Highlight XML tags
+ formatted_text.highlight_regex(
+ r"##[^#\n]+", style="bold magenta"
+ ) # Highlight headers
+ formatted_text.highlight_regex(
+ r"###[^#\n]+", style="bold cyan"
+ ) # Highlight sub-headers
+
+ # Display in a panel for better presentation
+ console.print(
+ Panel(
+ formatted_text,
+ title=f"[bold green]{title}[/bold green]",
+ border_style=border_style,
+ padding=(1, 2),
+ )
+ )
diff --git a/developer/uv.lock b/developer/uv.lock
new file mode 100644
index 0000000..01d26db
--- /dev/null
+++ b/developer/uv.lock
@@ -0,0 +1,1644 @@
+version = 1
+revision = 3
+requires-python = ">=3.11"
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
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+]
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+[[package]]
+name = "aiohttp"
+version = "3.13.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "aiohappyeyeballs" },
+ { name = "aiosignal" },
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