Add developer quickstart

README.md

@@ -16,6 +16,8 @@ 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
 

deep_research/research_agent.ipynb

@@ -2043,7 +2043,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,

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

developer/.gitignore

@@ -0,0 +1 @@
+agent_workspace

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 youw ant
+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: 
+
+<img width="2869" height="1512" alt="Screenshot 2025-11-17 at 11 42 59 AM" src="https://github.com/user-attachments/assets/03090057-c199-42fe-a0f7-769704c2124b" />
+
+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. 
+
+<img width="2039" height="1495" alt="Screenshot 2025-11-17 at 1 11 27 PM" src="https://github.com/user-attachments/assets/d559876b-4c90-46fb-8e70-c16c93793fa8" />
+
+
+### 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.

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